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Returns samples with: +- context_latents: (1, C, K, H//8, W//8) - 1 latent per context frame +- target_latents: (1, C, T, H//8, W//8) - 1 latent per 4 target frames +- prompt, video_name, start_frame, end_frame, actions + +Compatible with training that uses precomputed latents instead of encoding on the fly. +""" + +import json +import os +import warnings + +import torch + + +class LatentDataset(torch.utils.data.Dataset): + """ + Dataset that loads precomputed ctx and target latents. + """ + + def __init__( + self, + latent_dir, + metadata_path=None, + action_base_path=None, + repeat=1, + num_frames=81, + context_frames=5, + target_frames_per_latent=4, + ): + """ + Args: + latent_dir: Directory containing ctx_latents/ and target_latents/ subdirs. + metadata_path: Optional. If provided, used to get total_samples and validate. + action_base_path: Base path for action JSON files (for loading actions if not in .pt). + repeat: Dataset repeat factor. + num_frames: Expected num_frames per segment. + context_frames: Number of context frames (K). + target_frames_per_latent: Target: 1 latent per N frames. + """ + self.latent_dir = latent_dir + self.ctx_dir = os.path.join(latent_dir, "ctx_latents") + self.target_dir = os.path.join(latent_dir, "target_latents") + self.action_base_path = action_base_path or latent_dir + self.repeat = repeat + self.num_frames = num_frames + self.context_frames = context_frames + self.target_frames_per_latent = target_frames_per_latent + + # Infer valid indices from existing files (both ctx and target must exist) + self._indices = [] + if os.path.isdir(self.ctx_dir) and os.path.isdir(self.target_dir): + ctx_files = {f.replace(".pt", "") for f in os.listdir(self.ctx_dir) if f.endswith(".pt")} + target_files = {f.replace(".pt", "") for f in os.listdir(self.target_dir) if f.endswith(".pt")} + common = sorted([int(x) for x in ctx_files & target_files]) + self._indices = common + if not self._indices: + meta_path = os.path.join(latent_dir, "metadata_precompute.json") + if os.path.isfile(meta_path): + with open(meta_path) as f: + meta = json.load(f) + self._total = meta.get("total_samples", 0) + self._indices = list(range(self._total)) + else: + self._total = 0 + else: + self._total = len(self._indices) + + def __len__(self): + return self._total * self.repeat + + def __getitem__(self, idx): + real_idx = idx % self._total + if self._indices is not None: + real_idx = self._indices[real_idx] + + ctx_path = os.path.join(self.ctx_dir, f"{real_idx:08d}.pt") + target_path = os.path.join(self.target_dir, f"{real_idx:08d}.pt") + + if not os.path.isfile(ctx_path) or not os.path.isfile(target_path): + warnings.warn(f"Latent files not found for idx {real_idx}. Returning None.") + return None + + ctx_data = torch.load(ctx_path, map_location="cpu", weights_only=True) + target_data = torch.load(target_path, map_location="cpu", weights_only=True) + + ctx_latent = ctx_data["latent"] + target_latent = target_data["latent"] + + # Ensure batch dimension: (C, K, H, W) -> (1, C, K, H, W) + if ctx_latent.dim() == 4: + ctx_latent = ctx_latent.unsqueeze(0) + if target_latent.dim() == 4: + target_latent = target_latent.unsqueeze(0) + + out = { + "context_latents": ctx_latent, + "target_latents": target_latent, + "prompt": ctx_data.get("prompt", ""), + "video_name": ctx_data.get("video_name"), + "start_frame": ctx_data.get("start_frame"), + "end_frame": ctx_data.get("end_frame"), + } + if "actions" in ctx_data and ctx_data["actions"] is not None: + out["actions"] = ctx_data["actions"] + elif "actions" in target_data and target_data["actions"] is not None: + out["actions"] = target_data["actions"] + + return out + + +def get_latent_dataset_args(latent_dir, action_base_path=None, **kwargs): + """Build argparse.Namespace for LatentDataset from precompute metadata.""" + meta_path = os.path.join(latent_dir, "metadata_precompute.json") + if not os.path.isfile(meta_path): + return None + with open(meta_path) as f: + meta = json.load(f) + from argparse import Namespace + return Namespace( + latent_dir=latent_dir, + action_base_path=action_base_path or meta.get("dataset_base_path", latent_dir), + num_frames=meta.get("num_frames", 81), + context_frames=meta.get("context_frames", 5), + target_frames_per_latent=meta.get("target_frames_per_latent", 4), + **kwargs, + ) diff --git a/code/diffsynth/trainers/text_to_image.py b/code/diffsynth/trainers/text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..a76a7912da12f1ca665758542d92e50fea02713c --- /dev/null +++ b/code/diffsynth/trainers/text_to_image.py @@ -0,0 +1,318 @@ +import lightning as pl +from peft import LoraConfig, inject_adapter_in_model +import torch, os +from ..data.simple_text_image import TextImageDataset +from modelscope.hub.api import HubApi +from ..models.utils import load_state_dict + + + +class LightningModelForT2ILoRA(pl.LightningModule): + def __init__( + self, + learning_rate=1e-4, + use_gradient_checkpointing=True, + state_dict_converter=None, + ): + super().__init__() + # Set parameters + self.learning_rate = learning_rate + self.use_gradient_checkpointing = use_gradient_checkpointing + self.state_dict_converter = state_dict_converter + self.lora_alpha = None + + + def load_models(self): + # This function is implemented in other modules + self.pipe = None + + + def freeze_parameters(self): + # Freeze parameters + self.pipe.requires_grad_(False) + self.pipe.eval() + self.pipe.denoising_model().train() + + + def add_lora_to_model(self, model, lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, state_dict_converter=None): + # Add LoRA to UNet + self.lora_alpha = lora_alpha + if init_lora_weights == "kaiming": + init_lora_weights = True + + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + init_lora_weights=init_lora_weights, + target_modules=lora_target_modules.split(","), + ) + model = inject_adapter_in_model(lora_config, model) + for param in model.parameters(): + # Upcast LoRA parameters into fp32 + if param.requires_grad: + param.data = param.to(torch.float32) + + # Lora pretrained lora weights + if pretrained_lora_path is not None: + state_dict = load_state_dict(pretrained_lora_path) + if state_dict_converter is not None: + state_dict = state_dict_converter(state_dict) + missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False) + all_keys = [i for i, _ in model.named_parameters()] + num_updated_keys = len(all_keys) - len(missing_keys) + num_unexpected_keys = len(unexpected_keys) + print(f"{num_updated_keys} parameters are loaded from {pretrained_lora_path}. {num_unexpected_keys} parameters are unexpected.") + + + def training_step(self, batch, batch_idx): + # Data + text, image = batch["text"], batch["image"] + + # Prepare input parameters + self.pipe.device = self.device + prompt_emb = self.pipe.encode_prompt(text, positive=True) + if "latents" in batch: + latents = batch["latents"].to(dtype=self.pipe.torch_dtype, device=self.device) + else: + latents = self.pipe.vae_encoder(image.to(dtype=self.pipe.torch_dtype, device=self.device)) + noise = torch.randn_like(latents) + timestep_id = torch.randint(0, self.pipe.scheduler.num_train_timesteps, (1,)) + timestep = self.pipe.scheduler.timesteps[timestep_id].to(self.device) + extra_input = self.pipe.prepare_extra_input(latents) + noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep) + training_target = self.pipe.scheduler.training_target(latents, noise, timestep) + + # Compute loss + noise_pred = self.pipe.denoising_model()( + noisy_latents, timestep=timestep, **prompt_emb, **extra_input, + use_gradient_checkpointing=self.use_gradient_checkpointing + ) + loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float()) + loss = loss * self.pipe.scheduler.training_weight(timestep) + + # Record log + self.log("train_loss", loss, prog_bar=True) + return loss + + + def configure_optimizers(self): + trainable_modules = filter(lambda p: p.requires_grad, self.pipe.denoising_model().parameters()) + optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) + return optimizer + + + def on_save_checkpoint(self, checkpoint): + checkpoint.clear() + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.denoising_model().named_parameters())) + trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) + state_dict = self.pipe.denoising_model().state_dict() + lora_state_dict = {} + for name, param in state_dict.items(): + if name in trainable_param_names: + lora_state_dict[name] = param + if self.state_dict_converter is not None: + lora_state_dict = self.state_dict_converter(lora_state_dict, alpha=self.lora_alpha) + checkpoint.update(lora_state_dict) + + + +def add_general_parsers(parser): + parser.add_argument( + "--dataset_path", + type=str, + default=None, + required=True, + help="The path of the Dataset.", + ) + parser.add_argument( + "--output_path", + type=str, + default="./", + help="Path to save the model.", + ) + parser.add_argument( + "--steps_per_epoch", + type=int, + default=500, + help="Number of steps per epoch.", + ) + parser.add_argument( + "--height", + type=int, + default=1024, + help="Image height.", + ) + parser.add_argument( + "--width", + type=int, + default=1024, + help="Image width.", + ) + parser.add_argument( + "--center_crop", + default=False, + action="store_true", + help=( + "Whether to center crop the input images to the resolution. If not set, the images will be randomly" + " cropped. The images will be resized to the resolution first before cropping." + ), + ) + parser.add_argument( + "--random_flip", + default=False, + action="store_true", + help="Whether to randomly flip images horizontally", + ) + parser.add_argument( + "--batch_size", + type=int, + default=1, + help="Batch size (per device) for the training dataloader.", + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=0, + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + ) + parser.add_argument( + "--precision", + type=str, + default="16-mixed", + choices=["32", "16", "16-mixed", "bf16"], + help="Training precision", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Learning rate.", + ) + parser.add_argument( + "--lora_rank", + type=int, + default=4, + help="The dimension of the LoRA update matrices.", + ) + parser.add_argument( + "--lora_alpha", + type=float, + default=4.0, + help="The weight of the LoRA update matrices.", + ) + parser.add_argument( + "--init_lora_weights", + type=str, + default="kaiming", + choices=["gaussian", "kaiming"], + help="The initializing method of LoRA weight.", + ) + parser.add_argument( + "--use_gradient_checkpointing", + default=False, + action="store_true", + help="Whether to use gradient checkpointing.", + ) + parser.add_argument( + "--accumulate_grad_batches", + type=int, + default=1, + help="The number of batches in gradient accumulation.", + ) + parser.add_argument( + "--training_strategy", + type=str, + default="auto", + choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"], + help="Training strategy", + ) + parser.add_argument( + "--max_epochs", + type=int, + default=1, + help="Number of epochs.", + ) + parser.add_argument( + "--modelscope_model_id", + type=str, + default=None, + help="Model ID on ModelScope (https://www.modelscope.cn/). The model will be uploaded to ModelScope automatically if you provide a Model ID.", + ) + parser.add_argument( + "--modelscope_access_token", + type=str, + default=None, + help="Access key on ModelScope (https://www.modelscope.cn/). Required if you want to upload the model to ModelScope.", + ) + parser.add_argument( + "--pretrained_lora_path", + type=str, + default=None, + help="Pretrained LoRA path. Required if the training is resumed.", + ) + parser.add_argument( + "--use_swanlab", + default=False, + action="store_true", + help="Whether to use SwanLab logger.", + ) + parser.add_argument( + "--swanlab_mode", + default=None, + help="SwanLab mode (cloud or local).", + ) + return parser + + +def launch_training_task(model, args): + # dataset and data loader + dataset = TextImageDataset( + args.dataset_path, + steps_per_epoch=args.steps_per_epoch * args.batch_size, + height=args.height, + width=args.width, + center_crop=args.center_crop, + random_flip=args.random_flip + ) + train_loader = torch.utils.data.DataLoader( + dataset, + shuffle=True, + batch_size=args.batch_size, + num_workers=args.dataloader_num_workers + ) + # train + if args.use_swanlab: + from swanlab.integration.pytorch_lightning import SwanLabLogger + swanlab_config = {"UPPERFRAMEWORK": "DiffSynth-Studio"} + swanlab_config.update(vars(args)) + swanlab_logger = SwanLabLogger( + project="diffsynth_studio", + name="diffsynth_studio", + config=swanlab_config, + mode=args.swanlab_mode, + logdir=os.path.join(args.output_path, "swanlog"), + ) + logger = [swanlab_logger] + else: + logger = None + trainer = pl.Trainer( + max_epochs=args.max_epochs, + accelerator="gpu", + devices="auto", + precision=args.precision, + strategy=args.training_strategy, + default_root_dir=args.output_path, + accumulate_grad_batches=args.accumulate_grad_batches, + callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)], + logger=logger, + ) + trainer.fit(model=model, train_dataloaders=train_loader) + + # Upload models + if args.modelscope_model_id is not None and args.modelscope_access_token is not None: + print(f"Uploading models to modelscope. model_id: {args.modelscope_model_id} local_path: {trainer.log_dir}") + with open(os.path.join(trainer.log_dir, "configuration.json"), "w", encoding="utf-8") as f: + f.write('{"framework":"Pytorch","task":"text-to-image-synthesis"}\n') + api = HubApi() + api.login(args.modelscope_access_token) + api.push_model(model_id=args.modelscope_model_id, model_dir=trainer.log_dir) diff --git a/code/diffsynth/trainers/utils.py b/code/diffsynth/trainers/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0d8c9a024caf5cc450c7f2c76c7ed8ad12468af7 --- /dev/null +++ b/code/diffsynth/trainers/utils.py @@ -0,0 +1,1368 @@ +import imageio, os, torch, warnings, torchvision, argparse, json, random +from peft import LoraConfig, inject_adapter_in_model +from PIL import Image +import pandas as pd +from tqdm import tqdm +from accelerate import Accelerator + + + +class ImageDataset(torch.utils.data.Dataset): + def __init__( + self, + base_path=None, metadata_path=None, + max_pixels=1920*1080, height=None, width=None, + height_division_factor=16, width_division_factor=16, + data_file_keys=("image",), + image_file_extension=("jpg", "jpeg", "png", "webp"), + repeat=1, + args=None, + ): + if args is not None: + base_path = args.dataset_base_path + metadata_path = args.dataset_metadata_path + height = args.height + width = args.width + max_pixels = args.max_pixels + data_file_keys = args.data_file_keys.split(",") + repeat = args.dataset_repeat + + self.base_path = base_path + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.data_file_keys = data_file_keys + self.image_file_extension = image_file_extension + self.repeat = repeat + + if height is not None and width is not None: + print("Height and width are fixed. Setting `dynamic_resolution` to False.") + self.dynamic_resolution = False + elif height is None and width is None: + print("Height and width are none. Setting `dynamic_resolution` to True.") + self.dynamic_resolution = True + + if metadata_path is None: + print("No metadata. Trying to generate it.") + metadata = self.generate_metadata(base_path) + print(f"{len(metadata)} lines in metadata.") + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + else: + metadata = pd.read_csv(metadata_path) + # Ensure prompt column is string type to avoid float conversion for NaN values + if 'prompt' in metadata.columns: + metadata['prompt'] = metadata['prompt'].astype(str) + # Replace 'nan' string (from NaN) with empty string + metadata['prompt'] = metadata['prompt'].replace('nan', '') + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + + def generate_metadata(self, folder): + image_list, prompt_list = [], [] + file_set = set(os.listdir(folder)) + for file_name in file_set: + if "." not in file_name: + continue + file_ext_name = file_name.split(".")[-1].lower() + file_base_name = file_name[:-len(file_ext_name)-1] + if file_ext_name not in self.image_file_extension: + continue + prompt_file_name = file_base_name + ".txt" + if prompt_file_name not in file_set: + continue + with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f: + prompt = f.read().strip() + image_list.append(file_name) + prompt_list.append(prompt) + metadata = pd.DataFrame() + metadata["image"] = image_list + metadata["prompt"] = prompt_list + return metadata + + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height*scale), round(width*scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + + def load_image(self, file_path): + image = Image.open(file_path).convert("RGB") + image = self.crop_and_resize(image, *self.get_height_width(image)) + return image + + + def load_data(self, file_path): + return self.load_image(file_path) + + + def __getitem__(self, data_id): + data = self.data[data_id % len(self.data)].copy() + for key in self.data_file_keys: + if key in data: + path = os.path.join(self.base_path, data[key]) + data[key] = self.load_data(path) + if data[key] is None: + warnings.warn(f"cannot load file {data[key]}.") + return None + return data + + + def __len__(self): + return len(self.data) * self.repeat + + + +class VideoDataset(torch.utils.data.Dataset): + def __init__( + self, + base_path=None, metadata_path=None, + num_frames=81, + time_division_factor=4, time_division_remainder=1, + max_pixels=1920*1080, height=None, width=None, + height_division_factor=16, width_division_factor=16, + data_file_keys=("video",), + image_file_extension=("jpg", "jpeg", "png", "webp"), + video_file_extension=("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"), + repeat=1, + args=None, + action_base_path=None, + enable_icl=False, + icl_num_examples=2, + icl_context_frames=8, + ): + if args is not None: + base_path = args.dataset_base_path + metadata_path = args.dataset_metadata_path + height = args.height + width = args.width + max_pixels = args.max_pixels + num_frames = args.num_frames + data_file_keys = args.data_file_keys.split(",") + repeat = args.dataset_repeat + # In-context learning parameters + if hasattr(args, 'enable_icl'): + enable_icl = args.enable_icl + if hasattr(args, 'icl_num_examples'): + icl_num_examples = args.icl_num_examples + if hasattr(args, 'icl_context_frames'): + icl_context_frames = args.icl_context_frames + + self.base_path = base_path + self.num_frames = num_frames + self.time_division_factor = time_division_factor + self.time_division_remainder = time_division_remainder + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.data_file_keys = data_file_keys + self.image_file_extension = image_file_extension + self.video_file_extension = video_file_extension + self.repeat = repeat + + # In-context learning parameters + self.enable_icl = enable_icl + self.icl_num_examples = icl_num_examples + self.icl_context_frames = icl_context_frames + + if height is not None and width is not None: + print("Height and width are fixed. Setting `dynamic_resolution` to False.") + self.dynamic_resolution = False + elif height is None and width is None: + print("Height and width are none. Setting `dynamic_resolution` to True.") + self.dynamic_resolution = True + + if metadata_path is None: + print("No metadata. Trying to generate it.") + metadata = self.generate_metadata(base_path) + print(f"{len(metadata)} lines in metadata.") + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + else: + metadata = pd.read_csv(metadata_path) + # Ensure prompt column is string type to avoid float conversion for NaN values + if 'prompt' in metadata.columns: + metadata['prompt'] = metadata['prompt'].astype(str) + # Replace 'nan' string (from NaN) with empty string + metadata['prompt'] = metadata['prompt'].replace('nan', '') + + # CRITICAL FIX: Clean prompt - remove video path prefix if present + # Some CSV prompts start with "video_name.mp4 " prefix, which should be removed + def clean_prompt(prompt_str): + if not isinstance(prompt_str, str) or not prompt_str: + return prompt_str + # Check if prompt starts with a video path (contains .mp4 or /) + # Pattern: "VideoName/1234_5678.mp4 " or "VideoName.mp4 " + import re + # Match pattern: word/word.mp4 or word.mp4 at the start, followed by space + pattern = r'^[A-Za-z0-9_]+(/[A-Za-z0-9_]+)?\.mp4\s+' + cleaned = re.sub(pattern, '', prompt_str) + # Also handle truncated prompts ending with "..." + if cleaned.endswith('...'): + cleaned = cleaned[:-3].rstrip() + return cleaned.strip() + + metadata['prompt'] = metadata['prompt'].apply(clean_prompt) + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + self.action_base_path = action_base_path + + if self.enable_icl: + print(f"In-context learning enabled: {icl_num_examples} examples, {icl_context_frames} context frames each") + + + def generate_metadata(self, folder): + video_list, prompt_list = [], [] + file_set = set(os.listdir(folder)) + for file_name in file_set: + if "." not in file_name: + continue + file_ext_name = file_name.split(".")[-1].lower() + file_base_name = file_name[:-len(file_ext_name)-1] + if file_ext_name not in self.image_file_extension and file_ext_name not in self.video_file_extension: + continue + prompt_file_name = file_base_name + ".txt" + if prompt_file_name not in file_set: + continue + with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f: + prompt = f.read().strip() + video_list.append(file_name) + prompt_list.append(prompt) + metadata = pd.DataFrame() + metadata["video"] = video_list + metadata["prompt"] = prompt_list + return metadata + + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height*scale), round(width*scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + + def get_num_frames(self, reader): + num_frames = self.num_frames + if int(reader.count_frames()) < num_frames: + num_frames = int(reader.count_frames()) + while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder: + num_frames -= 1 + return num_frames + + + def load_video(self, file_path): + reader = imageio.get_reader(file_path) + num_frames = self.get_num_frames(reader) + frames = [] + for frame_id in range(num_frames): + frame = reader.get_data(frame_id) + frame = Image.fromarray(frame) + frame = self.crop_and_resize(frame, *self.get_height_width(frame)) + frames.append(frame) + reader.close() + return frames + + + def load_image(self, file_path): + image = Image.open(file_path).convert("RGB") + image = self.crop_and_resize(image, *self.get_height_width(image)) + frames = [image] + return frames + + + def is_image(self, file_path): + file_ext_name = file_path.split(".")[-1] + return file_ext_name.lower() in self.image_file_extension + + + def is_video(self, file_path): + file_ext_name = file_path.split(".")[-1] + return file_ext_name.lower() in self.video_file_extension + + + def load_data(self, file_path): + # Handle multiple frame paths separated by '|' (for frame sequences) + if '|' in str(file_path): + # Split the path by '|' to get individual frame paths + frame_paths = str(file_path).split('|') + frames = [] + + # Get base_path (dataset root) + if not hasattr(self, 'base_path') or not self.base_path: + warnings.warn(f"Cannot determine base directory for frame sequence: {file_path}") + return None + + base_dir = self.base_path # This is the dataset root + + # Check the first path to determine the format + first_frame = frame_paths[0].strip() if frame_paths else "" + + # If first frame is already an absolute path (from __getitem__ joining), + # extract the base directory from it + if os.path.isabs(first_frame): + # Extract base directory from first frame path + # First frame format: /path/to/dataset/frames/video_name/frame.png + # We need to get /path/to/dataset + parts = first_frame.split(os.sep) + # Find 'frames' in the path and get everything before it + if 'frames' in parts: + frames_idx = parts.index('frames') + base_dir = os.sep.join(parts[:frames_idx]) + else: + # Fallback: use self.base_path + base_dir = self.base_path + + for frame_path in frame_paths: + frame_path = frame_path.strip() + if not frame_path: + continue + + # Construct full path + if os.path.isabs(frame_path): + # Already absolute path (from __getitem__) + full_frame_path = frame_path + else: + # Relative path - need to construct full path + # Remove 'frames/' prefix if present (we'll add it consistently) + if frame_path.startswith('frames/'): + frame_path = frame_path[7:] # Remove 'frames/' prefix + + # Always join with base_dir + 'frames/' since base_dir is dataset root + full_frame_path = os.path.join(base_dir, 'frames', frame_path) + + # Load individual frame + if os.path.exists(full_frame_path): + if self.is_image(full_frame_path): + frame_data = self.load_image(full_frame_path) + if frame_data: + frames.extend(frame_data) + else: + warnings.warn(f"Frame is not an image: {full_frame_path}") + else: + warnings.warn(f"Frame not found: {full_frame_path}") + + if frames: + return frames + else: + warnings.warn(f"No frames loaded from sequence: {file_path}") + return None + + # Handle single file (image or video) + if self.is_image(file_path): + return self.load_image(file_path) + elif self.is_video(file_path): + return self.load_video(file_path) + else: + return None + + + def __getitem__(self, data_id): + data = self.data[data_id % len(self.data)].copy() + for key in self.data_file_keys: + if key in ["video_name", "start_frame", "end_frame"]: + if "actions" in data: + continue + try: + video_name = data.get("video_name") + if video_name is None: + warnings.warn(f"video_name is missing in metadata for data_id {data_id}. Skipping action loading.") + continue + + if video_name.endswith(".mp4"): + video_name = ".".join(video_name.split(".")[:-1]) + if "_" in video_name: + video_name = "_".join(video_name.split("_")[:4]) + + import json + json_path = os.path.join(self.action_base_path, video_name + ".json") + + # Check if action file exists + if not os.path.exists(json_path): + warnings.warn(f"Action file does not exist: {json_path}. Skipping action loading for data_id {data_id}.") + continue + + start_frame = data.get("start_frame") + end_frame = data.get("end_frame") + if start_frame is None or end_frame is None: + warnings.warn(f"start_frame or end_frame is missing in metadata for data_id {data_id}. Skipping action loading.") + continue + + json_data = json.load(open(json_path, "r"))['actions'] + actions = [] + current_yaw = 0.0 + for frame_id in range(start_frame+1, end_frame+1): + frame_str = str(frame_id) + if frame_str not in json_data: + warnings.warn(f"Frame {frame_id} not found in action file {json_path}. Skipping this frame.") + continue + + action = json_data[frame_str] + new_action = [0.0] * (2 + 2 + 3 + 1 + 2) + if action['ws'] == 1: + new_action[0] = 1 + elif action['ws'] == 2: + new_action[1] = 1 + + if action['ad'] == 1: + new_action[2] = 1 + elif action['ad'] == 2: + new_action[3] = 1 + + if action['scs'] == 1 and action.get("jump_invalid", 0) == 0: + new_action[4] = 1 + elif action['scs'] == 2: + new_action[5] = 1 + elif action['scs'] == 3: + new_action[6] = 1 + + if action.get('collision', 0) == 1: + new_action[7] = 1 + new_action[0] = 0 + new_action[1] = 0 + new_action[2] = 0 + new_action[3] = 0 + + pre_pitch = action.get('pre_pitch', 0.0) + current_pitch = pre_pitch + action.get('pitch_delta', 0.0) * 15.0 + current_yaw += action.get('yaw_delta', 0.0) * 15.0 + new_action[8] = current_pitch + new_action[9] = current_yaw + + actions.append(new_action) + data["actions"] = actions + except Exception as e: + warnings.warn(f"Exception while loading actions for data_id {data_id}: {e}. Continuing without actions.") + # Don't return None, just continue without actions + continue + elif key == "video": + # Check if data[key] exists and is not None + if key not in data or data[key] is None: + warnings.warn(f"Video key '{key}' is missing or None in metadata for data_id {data_id}. Skipping this sample.") + return None + + # Handle frame sequences (paths with '|' separator) + video_path_str = str(data[key]) + if '|' in video_path_str: + # For frame sequences, pass the full path string to load_data + # load_data will handle splitting and loading individual frames + path = os.path.join(self.base_path, video_path_str) + # Don't check path existence here for frame sequences + # load_data will handle individual frame loading + else: + path = os.path.join(self.base_path, data[key]) + # Check if path exists (only for single files) + if not os.path.exists(path): + warnings.warn(f"Video file does not exist: {path}. Skipping this sample.") + return None + try: + data[key] = self.load_data(path) + if data[key] is None: + warnings.warn(f"Failed to load video file: {path}. load_data returned None.") + return None + except Exception as e: + warnings.warn(f"Exception while loading video file {path}: {e}. Skipping this sample.") + return None + + # In-context learning: sample context examples from dataset + if self.enable_icl and len(self.data) > 1: + context_frames_list = [] + context_actions_list = [] + + # Sample random examples from dataset (excluding current one) + current_idx = data_id % len(self.data) + candidate_indices = [i for i in range(len(self.data)) if i != current_idx] + if len(candidate_indices) > 0: + num_samples = min(self.icl_num_examples, len(candidate_indices)) + sampled_indices = random.sample(candidate_indices, num_samples) + + for sample_idx in sampled_indices: + sample_data = self.data[sample_idx].copy() + # Load video for context + if "video" in self.data_file_keys and "video" in sample_data: + video_path = os.path.join(self.base_path, sample_data["video"]) + sample_video = self.load_data(video_path) + if sample_video is not None and len(sample_video) >= self.icl_context_frames: + # Sample context_frames from the video + start_idx = random.randint(0, max(0, len(sample_video) - self.icl_context_frames)) + context_frames = sample_video[start_idx:start_idx + self.icl_context_frames] + context_frames_list.extend(context_frames) + + # Load corresponding actions if available + if self.action_base_path is not None and "video_name" in sample_data: + try: + sample_video_name = sample_data["video_name"] + if sample_video_name.endswith(".mp4"): + sample_video_name = ".".join(sample_video_name.split(".")[:-1]) + if "_" in sample_video_name: + sample_video_name = "_".join(sample_video_name.split("_")[:4]) + sample_json_path = os.path.join(self.action_base_path, sample_video_name + ".json") + if os.path.exists(sample_json_path): + sample_json_data = json.load(open(sample_json_path, "r"))['actions'] + sample_start_frame = sample_data.get("start_frame", 0) + sample_end_frame = sample_data.get("end_frame", len(sample_video)) + + # Get actions for the context frames + context_actions = [] + context_yaw = 0.0 + for frame_idx in range(sample_start_frame + start_idx + 1, + min(sample_start_frame + start_idx + self.icl_context_frames + 1, sample_end_frame + 1)): + if str(frame_idx) in sample_json_data: + action = sample_json_data[str(frame_idx)] + new_action = [0.0] * (2 + 2 + 3 + 1 + 2) + if action['ws'] == 1: + new_action[0] = 1 + elif action['ws'] == 2: + new_action[1] = 1 + if action['ad'] == 1: + new_action[2] = 1 + elif action['ad'] == 2: + new_action[3] = 1 + if action['scs'] == 1 and action.get("jump_invalid", 0) == 0: + new_action[4] = 1 + elif action['scs'] == 2: + new_action[5] = 1 + elif action['scs'] == 3: + new_action[6] = 1 + if action.get('collision', 0) == 1: + new_action[7] = 1 + new_action[0] = 0 + new_action[1] = 0 + new_action[2] = 0 + new_action[3] = 0 + pre_pitch = action.get('pre_pitch', 0.0) + current_pitch = pre_pitch + action.get('pitch_delta', 0.0) * 15.0 + context_yaw += action.get('yaw_delta', 0.0) * 15.0 + new_action[8] = current_pitch + new_action[9] = context_yaw + context_actions.append(new_action) + context_actions_list.extend(context_actions[:len(context_frames)]) + except Exception as e: + # If loading actions fails, just skip + pass + + if context_frames_list: + data["context_frames"] = context_frames_list + if context_actions_list and len(context_actions_list) == len(context_frames_list): + data["context_actions"] = context_actions_list + + return data + + + def __len__(self): + return len(self.data) * self.repeat + + @staticmethod + def get_one_hot(action, range=2): + one_hot = [0] * (range + 1) + one_hot[action] = 1 + return one_hot + + + +import numpy as np + + +class CamVideoDataset(torch.utils.data.Dataset): + """Dataset for Context-as-Memory camera pose conditioned training (ported from VWM). + + Loads 81 PNG frames from UE scenes with random temporal cropping and extracts + corresponding camera poses as 12-dim relative RT vectors subsampled to match + the 21 latent frames. + """ + def __init__( + self, + base_path=None, metadata_path=None, + num_frames=81, + height=None, width=None, + max_pixels=1920*1080, + height_division_factor=16, width_division_factor=16, + repeat=1, + args=None, + cam_position_scale=None, + ): + if args is not None: + base_path = args.dataset_base_path + metadata_path = getattr(args, "dataset_metadata_path", metadata_path) + height = args.height + width = args.width + max_pixels = args.max_pixels + num_frames = args.num_frames + repeat = args.dataset_repeat + cam_position_scale = getattr(args, "cam_position_scale", 0.01) + self.use_condition_context_frames = getattr(args, "use_condition_context_frames", False) + self.condition_first_frame = getattr(args, "condition_first_frame", False) + self.condition_history_keyframes = getattr(args, "condition_history_keyframes", False) + self.condition_use_camera_pose = getattr(args, "condition_use_camera_pose", True) + self.num_condition_frames = getattr(args, "num_condition_frames", 1) + self.condition_frame_mode = getattr(args, "condition_frame_mode", "first_frame_only") + self.overlap_labels_root = getattr(args, "overlap_labels_root", None) + self.condition_t2v_ratio = getattr(args, "condition_t2v_ratio", 0.10) + self.condition_i2v_ratio = getattr(args, "condition_i2v_ratio", 0.10) + self.use_geometry_spatial_memory = getattr(args, "use_geometry_spatial_memory", False) + self.geometry_memory_column = getattr(args, "geometry_memory_column", "geometry_memory") + self.geometry_memory_root = getattr(args, "geometry_memory_root", None) + else: + self.use_condition_context_frames = False + self.condition_first_frame = False + self.condition_history_keyframes = False + self.condition_use_camera_pose = True + self.num_condition_frames = 1 + self.condition_frame_mode = "first_frame_only" + self.overlap_labels_root = None + self.condition_t2v_ratio = 0.10 + self.condition_i2v_ratio = 0.10 + self.use_geometry_spatial_memory = False + self.geometry_memory_column = "geometry_memory" + self.geometry_memory_root = None + + if cam_position_scale is None: + cam_position_scale = 0.01 + self.cam_position_scale = float(cam_position_scale) + + self.base_path = base_path + self.frames_dir = os.path.join(base_path, "frames") + self.jsons_dir = os.path.join(base_path, "jsons") + self.num_frames = num_frames + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.repeat = repeat + + if height is not None and width is not None: + self.dynamic_resolution = False + else: + self.dynamic_resolution = True + + if metadata_path is None: + print("No metadata. Trying to generate it.") + metadata = self.generate_metadata(base_path) + print(f"{len(metadata)} lines in metadata.") + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + else: + metadata = pd.read_csv(metadata_path) + # Ensure prompt column is string type to avoid float conversion for NaN values + if 'prompt' in metadata.columns: + metadata['prompt'] = metadata['prompt'].astype(str) + # Replace 'nan' string (from NaN) with empty string + metadata['prompt'] = metadata['prompt'].replace('nan', '') + + # CRITICAL FIX: Clean prompt - remove video path prefix if present + # Some CSV prompts start with "video_name.mp4 " prefix, which should be removed + def clean_prompt(prompt_str): + if not isinstance(prompt_str, str) or not prompt_str: + return prompt_str + # Check if prompt starts with a video path (contains .mp4 or /) + # Pattern: "VideoName/1234_5678.mp4 " or "VideoName.mp4 " + import re + # Match pattern: word/word.mp4 or word.mp4 at the start, followed by space + pattern = r'^[A-Za-z0-9_]+(/[A-Za-z0-9_]+)?\.mp4\s+' + cleaned = re.sub(pattern, '', prompt_str) + # Also handle truncated prompts ending with "..." + if cleaned.endswith('...'): + cleaned = cleaned[:-3].rstrip() + return cleaned.strip() + + metadata['prompt'] = metadata['prompt'].apply(clean_prompt) + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + captions_path = os.path.join(base_path, "captions.txt") + self.scene_captions = {} + with open(captions_path, "r") as f: + for line in f: + parts = line.strip().split("\t", 1) + if len(parts) < 2: + continue + clip_path, caption = parts + scene_name = "/".join(clip_path.split("/")[:-1]) + fname = clip_path.split("/")[-1].replace(".mp4", "") + clip_start = int(fname.split("_")[0]) + if scene_name not in self.scene_captions: + self.scene_captions[scene_name] = [] + self.scene_captions[scene_name].append((clip_start, caption)) + + for scene_name in self.scene_captions: + self.scene_captions[scene_name].sort(key=lambda x: x[0]) + + self.scene_names = sorted(self.scene_captions.keys()) + self.metadata_rows = [] + if metadata_path and os.path.isfile(metadata_path): + metadata = pd.read_csv(metadata_path) + if "prompt" in metadata.columns: + metadata["prompt"] = metadata["prompt"].astype(str) + self.metadata_rows = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + self.pose_cache = {} + self.overlap_cache = {} + self.invalid_scenes = set() + self.invalid_metadata_indices = set() + self.overlap_labels_root = self._resolve_overlap_labels_root(base_path, self.overlap_labels_root) + self._validate_condition_config() + + total_items = len(self.data) + total_scenes = len(self.scene_names) + total_captions = sum(len(v) for v in self.scene_captions.values()) + metadata_msg = f", metadata_rows={len(self.metadata_rows)}" if self.metadata_rows else "" + effective_len = (len(self.metadata_rows) if self.metadata_rows else total_scenes) * repeat + print(f"CamVideoDataset: {total_scenes} scenes, {total_captions} captions{metadata_msg}, " + f"repeat={repeat}, cam_position_scale={self.cam_position_scale}, " + f"effective length={total_items}") + + def _resolve_overlap_labels_root(self, base_path, overlap_labels_root): + candidate_roots = [] + if overlap_labels_root is not None: + candidate_roots.append(overlap_labels_root) + if base_path is not None: + candidate_roots.append(os.path.join(base_path, "overlap_labels")) + for root in candidate_roots: + if root is not None and os.path.isdir(root): + return root + return overlap_labels_root + + def _validate_condition_config(self): + if self.condition_t2v_ratio < 0 or self.condition_i2v_ratio < 0: + raise ValueError("Condition sampling ratios must be non-negative.") + if self.condition_t2v_ratio + self.condition_i2v_ratio >= 1.0: + raise ValueError("condition_t2v_ratio + condition_i2v_ratio must be < 1.0.") + needs_overlap = ( + self.use_condition_context_frames + and self.condition_frame_mode == "first_plus_overlap" + and self.condition_history_keyframes + and self.num_condition_frames > 1 + ) + if needs_overlap and (self.overlap_labels_root is None or not os.path.isdir(self.overlap_labels_root)): + raise FileNotFoundError( + "K-frame condition mode requires overlap_labels_root. " + "Pass --overlap_labels_root or keep overlap_labels under dataset_base_path/overlap_labels." + ) + + def _load_scene_poses(self, scene_name): + if scene_name not in self.pose_cache: + json_path = os.path.join(self.jsons_dir, scene_name + ".json") + try: + with open(json_path, "r") as f: + data = json.load(f) + except (FileNotFoundError, json.JSONDecodeError) as e: + raise ValueError(f"Pose JSON for scene '{scene_name}' is missing or corrupt: {e}") + if not isinstance(data, dict) or "CineCameraActor" not in data: + raise ValueError( + f"Pose JSON for scene '{scene_name}' lacks 'CineCameraActor' key " + f"(found keys: {list(data.keys()) if isinstance(data, dict) else type(data).__name__})." + ) + cine = data["CineCameraActor"] + if not isinstance(cine, dict) or len(cine) == 0: + raise ValueError(f"Pose JSON for scene '{scene_name}' has empty 'CineCameraActor' entries.") + self.pose_cache[scene_name] = cine + return self.pose_cache[scene_name] + + def _find_nearest_caption(self, scene_name, start_frame): + captions = self.scene_captions[scene_name] + best_idx = 0 + best_dist = abs(captions[0][0] - start_frame) + for i, (clip_start, _) in enumerate(captions): + dist = abs(clip_start - start_frame) + if dist < best_dist: + best_dist = dist + best_idx = i + return captions[best_idx][1] + + @staticmethod + def _compute_rt(position, rotation): + x, y, z = position + yaw_rad = np.radians(rotation[2]) + cos_y, sin_y = np.cos(yaw_rad), np.sin(yaw_rad) + R = np.array([[cos_y, -sin_y, 0], [sin_y, cos_y, 0], [0, 0, 1]]) + return [x, y, z] + R.flatten().tolist() + + @staticmethod + def _to_relative_rt(rt_list, ref_rt): + R_ref = np.array(ref_rt[3:]).reshape(3, 3) + T_ref = np.array(ref_rt[:3]).reshape(3, 1) + R_ref_inv = R_ref.T + T_ref_inv = -R_ref_inv @ T_ref + result = [] + for rt in rt_list: + R_i = np.array(rt[3:]).reshape(3, 3) + T_i = np.array(rt[:3]).reshape(3, 1) + R_new = R_ref_inv @ R_i + T_new = R_ref_inv @ T_i + T_ref_inv + result.append(T_new.flatten().tolist() + R_new.flatten().tolist()) + return result + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height * scale), round(width * scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + def _load_resized_frame(self, scene_name, frame_index, target_height, target_width): + frame_path = os.path.join(self.frames_dir, scene_name, f"{frame_index:04d}.png") + img = Image.open(frame_path).convert("RGB") + return self.crop_and_resize(img, target_height, target_width) + + @staticmethod + def _parse_frame_token(token): + token = str(token).strip() + if not token: + return None, None + parts = token.split("/") + if parts and parts[0] == "frames": + parts = parts[1:] + if len(parts) < 2: + return None, None + scene_name = "/".join(parts[:-1]) + stem = os.path.splitext(parts[-1])[0] + try: + frame_index = int(stem) + except ValueError: + return None, None + return scene_name, frame_index + + def _metadata_scene_and_indices(self, row): + video_field = str(row.get("video", "") or "") + tokens = [t for t in video_field.split("|") if t] + parsed = [self._parse_frame_token(t) for t in tokens] + parsed = [(s, i) for s, i in parsed if s is not None and i is not None] + if parsed: + scene_name = str(row.get("video_name", "") or parsed[0][0]) + frame_indices = [i for _, i in parsed[: self.num_frames]] + else: + scene_name = str(row.get("video_name", "") or "").strip() + if not scene_name: + raise ValueError("metadata row lacks video_name and parseable video paths") + start_frame = int(row.get("start_frame", 0) or 0) + frame_indices = list(range(start_frame, start_frame + self.num_frames)) + if len(frame_indices) < self.num_frames: + raise ValueError(f"metadata row has {len(frame_indices)} frames, expected {self.num_frames}") + return scene_name, frame_indices[: self.num_frames] + + def _resolve_geometry_path(self, token): + token = str(token).strip() + if not token: + return None + if os.path.isabs(token): + return token + root = self.geometry_memory_root or self.base_path + return os.path.join(root, token) + + def _load_geometry_memory_frames(self, row): + value = row.get(self.geometry_memory_column, None) + if value is None or str(value).strip() == "" or str(value).lower() == "nan": + if self.use_geometry_spatial_memory: + raise ValueError( + f"metadata row lacks required geometry column '{self.geometry_memory_column}'" + ) + return [] + tokens = [token for token in str(value).split("|") if token.strip()] + if not tokens: + raise ValueError(f"empty geometry memory field '{self.geometry_memory_column}'") + + if len(tokens) == 1: + path = self._resolve_geometry_path(tokens[0]) + if path is None: + return [] + if os.path.isdir(path): + names = sorted( + name + for name in os.listdir(path) + if os.path.splitext(name)[1].lower() in (".png", ".jpg", ".jpeg", ".webp") + ) + paths = [os.path.join(path, name) for name in names] + return [Image.open(frame_path).convert("RGB") for frame_path in paths] + if os.path.splitext(path)[1].lower() in (".mp4", ".mov", ".avi", ".mkv", ".webm"): + reader = imageio.get_reader(path) + try: + frames = [Image.fromarray(frame).convert("RGB") for frame in reader] + finally: + reader.close() + if not frames: + raise ValueError(f"geometry memory video has no frames: {path}") + return frames + + frames = [] + for token in tokens: + path = self._resolve_geometry_path(token) + if path is None or not os.path.isfile(path): + raise FileNotFoundError(f"geometry memory frame not found: {path}") + frames.append(Image.open(path).convert("RGB")) + return frames + + def _load_overlap_frames(self, scene_name, frame_index): + if self.overlap_labels_root is None: + return [] + cache_key = (scene_name, int(frame_index)) + if cache_key not in self.overlap_cache: + overlap_path = os.path.join(self.overlap_labels_root, scene_name, f"{int(frame_index)}.json") + if not os.path.exists(overlap_path): + self.overlap_cache[cache_key] = [] + else: + with open(overlap_path, "r") as f: + overlap_data = json.load(f) + overlaps = overlap_data.get("overlapping_frames", []) + self.overlap_cache[cache_key] = [int(idx) for idx in overlaps] + return self.overlap_cache[cache_key] + + def _compute_scene_rt(self, scene_name, frame_index): + frame_data = self._load_scene_poses(scene_name)[str(int(frame_index))] + raw_pos = frame_data["position"] + pos = [float(p) * self.cam_position_scale for p in raw_pos] + return self._compute_rt(pos, frame_data["rotation"]) + + def _sample_condition_mode(self): + if not self.use_condition_context_frames: + return "disabled" + if ( + self.condition_frame_mode != "first_plus_overlap" + or not self.condition_history_keyframes + or self.num_condition_frames <= 1 + ): + return "first_frame_only" + sample = random.random() + if sample < self.condition_t2v_ratio: + return "text_only" + if sample < self.condition_t2v_ratio + self.condition_i2v_ratio: + return "first_frame_only" + return "first_plus_overlap" + + def _sample_overlap_conditions(self, scene_name, start_frame, ref_rt, target_height, target_width, num_extra_conditions): + if num_extra_conditions <= 0: + return [], [], [] + window_indices = set(range(start_frame, start_frame + self.num_frames)) + target_candidates = list(range(start_frame + 1, start_frame + self.num_frames)) + sampled_target_frames = random.sample(target_candidates, k=min(num_extra_conditions, len(target_candidates))) + overlap_frames = [] + overlap_indices = [] + overlap_actions = [] + used_condition_indices = set() + for target_frame_idx in sampled_target_frames: + candidate_indices = [ + idx for idx in self._load_overlap_frames(scene_name, target_frame_idx) + if idx not in window_indices and idx != target_frame_idx and idx not in used_condition_indices + ] + if len(candidate_indices) == 0: + return None + chosen_idx = random.choice(candidate_indices) + used_condition_indices.add(chosen_idx) + overlap_indices.append(chosen_idx) + overlap_frames.append(self._load_resized_frame(scene_name, chosen_idx, target_height, target_width)) + if self.condition_use_camera_pose: + overlap_rt = self._compute_scene_rt(scene_name, chosen_idx) + overlap_actions.append(self._to_relative_rt([overlap_rt], ref_rt)[0]) + if len(overlap_frames) != num_extra_conditions: + return None + return overlap_frames, overlap_indices, overlap_actions + + def _try_get_sample(self, data_item): + scene_name = data_item["video_name"] + cam_data = self._load_scene_poses(scene_name) + max_start = len(cam_data) - self.num_frames + if max_start < 0: + raise ValueError(f"Scene {scene_name} has fewer than {self.num_frames} frames.") + start_frame = data_item["start_frame"] + end_frame = start_frame + self.num_frames - 1 + assert end_frame == data_item["end_frame"] + + frames = [] + for i in range(start_frame, end_frame + 1): + frame_path = os.path.join(self.frames_dir, scene_name, f"{i:04d}.png") + img = Image.open(frame_path).convert("RGB") + img = self.crop_and_resize(img, *self.get_height_width(img)) + frames.append(img) + + # prompt = self._find_nearest_caption(scene_name, start_frame) + prompt = data_item["prompt"] + + rt_list_abs = [] + for i in range(start_frame, end_frame + 1): + key = str(i) + if key not in cam_data: + raise ValueError(f"Scene {scene_name} missing pose for frame {i}.") + frame_data = cam_data[key] + raw_pos = frame_data["position"] + pos = [float(p) * self.cam_position_scale for p in raw_pos] + rt = self._compute_rt(pos, frame_data["rotation"]) + rt_list_abs.append(rt) + + rt_list = self._to_relative_rt(rt_list_abs, rt_list_abs[0]) + pose_indices = list(range(0, self.num_frames, 4)) + actions = [rt_list[i] for i in pose_indices] + + return { + "video": frames, + "prompt": prompt, + "actions": actions, + "video_name": scene_name, + "start_frame": start_frame, + "end_frame": end_frame, + **self._build_condition_context_payload( + frames=frames, + scene_name=scene_name, + start_frame=start_frame, + ref_rt=rt_list_abs[0], + actions=actions, + ), + } + + def _try_get_metadata_sample(self, row): + scene_name, frame_indices = self._metadata_scene_and_indices(row) + start_frame = int(frame_indices[0]) + end_frame = int(frame_indices[-1]) + cam_data = self._load_scene_poses(scene_name) + + frames = [] + for frame_idx in frame_indices: + frame_path = os.path.join(self.frames_dir, scene_name, f"{int(frame_idx):04d}.png") + img = Image.open(frame_path).convert("RGB") + img = self.crop_and_resize(img, *self.get_height_width(img)) + frames.append(img) + + prompt = row.get("prompt", None) + if prompt is None or str(prompt).strip() == "" or str(prompt).lower() == "nan": + prompt = self._find_nearest_caption(scene_name, start_frame) + else: + prompt = str(prompt) + + rt_list_abs = [] + for frame_idx in frame_indices: + key = str(int(frame_idx)) + if key not in cam_data: + raise ValueError(f"Scene {scene_name} missing pose for frame {frame_idx}.") + frame_data = cam_data[key] + raw_pos = frame_data["position"] + pos = [float(p) * self.cam_position_scale for p in raw_pos] + rt = self._compute_rt(pos, frame_data["rotation"]) + rt_list_abs.append(rt) + + rt_list = self._to_relative_rt(rt_list_abs, rt_list_abs[0]) + pose_indices = list(range(0, len(frame_indices), 4)) + actions = [rt_list[i] for i in pose_indices] + geometry_memory_frames = self._load_geometry_memory_frames(row) + + return { + "video": frames, + "prompt": prompt, + "actions": actions, + "video_name": scene_name, + "start_frame": start_frame, + "end_frame": end_frame, + "geometry_memory_frames": geometry_memory_frames, + **self._build_condition_context_payload( + frames=frames, + scene_name=scene_name, + start_frame=start_frame, + ref_rt=rt_list_abs[0], + actions=actions, + ), + } + + def __getitem__(self, data_id): + n = len(self.data) + if n == 0: + raise RuntimeError("CamVideoDataset has no scenes.") + max_attempts = min(64, n) + last_error = None + for attempt in range(max_attempts): + idx = (data_id + attempt) % n + data_item = self.data[idx] + scene_name = data_item.get("video_name", "?") if isinstance(data_item, dict) else "?" + if scene_name in self.invalid_scenes: + continue + try: + return self._try_get_sample(data_item) + except (ValueError, FileNotFoundError, KeyError, OSError) as e: + self.invalid_scenes.add(scene_name) + last_error = e + if attempt < 3 or attempt % 8 == 0: + print( + f"[CamVideoDataset] Skipping invalid scene '{scene_name}' " + f"({type(e).__name__}: {e}); attempt {attempt + 1}/{max_attempts}" + ) + continue + raise RuntimeError( + f"CamVideoDataset: exhausted {max_attempts} attempts starting from index {data_id}; " + f"last error: {type(last_error).__name__}: {last_error}" + ) + + def _build_condition_context_payload(self, frames, scene_name, start_frame, ref_rt, actions): + if not self.use_condition_context_frames: + return {} + payload = { + "use_condition_context_frames": False, + "condition_frames": [], + "condition_frame_indices": [], + "condition_source": None, + "condition_actions": [], + } + condition_mode = self._sample_condition_mode() + payload["condition_source"] = condition_mode + if condition_mode == "text_only": + return payload + payload["use_condition_context_frames"] = True + if self.condition_first_frame: + payload["condition_frames"].append(frames[0]) + payload["condition_frame_indices"].append(start_frame) + payload["condition_source"] = "first_frame_only" + if self.condition_use_camera_pose and actions: + payload["condition_actions"].append(list(actions[0])) + if ( + condition_mode == "first_plus_overlap" + and self.condition_history_keyframes + and self.num_condition_frames > len(payload["condition_frames"]) + ): + num_extra_conditions = self.num_condition_frames - len(payload["condition_frames"]) + overlap_payload = self._sample_overlap_conditions( + scene_name=scene_name, + start_frame=start_frame, + ref_rt=ref_rt, + target_height=frames[0].size[1], + target_width=frames[0].size[0], + num_extra_conditions=num_extra_conditions, + ) + if overlap_payload is None: + return payload + overlap_frames, overlap_indices, overlap_actions = overlap_payload + payload["condition_frames"].extend(overlap_frames) + payload["condition_frame_indices"].extend(overlap_indices) + if self.condition_use_camera_pose: + payload["condition_actions"].extend(overlap_actions) + payload["condition_source"] = "first_plus_overlap" + return payload + + def __len__(self): + return len(self.data) + + +class DiffusionTrainingModule(torch.nn.Module): + def __init__(self): + super().__init__() + + + def to(self, *args, **kwargs): + for name, model in self.named_children(): + model.to(*args, **kwargs) + return self + + + def trainable_modules(self): + trainable_modules = filter(lambda p: p.requires_grad, self.parameters()) + return trainable_modules + + + def trainable_param_names(self): + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.named_parameters())) + trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) + return trainable_param_names + + + def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None): + if lora_alpha is None: + lora_alpha = lora_rank + lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules) + model = inject_adapter_in_model(lora_config, model) + return model + + + def export_trainable_state_dict(self, state_dict, remove_prefix=None): + trainable_param_names = self.trainable_param_names() + state_dict = {name: param for name, param in state_dict.items() if name in trainable_param_names} + if remove_prefix is not None: + state_dict_ = {} + for name, param in state_dict.items(): + if name.startswith(remove_prefix): + name = name[len(remove_prefix):] + state_dict_[name] = param + state_dict = state_dict_ + return state_dict + + + +class ModelLogger: + def __init__(self, output_path, remove_prefix_in_ckpt=None, state_dict_converter=lambda x:x): + self.output_path = output_path + self.remove_prefix_in_ckpt = remove_prefix_in_ckpt + self.state_dict_converter = state_dict_converter + + + def on_step_end(self, loss): + pass + + + def on_epoch_end(self, accelerator, model, epoch_id): + accelerator.wait_for_everyone() + if accelerator.is_main_process: + state_dict = accelerator.get_state_dict(model) + state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt) + state_dict = self.state_dict_converter(state_dict) + os.makedirs(self.output_path, exist_ok=True) + path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors") + accelerator.save(state_dict, path, safe_serialization=True) + + + +def launch_training_task( + dataset: torch.utils.data.Dataset, + model: DiffusionTrainingModule, + model_logger: ModelLogger, + optimizer: torch.optim.Optimizer, + scheduler: torch.optim.lr_scheduler.LRScheduler, + num_epochs: int = 1, + gradient_accumulation_steps: int = 1, +): + dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], drop_last=True) + accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps) + model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler) + + for epoch_id in range(num_epochs): + for data in tqdm(dataloader): + with accelerator.accumulate(model): + optimizer.zero_grad() + loss = model(data) + accelerator.backward(loss) + optimizer.step() + model_logger.on_step_end(loss) + scheduler.step() + model_logger.on_epoch_end(accelerator, model, epoch_id) + +def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_path="./models"): + dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], drop_last=True) + accelerator = Accelerator() + model, dataloader = accelerator.prepare(model, dataloader) + os.makedirs(os.path.join(output_path, "data_cache"), exist_ok=True) + for data_id, data in enumerate(tqdm(dataloader)): + with torch.no_grad(): + inputs = model.forward_preprocess(data) + inputs = {key: inputs[key] for key in model.model_input_keys if key in inputs} + torch.save(inputs, os.path.join(output_path, "data_cache", f"{data_id}.pth")) + + + +def wan_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") + parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") + parser.add_argument("--max_pixels", type=int, default=1280*720, help="Maximum number of pixels per frame, used for dynamic resolution..") + parser.add_argument("--height", type=int, default=None, help="Height of images or videos. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--width", type=int, default=None, help="Width of images or videos. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--num_frames", type=int, default=81, help="Number of frames per video. Frames are sampled from the video prefix.") + parser.add_argument("--data_file_keys", type=str, default="image,video", help="Data file keys in the metadata. Comma-separated.") + parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") + parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") + parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") + parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") + parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") + parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") + parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") + parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") + parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") + parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") + parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") + parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") + parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") + parser.add_argument("--use_condition_context_frames", default=False, action="store_true", help="Enable appended clean condition latents.") + parser.add_argument("--condition_first_frame", default=False, action="store_true", help="Use the current clip first frame as a clean condition frame.") + parser.add_argument("--condition_history_keyframes", default=False, action="store_true", help="Use overlap-based keyframes as conditions.") + parser.add_argument("--condition_use_camera_pose", default=True, action="store_true", help="Inject camera pose for condition frames.") + parser.add_argument("--num_condition_frames", type=int, default=1, help="Number of condition frames.") + parser.add_argument("--condition_frame_mode", type=str, default="first_frame_only", help="Condition frame selection mode.") + parser.add_argument("--overlap_labels_root", type=str, default=None, help="Root dir for overlap label JSONs.") + parser.add_argument("--condition_t2v_ratio", type=float, default=0.10, help="Ratio of text-only condition samples.") + parser.add_argument("--condition_i2v_ratio", type=float, default=0.10, help="Ratio of first-frame-only condition samples.") + return parser + + + +def flux_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") + parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") + parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..") + parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--data_file_keys", type=str, default="image", help="Data file keys in the metadata. Comma-separated.") + parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") + parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") + parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") + parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") + parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") + parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") + parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") + parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") + parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") + parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") + parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") + parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.") + parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.") + parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") + parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") + return parser diff --git a/code/diffsynth/trainers/utils.py.p0bak-getitem b/code/diffsynth/trainers/utils.py.p0bak-getitem new file mode 100644 index 0000000000000000000000000000000000000000..f77ac98aad40c118ebf2519976e64794c0df0338 --- /dev/null +++ b/code/diffsynth/trainers/utils.py.p0bak-getitem @@ -0,0 +1,1367 @@ +import imageio, os, torch, warnings, torchvision, argparse, json, random +from peft import LoraConfig, inject_adapter_in_model +from PIL import Image +import pandas as pd +from tqdm import tqdm +from accelerate import Accelerator + + + +class ImageDataset(torch.utils.data.Dataset): + def __init__( + self, + base_path=None, metadata_path=None, + max_pixels=1920*1080, height=None, width=None, + height_division_factor=16, width_division_factor=16, + data_file_keys=("image",), + image_file_extension=("jpg", "jpeg", "png", "webp"), + repeat=1, + args=None, + ): + if args is not None: + base_path = args.dataset_base_path + metadata_path = args.dataset_metadata_path + height = args.height + width = args.width + max_pixels = args.max_pixels + data_file_keys = args.data_file_keys.split(",") + repeat = args.dataset_repeat + + self.base_path = base_path + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.data_file_keys = data_file_keys + self.image_file_extension = image_file_extension + self.repeat = repeat + + if height is not None and width is not None: + print("Height and width are fixed. Setting `dynamic_resolution` to False.") + self.dynamic_resolution = False + elif height is None and width is None: + print("Height and width are none. Setting `dynamic_resolution` to True.") + self.dynamic_resolution = True + + if metadata_path is None: + print("No metadata. Trying to generate it.") + metadata = self.generate_metadata(base_path) + print(f"{len(metadata)} lines in metadata.") + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + else: + metadata = pd.read_csv(metadata_path) + # Ensure prompt column is string type to avoid float conversion for NaN values + if 'prompt' in metadata.columns: + metadata['prompt'] = metadata['prompt'].astype(str) + # Replace 'nan' string (from NaN) with empty string + metadata['prompt'] = metadata['prompt'].replace('nan', '') + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + + def generate_metadata(self, folder): + image_list, prompt_list = [], [] + file_set = set(os.listdir(folder)) + for file_name in file_set: + if "." not in file_name: + continue + file_ext_name = file_name.split(".")[-1].lower() + file_base_name = file_name[:-len(file_ext_name)-1] + if file_ext_name not in self.image_file_extension: + continue + prompt_file_name = file_base_name + ".txt" + if prompt_file_name not in file_set: + continue + with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f: + prompt = f.read().strip() + image_list.append(file_name) + prompt_list.append(prompt) + metadata = pd.DataFrame() + metadata["image"] = image_list + metadata["prompt"] = prompt_list + return metadata + + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height*scale), round(width*scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + + def load_image(self, file_path): + image = Image.open(file_path).convert("RGB") + image = self.crop_and_resize(image, *self.get_height_width(image)) + return image + + + def load_data(self, file_path): + return self.load_image(file_path) + + + def __getitem__(self, data_id): + data = self.data[data_id % len(self.data)].copy() + for key in self.data_file_keys: + if key in data: + path = os.path.join(self.base_path, data[key]) + data[key] = self.load_data(path) + if data[key] is None: + warnings.warn(f"cannot load file {data[key]}.") + return None + return data + + + def __len__(self): + return len(self.data) * self.repeat + + + +class VideoDataset(torch.utils.data.Dataset): + def __init__( + self, + base_path=None, metadata_path=None, + num_frames=81, + time_division_factor=4, time_division_remainder=1, + max_pixels=1920*1080, height=None, width=None, + height_division_factor=16, width_division_factor=16, + data_file_keys=("video",), + image_file_extension=("jpg", "jpeg", "png", "webp"), + video_file_extension=("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"), + repeat=1, + args=None, + action_base_path=None, + enable_icl=False, + icl_num_examples=2, + icl_context_frames=8, + ): + if args is not None: + base_path = args.dataset_base_path + metadata_path = args.dataset_metadata_path + height = args.height + width = args.width + max_pixels = args.max_pixels + num_frames = args.num_frames + data_file_keys = args.data_file_keys.split(",") + repeat = args.dataset_repeat + # In-context learning parameters + if hasattr(args, 'enable_icl'): + enable_icl = args.enable_icl + if hasattr(args, 'icl_num_examples'): + icl_num_examples = args.icl_num_examples + if hasattr(args, 'icl_context_frames'): + icl_context_frames = args.icl_context_frames + + self.base_path = base_path + self.num_frames = num_frames + self.time_division_factor = time_division_factor + self.time_division_remainder = time_division_remainder + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.data_file_keys = data_file_keys + self.image_file_extension = image_file_extension + self.video_file_extension = video_file_extension + self.repeat = repeat + + # In-context learning parameters + self.enable_icl = enable_icl + self.icl_num_examples = icl_num_examples + self.icl_context_frames = icl_context_frames + + if height is not None and width is not None: + print("Height and width are fixed. Setting `dynamic_resolution` to False.") + self.dynamic_resolution = False + elif height is None and width is None: + print("Height and width are none. Setting `dynamic_resolution` to True.") + self.dynamic_resolution = True + + if metadata_path is None: + print("No metadata. Trying to generate it.") + metadata = self.generate_metadata(base_path) + print(f"{len(metadata)} lines in metadata.") + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + else: + metadata = pd.read_csv(metadata_path) + # Ensure prompt column is string type to avoid float conversion for NaN values + if 'prompt' in metadata.columns: + metadata['prompt'] = metadata['prompt'].astype(str) + # Replace 'nan' string (from NaN) with empty string + metadata['prompt'] = metadata['prompt'].replace('nan', '') + + # CRITICAL FIX: Clean prompt - remove video path prefix if present + # Some CSV prompts start with "video_name.mp4 " prefix, which should be removed + def clean_prompt(prompt_str): + if not isinstance(prompt_str, str) or not prompt_str: + return prompt_str + # Check if prompt starts with a video path (contains .mp4 or /) + # Pattern: "VideoName/1234_5678.mp4 " or "VideoName.mp4 " + import re + # Match pattern: word/word.mp4 or word.mp4 at the start, followed by space + pattern = r'^[A-Za-z0-9_]+(/[A-Za-z0-9_]+)?\.mp4\s+' + cleaned = re.sub(pattern, '', prompt_str) + # Also handle truncated prompts ending with "..." + if cleaned.endswith('...'): + cleaned = cleaned[:-3].rstrip() + return cleaned.strip() + + metadata['prompt'] = metadata['prompt'].apply(clean_prompt) + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + self.action_base_path = action_base_path + + if self.enable_icl: + print(f"In-context learning enabled: {icl_num_examples} examples, {icl_context_frames} context frames each") + + + def generate_metadata(self, folder): + video_list, prompt_list = [], [] + file_set = set(os.listdir(folder)) + for file_name in file_set: + if "." not in file_name: + continue + file_ext_name = file_name.split(".")[-1].lower() + file_base_name = file_name[:-len(file_ext_name)-1] + if file_ext_name not in self.image_file_extension and file_ext_name not in self.video_file_extension: + continue + prompt_file_name = file_base_name + ".txt" + if prompt_file_name not in file_set: + continue + with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f: + prompt = f.read().strip() + video_list.append(file_name) + prompt_list.append(prompt) + metadata = pd.DataFrame() + metadata["video"] = video_list + metadata["prompt"] = prompt_list + return metadata + + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height*scale), round(width*scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + + def get_num_frames(self, reader): + num_frames = self.num_frames + if int(reader.count_frames()) < num_frames: + num_frames = int(reader.count_frames()) + while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder: + num_frames -= 1 + return num_frames + + + def load_video(self, file_path): + reader = imageio.get_reader(file_path) + num_frames = self.get_num_frames(reader) + frames = [] + for frame_id in range(num_frames): + frame = reader.get_data(frame_id) + frame = Image.fromarray(frame) + frame = self.crop_and_resize(frame, *self.get_height_width(frame)) + frames.append(frame) + reader.close() + return frames + + + def load_image(self, file_path): + image = Image.open(file_path).convert("RGB") + image = self.crop_and_resize(image, *self.get_height_width(image)) + frames = [image] + return frames + + + def is_image(self, file_path): + file_ext_name = file_path.split(".")[-1] + return file_ext_name.lower() in self.image_file_extension + + + def is_video(self, file_path): + file_ext_name = file_path.split(".")[-1] + return file_ext_name.lower() in self.video_file_extension + + + def load_data(self, file_path): + # Handle multiple frame paths separated by '|' (for frame sequences) + if '|' in str(file_path): + # Split the path by '|' to get individual frame paths + frame_paths = str(file_path).split('|') + frames = [] + + # Get base_path (dataset root) + if not hasattr(self, 'base_path') or not self.base_path: + warnings.warn(f"Cannot determine base directory for frame sequence: {file_path}") + return None + + base_dir = self.base_path # This is the dataset root + + # Check the first path to determine the format + first_frame = frame_paths[0].strip() if frame_paths else "" + + # If first frame is already an absolute path (from __getitem__ joining), + # extract the base directory from it + if os.path.isabs(first_frame): + # Extract base directory from first frame path + # First frame format: /path/to/dataset/frames/video_name/frame.png + # We need to get /path/to/dataset + parts = first_frame.split(os.sep) + # Find 'frames' in the path and get everything before it + if 'frames' in parts: + frames_idx = parts.index('frames') + base_dir = os.sep.join(parts[:frames_idx]) + else: + # Fallback: use self.base_path + base_dir = self.base_path + + for frame_path in frame_paths: + frame_path = frame_path.strip() + if not frame_path: + continue + + # Construct full path + if os.path.isabs(frame_path): + # Already absolute path (from __getitem__) + full_frame_path = frame_path + else: + # Relative path - need to construct full path + # Remove 'frames/' prefix if present (we'll add it consistently) + if frame_path.startswith('frames/'): + frame_path = frame_path[7:] # Remove 'frames/' prefix + + # Always join with base_dir + 'frames/' since base_dir is dataset root + full_frame_path = os.path.join(base_dir, 'frames', frame_path) + + # Load individual frame + if os.path.exists(full_frame_path): + if self.is_image(full_frame_path): + frame_data = self.load_image(full_frame_path) + if frame_data: + frames.extend(frame_data) + else: + warnings.warn(f"Frame is not an image: {full_frame_path}") + else: + warnings.warn(f"Frame not found: {full_frame_path}") + + if frames: + return frames + else: + warnings.warn(f"No frames loaded from sequence: {file_path}") + return None + + # Handle single file (image or video) + if self.is_image(file_path): + return self.load_image(file_path) + elif self.is_video(file_path): + return self.load_video(file_path) + else: + return None + + + def __getitem__(self, data_id): + data = self.data[data_id % len(self.data)].copy() + for key in self.data_file_keys: + if key in ["video_name", "start_frame", "end_frame"]: + if "actions" in data: + continue + try: + video_name = data.get("video_name") + if video_name is None: + warnings.warn(f"video_name is missing in metadata for data_id {data_id}. Skipping action loading.") + continue + + if video_name.endswith(".mp4"): + video_name = ".".join(video_name.split(".")[:-1]) + if "_" in video_name: + video_name = "_".join(video_name.split("_")[:4]) + + import json + json_path = os.path.join(self.action_base_path, video_name + ".json") + + # Check if action file exists + if not os.path.exists(json_path): + warnings.warn(f"Action file does not exist: {json_path}. Skipping action loading for data_id {data_id}.") + continue + + start_frame = data.get("start_frame") + end_frame = data.get("end_frame") + if start_frame is None or end_frame is None: + warnings.warn(f"start_frame or end_frame is missing in metadata for data_id {data_id}. Skipping action loading.") + continue + + json_data = json.load(open(json_path, "r"))['actions'] + actions = [] + current_yaw = 0.0 + for frame_id in range(start_frame+1, end_frame+1): + frame_str = str(frame_id) + if frame_str not in json_data: + warnings.warn(f"Frame {frame_id} not found in action file {json_path}. Skipping this frame.") + continue + + action = json_data[frame_str] + new_action = [0.0] * (2 + 2 + 3 + 1 + 2) + if action['ws'] == 1: + new_action[0] = 1 + elif action['ws'] == 2: + new_action[1] = 1 + + if action['ad'] == 1: + new_action[2] = 1 + elif action['ad'] == 2: + new_action[3] = 1 + + if action['scs'] == 1 and action.get("jump_invalid", 0) == 0: + new_action[4] = 1 + elif action['scs'] == 2: + new_action[5] = 1 + elif action['scs'] == 3: + new_action[6] = 1 + + if action.get('collision', 0) == 1: + new_action[7] = 1 + new_action[0] = 0 + new_action[1] = 0 + new_action[2] = 0 + new_action[3] = 0 + + pre_pitch = action.get('pre_pitch', 0.0) + current_pitch = pre_pitch + action.get('pitch_delta', 0.0) * 15.0 + current_yaw += action.get('yaw_delta', 0.0) * 15.0 + new_action[8] = current_pitch + new_action[9] = current_yaw + + actions.append(new_action) + data["actions"] = actions + except Exception as e: + warnings.warn(f"Exception while loading actions for data_id {data_id}: {e}. Continuing without actions.") + # Don't return None, just continue without actions + continue + elif key == "video": + # Check if data[key] exists and is not None + if key not in data or data[key] is None: + warnings.warn(f"Video key '{key}' is missing or None in metadata for data_id {data_id}. Skipping this sample.") + return None + + # Handle frame sequences (paths with '|' separator) + video_path_str = str(data[key]) + if '|' in video_path_str: + # For frame sequences, pass the full path string to load_data + # load_data will handle splitting and loading individual frames + path = os.path.join(self.base_path, video_path_str) + # Don't check path existence here for frame sequences + # load_data will handle individual frame loading + else: + path = os.path.join(self.base_path, data[key]) + # Check if path exists (only for single files) + if not os.path.exists(path): + warnings.warn(f"Video file does not exist: {path}. Skipping this sample.") + return None + try: + data[key] = self.load_data(path) + if data[key] is None: + warnings.warn(f"Failed to load video file: {path}. load_data returned None.") + return None + except Exception as e: + warnings.warn(f"Exception while loading video file {path}: {e}. Skipping this sample.") + return None + + # In-context learning: sample context examples from dataset + if self.enable_icl and len(self.data) > 1: + context_frames_list = [] + context_actions_list = [] + + # Sample random examples from dataset (excluding current one) + current_idx = data_id % len(self.data) + candidate_indices = [i for i in range(len(self.data)) if i != current_idx] + if len(candidate_indices) > 0: + num_samples = min(self.icl_num_examples, len(candidate_indices)) + sampled_indices = random.sample(candidate_indices, num_samples) + + for sample_idx in sampled_indices: + sample_data = self.data[sample_idx].copy() + # Load video for context + if "video" in self.data_file_keys and "video" in sample_data: + video_path = os.path.join(self.base_path, sample_data["video"]) + sample_video = self.load_data(video_path) + if sample_video is not None and len(sample_video) >= self.icl_context_frames: + # Sample context_frames from the video + start_idx = random.randint(0, max(0, len(sample_video) - self.icl_context_frames)) + context_frames = sample_video[start_idx:start_idx + self.icl_context_frames] + context_frames_list.extend(context_frames) + + # Load corresponding actions if available + if self.action_base_path is not None and "video_name" in sample_data: + try: + sample_video_name = sample_data["video_name"] + if sample_video_name.endswith(".mp4"): + sample_video_name = ".".join(sample_video_name.split(".")[:-1]) + if "_" in sample_video_name: + sample_video_name = "_".join(sample_video_name.split("_")[:4]) + sample_json_path = os.path.join(self.action_base_path, sample_video_name + ".json") + if os.path.exists(sample_json_path): + sample_json_data = json.load(open(sample_json_path, "r"))['actions'] + sample_start_frame = sample_data.get("start_frame", 0) + sample_end_frame = sample_data.get("end_frame", len(sample_video)) + + # Get actions for the context frames + context_actions = [] + context_yaw = 0.0 + for frame_idx in range(sample_start_frame + start_idx + 1, + min(sample_start_frame + start_idx + self.icl_context_frames + 1, sample_end_frame + 1)): + if str(frame_idx) in sample_json_data: + action = sample_json_data[str(frame_idx)] + new_action = [0.0] * (2 + 2 + 3 + 1 + 2) + if action['ws'] == 1: + new_action[0] = 1 + elif action['ws'] == 2: + new_action[1] = 1 + if action['ad'] == 1: + new_action[2] = 1 + elif action['ad'] == 2: + new_action[3] = 1 + if action['scs'] == 1 and action.get("jump_invalid", 0) == 0: + new_action[4] = 1 + elif action['scs'] == 2: + new_action[5] = 1 + elif action['scs'] == 3: + new_action[6] = 1 + if action.get('collision', 0) == 1: + new_action[7] = 1 + new_action[0] = 0 + new_action[1] = 0 + new_action[2] = 0 + new_action[3] = 0 + pre_pitch = action.get('pre_pitch', 0.0) + current_pitch = pre_pitch + action.get('pitch_delta', 0.0) * 15.0 + context_yaw += action.get('yaw_delta', 0.0) * 15.0 + new_action[8] = current_pitch + new_action[9] = context_yaw + context_actions.append(new_action) + context_actions_list.extend(context_actions[:len(context_frames)]) + except Exception as e: + # If loading actions fails, just skip + pass + + if context_frames_list: + data["context_frames"] = context_frames_list + if context_actions_list and len(context_actions_list) == len(context_frames_list): + data["context_actions"] = context_actions_list + + return data + + + def __len__(self): + return len(self.data) * self.repeat + + @staticmethod + def get_one_hot(action, range=2): + one_hot = [0] * (range + 1) + one_hot[action] = 1 + return one_hot + + + +import numpy as np + + +class CamVideoDataset(torch.utils.data.Dataset): + """Dataset for Context-as-Memory camera pose conditioned training (ported from VWM). + + Loads 81 PNG frames from UE scenes with random temporal cropping and extracts + corresponding camera poses as 12-dim relative RT vectors subsampled to match + the 21 latent frames. + """ + def __init__( + self, + base_path=None, metadata_path=None, + num_frames=81, + height=None, width=None, + max_pixels=1920*1080, + height_division_factor=16, width_division_factor=16, + repeat=1, + args=None, + cam_position_scale=None, + ): + if args is not None: + base_path = args.dataset_base_path + metadata_path = getattr(args, "dataset_metadata_path", metadata_path) + height = args.height + width = args.width + max_pixels = args.max_pixels + num_frames = args.num_frames + repeat = args.dataset_repeat + cam_position_scale = getattr(args, "cam_position_scale", 0.01) + self.use_condition_context_frames = getattr(args, "use_condition_context_frames", False) + self.condition_first_frame = getattr(args, "condition_first_frame", False) + self.condition_history_keyframes = getattr(args, "condition_history_keyframes", False) + self.condition_use_camera_pose = getattr(args, "condition_use_camera_pose", True) + self.num_condition_frames = getattr(args, "num_condition_frames", 1) + self.condition_frame_mode = getattr(args, "condition_frame_mode", "first_frame_only") + self.overlap_labels_root = getattr(args, "overlap_labels_root", None) + self.condition_t2v_ratio = getattr(args, "condition_t2v_ratio", 0.10) + self.condition_i2v_ratio = getattr(args, "condition_i2v_ratio", 0.10) + self.use_geometry_spatial_memory = getattr(args, "use_geometry_spatial_memory", False) + self.geometry_memory_column = getattr(args, "geometry_memory_column", "geometry_memory") + self.geometry_memory_root = getattr(args, "geometry_memory_root", None) + else: + self.use_condition_context_frames = False + self.condition_first_frame = False + self.condition_history_keyframes = False + self.condition_use_camera_pose = True + self.num_condition_frames = 1 + self.condition_frame_mode = "first_frame_only" + self.overlap_labels_root = None + self.condition_t2v_ratio = 0.10 + self.condition_i2v_ratio = 0.10 + self.use_geometry_spatial_memory = False + self.geometry_memory_column = "geometry_memory" + self.geometry_memory_root = None + + if cam_position_scale is None: + cam_position_scale = 0.01 + self.cam_position_scale = float(cam_position_scale) + + self.base_path = base_path + self.frames_dir = os.path.join(base_path, "frames") + self.jsons_dir = os.path.join(base_path, "jsons") + self.num_frames = num_frames + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.repeat = repeat + + if height is not None and width is not None: + self.dynamic_resolution = False + else: + self.dynamic_resolution = True + + if metadata_path is None: + print("No metadata. Trying to generate it.") + metadata = self.generate_metadata(base_path) + print(f"{len(metadata)} lines in metadata.") + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + else: + metadata = pd.read_csv(metadata_path) + # Ensure prompt column is string type to avoid float conversion for NaN values + if 'prompt' in metadata.columns: + metadata['prompt'] = metadata['prompt'].astype(str) + # Replace 'nan' string (from NaN) with empty string + metadata['prompt'] = metadata['prompt'].replace('nan', '') + + # CRITICAL FIX: Clean prompt - remove video path prefix if present + # Some CSV prompts start with "video_name.mp4 " prefix, which should be removed + def clean_prompt(prompt_str): + if not isinstance(prompt_str, str) or not prompt_str: + return prompt_str + # Check if prompt starts with a video path (contains .mp4 or /) + # Pattern: "VideoName/1234_5678.mp4 " or "VideoName.mp4 " + import re + # Match pattern: word/word.mp4 or word.mp4 at the start, followed by space + pattern = r'^[A-Za-z0-9_]+(/[A-Za-z0-9_]+)?\.mp4\s+' + cleaned = re.sub(pattern, '', prompt_str) + # Also handle truncated prompts ending with "..." + if cleaned.endswith('...'): + cleaned = cleaned[:-3].rstrip() + return cleaned.strip() + + metadata['prompt'] = metadata['prompt'].apply(clean_prompt) + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + captions_path = os.path.join(base_path, "captions.txt") + self.scene_captions = {} + with open(captions_path, "r") as f: + for line in f: + parts = line.strip().split("\t", 1) + if len(parts) < 2: + continue + clip_path, caption = parts + scene_name = "/".join(clip_path.split("/")[:-1]) + fname = clip_path.split("/")[-1].replace(".mp4", "") + clip_start = int(fname.split("_")[0]) + if scene_name not in self.scene_captions: + self.scene_captions[scene_name] = [] + self.scene_captions[scene_name].append((clip_start, caption)) + + for scene_name in self.scene_captions: + self.scene_captions[scene_name].sort(key=lambda x: x[0]) + + self.scene_names = sorted(self.scene_captions.keys()) + self.metadata_rows = [] + if metadata_path and os.path.isfile(metadata_path): + metadata = pd.read_csv(metadata_path) + if "prompt" in metadata.columns: + metadata["prompt"] = metadata["prompt"].astype(str) + self.metadata_rows = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + self.pose_cache = {} + self.overlap_cache = {} + self.invalid_scenes = set() + self.invalid_metadata_indices = set() + self.overlap_labels_root = self._resolve_overlap_labels_root(base_path, self.overlap_labels_root) + self._validate_condition_config() + + total_items = len(self.data) + total_scenes = len(self.scene_names) + total_captions = sum(len(v) for v in self.scene_captions.values()) + metadata_msg = f", metadata_rows={len(self.metadata_rows)}" if self.metadata_rows else "" + effective_len = (len(self.metadata_rows) if self.metadata_rows else total_scenes) * repeat + print(f"CamVideoDataset: {total_scenes} scenes, {total_captions} captions{metadata_msg}, " + f"repeat={repeat}, cam_position_scale={self.cam_position_scale}, " + f"effective length={total_items}") + + def _resolve_overlap_labels_root(self, base_path, overlap_labels_root): + candidate_roots = [] + if overlap_labels_root is not None: + candidate_roots.append(overlap_labels_root) + if base_path is not None: + candidate_roots.append(os.path.join(base_path, "overlap_labels")) + for root in candidate_roots: + if root is not None and os.path.isdir(root): + return root + return overlap_labels_root + + def _validate_condition_config(self): + if self.condition_t2v_ratio < 0 or self.condition_i2v_ratio < 0: + raise ValueError("Condition sampling ratios must be non-negative.") + if self.condition_t2v_ratio + self.condition_i2v_ratio >= 1.0: + raise ValueError("condition_t2v_ratio + condition_i2v_ratio must be < 1.0.") + needs_overlap = ( + self.use_condition_context_frames + and self.condition_frame_mode == "first_plus_overlap" + and self.condition_history_keyframes + and self.num_condition_frames > 1 + ) + if needs_overlap and (self.overlap_labels_root is None or not os.path.isdir(self.overlap_labels_root)): + raise FileNotFoundError( + "K-frame condition mode requires overlap_labels_root. " + "Pass --overlap_labels_root or keep overlap_labels under dataset_base_path/overlap_labels." + ) + + def _load_scene_poses(self, scene_name): + if scene_name not in self.pose_cache: + json_path = os.path.join(self.jsons_dir, scene_name + ".json") + try: + with open(json_path, "r") as f: + data = json.load(f) + except (FileNotFoundError, json.JSONDecodeError) as e: + raise ValueError(f"Pose JSON for scene '{scene_name}' is missing or corrupt: {e}") + if not isinstance(data, dict) or "CineCameraActor" not in data: + raise ValueError( + f"Pose JSON for scene '{scene_name}' lacks 'CineCameraActor' key " + f"(found keys: {list(data.keys()) if isinstance(data, dict) else type(data).__name__})." + ) + cine = data["CineCameraActor"] + if not isinstance(cine, dict) or len(cine) == 0: + raise ValueError(f"Pose JSON for scene '{scene_name}' has empty 'CineCameraActor' entries.") + self.pose_cache[scene_name] = cine + return self.pose_cache[scene_name] + + def _find_nearest_caption(self, scene_name, start_frame): + captions = self.scene_captions[scene_name] + best_idx = 0 + best_dist = abs(captions[0][0] - start_frame) + for i, (clip_start, _) in enumerate(captions): + dist = abs(clip_start - start_frame) + if dist < best_dist: + best_dist = dist + best_idx = i + return captions[best_idx][1] + + @staticmethod + def _compute_rt(position, rotation): + x, y, z = position + yaw_rad = np.radians(rotation[2]) + cos_y, sin_y = np.cos(yaw_rad), np.sin(yaw_rad) + R = np.array([[cos_y, -sin_y, 0], [sin_y, cos_y, 0], [0, 0, 1]]) + return [x, y, z] + R.flatten().tolist() + + @staticmethod + def _to_relative_rt(rt_list, ref_rt): + R_ref = np.array(ref_rt[3:]).reshape(3, 3) + T_ref = np.array(ref_rt[:3]).reshape(3, 1) + R_ref_inv = R_ref.T + T_ref_inv = -R_ref_inv @ T_ref + result = [] + for rt in rt_list: + R_i = np.array(rt[3:]).reshape(3, 3) + T_i = np.array(rt[:3]).reshape(3, 1) + R_new = R_ref_inv @ R_i + T_new = R_ref_inv @ T_i + T_ref_inv + result.append(T_new.flatten().tolist() + R_new.flatten().tolist()) + return result + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height * scale), round(width * scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + def _load_resized_frame(self, scene_name, frame_index, target_height, target_width): + frame_path = os.path.join(self.frames_dir, scene_name, f"{frame_index:04d}.png") + img = Image.open(frame_path).convert("RGB") + return self.crop_and_resize(img, target_height, target_width) + + @staticmethod + def _parse_frame_token(token): + token = str(token).strip() + if not token: + return None, None + parts = token.split("/") + if parts and parts[0] == "frames": + parts = parts[1:] + if len(parts) < 2: + return None, None + scene_name = "/".join(parts[:-1]) + stem = os.path.splitext(parts[-1])[0] + try: + frame_index = int(stem) + except ValueError: + return None, None + return scene_name, frame_index + + def _metadata_scene_and_indices(self, row): + video_field = str(row.get("video", "") or "") + tokens = [t for t in video_field.split("|") if t] + parsed = [self._parse_frame_token(t) for t in tokens] + parsed = [(s, i) for s, i in parsed if s is not None and i is not None] + if parsed: + scene_name = str(row.get("video_name", "") or parsed[0][0]) + frame_indices = [i for _, i in parsed[: self.num_frames]] + else: + scene_name = str(row.get("video_name", "") or "").strip() + if not scene_name: + raise ValueError("metadata row lacks video_name and parseable video paths") + start_frame = int(row.get("start_frame", 0) or 0) + frame_indices = list(range(start_frame, start_frame + self.num_frames)) + if len(frame_indices) < self.num_frames: + raise ValueError(f"metadata row has {len(frame_indices)} frames, expected {self.num_frames}") + return scene_name, frame_indices[: self.num_frames] + + def _resolve_geometry_path(self, token): + token = str(token).strip() + if not token: + return None + if os.path.isabs(token): + return token + root = self.geometry_memory_root or self.base_path + return os.path.join(root, token) + + def _load_geometry_memory_frames(self, row): + value = row.get(self.geometry_memory_column, None) + if value is None or str(value).strip() == "" or str(value).lower() == "nan": + if self.use_geometry_spatial_memory: + raise ValueError( + f"metadata row lacks required geometry column '{self.geometry_memory_column}'" + ) + return [] + tokens = [token for token in str(value).split("|") if token.strip()] + if not tokens: + raise ValueError(f"empty geometry memory field '{self.geometry_memory_column}'") + + if len(tokens) == 1: + path = self._resolve_geometry_path(tokens[0]) + if path is None: + return [] + if os.path.isdir(path): + names = sorted( + name + for name in os.listdir(path) + if os.path.splitext(name)[1].lower() in (".png", ".jpg", ".jpeg", ".webp") + ) + paths = [os.path.join(path, name) for name in names] + return [Image.open(frame_path).convert("RGB") for frame_path in paths] + if os.path.splitext(path)[1].lower() in (".mp4", ".mov", ".avi", ".mkv", ".webm"): + reader = imageio.get_reader(path) + try: + frames = [Image.fromarray(frame).convert("RGB") for frame in reader] + finally: + reader.close() + if not frames: + raise ValueError(f"geometry memory video has no frames: {path}") + return frames + + frames = [] + for token in tokens: + path = self._resolve_geometry_path(token) + if path is None or not os.path.isfile(path): + raise FileNotFoundError(f"geometry memory frame not found: {path}") + frames.append(Image.open(path).convert("RGB")) + return frames + + def _load_overlap_frames(self, scene_name, frame_index): + if self.overlap_labels_root is None: + return [] + cache_key = (scene_name, int(frame_index)) + if cache_key not in self.overlap_cache: + overlap_path = os.path.join(self.overlap_labels_root, scene_name, f"{int(frame_index)}.json") + if not os.path.exists(overlap_path): + self.overlap_cache[cache_key] = [] + else: + with open(overlap_path, "r") as f: + overlap_data = json.load(f) + overlaps = overlap_data.get("overlapping_frames", []) + self.overlap_cache[cache_key] = [int(idx) for idx in overlaps] + return self.overlap_cache[cache_key] + + def _compute_scene_rt(self, scene_name, frame_index): + frame_data = self._load_scene_poses(scene_name)[str(int(frame_index))] + raw_pos = frame_data["position"] + pos = [float(p) * self.cam_position_scale for p in raw_pos] + return self._compute_rt(pos, frame_data["rotation"]) + + def _sample_condition_mode(self): + if not self.use_condition_context_frames: + return "disabled" + if ( + self.condition_frame_mode != "first_plus_overlap" + or not self.condition_history_keyframes + or self.num_condition_frames <= 1 + ): + return "first_frame_only" + sample = random.random() + if sample < self.condition_t2v_ratio: + return "text_only" + if sample < self.condition_t2v_ratio + self.condition_i2v_ratio: + return "first_frame_only" + return "first_plus_overlap" + + def _sample_overlap_conditions(self, scene_name, start_frame, ref_rt, target_height, target_width, num_extra_conditions): + if num_extra_conditions <= 0: + return [], [], [] + window_indices = set(range(start_frame, start_frame + self.num_frames)) + target_candidates = list(range(start_frame + 1, start_frame + self.num_frames)) + sampled_target_frames = random.sample(target_candidates, k=min(num_extra_conditions, len(target_candidates))) + overlap_frames = [] + overlap_indices = [] + overlap_actions = [] + used_condition_indices = set() + for target_frame_idx in sampled_target_frames: + candidate_indices = [ + idx for idx in self._load_overlap_frames(scene_name, target_frame_idx) + if idx not in window_indices and idx != target_frame_idx and idx not in used_condition_indices + ] + if len(candidate_indices) == 0: + return None + chosen_idx = random.choice(candidate_indices) + used_condition_indices.add(chosen_idx) + overlap_indices.append(chosen_idx) + overlap_frames.append(self._load_resized_frame(scene_name, chosen_idx, target_height, target_width)) + if self.condition_use_camera_pose: + overlap_rt = self._compute_scene_rt(scene_name, chosen_idx) + overlap_actions.append(self._to_relative_rt([overlap_rt], ref_rt)[0]) + if len(overlap_frames) != num_extra_conditions: + return None + return overlap_frames, overlap_indices, overlap_actions + + def _try_get_sample(self, data_item): + scene_name = data_item["video_name"] + cam_data = self._load_scene_poses(scene_name) + max_start = len(cam_data) - self.num_frames + if max_start < 0: + raise ValueError(f"Scene {scene_name} has fewer than {self.num_frames} frames.") + start_frame = data_item["start_frame"] + end_frame = start_frame + self.num_frames - 1 + assert end_frame == data_item["end_frame"] + + frames = [] + for i in range(start_frame, end_frame + 1): + frame_path = os.path.join(self.frames_dir, scene_name, f"{i:04d}.png") + img = Image.open(frame_path).convert("RGB") + img = self.crop_and_resize(img, *self.get_height_width(img)) + frames.append(img) + + # prompt = self._find_nearest_caption(scene_name, start_frame) + prompt = data_item["prompt"] + + rt_list_abs = [] + for i in range(start_frame, end_frame + 1): + key = str(i) + if key not in cam_data: + raise ValueError(f"Scene {scene_name} missing pose for frame {i}.") + frame_data = cam_data[key] + raw_pos = frame_data["position"] + pos = [float(p) * self.cam_position_scale for p in raw_pos] + rt = self._compute_rt(pos, frame_data["rotation"]) + rt_list_abs.append(rt) + + rt_list = self._to_relative_rt(rt_list_abs, rt_list_abs[0]) + pose_indices = list(range(0, self.num_frames, 4)) + actions = [rt_list[i] for i in pose_indices] + + return { + "video": frames, + "prompt": prompt, + "actions": actions, + "video_name": scene_name, + "start_frame": start_frame, + "end_frame": end_frame, + **self._build_condition_context_payload( + frames=frames, + scene_name=scene_name, + start_frame=start_frame, + ref_rt=rt_list_abs[0], + actions=actions, + ), + } + + def _try_get_metadata_sample(self, row): + scene_name, frame_indices = self._metadata_scene_and_indices(row) + start_frame = int(frame_indices[0]) + end_frame = int(frame_indices[-1]) + cam_data = self._load_scene_poses(scene_name) + + frames = [] + for frame_idx in frame_indices: + frame_path = os.path.join(self.frames_dir, scene_name, f"{int(frame_idx):04d}.png") + img = Image.open(frame_path).convert("RGB") + img = self.crop_and_resize(img, *self.get_height_width(img)) + frames.append(img) + + prompt = row.get("prompt", None) + if prompt is None or str(prompt).strip() == "" or str(prompt).lower() == "nan": + prompt = self._find_nearest_caption(scene_name, start_frame) + else: + prompt = str(prompt) + + rt_list_abs = [] + for frame_idx in frame_indices: + key = str(int(frame_idx)) + if key not in cam_data: + raise ValueError(f"Scene {scene_name} missing pose for frame {frame_idx}.") + frame_data = cam_data[key] + raw_pos = frame_data["position"] + pos = [float(p) * self.cam_position_scale for p in raw_pos] + rt = self._compute_rt(pos, frame_data["rotation"]) + rt_list_abs.append(rt) + + rt_list = self._to_relative_rt(rt_list_abs, rt_list_abs[0]) + pose_indices = list(range(0, len(frame_indices), 4)) + actions = [rt_list[i] for i in pose_indices] + geometry_memory_frames = self._load_geometry_memory_frames(row) + + return { + "video": frames, + "prompt": prompt, + "actions": actions, + "video_name": scene_name, + "start_frame": start_frame, + "end_frame": end_frame, + "geometry_memory_frames": geometry_memory_frames, + **self._build_condition_context_payload( + frames=frames, + scene_name=scene_name, + start_frame=start_frame, + ref_rt=rt_list_abs[0], + actions=actions, + ), + } + + def __getitem__(self, data_id): + n = len(self.data) + if n == 0: + raise RuntimeError("CamVideoDataset has no scenes.") + max_attempts = min(64, n) + last_error = None + for attempt in range(max_attempts): + idx = (data_id + attempt) % n + data_item = self.data[idx] + # if scene_name in self.invalid_scenes: + # continue + # try: + return self._try_get_sample(data_item) + # except (ValueError, FileNotFoundError, KeyError, OSError) as e: + # self.invalid_scenes.add(scene_name) + # last_error = e + # if attempt < 3 or attempt % 8 == 0: + # print( + # f"[CamVideoDataset] Skipping invalid scene '{scene_name}' " + # f"({type(e).__name__}: {e}); attempt {attempt + 1}/{max_attempts}" + # ) + # continue + raise RuntimeError( + f"CamVideoDataset: exhausted {max_attempts} attempts starting from index {data_id}; " + f"last error: {type(last_error).__name__}: {last_error}" + ) + + def _build_condition_context_payload(self, frames, scene_name, start_frame, ref_rt, actions): + if not self.use_condition_context_frames: + return {} + payload = { + "use_condition_context_frames": False, + "condition_frames": [], + "condition_frame_indices": [], + "condition_source": None, + "condition_actions": [], + } + condition_mode = self._sample_condition_mode() + payload["condition_source"] = condition_mode + if condition_mode == "text_only": + return payload + payload["use_condition_context_frames"] = True + if self.condition_first_frame: + payload["condition_frames"].append(frames[0]) + payload["condition_frame_indices"].append(start_frame) + payload["condition_source"] = "first_frame_only" + if self.condition_use_camera_pose and actions: + payload["condition_actions"].append(list(actions[0])) + if ( + condition_mode == "first_plus_overlap" + and self.condition_history_keyframes + and self.num_condition_frames > len(payload["condition_frames"]) + ): + num_extra_conditions = self.num_condition_frames - len(payload["condition_frames"]) + overlap_payload = self._sample_overlap_conditions( + scene_name=scene_name, + start_frame=start_frame, + ref_rt=ref_rt, + target_height=frames[0].size[1], + target_width=frames[0].size[0], + num_extra_conditions=num_extra_conditions, + ) + if overlap_payload is None: + return payload + overlap_frames, overlap_indices, overlap_actions = overlap_payload + payload["condition_frames"].extend(overlap_frames) + payload["condition_frame_indices"].extend(overlap_indices) + if self.condition_use_camera_pose: + payload["condition_actions"].extend(overlap_actions) + payload["condition_source"] = "first_plus_overlap" + return payload + + def __len__(self): + return len(self.data) + + +class DiffusionTrainingModule(torch.nn.Module): + def __init__(self): + super().__init__() + + + def to(self, *args, **kwargs): + for name, model in self.named_children(): + model.to(*args, **kwargs) + return self + + + def trainable_modules(self): + trainable_modules = filter(lambda p: p.requires_grad, self.parameters()) + return trainable_modules + + + def trainable_param_names(self): + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.named_parameters())) + trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) + return trainable_param_names + + + def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None): + if lora_alpha is None: + lora_alpha = lora_rank + lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules) + model = inject_adapter_in_model(lora_config, model) + return model + + + def export_trainable_state_dict(self, state_dict, remove_prefix=None): + trainable_param_names = self.trainable_param_names() + state_dict = {name: param for name, param in state_dict.items() if name in trainable_param_names} + if remove_prefix is not None: + state_dict_ = {} + for name, param in state_dict.items(): + if name.startswith(remove_prefix): + name = name[len(remove_prefix):] + state_dict_[name] = param + state_dict = state_dict_ + return state_dict + + + +class ModelLogger: + def __init__(self, output_path, remove_prefix_in_ckpt=None, state_dict_converter=lambda x:x): + self.output_path = output_path + self.remove_prefix_in_ckpt = remove_prefix_in_ckpt + self.state_dict_converter = state_dict_converter + + + def on_step_end(self, loss): + pass + + + def on_epoch_end(self, accelerator, model, epoch_id): + accelerator.wait_for_everyone() + if accelerator.is_main_process: + state_dict = accelerator.get_state_dict(model) + state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt) + state_dict = self.state_dict_converter(state_dict) + os.makedirs(self.output_path, exist_ok=True) + path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors") + accelerator.save(state_dict, path, safe_serialization=True) + + + +def launch_training_task( + dataset: torch.utils.data.Dataset, + model: DiffusionTrainingModule, + model_logger: ModelLogger, + optimizer: torch.optim.Optimizer, + scheduler: torch.optim.lr_scheduler.LRScheduler, + num_epochs: int = 1, + gradient_accumulation_steps: int = 1, +): + dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], drop_last=True) + accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps) + model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler) + + for epoch_id in range(num_epochs): + for data in tqdm(dataloader): + with accelerator.accumulate(model): + optimizer.zero_grad() + loss = model(data) + accelerator.backward(loss) + optimizer.step() + model_logger.on_step_end(loss) + scheduler.step() + model_logger.on_epoch_end(accelerator, model, epoch_id) + +def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_path="./models"): + dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], drop_last=True) + accelerator = Accelerator() + model, dataloader = accelerator.prepare(model, dataloader) + os.makedirs(os.path.join(output_path, "data_cache"), exist_ok=True) + for data_id, data in enumerate(tqdm(dataloader)): + with torch.no_grad(): + inputs = model.forward_preprocess(data) + inputs = {key: inputs[key] for key in model.model_input_keys if key in inputs} + torch.save(inputs, os.path.join(output_path, "data_cache", f"{data_id}.pth")) + + + +def wan_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") + parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") + parser.add_argument("--max_pixels", type=int, default=1280*720, help="Maximum number of pixels per frame, used for dynamic resolution..") + parser.add_argument("--height", type=int, default=None, help="Height of images or videos. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--width", type=int, default=None, help="Width of images or videos. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--num_frames", type=int, default=81, help="Number of frames per video. Frames are sampled from the video prefix.") + parser.add_argument("--data_file_keys", type=str, default="image,video", help="Data file keys in the metadata. Comma-separated.") + parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") + parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") + parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") + parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") + parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") + parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") + parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") + parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") + parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") + parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") + parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") + parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") + parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") + parser.add_argument("--use_condition_context_frames", default=False, action="store_true", help="Enable appended clean condition latents.") + parser.add_argument("--condition_first_frame", default=False, action="store_true", help="Use the current clip first frame as a clean condition frame.") + parser.add_argument("--condition_history_keyframes", default=False, action="store_true", help="Use overlap-based keyframes as conditions.") + parser.add_argument("--condition_use_camera_pose", default=True, action="store_true", help="Inject camera pose for condition frames.") + parser.add_argument("--num_condition_frames", type=int, default=1, help="Number of condition frames.") + parser.add_argument("--condition_frame_mode", type=str, default="first_frame_only", help="Condition frame selection mode.") + parser.add_argument("--overlap_labels_root", type=str, default=None, help="Root dir for overlap label JSONs.") + parser.add_argument("--condition_t2v_ratio", type=float, default=0.10, help="Ratio of text-only condition samples.") + parser.add_argument("--condition_i2v_ratio", type=float, default=0.10, help="Ratio of first-frame-only condition samples.") + return parser + + + +def flux_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") + parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") + parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..") + parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--data_file_keys", type=str, default="image", help="Data file keys in the metadata. Comma-separated.") + parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") + parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") + parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") + parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") + parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") + parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") + parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") + parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") + parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") + parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") + parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") + parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.") + parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.") + parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") + parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") + return parser diff --git a/code/diffsynth/vram_management/__init__.py b/code/diffsynth/vram_management/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5b075800b6fd5b1702eeb7ff9c619aef1093ed46 --- /dev/null +++ b/code/diffsynth/vram_management/__init__.py @@ -0,0 +1,2 @@ +from .layers import * +from .gradient_checkpointing import * diff --git a/code/diffsynth/vram_management/gradient_checkpointing.py b/code/diffsynth/vram_management/gradient_checkpointing.py new file mode 100644 index 0000000000000000000000000000000000000000..b356415a004f3d74afdd45840f1fc4caf6659e16 --- /dev/null +++ b/code/diffsynth/vram_management/gradient_checkpointing.py @@ -0,0 +1,34 @@ +import torch + + +def create_custom_forward(module): + def custom_forward(*inputs, **kwargs): + return module(*inputs, **kwargs) + return custom_forward + + +def gradient_checkpoint_forward( + model, + use_gradient_checkpointing, + use_gradient_checkpointing_offload, + *args, + **kwargs, +): + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + model_output = torch.utils.checkpoint.checkpoint( + create_custom_forward(model), + *args, + **kwargs, + use_reentrant=False, + ) + elif use_gradient_checkpointing: + model_output = torch.utils.checkpoint.checkpoint( + create_custom_forward(model), + *args, + **kwargs, + use_reentrant=False, + ) + else: + model_output = model(*args, **kwargs) + return model_output diff --git a/code/diffsynth/vram_management/layers.py b/code/diffsynth/vram_management/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..cca7c50b8b7fcb068e39e3e82eef8f9d7f3c118f --- /dev/null +++ b/code/diffsynth/vram_management/layers.py @@ -0,0 +1,170 @@ +import torch, copy +from ..models.utils import init_weights_on_device + + +def cast_to(weight, dtype, device): + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight) + return r + + +class AutoTorchModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def check_free_vram(self): + _dev = self.computation_device + if not (isinstance(_dev, torch.device) and _dev.index is not None): + _dev = 0 + gpu_mem_state = torch.cuda.mem_get_info(_dev) + used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024 ** 3) + return used_memory < self.vram_limit + + def offload(self): + if self.state != 0: + self.to(dtype=self.offload_dtype, device=self.offload_device) + self.state = 0 + + def onload(self): + if self.state != 1: + self.to(dtype=self.onload_dtype, device=self.onload_device) + self.state = 1 + + def keep(self): + if self.state != 2: + self.to(dtype=self.computation_dtype, device=self.computation_device) + self.state = 2 + + +class AutoWrappedModule(AutoTorchModule): + def __init__(self, module: torch.nn.Module, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, **kwargs): + super().__init__() + self.module = module.to(dtype=offload_dtype, device=offload_device) + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.vram_limit = vram_limit + self.state = 0 + + def forward(self, *args, **kwargs): + if self.state == 2: + module = self.module + else: + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + module = self.module + elif self.vram_limit is not None and self.check_free_vram(): + self.keep() + module = self.module + else: + module = copy.deepcopy(self.module).to(dtype=self.computation_dtype, device=self.computation_device) + return module(*args, **kwargs) + + +class WanAutoCastLayerNorm(torch.nn.LayerNorm, AutoTorchModule): + def __init__(self, module: torch.nn.LayerNorm, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, **kwargs): + with init_weights_on_device(device=torch.device("meta")): + super().__init__(module.normalized_shape, eps=module.eps, elementwise_affine=module.elementwise_affine, bias=module.bias is not None, dtype=offload_dtype, device=offload_device) + self.weight = module.weight + self.bias = module.bias + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.vram_limit = vram_limit + self.state = 0 + + def forward(self, x, *args, **kwargs): + if self.state == 2: + weight, bias = self.weight, self.bias + else: + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + weight, bias = self.weight, self.bias + elif self.vram_limit is not None and self.check_free_vram(): + self.keep() + weight, bias = self.weight, self.bias + else: + weight = None if self.weight is None else cast_to(self.weight, self.computation_dtype, self.computation_device) + bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device) + with torch.amp.autocast(device_type=x.device.type): + x = torch.nn.functional.layer_norm(x.float(), self.normalized_shape, weight, bias, self.eps).type_as(x) + return x + + +class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule): + def __init__(self, module: torch.nn.Linear, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, name="", **kwargs): + with init_weights_on_device(device=torch.device("meta")): + super().__init__(in_features=module.in_features, out_features=module.out_features, bias=module.bias is not None, dtype=offload_dtype, device=offload_device) + self.weight = module.weight + self.bias = module.bias + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.vram_limit = vram_limit + self.state = 0 + self.name = name + self.lora_A_weights = [] + self.lora_B_weights = [] + self.lora_merger = None + + def forward(self, x, *args, **kwargs): + if self.state == 2: + weight, bias = self.weight, self.bias + else: + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + weight, bias = self.weight, self.bias + elif self.vram_limit is not None and self.check_free_vram(): + self.keep() + weight, bias = self.weight, self.bias + else: + weight = cast_to(self.weight, self.computation_dtype, self.computation_device) + bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device) + out = torch.nn.functional.linear(x, weight, bias) + + if len(self.lora_A_weights) == 0: + # No LoRA + return out + elif self.lora_merger is None: + # Native LoRA inference + for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights): + out = out + x @ lora_A.T @ lora_B.T + else: + # LoRA fusion + lora_output = [] + for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights): + lora_output.append(x @ lora_A.T @ lora_B.T) + lora_output = torch.stack(lora_output) + out = self.lora_merger(out, lora_output) + return out + + +def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, total_num_param=0, vram_limit=None, name_prefix=""): + for name, module in model.named_children(): + layer_name = name if name_prefix == "" else name_prefix + "." + name + for source_module, target_module in module_map.items(): + if isinstance(module, source_module): + num_param = sum(p.numel() for p in module.parameters()) + if max_num_param is not None and total_num_param + num_param > max_num_param: + module_config_ = overflow_module_config + else: + module_config_ = module_config + module_ = target_module(module, **module_config_, vram_limit=vram_limit, name=layer_name) + setattr(model, name, module_) + total_num_param += num_param + break + else: + total_num_param = enable_vram_management_recursively(module, module_map, module_config, max_num_param, overflow_module_config, total_num_param, vram_limit=vram_limit, name_prefix=layer_name) + return total_num_param + + +def enable_vram_management(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, vram_limit=None): + enable_vram_management_recursively(model, module_map, module_config, max_num_param, overflow_module_config, total_num_param=0, vram_limit=vram_limit) + model.vram_management_enabled = True + diff --git a/code/doc/DEVELOPER.md b/code/doc/DEVELOPER.md new file mode 100644 index 0000000000000000000000000000000000000000..b13a3795091d42e103d2442a5081b9ee64caeed6 --- /dev/null +++ b/code/doc/DEVELOPER.md @@ -0,0 +1,116 @@ +# Developer Guide / 开发者指南 + +Bilingual interactive version: [Project Page → Developer Guide](https://echo-team-joy-future-academy-jd.github.io/Echo-Memory/developer.html) + +Hands-on development, training/eval workflows, and **Cursor Agent skills** for Echo-Memory. + +实战开发、训练评测与 **Cursor Agent 技能**。 + +--- + +## Cursor skills / 项目 Skills + +Project skills live in **`.cursor/skills/`** — reference them in Agent chat (e.g. *use echo-memory-eval to …*). + +| Skill | English | 中文 | +| --- | --- | --- | +| `echo-memory-agent` | Scope prompts, rules, skill index | Prompt 范围、Rules、技能索引 | +| `echo-memory-train` | Memory baselines & context training | Baseline 与 Context 训练 | +| `echo-memory-eval` | Replay / revisit & HF checkpoint checks | 回放 / revisit、HF checkpoint check | +| `echo-memory-release` | gh-pages, i18n, checkpoints doc | gh-pages、i18n、权重文档 | + +Index: [.cursor/skills/README.md](../.cursor/skills/README.md) + +--- + +## 1. Guide map / 文档地图 + +| | English | 中文 | +| --- | --- | --- | +| **README** | Paper overview, quick start, checkpoints, community | 论文概览、快速上手、权重、社区 | +| **This guide** | Workflows, Cursor skills, Agent tips | 工作流、Skills、Agent 技巧 | +| **`doc/`** | Dataset & checkpoint reference | 数据集与权重参考 | + +--- + +## 2. Environment & paths / 环境与路径 + +```bash +export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B +export DATASET_BASE_PATH=data/Context-as-Memory-Dataset +export PYTHONPATH=$PWD:${PYTHONPATH:-} +export OUTPUT_BASE_ROOT=$PWD/outputs +``` + +| Pool | English | 中文 | +| --- | --- | --- | +| Static in-domain | Default root above — [dataset_preprocessing.md](dataset_preprocessing.md) | 默认路径 — 同上 | +| Dynamic training | e.g. `data/dynamic-memory-dataset` — [dynamic_dataset_preprocessing.md](dynamic_dataset_preprocessing.md) | 如 `data/dynamic-memory-dataset` — 同上 | +| Checkpoints | [Echo-Team/Echo-Memory](https://huggingface.co/Echo-Team/Echo-Memory) — [checkpoints.md](checkpoints.md) | 同上 | + +--- + +## 3. Code map / 代码地图 + +| Path | English | 中文 | +| --- | --- | --- | +| `.cursor/skills/` | Cursor Agent skills | Agent 技能 | +| `train/memory_baselines_basic/` | Spatial / SSM / compression | Spatial / SSM / 压缩 | +| `train/context_learning/` | Context K=1/5/20 | Context 配方 | +| `eval/v2/` | Replay, revisit | 回放、revisit | +| `env/memory_baseline_runtime.py` | CKPT → memory profile | 权重 → 记忆配置 | +| `docs/` | GitHub Pages | 项目页 | + +--- + +## 4. Common workflows / 常用工作流 + +**Train / 训练** + +```bash +bash train/memory_baselines_basic/run_spatial_memory_baseline.sh +bash train/context_learning/run_pre_qkv_ctx20.sh +``` + +**Checkpoint eval / checkpoint 检查** + +```bash +huggingface-cli download Echo-Team/Echo-Memory context_k1/epoch-0.safetensors --local-dir ./ckpts +export CKPT=./ckpts/context_k1/epoch-0.safetensors +bash eval/v2/run_static_consistency_loop_and_revisit.sh +``` + +Keep the row folder name in `CKPT`. + +--- + +## 5. Agent prompts / 示例 Prompt + +```text +Using echo-memory-eval: download context_k1 from Echo-Team/Echo-Memory +and run eval/v2/run_basic_replay_gt.sh with the static in-domain pool. + +Using echo-memory-train: document OUTPUT_BASE_ROOT override in +run_ablation_block_wise_ssm_two_chunk.sh. +``` + +**Public repo hygiene / 公开仓库规范:** no upload bash, internal benchmark names, or machine paths in GitHub. + +--- + +## 6. Site & release / 站点与发布 + +```bash +bash scripts/publish_gh_pages.sh +``` + +Community QR: [project page → Updates](https://echo-team-joy-future-academy-jd.github.io/Echo-Memory/#updates) or README **Community**. + +--- + +## 7. Checklist / 检查清单 + +- [ ] Quick eval with one HF checkpoint +- [ ] `doc/checkpoints.md` matches HF folders +- [ ] Public docs use Echo pool names +- [ ] Publish gh-pages after site edits; verify EN/中文 toggle diff --git a/code/doc/README.md b/code/doc/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2283fad94153918778d0d533d79337e66f91092e --- /dev/null +++ b/code/doc/README.md @@ -0,0 +1,13 @@ +# Echo-Memory documentation + +| Doc | Echo pool | Covers | +| --- | --- | --- | +| [DEVELOPER.md](DEVELOPER.md) | **Developer guide** — workflows, `.cursor/skills/`, Cursor Agent | +| [checkpoints.md](checkpoints.md) | **Hugging Face weights** — [Echo-Team/Echo-Memory](https://huggingface.co/Echo-Team/Echo-Memory) baseline index | +| [memory_mechanisms.md](memory_mechanisms.md) | **Memory mechanisms** — paper row names, code modules, and training scripts | +| [dataset_preprocessing.md](dataset_preprocessing.md) | Static in-domain pool | Echo-Team package download → layout → metadata → latents | +| [dynamic_dataset_preprocessing.md](dynamic_dataset_preprocessing.md) | Dynamic training pool | subset download → export → training settings | + +**Static in-domain pool:** download the Echo-Team package before in-domain replay/revisit eval. + +**Dynamic training pool:** SpatialVID subset export + `DATASET_BASE_PATH` before training on the dynamic pool. diff --git a/code/doc/checkpoints.md b/code/doc/checkpoints.md new file mode 100644 index 0000000000000000000000000000000000000000..86f259b55e1e858a86e4fa2247183d24f9516258 --- /dev/null +++ b/code/doc/checkpoints.md @@ -0,0 +1,72 @@ +# Checkpoints (Hugging Face) + +**Repo:** [Echo-Team/Echo-Memory](https://huggingface.co/Echo-Team/Echo-Memory) + +Fine-tuned DiT weights on top of [Wan-AI/Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B). Released rows are saved as `{row_id}/epoch-0.safetensors` after **1 epoch / 30,000 steps** on the static in-domain pool (640×352, 81-frame chunks). Mechanism names follow [memory_mechanisms.md](memory_mechanisms.md). + +## Checkpoint index + +| Family | Paper row | HF path | Steps | Echo-Memory recipe | +| --- | --- | --- | ---: | --- | +| Raw context | Context K=1 | [`context_k1/epoch-0.safetensors`](https://huggingface.co/Echo-Team/Echo-Memory/tree/main/context_k1) | 30,000 | `train/context_learning/run_pre_qkv_ctx1.sh` | +| Raw context | Context K=20 | TODO | TODO | `train/context_learning/run_pre_qkv_ctx20.sh` | +| Spatial | Spatial Memory | TODO | TODO | `train/memory_baselines_basic/run_spatial_memory_baseline.sh` | +| State-space | Block-wise SSM | TODO | TODO | `train/memory_baselines_basic/run_ablation_block_wise_ssm_two_chunk.sh` | +| State-space | Legacy Hybrid (VideoSSM) | TODO | TODO | `train/memory_baselines_basic/run_videossm_hybrid_baseline.sh` | +| Spatial | concat text (ablation) | TODO | TODO | `train/memory_baselines_basic/run_ablation_spatial_concat_text_two_chunk.sh` | +| Spatial | inject none (ablation) | TODO | TODO | `train/memory_baselines_basic/run_ablation_spatial_inject_none_two_chunk.sh` | +| Spatial | cross-attn t32 (ablation) | TODO | TODO | `train/memory_baselines_basic/run_ablation_spatial_cross_attn_readout_two_chunk.sh` | +| State-space | SSM ctx1 / every4 / hint21 | TODO | TODO | SSM ablation | +| State-space | SSM ctx5 / every1 / hint21 | TODO | TODO | SSM ablation | +| State-space | SSM ctx5 / every4 / hint81 | TODO | TODO | SSM ablation | + +Context K=5, Context K=20, Spatial memory, FramePack compression, and State-space / SSM rows are TODO and not yet released as `epoch-0` weights. + +## Download + +```bash +pip install -U "huggingface_hub[cli]" + +# one row (keeps HF folder layout under ./ckpts/) +huggingface-cli download Echo-Team/Echo-Memory context_k1/epoch-0.safetensors --local-dir ./ckpts + +# all currently released rows +huggingface-cli download Echo-Team/Echo-Memory --local-dir ./ckpts +``` + +Keep the subdirectory name in the local path (e.g. `./ckpts/context_k1/epoch-0.safetensors`). Eval scripts use `env/memory_baseline_runtime.py` to infer memory flags from path substrings; Spatial and SSM checkpoint rows remain TODO. + +## Use with Echo-Memory + +Set the Wan backbone, static in-domain data pool, and checkpoint path: + +```bash +export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B +export DATASET_BASE_PATH=data/Context-as-Memory-Dataset +export PYTHONPATH=$PWD:${PYTHONPATH:-} +export CKPT=./ckpts/context_k1/epoch-0.safetensors +``` + +**In-domain replay + revisit (paper bundle):** + +```bash +bash eval/v2/run_static_consistency_loop_and_revisit.sh +bash eval/v2/run_basic_replay_gt.sh +``` + +**Open-domain revisit** (first frames already in `assets/opendomain_revisit/`): + +```bash +PHASE=stage1 OOD_DIR=assets/opendomain_revisit \ + bash eval/v2/revisit_suite/run_one_click_revisit_eval.sh +``` + +**Visual comparison** (fixed prompt + first frame): + +```bash +python eval/metrics/run_visual_eval.py \ + --ckpt "$CKPT" \ + --output_root ./evals_visual +``` + +See [eval/v2/README.md](../eval/v2/README.md) and [eval/metrics/README.md](../eval/metrics/README.md) for full options. diff --git a/code/doc/dataset_preprocessing.md b/code/doc/dataset_preprocessing.md new file mode 100644 index 0000000000000000000000000000000000000000..9f5cab17074b78d4ef3be78eb644044a80f2848b --- /dev/null +++ b/code/doc/dataset_preprocessing.md @@ -0,0 +1,181 @@ +# Static in-domain pool — download & preprocessing + +Echo-Memory’s **static in-domain pool** is released through [Echo-Team/Echo-Memory-Data](https://huggingface.co/datasets/Echo-Team/Echo-Memory-Data) as tar parts under `static_pool_tar_parts/`. The underlying pool is sourced from [KlingTeam/Context-as-Memory-Dataset](https://huggingface.co/datasets/KlingTeam/Context-as-Memory-Dataset) on Hugging Face (Kling Team, SIGGRAPH Asia 2025; [arXiv:2506.03141](https://arxiv.org/abs/2506.03141)). Total size is about **340 GB** — plan disk space before downloading and unpacking. + +--- + +## 1. Download + +### Option A — Echo-Team packaged release + +```bash +pip install -U "huggingface_hub[cli]" + +mkdir -p data + +huggingface-cli download Echo-Team/Echo-Memory-Data \ + --repo-type dataset \ + --include "static_pool_tar_parts/*" \ + --local-dir ./data/echo-memory-data-release + +cat ./data/echo-memory-data-release/static_pool_tar_parts/echo-memory-data.tar.part-* | tar -xf - -C ./data +``` + +You should end up with `data/Context-as-Memory-Dataset/`. + +### Option B — original KlingTeam source + +If you prefer the upstream release, download or merge the original parts from the [KlingTeam dataset card](https://huggingface.co/datasets/KlingTeam/Context-as-Memory-Dataset): + +```bash +mkdir -p data +cd data + +# after all Context-as-Memory-Dataset_* parts are downloaded into this directory: +cat Context-as-Memory-Dataset_* > Context-as-Memory-Dataset.zip +unzip Context-as-Memory-Dataset.zip -d . +``` + +You should end up with a directory named `Context-as-Memory-Dataset/` (adjust the path below if your folder name differs). + +--- + +## 2. Expected layout (static in-domain pool) + +After extraction, point `DATASET_BASE_PATH` at the pool root (default: `data/Context-as-Memory-Dataset/`): + +```text +data/Context-as-Memory-Dataset/ +├── frames/ # 100 scene folders, ~7601 PNGs each +│ ├── AncientTempleEnv_0/ +│ │ ├── 0000.png +│ │ └── ... +│ └── ... +├── jsons/ # per-scene camera pose JSON (one file per scene) +│ ├── AncientTempleEnv_0.json +│ └── ... +├── overlap_labels/ # per-frame overlap indices (used by context retrieval / latent precompute) +│ ├── AncientTempleEnv_0/ +│ │ ├── 0.json +│ │ └── ... +│ └── ... +├── captions.txt # segment captions (optional for some workflows) +└── metadata_full.csv # released Echo-Memory segment metadata +``` + +Quick sanity check: + +```bash +export DATASET_BASE_PATH=data/Context-as-Memory-Dataset + +test -d "${DATASET_BASE_PATH}/frames" && echo "frames OK" +test -d "${DATASET_BASE_PATH}/jsons" && echo "jsons OK" +test -d "${DATASET_BASE_PATH}/overlap_labels" && echo "overlap_labels OK" +ls "${DATASET_BASE_PATH}/frames" | head +ls "${DATASET_BASE_PATH}/jsons" | head +``` + +--- + +## 3. Point Echo-Memory at the static in-domain pool + +```bash +export DATASET_BASE_PATH=data/Context-as-Memory-Dataset +export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B +export PYTHONPATH=$PWD:${PYTHONPATH:-} +``` + +Training scripts also accept `data/Context-as-Memory-Dataset` under the repo root if `DATASET_BASE_PATH` is unset. + +--- + +## 4. Metadata (required) + +`metadata_full.csv` is included in the Echo-Team packaged release. If you downloaded the upstream KlingTeam source instead, fetch the released metadata into the pool root: + +```bash +cd /path/to/Echo-Memory +export DATASET_BASE_PATH=data/Context-as-Memory-Dataset + +huggingface-cli download Echo-Team/Echo-Memory-Data metadata_full.csv \ + --repo-type dataset \ + --local-dir "${DATASET_BASE_PATH}" +``` + +If you modify the pool or need to rebuild metadata locally, regenerate it from `frames/` and `captions.txt`: + +```bash +bash scripts/run_generate_metadata.sh +``` + +You can also generate a smaller custom index for ablations or reduced-size training: + +```bash +OUTPUT_CSV="${DATASET_BASE_PATH}/metadata_1000.csv" \ +METADATA_MAX_ROWS=1000 \ +bash scripts/run_generate_metadata.sh +``` + +Pass the custom CSV to training/evaluation with `--dataset_metadata_path "${DATASET_BASE_PATH}/metadata_1000.csv"`. + +Defaults (override via env vars): + +| Variable | Default | Meaning | +| --- | --- | --- | +| `OUTPUT_CSV` | `${DATASET_BASE_PATH}/metadata_full.csv` | Output metadata path | +| `SEGMENT_LENGTH` | `81` | Frames per training segment | +| `CONTEXT_FRAMES` | `5` | Context window used when building metadata | +| `NUM_WORKERS` | CPU count − 2 | Parallel workers | +| `METADATA_MAX_ROWS` / `DATASET_SIZE_ROWS` | `0` | Keep only the first N metadata rows after generation; `0` keeps the full CSV | + +Verify: + +```bash +wc -l "${DATASET_BASE_PATH}/metadata_full.csv" +head -n 3 "${DATASET_BASE_PATH}/metadata_full.csv" +``` + +--- + +## 5. Precompute latents (optional, speeds training) + +If you train with precomputed VAE latents: + +```bash +export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B +export DATASET_BASE_PATH=data/Context-as-Memory-Dataset +NUM_PROCESSES=8 bash scripts/run_precompute_ctx_target_latents.sh +``` + +Latents are written under `${DATASET_BASE_PATH}/latents/`. The script can use `overlap_labels/` when `--use_overlap_labels` is enabled (see `scripts/run_precompute_ctx_target_latents.sh`). + +--- + +## 6. Training pools vs. open-domain assets + +| Echo pool / asset | Location | Purpose | +| --- | --- | --- | +| Static in-domain pool | `DATASET_BASE_PATH` → `data/Context-as-Memory-Dataset` | Training, in-domain replay/revisit, metadata | +| Dynamic training pool | `DATASET_BASE_PATH` → `data/dynamic-memory-dataset` | Training on the dynamic pool ([guide](dynamic_dataset_preprocessing.md)) | +| Open-domain first frames | `assets/opendomain_revisit/` | Held-out OOD revisit probes (already in repo) | + +You do **not** need to rebuild open-domain anchors for the released revisit suite. + +--- + +## 7. Troubleshooting + +**`DATASET_BASE_PATH is not set`** — export the variable or place data at `data/Context-as-Memory-Dataset` relative to the repo root. + +**Missing `frames/` or `jsons/`** — re-check unzip path; the root folder name must match what you pass to `DATASET_BASE_PATH`. + +**Metadata script missing** — ensure you are on the latest Echo-Memory `main` branch; metadata generation is invoked via `scripts/run_generate_metadata.sh`. + +**Disk space** — keep ~340 GB for raw frames plus extra space for `metadata_full.csv`, `latents/`, and training outputs. + +--- + +## Reference + +- Static in-domain pool: [dataset_preprocessing.md](dataset_preprocessing.md) +- Dynamic training pool: [dynamic_dataset_preprocessing.md](dynamic_dataset_preprocessing.md) diff --git a/code/doc/dynamic_dataset_preprocessing.md b/code/doc/dynamic_dataset_preprocessing.md new file mode 100644 index 0000000000000000000000000000000000000000..50ace195af3115f46c86fdbf3e6caf6c153923ce --- /dev/null +++ b/code/doc/dynamic_dataset_preprocessing.md @@ -0,0 +1,159 @@ +# Dynamic training pool — SpatialVID subset + +Echo-Memory’s **dynamic training pool** uses a motion-filtered subset of [SpatialVID/SpatialVID](https://huggingface.co/datasets/SpatialVID/SpatialVID): ego-centric clips with camera poses and captions, exported into the same sample format used by the static pool. + +This guide covers **download → export → training/inference settings** only. Dynamic eval is TODO; current public support is training and inference. + +**License:** SpatialVID is [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (non-commercial). Static and dynamic pools may have different licenses — check before mixing runs. + +--- + +## 1. Download (subset) + +**Hugging Face:** [SpatialVID/SpatialVID](https://huggingface.co/datasets/SpatialVID/SpatialVID) + +- Accept the dataset terms on Hugging Face before download. +- Full corpus is large (~7 TB+). For Echo-Memory dynamic training, download **selected groups** only — you do not need the full 545 groups. + +```bash +pip install -U "huggingface_hub[cli]" +huggingface-cli login + +export SPATIALVID_ROOT=/path/to/SpatialVID +hf download SpatialVID/SpatialVID --repo-type dataset --local-dir "${SPATIALVID_ROOT}" +``` + +To fetch specific groups, use include patterns or the helper script linked from the [dataset card](https://huggingface.co/datasets/SpatialVID/SpatialVID) (`download_SpatialVID.py` on the SpatialVID GitHub). + +Extract downloaded `.tar.gz` groups: + +```bash +cd "${SPATIALVID_ROOT}" +tar -xzvf annotations/group_0001.tar.gz +tar -xzvf videos/group_0001.tar.gz +``` + +### Raw layout (per clip) + +```text +SPATIALVID_ROOT/ +├── annotations/group_0001/{clip_id}/ +│ ├── poses.npy # (N, 7) = tx,ty,tz,qx,qy,qz,qw +│ ├── indexes.txt # pose index → source frame index +│ ├── caption.json # scene / motion text +│ └── dyn_masks.npz # optional dynamic-region masks +├── videos/group_0001/{clip_id}.mp4 +└── data/train/SpatialVID_metadata.csv +``` + +Use `SpatialVID_metadata.csv` to filter clips (e.g. `motion score`, `dynamicRatio`, `sceneType`) when building your subset. + +--- + +## 2. Export to Echo layout (dynamic training pool) + +Use `data/dynamic-spatialvid-motion60/mixed/` as the public training root and set `DATASET_BASE_PATH` to it: + +```text +data/dynamic-spatialvid-motion60/ +├── L1/ # single-level exports are also valid roots +├── L2/ +├── L3/ +└── mixed/ + ├── frames/L{1,2,3}/{clip_id}/0000.png ... 0080.png + ├── jsons/L{1,2,3}/{clip_id}.json + ├── overlap_labels/L{1,2,3}/{clip_id}/ + ├── captions.txt + ├── metadata_train.csv + ├── metadata_train_sample.csv + ├── metadata_train_sample_L1.csv + ├── metadata_eval.csv + └── metadata_eval_2chunk.csv +``` + +**Per-clip steps:** + +| Step | Setting | +| --- | --- | +| Frame sample | **81** PNGs per clip, **640×352** | +| Pose | Interpolate `poses.npy` + `indexes.txt` → `jsons/{clip_id}.json` (Euler `CineCameraActor` format, same as static data) | +| Prompt | Short caption from `caption.json` (`SceneSummary` or `SceneDescription`) | +| Overlap | Build `overlap_labels/` for FOV-based context retrieval | +| Metadata row | `video`, `prompt`, `video_name`, `start_frame`, `end_frame`, optional `level` | + +`metadata_train.csv` is written at export time. Use `metadata_train_sample.csv` or `metadata_train_sample_L1.csv` for local step checks. Do not re-run `run_generate_metadata.sh` unless you regenerate from raw frames only. + +--- + +## 3. Training settings + +Same env vars and on-disk layout as the static in-domain pool — only `DATASET_BASE_PATH` changes. + +```bash +export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B +export DATASET_BASE_PATH=data/dynamic-spatialvid-motion60/mixed +export PYTHONPATH=$PWD:${PYTHONPATH:-} +``` + +Recommended settings for the dynamic training pool (match memory baseline scripts): + +| Parameter | Typical value | +| --- | --- | +| Resolution | **640 × 352** | +| Frames / chunk | **81** | +| Context frames | **1–20** (recipe-dependent) | +| `--use_rt_relative` | on | +| `--enable_fov_retrieval` | on (when `overlap_labels/` present) | +| `--enable_context_memory` | on for context / spatial / SSM rows | +| `--timestep_shift` | **15** | +| Learning rate | **5e-5** (adjust per row) | + +Example — run a dynamic row: + +```bash +METADATA_NAME=metadata_train.csv bash train/dynamic_spatialvid/run_dyn_spatial_mem.sh +``` + +For local one-step validation: + +```bash +METADATA_NAME=metadata_train_sample_L1.csv \ +MAX_TRAIN_STEPS=1 \ +PROGRESS_TOTAL_STEPS=30000 \ +NUM_WORKERS=0 \ +bash train/dynamic_spatialvid/run_dyn_block_wise_ssm.sh +``` + +Inference wrappers live under `inference/dynamic_spatialvid/`. + +--- + +## 4. Demo selection + +Dynamic demos are selected from training-scene replay rather than from fixed eval scripts: + +1. Randomly sample candidate scenes from `metadata_train.csv` or `metadata_train_sample.csv`. +2. Use the same prompt, first frame, and GT action trajectory for all six dynamic rows. +3. Run `inference/unified_inference.py` or `inference/dynamic_spatialvid/*.sh` for each checkpoint. +4. Manually pick a representative scene where all rows are viewable. + +The checked-in README previews are compressed GIFs under `assets/readme_previews/`. + +--- + +## 5. Checklist + +- [ ] Hugging Face access approved for [SpatialVID/SpatialVID](https://huggingface.co/datasets/SpatialVID/SpatialVID) +- [ ] Subset of `group_****` archives downloaded and extracted +- [ ] Clips filtered (poses + caption present; optional motion / dynamic filters) +- [ ] `frames/`, `jsons/`, `metadata_train.csv` under one root +- [ ] (Recommended) `overlap_labels/` for FOV retrieval +- [ ] `DATASET_BASE_PATH` exported before training/inference + +--- + +## Reference + +- SpatialVID: [SpatialVID/SpatialVID](https://huggingface.co/datasets/SpatialVID/SpatialVID) · [arXiv:2509.09676](https://arxiv.org/abs/2509.09676) +- Static in-domain pool: [dataset_preprocessing.md](dataset_preprocessing.md) +- Dynamic training pool: [dynamic_dataset_preprocessing.md](dynamic_dataset_preprocessing.md) diff --git a/code/doc/memory_mechanisms.md b/code/doc/memory_mechanisms.md new file mode 100644 index 0000000000000000000000000000000000000000..bb03608c986c7e299774261b253296825bf0f33f --- /dev/null +++ b/code/doc/memory_mechanisms.md @@ -0,0 +1,41 @@ +# Memory Mechanisms + +This note maps the paper's memory rows to the repository implementation and explains the modeling role of each family. Echo-Memory treats memory as a controlled intervention on what information from chunk 1 is stored and how chunk 2 reads it back during denoising. + +## Modeling View + +All rows use the same action-conditioned Wan DiT backbone and the same two-chunk training/evaluation setup: + +1. **Context chunk:** clean history frames are encoded into latent/context tokens, optionally with matched camera RT actions. +2. **Target chunk:** noisy target latents are denoised while the selected memory mechanism exposes information from the context chunk. +3. **Read-out:** memory is injected through raw context concatenation, compressed context tokens, spatial memory tokens, or recurrent state-space modules attached to DiT blocks. + +The ablations are designed to change only the memory pathway while keeping the backbone, action conditioning, resolution, chunk length, and training schedule aligned. + +## Paper Rows + +| Paper family | Paper row / repo name | What is stored or read | Main code path | Training entry | +| --- | --- | --- | --- | --- | +| Raw context | `context_k1`, `context_k5`, `context_k20` | Uncompressed retrieved context frames. `K=1` is the anchor/I2V floor; `K=5/20` are context-learning capacity rows. | `diffsynth/pipelines/wan_video_new.py` context latent path | `train/context_learning/run_pre_qkv_ctx{1,5,20}.sh` | +| Compression | `framepack_weight` | Context tokens are kept at the same length but temporally reweighted. | `diffsynth/models/memory/framepack_weight.py` | `train/memory_baselines_basic/run_ablation_framepack_weight_two_chunk.sh` | +| Compression | `framepack_len_r2`, `framepack_len_r4` | Context latents and matched RT actions are pooled along time. | `diffsynth/models/memory/framepack_length.py` | `train/memory_baselines_basic/run_ablation_framepack_len_r{2,4}_two_chunk.sh` | +| Compression | `framepack_hybrid_r2`, `framepack_hybrid_r4` | Length compression plus token reweighting. | `wan_video_new.py` + FramePack helpers | `train/memory_baselines_basic/run_ablation_framepack_hybrid_r*_weight_two_chunk.sh` | +| Token-grid | `spatial_mem` | Context tokens are time-averaged and summarized into learned grid tokens. This is the implementation behind the currently reported `spatial_mem` row; it does **not** reconstruct depth or 3D geometry. | `diffsynth/models/memory/spatial_grid_memory.py` | `train/memory_baselines_basic/run_spatial_memory_baseline.sh` | +| Token-grid | `spatial_inject_none`, `spatial_concat_text`, `spatial_cross_attn_readout` | Same token-grid storage, different read-out: withheld, text-KV concat, or dedicated cross-attention. | `spatial_grid_memory.py` read-out helpers | matching `run_ablation_spatial_*_two_chunk.sh` scripts | +| Geometry-grounded spatial | `geometry_spatial_mem` | A static scene is reconstructed outside the DiT using depth, intrinsics, extrinsics, and TSDF fusion. The fused point cloud is rendered along the target trajectory, VAE-encoded, and converted into conditioning tokens. | `diffsynth/models/memory/geometry_spatial_memory.py` | `train/memory_baselines_basic/run_geometry_spatial_memory_baseline.sh` | +| State-space | `block_wise_ssm` | Paper-aligned recurrent state attached to selected DiT blocks. Checkpoint keys contain `block_wise_ssm.*`. | `diffsynth/models/memory/block_wise_ssm.py` | `train/memory_baselines_basic/run_ablation_block_wise_ssm_two_chunk.sh` | +| State-space | `videossm_hybrid` | Legacy VideoSSM hybrid baseline: depthwise temporal-conv state-space-like module. Checkpoint keys contain `videossm_hybrid.*`. | `diffsynth/models/memory/videossm_hybrid.py` | `train/memory_baselines_basic/run_videossm_hybrid_baseline.sh` | + +## Naming Rules + +- Do not describe `SpatialGridMemory` or the existing `spatial_mem` results as the + geometry-grounded method from arXiv:2506.05284. It is a token-grid baseline. +- Use **Geometry-grounded Spatial Memory** only when the metadata supplies + rendered static geometry through `geometry_memory` (or a configured column). + The geometry extractor is the external reconstruction pipeline: depth and + cameras → TSDF-fused static point cloud → target-view renders. The model-side + encoder does not estimate depth itself. +- Use **Block-wise SSM** only for `--use_block_wise_ssm` / `BlockWiseStateSpaceMemory`. +- Use **VideoSSM hybrid** only for the legacy `--use_videossm_hybrid` / `HybridStateSpaceMemory` baseline. +- Use **Context learning** for raw-context capacity rows (`K=1/5/20`), not for compact memory modules. +- Keep checkpoint folder names stable; `env/memory_baseline_runtime.py` and `inference/unified_inference.py` infer memory profiles from those names. diff --git a/code/docs/.nojekyll b/code/docs/.nojekyll new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/code/docs/README.md b/code/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bb8e10de74ae7d8cb6c3e3d7bb2df6c82ff0d36c --- /dev/null +++ b/code/docs/README.md @@ -0,0 +1,16 @@ +# Project page (local preview) + +The **official** GitHub Pages site is deployed from the **`gh-pages`** branch (root `index.html` + `style.css`), not from this folder. + +This `docs/` directory mirrors that pink-themed project page so you can preview locally: + +```bash +cd docs +python -m http.server 18876 --bind 0.0.0.0 +``` + +Then open `http://localhost:18876/` (with port forwarding if remote). + +**Live site:** https://echo-team-joy-future-academy-jd.github.io/Echo-Memory/ + +Edit `docs/index.html`, `docs/style.css`, `docs/site.js`, and `docs/assets/`, then run `bash scripts/publish_gh_pages.sh` (or push `main` to trigger CI). diff --git a/code/docs/assets/echo-memory-paper.pdf b/code/docs/assets/echo-memory-paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..da5c173036867f299d63ff26d7484bd303e59ca9 --- /dev/null +++ b/code/docs/assets/echo-memory-paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d7b4abd1c79f36e21bafff4d972a9562058b814fd40b0e9225309833f1adf11a +size 4038848 diff --git a/code/docs/assets/opendomain_revisit/README.md b/code/docs/assets/opendomain_revisit/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c214de9069450bee7540a8ad45b9f624a05d980e --- /dev/null +++ b/code/docs/assets/opendomain_revisit/README.md @@ -0,0 +1,19 @@ +# Open-Domain Revisit Sources + +This folder contains the eight held-out first-frame sources used by the +open-domain revisit probe in the Echo-Memory paper. + +Each image is treated as a first frame for a short controlled camera-return +probe. The default prompt used by `eval/v2/revisit_suite` is: + +```text +A toy bear in the same static scene. Preserve the bear appearance and the scene layout after camera revisit. +``` + +To run the probe, point `OOD_DIR` here or use the default: + +```bash +export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B +PHASE=stage1 OOD_DIR=assets/opendomain_revisit \ + bash eval/v2/revisit_suite/run_one_click_revisit_eval.sh +``` diff --git a/code/docs/assets/paper_cases/README.md b/code/docs/assets/paper_cases/README.md new file mode 100644 index 0000000000000000000000000000000000000000..897bd3dd69a22bcc0f309cf48f2a41bc705d3be1 --- /dev/null +++ b/code/docs/assets/paper_cases/README.md @@ -0,0 +1,13 @@ +# Paper Case Visual Assets + +This directory contains paper-facing figures copied into the release for direct README rendering. + +- `figure_1_abs_framework.png`: paper teaser and workflow figure for the project landing page. +- `figure_2_mem_overview.png`: overview of the memory design matrix. + +To generate new open-domain revisit videos and evidence frames for a checkpoint, use: + +```bash +PHASE=stage1 OOD_DIR=assets/opendomain_revisit \ + bash eval/v2/revisit_suite/run_one_click_revisit_eval.sh +``` diff --git a/code/docs/assets/readme_previews/context_k1_replay_gt.gif b/code/docs/assets/readme_previews/context_k1_replay_gt.gif new file mode 100644 index 0000000000000000000000000000000000000000..e90860cdfe9367ac42935740fe3dfdf9794414e6 --- /dev/null +++ b/code/docs/assets/readme_previews/context_k1_replay_gt.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c06f6c8683c1adc6021207cf7118d266e2adc9dccc43e79eea701b879f55cc35 +size 1178061 diff --git a/code/docs/assets/readme_previews/context_k5_replay_gt.gif b/code/docs/assets/readme_previews/context_k5_replay_gt.gif new file mode 100644 index 0000000000000000000000000000000000000000..73f5ddca25b8c8bed35dad89086931ae2742c923 --- /dev/null +++ b/code/docs/assets/readme_previews/context_k5_replay_gt.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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sha256:7b139ed864a3e813aa89bdfa4582bdbea7e19b87a7b6786e6510195ed708b3a0 +size 1594468 diff --git a/code/docs/assets/readme_previews/dyn_ssm_blockwise_replay.gif b/code/docs/assets/readme_previews/dyn_ssm_blockwise_replay.gif new file mode 100644 index 0000000000000000000000000000000000000000..1486e6651e349517fb12332d71f8f7e8efc2cd67 --- /dev/null +++ b/code/docs/assets/readme_previews/dyn_ssm_blockwise_replay.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:784d33b8ee707a1d2530b26ebee93431d4e4a43a01f72d7323601fd931eebe17 +size 1421152 diff --git a/code/docs/assets/readme_previews/dyn_ssm_legacy_replay.gif b/code/docs/assets/readme_previews/dyn_ssm_legacy_replay.gif new file mode 100644 index 0000000000000000000000000000000000000000..f68c32131584be099e118d3ce9eccb2fd229d33b --- /dev/null +++ b/code/docs/assets/readme_previews/dyn_ssm_legacy_replay.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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sha256:02e00f0c1abba304715ae6821a6c05bef3fd132ceaeb50d8ef2dc9fb77a9ee89 +size 1099427 diff --git a/code/docs/assets/readme_previews/ssm_blockwise_replay_gt.gif b/code/docs/assets/readme_previews/ssm_blockwise_replay_gt.gif new file mode 100644 index 0000000000000000000000000000000000000000..cf5403d528bacf5912c20ad004172ec219c44343 --- /dev/null +++ b/code/docs/assets/readme_previews/ssm_blockwise_replay_gt.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11d5c93b3ac8a35c94c89be513f3964059ce33ab68e2a0b75ab0ce748b06985c +size 1096158 diff --git a/code/docs/assets/readme_previews/ssm_legacy_replay_gt.gif b/code/docs/assets/readme_previews/ssm_legacy_replay_gt.gif new file mode 100644 index 0000000000000000000000000000000000000000..f32e09e79d708b5043aefaf58aad477880eb40c5 --- /dev/null +++ b/code/docs/assets/readme_previews/ssm_legacy_replay_gt.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75b24e6a0d4a944d9dadde6719fd9d523d1d355ab32596f990185aaca7b74da9 +size 1183326 diff --git a/code/docs/developer.html b/code/docs/developer.html new file mode 100644 index 0000000000000000000000000000000000000000..e70c5e233d05ce24f91c3bbd9c2cb9f282043bb0 --- /dev/null +++ b/code/docs/developer.html @@ -0,0 +1,109 @@ + + + + + + Echo-Memory Developer Guide + + + + + + + +
+ + +
+

Development · Cursor

+

Developer Guide

+

+ +
+

1. What this guide is

+
    +
    + +
    +

    2. Environment & paths

    +

    Set these before training or eval:

    + 
    export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B
+export DATASET_BASE_PATH=data/Context-as-Memory-Dataset
+export PYTHONPATH=$PWD:${PYTHONPATH:-}
+export OUTPUT_BASE_ROOT=$PWD/outputs
    +
      +
      + +
      +

      3. Code map

      +
      +
      + +
      +

      4. Common workflows

      +

      Train one memory row (from repo root):

      + 
      bash train/memory_baselines_basic/run_spatial_memory_baseline.sh
+bash train/context_learning/run_pre_qkv_ctx20.sh
      +

      Smoke eval with a HF checkpoint:

      + 
      huggingface-cli download Echo-Team/Echo-Memory \
+  context_k1/epoch-0.safetensors --local-dir ./ckpts
+export CKPT=./ckpts/context_k1/epoch-0.safetensors
+bash eval/v2/run_static_consistency_loop_and_revisit.sh
      +

      +
      + +
      +

      5. Cursor vibe coding

      +

      +

      Project skills

      +
      +
        +

        Example prompt

        +
        +

        +
        + +
        +

        6. Site & release (maintainers)

        +

        + 
        bash scripts/publish_gh_pages.sh
        +

        +
        + +
        +

        7. Checklist

        +
          +
        • Smoke eval with one HF checkpoint before tagging a release.
          • +
        • +
        • Public docs use Echo pool names — no internal paths or benchmark codenames.
          • +
        • +
        +
        + +

        +
        + +
        +
        +

        © Echo Team · Joy Future Academy, JD

        +
        +
        +
        + + + + + diff --git a/code/docs/i18n-runtime.js b/code/docs/i18n-runtime.js new file mode 100644 index 0000000000000000000000000000000000000000..c21e5118b7d918a136a4abfbadc5294a9a07cca2 --- /dev/null +++ b/code/docs/i18n-runtime.js @@ -0,0 +1,141 @@ +(function () { + "use strict"; + + var STORAGE_KEY = "echo-memory-lang"; + var NAV_MAP = { + hero: "nav.home", + overview: "nav.overview", + design: "nav.design", + checkpoints: "nav.checkpoints", + evaluation: "nav.eval", + evidence: "nav.evidence", + findings: "nav.findings", + updates: "nav.updates", + bibtex: "nav.bibtex" + }; + + function detectDefaultLang() { + var saved = localStorage.getItem(STORAGE_KEY); + if (saved === "en" || saved === "zh") return saved; + return "en"; + } + + function dict(lang) { + return (window.ECHO_I18N && window.ECHO_I18N[lang]) || {}; + } + + function t(lang, key) { + var d = dict(lang); + return Object.prototype.hasOwnProperty.call(d, key) ? d[key] : ""; + } + + function applyLang(lang) { + var d = dict(lang); + if (!Object.keys(d).length) return; + + document.documentElement.lang = lang === "zh" ? "zh-CN" : "en"; + + var descMeta = document.querySelector('meta[name="description"]'); + var pageTitleKey = document.body.getAttribute("data-title-key"); + if (pageTitleKey && d[pageTitleKey]) { + document.title = d[pageTitleKey]; + } else if (d["meta.title"]) { + document.title = d["meta.title"]; + } + if (descMeta) { + if (pageTitleKey === "dev.meta.title" && d["dev.meta.description"]) { + descMeta.setAttribute("content", d["dev.meta.description"]); + } else if (d["meta.description"]) { + descMeta.setAttribute("content", d["meta.description"]); + } + } + + document.querySelectorAll("[data-i18n]").forEach(function (el) { + var key = el.getAttribute("data-i18n"); + if (!key || !(key in d)) return; + el.textContent = d[key]; + }); + + document.querySelectorAll("[data-i18n-html]").forEach(function (el) { + var key = el.getAttribute("data-i18n-html"); + if (!key || !(key in d)) return; + el.innerHTML = d[key]; + }); + + document.querySelectorAll("[data-i18n-attr]").forEach(function (el) { + el.getAttribute("data-i18n-attr").split(";").forEach(function (pair) { + var parts = pair.split(":"); + if (parts.length !== 2) return; + var attr = parts[0].trim(); + var key = parts[1].trim(); + if (key in d) el.setAttribute(attr, d[key]); + }); + }); + + document.querySelectorAll("[data-nav]").forEach(function (el) { + var navKey = NAV_MAP[el.getAttribute("data-nav")]; + if (navKey && d[navKey]) el.textContent = d[navKey]; + }); + + document.querySelectorAll(".slide-dots button[data-slide]").forEach(function (btn) { + var navKey = NAV_MAP[btn.getAttribute("data-slide")]; + if (navKey && d[navKey]) { + btn.setAttribute("data-label", d[navKey]); + btn.setAttribute("aria-label", d[navKey]); + } + }); + + document.querySelectorAll(".qual-chip").forEach(function (chip, idx) { + var keys = ["evidence.chip1", "evidence.chip2", "evidence.chip3"]; + if (keys[idx] && d[keys[idx]]) chip.textContent = d[keys[idx]]; + }); + + var activeQual = document.querySelector(".qual-chip.is-active"); + var qualCaption = document.querySelector("[data-qual-caption]"); + if (activeQual && qualCaption && activeQual.dataset.captionKey && d[activeQual.dataset.captionKey]) { + qualCaption.innerHTML = d[activeQual.dataset.captionKey] + ' ' + (d["zoom.hint"] || "Click to expand") + ""; + } else if (qualCaption && d["evidence.cap1"] && document.querySelector(".qual-chip.is-active") === document.querySelector(".qual-chip")) { + qualCaption.innerHTML = d["evidence.cap1"]; + } + + var langCurrent = document.querySelector("[data-lang-current]"); + var langAlt = document.querySelector("[data-lang-alt]"); + if (langCurrent && d["lang.current"]) langCurrent.textContent = d["lang.current"]; + if (langAlt && d["lang.toggle"]) langAlt.textContent = d["lang.toggle"]; + + var toggle = document.getElementById("lang-toggle"); + if (toggle && d["lang.switch"]) toggle.setAttribute("aria-label", d["lang.switch"]); + + document.querySelectorAll("[data-copy-target]").forEach(function (btn) { + if (!btn.classList.contains("is-copied") && d["bibtex.copy"]) { + btn.textContent = d["bibtex.copy"]; + } + }); + + localStorage.setItem(STORAGE_KEY, lang); + document.dispatchEvent(new CustomEvent("echo-lang-change", { detail: { lang: lang } })); + } + + window.EchoI18n = { + getLang: function () { + return localStorage.getItem(STORAGE_KEY) || detectDefaultLang(); + }, + setLang: applyLang, + t: function (key) { + return t(window.EchoI18n.getLang(), key); + } + }; + + document.addEventListener("DOMContentLoaded", function () { + var lang = detectDefaultLang(); + applyLang(lang); + + var toggle = document.getElementById("lang-toggle"); + if (toggle) { + toggle.addEventListener("click", function () { + var next = window.EchoI18n.getLang() === "zh" ? "en" : "zh"; + applyLang(next); + }); + } + }); +})(); diff --git a/code/docs/i18n.js b/code/docs/i18n.js new file mode 100644 index 0000000000000000000000000000000000000000..128dad7f9e501488dacc66fa6536378e9fe496a1 --- /dev/null +++ b/code/docs/i18n.js @@ -0,0 +1,329 @@ +/* Echo-Memory project page — EN / ZH strings */ +window.ECHO_I18N = { + en: { + "meta.title": "Echo-Memory | Echo Team", + "meta.description": "Echo-Memory: A controlled study of memory mechanisms in action-conditioned world models.", + "nav.home": "Home", + "nav.overview": "Overview", + "nav.design": "Design", + "nav.checkpoints": "Ckpt", + "nav.eval": "Eval", + "nav.evidence": "Qual", + "nav.findings": "Results", + "nav.updates": "Updates", + "nav.bibtex": "BibTeX", + "nav.developer": "Dev Guide", + "nav.github": "GitHub", + "nav.menu": "Menu", + "lang.toggle": "中文", + "lang.current": "EN", + "lang.switch": "Switch language", + "hero.badge": "Echo Team · Joy Future Academy, JD · June 2026 · CC BY 4.0", + "hero.title.line2": "A Controlled Study of Memory in Action World Models", + "hero.subtitle": "When the camera leaves and returns, which memory keeps the same world instead of a plausible but different scene?", + "hero.cta.paper": "Paper", + "hero.cta.pdf": "PDF", + "hero.cta.ckpt": "Checkpoints", + "hero.cta.code": "Code", + "hero.metric.stars": "GitHub Stars", + "hero.metric.forks": "Forks", + "hero.note": "Controlled memory ablations on a shared Wan action-to-video stack — reproducible rows, evaluation scripts, and qualitative revisit panels.", + "hero.authors.summary": "Authors & affiliations", + "hero.affiliations": "HKU · Joy Future Academy, JD · CUHK · PKU · Fudan · Tsinghua · HKUST · UMich", + "overview.kicker": "01 · Overview", + "overview.title": "One backbone, one protocol — only memory changes.", + "overview.lead": "Echo-Memory holds the video backbone, training recipe, and data protocol fixed, and swaps only the memory module. The goal is to separate replay fidelity from return memory when the camera leaves and comes back to the same place.", + "overview.b1": "Shared stack — chunk-wise action-conditioned world generation on Wan.", + "overview.b2": "Controlled variable — Context, Compression, Spatial, or State-Space memory.", + "overview.b3": "Three probes — replay metrics, in-domain 180° loop, open-domain edited return.", + "overview.b4": "Release — ablation scripts, GT replay, revisit assets, and paper-aligned figures.", + "overview.fig.cap": "Controlled memory study over chunk-wise action-world generation. Click to expand", + "overview.fig.alt": "Echo-Memory framework overview", + "design.kicker": "02 · Memory Design", + "design.title": "Context · Compression · Spatial · State-Space", + "design.lead": "All variants plug into the same write–read interface; we only change what is stored and how history is retrieved. A no-memory I2V floor re-generates from the first frame as a lower bound.", + "design.context.title": "Context", + "design.context.body": "Raw recent frames at K = 1, 5, or 20 chunks — tests whether longer windows alone stop drift.", + "design.compression.title": "Compression", + "design.compression.body": "Learned compact tokens at ratio r = 4 — history without growing raw-frame storage.", + "design.spatial.title": "Spatial", + "design.spatial.body": "Explicit spatial read/write state — targets layout, object pose, and viewpoint carry.", + "design.ssm.title": "State-Space", + "design.ssm.body": "Block-wise SSM updates — recurrent carry beyond short context windows on revisit.", + "design.fig.cap": "Four memory families under a shared write–read interface. Click to expand", + "design.fig.alt": "Memory design matrix", + "ckpt.kicker": "03 · Checkpoints", + "ckpt.title": "Paper baselines on Hugging Face", + "ckpt.lead": "Wan 2.1 1.3B memory rows — epoch-0, 30,000 steps, static in-domain pool. Released weights: Echo-Team/Echo-Memory", + "ckpt.th.family": "Family", + "ckpt.th.row": "Paper row", + "ckpt.th.path": "HF path", + "ckpt.th.steps": "Steps", + "ckpt.label.download": "Download", + "ckpt.label.eval": "In-domain eval (Echo-Memory repo)", + "ckpt.note": "Keep the row folder in CKPT — env/memory_baseline_runtime.py infers memory flags from the path. Full index: doc/checkpoints.md.", + "eval.kicker": "04 · Evaluation", + "eval.title": "Replay · In-domain revisit · Open-domain return", + "eval.lead": "Each branch asks a different question: Can the model reconstruct the past? Can it close a loop in-domain? After an edited first frame, does it return to the same world?", + "eval.replay.title": "Replay", + "eval.replay.body": "PSNR, SSIM, LPIPS on chunk-wise reconstruction — measures short-horizon pixel fidelity.", + "eval.indomain.title": "In-domain", + "eval.indomain.body": "180° trajectory loop closure with VLM-assisted scoring on held layouts.", + "eval.opendomain.title": "Open-domain", + "eval.opendomain.body": "Edited first frames and 45° return probes — stresses object identity and scene persistence.", + "eval.dynamic.title": "Dynamic SpatialVID", + "eval.dynamic.body": "Training and inference wrappers are public; the dynamic eval protocol is TODO.", + "eval.fig.cap": "Replay health vs. return memory under the same stack. Click to expand", + "eval.fig.alt": "Three-branch evaluation summary", + "evidence.kicker": "05 · Qualitative Evidence", + "evidence.title": "Return probes expose identity drift.", + "evidence.lead": "Qualitative panels follow a simple diagnostic: first frame → leave the view → revisit tail. We compare whether memory restores the same object, pose, background, and camera geometry — not merely a plausible new scene.", + "evidence.chip1": "Memory Results", + "evidence.chip2": "Open-Domain Sweep", + "evidence.chip3": "Identity Anchors", + "evidence.cap1": "Representative memory comparisons across variants. Click to expand", + "evidence.dynamic.note": "SpatialVID previews use one selected training scene and the same first frame, prompt, and GT camera trajectory for a 5-second first-chunk replay across all six rows.", + "findings.kicker": "06 · Main Conclusions", + "findings.title": "Replay quality ≠ memory quality.", + "findings.lead": "Replay metrics and return probes do not always agree — a model can look sharp on reconstruction yet fail when the camera returns. Rankings reorder once identity under revisit is measured.", + "findings.b1": "Raw context — more history helps open-domain return more than replay alone.", + "findings.b2": "Compression — compact tokens can preserve replay while losing identity on return.", + "findings.b3": "Spatial vs. SSM — explicit state and block-wise SSM trade off layout carry and long-horizon stability.", + "findings.b4": "Takeaway — treat replay as a health check, not the final memory benchmark.", + "findings.fig.cap": "Rank shift from replay to return — replay is not the final memory score. Click to expand", + "updates.kicker": "07 · News & Roadmap", + "updates.title": "Release notes and next steps.", + "updates.news": "News", + "updates.roadmap": "Roadmap", + "updates.news0": "SpatialVID support added: dynamic training/inference recipes, 5-second first-chunk replay previews, and dynamic eval marked as TODO.", + "updates.news1": "Echo-Memory released: paper on arXiv (PDF), project page, public code, replay/revisit eval assets, and baseline checkpoints on Echo-Team/Echo-Memory.", + "updates.models": "Models", + "updates.eval": "Eval", + "updates.rm1": "Wan 2.1 1.3B backbone and training recipes", + "updates.rm2": "Four memory families — Context, Compression, Spatial, State-Space", + "updates.rm3": "Dynamic training pool — SpatialVID subset export & settings", + "updates.rm4": "Paper checkpoints — Echo-Team/Echo-Memory", + "updates.rm5": "Wan 2.2 + multi-scale 5B / 14B", + "updates.re1": "Dynamic eval beyond static replay/revisit", + "updates.re2": "More revisit probes and scoring presets", + "community.title": "Community", + "community.lead": "Join the Echo-Memory WeChat group for release updates, checkpoint questions, and maintainer coordination.", + "community.qr.alt": "Echo-Memory WeChat group QR code", + "community.qr.caption": "Echo-Memory 交流群 · scan to join (QR refreshes periodically)", + "bibtex.kicker": "08 · Citation", + "bibtex.title": "BibTeX", + "bibtex.lead": "Echo-Memory: A Controlled Study of Memory in Action World Models (June 2026). Licensed under CC BY 4.0. Cite the arXiv preprint below.", + "bibtex.arxiv": "arXiv", + "bibtex.source": "Source", + "bibtex.doi": "DOI", + "bibtex.arxivid": "arXiv ID", + "bibtex.pdf": "PDF", + "bibtex.license": "License", + "bibtex.copy": "Copy", + "bibtex.copied": "Copied", + "bibtex.failed": "Failed", + "footer.copy": "© Echo Team · Joy Future Academy, JD", + "footer.pp": "Project Page", + "zoom.hint": "Click to expand", + "backtop": "Back to top", + "lightbox.close": "Close", + "lightbox.label": "Expanded figure", + "dev.meta.title": "Echo-Memory Developer Guide", + "dev.meta.description": "Echo-Memory development guide — workflows, eval, and Cursor vibe coding.", + "dev.kicker": "Development · Cursor", + "dev.title": "Developer Guide", + "dev.subtitle": "Hands-on coding, training, eval, and Cursor vibe coding for Echo-Memory.", + "dev.back": "← Back to project page", + "dev.s1.title": "1. What this guide is", + "dev.s1.body": "
      • README — paper overview, quick start, checkpoints, community.
      • This guide — workflows, project Cursor skills, Agent tips.
      • doc/ — dataset & checkpoint reference.
      • .cursor/skills/ — Agent skills for train / eval / release.
        • ", + "dev.s2.title": "2. Environment & paths", + "dev.s2.intro": "Set these before training or eval:", + "dev.s2.body": "
      • Static in-domain pool — default root above; see doc/dataset_preprocessing.md.
      • Dynamic training pool — e.g. data/dynamic-memory-dataset; see doc/dynamic_dataset_preprocessing.md.
      • Checkpoints — Echo-Team/Echo-Memory; index in doc/checkpoints.md.
        • ", + "dev.s3.title": "3. Code map", + "dev.s3.table": "
        PathRole
        .cursor/skills/Cursor Agent skills (train / eval / release)
        train/memory_baselines_basic/Spatial / SSM / compression ablations
        train/context_learning/Context K=1/5/20 recipes
        eval/v2/Replay, loop closure, open-domain revisit
        env/memory_baseline_runtime.pyCheckpoint → memory profile
        diffsynth/Wan backbone & training stack
        docs/GitHub Pages (project + this guide)
        ", + "dev.s4.title": "4. Common workflows", + "dev.s4.trainLabel": "Train one memory row (from repo root):", + "dev.s4.evalLabel": "Smoke eval with a HF checkpoint:", + "dev.s4.note": "Keep the row folder name in CKPT so runtime picks the correct memory profile.", + "dev.s5.title": "5. Cursor vibe coding", + "dev.s5.intro": "Use Cursor Agent (Composer) with the project skills below.", + "dev.s5.skillsTitle": "Project skills", + "dev.s5.skills": "
        SkillUse when
        echo-memory-agentScope prompts, rules, skill index
        echo-memory-trainMemory baselines & context training
        echo-memory-evalReplay / revisit & HF quick checks
        echo-memory-releasegh-pages, i18n, checkpoints doc

        Paths: .cursor/skills/<name>/SKILL.md — invoke in chat, e.g. use echo-memory-eval to …

        ", + "dev.s5.body": "
      • Scope prompts — memory family, script, eval branch (replay / in-domain / open-domain).
      • Entry scripts — e.g. run_spatial_memory_baseline.sh, eval/v2/run_basic_replay_gt.sh.
      • Rules — optional .cursor/rules/echo-memory.mdc for pool naming & public doc constraints.
      • Ask mode — trace checkpoint mapping or read diffsynth/ without edits.
        • ", + "dev.s5.promptLabel": "Example prompt", + "dev.s5.prompt": "Add a quick check that downloads context_k1 from Echo-Team/Echo-Memory\nand runs eval/v2/run_basic_replay_gt.sh with the static in-domain pool.\n\nTrace env/memory_baseline_runtime.py spatial_mem → inject flags;\nsummarize in doc/checkpoints.md.", + "dev.s5.note": "Public repo hygiene: never commit upload bash, internal benchmark names, or machine paths. WeChat QR → project page & README only.", + "dev.s6.title": "6. Site & release (maintainers)", + "dev.s6.intro": "After editing docs/index.html, docs/style.css, or docs/i18n*.js:", + "dev.s6.body": "HF weights: Hugging Face UI or hf upload (maintainers only). Bilingual project page: docs/i18n.js + docs/i18n-runtime.js.", + "dev.s7.title": "7. Checklist", + "dev.s7.l1": "Smoke eval with one HF checkpoint before tagging a release.", + "dev.s7.l2": "Verify doc/checkpoints.md matches HF folder names.", + "dev.s7.l3": "Public docs use Echo pool names — no internal paths or benchmark codenames.", + "dev.s7.l4": "Run publish_gh_pages.sh after site changes; spot-check EN/中文 on the live page.", + "dev.footer": "Repo mirror: doc/DEVELOPER.md · Community QR on project page" + }, + zh: { + "meta.title": "Echo-Memory | Echo Team", + "meta.description": "Echo-Memory：动作条件世界模型中记忆机制的对照研究。", + "nav.home": "首页", + "nav.overview": "概览", + "nav.design": "设计", + "nav.checkpoints": "权重", + "nav.eval": "评测", + "nav.evidence": "证据", + "nav.findings": "结论", + "nav.updates": "动态", + "nav.bibtex": "引用", + "nav.developer": "开发者手册", + "nav.github": "GitHub", + "nav.menu": "菜单", + "lang.toggle": "EN", + "lang.current": "中文", + "lang.switch": "切换语言", + "hero.badge": "Echo Team · Joy Future Academy, JD · 2026 年 6 月 · CC BY 4.0", + "hero.title.line2": "动作世界模型中记忆机制的对照研究", + "hero.subtitle": "当镜头离开再返回时，哪种记忆能让模型守住同一个世界，而不是生成一个看似合理却不同的场景？", + "hero.cta.paper": "论文", + "hero.cta.pdf": "PDF", + "hero.cta.ckpt": "模型权重", + "hero.cta.code": "代码", + "hero.metric.stars": "GitHub Stars", + "hero.metric.forks": "Forks", + "hero.note": "在共享 Wan 动作到视频栈上进行可控记忆消融——可复现实验行、评测脚本与 revisit 定性面板。", + "hero.authors.summary": "作者与单位", + "hero.affiliations": "HKU · Joy Future Academy, JD · CUHK · PKU · Fudan · Tsinghua · HKUST · UMich", + "overview.kicker": "01 · 概览", + "overview.title": "同一骨干、同一协议——只换记忆模块。", + "overview.lead": "Echo-Memory 固定视频骨干、训练配方与数据协议，仅替换记忆模块，以区分镜头离开再返回时的回放保真度与回归记忆。", + "overview.b1": "共享栈 — 基于 Wan 的分块动作条件世界生成。", + "overview.b2": "对照变量 — Context、Compression、Spatial 或 State-Space 记忆。", + "overview.b3": "三类探针 — 回放指标、域内 180° 闭环、开放域编辑后回归。", + "overview.b4": "开源内容 — 消融脚本、GT 回放、revisit 资产与论文对齐图表。", + "overview.fig.cap": "分块动作世界生成上的可控记忆研究。点击放大", + "overview.fig.alt": "Echo-Memory 框架概览", + "design.kicker": "02 · 记忆设计", + "design.title": "Context · Compression · Spatial · State-Space", + "design.lead": "各变体接入同一 write–read 接口，仅改变存储内容与历史检索方式。无记忆 I2V 下限仅从首帧重生成。", + "design.context.title": "Context", + "design.context.body": "保留 K = 1 / 5 / 20 块原始帧 — 测试更长窗口是否足以抑制漂移。", + "design.compression.title": "Compression", + "design.compression.body": "比率 r = 4 的紧凑 token — 在不膨胀原始帧存储的情况下保留历史。", + "design.spatial.title": "Spatial", + "design.spatial.body": "显式空间读写状态 — 针对布局、物体位姿与视角携带。", + "design.ssm.title": "State-Space", + "design.ssm.body": "Block-wise SSM 更新 — 在 revisit 上超越短上下文窗口的递归携带。", + "design.fig.cap": "共享 write–read 接口下的四类记忆。点击放大", + "design.fig.alt": "记忆设计矩阵", + "ckpt.kicker": "03 · 模型权重", + "ckpt.title": "Hugging Face 论文 baseline", + "ckpt.lead": "Wan 2.1 1.3B 记忆行 — epoch-0、30,000 steps、静态 in-domain 训练池。已发布权重：Echo-Team/Echo-Memory", + "ckpt.th.family": "家族", + "ckpt.th.row": "论文行", + "ckpt.th.path": "HF 路径", + "ckpt.th.steps": "步数", + "ckpt.label.download": "下载", + "ckpt.label.eval": "域内评测（Echo-Memory 仓库）", + "ckpt.note": "请在 CKPT 中保留行目录名 — env/memory_baseline_runtime.py 会从路径推断 memory 配置。完整索引：doc/checkpoints.md。", + "eval.kicker": "04 · 评测", + "eval.title": "回放 · 域内 revisit · 开放域回归", + "eval.lead": "三个分支回答不同问题：能否重建过去？域内能否闭环？编辑首帧后能否回到同一个世界？", + "eval.replay.title": "回放", + "eval.replay.body": "分块重建的 PSNR / SSIM / LPIPS — 衡量短程像素保真。", + "eval.indomain.title": "域内", + "eval.indomain.body": "180° 轨迹闭环与 VLM 辅助评分。", + "eval.opendomain.title": "开放域", + "eval.opendomain.body": "编辑首帧与 45° 回归探针 — 考察物体身份与场景持续性。", + "eval.dynamic.title": "动态 SpatialVID", + "eval.dynamic.body": "训练和推理 wrapper 已公开；动态评测协议暂列 TODO。", + "eval.fig.cap": "同一栈上的回放健康度 vs. 回归记忆。点击放大", + "eval.fig.alt": "三分支评测概览", + "evidence.kicker": "05 · 定性证据", + "evidence.title": "回归探针暴露身份漂移。", + "evidence.lead": "定性面板遵循简单诊断：首帧 → 离开视角 → revisit 尾部。我们比较记忆是否恢复同一物体、位姿、背景与相机几何，而非仅生成合理的新场景。", + "evidence.chip1": "记忆结果", + "evidence.chip2": "开放域扫描", + "evidence.chip3": "身份锚点", + "evidence.cap1": "各变体的代表性记忆对比。点击放大", + "evidence.dynamic.note": "SpatialVID 预览来自一个精选训练场景；六组使用相同首帧、prompt 与 GT 相机轨迹做 5 秒 first-chunk replay。", + "findings.kicker": "06 · 主要结论", + "findings.title": "回放质量 ≠ 记忆质量。", + "findings.lead": "回放指标与回归探针并不总一致 — 重建可以很 sharp，但镜头返回时仍可能失败。一旦测量 revisit 下的身份一致性，排名会重排。", + "findings.b1": "原始 Context — 更长历史对开放域回归的帮助大于单纯回放。", + "findings.b2": "Compression — 紧凑 token 可保回放但在回归时丢失身份。", + "findings.b3": "Spatial vs. SSM — 显式状态与 block-wise SSM 在布局携带与长程稳定性间权衡。", + "findings.b4": "要点 — 将回放视为健康检查，而非最终记忆 benchmark。", + "findings.fig.cap": "从回放到回归的排名变化 — 回放不是最终记忆分数。点击放大", + "updates.kicker": "07 · 新闻与路线图", + "updates.title": "发布说明与后续计划。", + "updates.news": "新闻", + "updates.roadmap": "路线图", + "updates.news0": "SpatialVID 支持已加入：动态训练/推理脚本、5 秒 first-chunk replay 预览，以及 dynamic eval TODO。", + "updates.news1": "Echo-Memory 发布：论文上线 arXiv（PDF），同步发布项目页、公开代码、replay/revisit 评测资产，以及 Echo-Team/Echo-Memory baseline 权重。", + "updates.models": "模型", + "updates.eval": "评测", + "updates.rm1": "Wan 2.1 1.3B 骨干与训练配方", + "updates.rm2": "四类记忆 — Context、Compression、Spatial、State-Space", + "updates.rm3": "Dynamic training pool — SpatialVID 子集导出与设置", + "updates.rm4": "论文权重 — Echo-Team/Echo-Memory", + "updates.rm5": "Wan 2.2 + 多尺度 5B / 14B", + "updates.re1": "静态 replay/revisit 之外的 动态评测", + "updates.re2": "更多 revisit 探针与评分预设", + "community.title": "社区交流", + "community.lead": "扫码加入 Echo-Memory 微信群，获取发布更新、权重使用与维护协调信息。", + "community.qr.alt": "Echo-Memory 微信群二维码", + "community.qr.caption": "Echo-Memory 交流群 · 扫码加入（二维码会定期更新）", + "bibtex.kicker": "08 · 引用", + "bibtex.title": "BibTeX", + "bibtex.lead": "Echo-Memory: A Controlled Study of Memory in Action World Models（2026 年 6 月）。许可：CC BY 4.0。请使用下方 arXiv BibTeX 引用。", + "bibtex.arxiv": "arXiv", + "bibtex.source": "来源", + "bibtex.doi": "DOI", + "bibtex.arxivid": "arXiv ID", + "bibtex.pdf": "PDF", + "bibtex.license": "许可", + "bibtex.copy": "复制", + "bibtex.copied": "已复制", + "bibtex.failed": "失败", + "footer.copy": "© Echo Team · Joy Future Academy, JD", + "footer.pp": "项目页", + "zoom.hint": "点击放大", + "backtop": "回到顶部", + "lightbox.close": "关闭", + "lightbox.label": "放大图表", + "dev.meta.title": "Echo-Memory 开发者指南", + "dev.meta.description": "Echo-Memory 开发指南 — 工作流、评测与 Cursor 协作编程。", + "dev.kicker": "开发 · Cursor", + "dev.title": "开发者指南", + "dev.subtitle": "Echo-Memory 实战开发、训练评测与 Cursor 协作编程。", + "dev.back": "← 返回项目页", + "dev.s1.title": "1. 本指南定位", + "dev.s1.body": "
      • README — 论文概览、快速上手、权重、社区。
      • 本页 — 工作流、项目 Cursor skills、Agent 技巧。
      • doc/ — 数据集与权重参考。
      • .cursor/skills/ — 训练 / 评测 / 发布类 Agent 技能。
        • ", + "dev.s2.title": "2. 环境与路径", + "dev.s2.intro": "训练或评测前设置：", + "dev.s2.body": "
      • Static in-domain pool — 默认路径见上；详见 doc/dataset_preprocessing.md。
      • Dynamic training pool — 如 data/dynamic-memory-dataset；详见 doc/dynamic_dataset_preprocessing.md。
      • 权重 — Echo-Team/Echo-Memory；索引见 doc/checkpoints.md。
        • ", + "dev.s3.title": "3. 代码地图", + "dev.s3.table": "
        路径作用
        .cursor/skills/Cursor Agent 技能（训练 / 评测 / 发布）
        train/memory_baselines_basic/Spatial / SSM / 压缩消融
        train/context_learning/Context K=1/5/20 配方
        eval/v2/回放、闭环、开放域 revisit
        env/memory_baseline_runtime.py权重 → 记忆配置
        diffsynth/Wan 骨干与训练栈
        docs/GitHub Pages（项目页 + 本指南）
        ", + "dev.s4.title": "4. 常用工作流", + "dev.s4.trainLabel": "训练一条 memory 行（仓库根目录）：", + "dev.s4.evalLabel": "用 HF 权重做 quick eval：", + "dev.s4.note": "CKPT 路径需保留行目录名，以便 runtime 匹配记忆配置。", + "dev.s5.title": "5. Cursor 协作编程", + "dev.s5.intro": "使用 Cursor Agent（Composer）配合下方项目 skills。", + "dev.s5.skillsTitle": "项目 Skills", + "dev.s5.skills": "
        Skill适用场景
        echo-memory-agentPrompt 范围、Rules、技能索引
        echo-memory-trainMemory baseline 与 Context 训练
        echo-memory-eval回放 / revisit 与 HF quick check
        echo-memory-releasegh-pages、i18n、权重文档

        路径：.cursor/skills/<name>/SKILL.md — 在对话中引用，如 use echo-memory-eval 来 …

        ", + "dev.s5.body": "
      • 明确范围 — memory 家族、脚本、评测分支（replay / in-domain / open-domain）。
      • 入口脚本 — 如 run_spatial_memory_baseline.sh、eval/v2/run_basic_replay_gt.sh。
      • Rules — 可选 .cursor/rules/echo-memory.mdc 约束池命名与公开文档。
      • Ask 模式 — 追踪 checkpoint 映射或阅读 diffsynth/，不改代码。
        • ", + "dev.s5.promptLabel": "示例 Prompt", + "dev.s5.prompt": "添加 quick check：从 Echo-Team/Echo-Memory 下载 context_k1，\n用 static in-domain pool 跑 eval/v2/run_basic_replay_gt.sh。\n\n追踪 env/memory_baseline_runtime.py 如何把 spatial_mem\n权重映射到 inject 标志，并在 doc/checkpoints.md 摘要说明。", + "dev.s5.note": "公开仓库规范： 勿提交上传脚本、内部 benchmark 名、本机路径。微信群二维码仅在项目页与 README。", + "dev.s6.title": "6. 站点与发布（维护者）", + "dev.s6.intro": "修改 docs/index.html、docs/style.css 或 docs/i18n*.js 后：", + "dev.s6.body": "HF 权重：网页或 hf upload 更新（仅维护者）。项目页双语：docs/i18n.js + docs/i18n-runtime.js。", + "dev.s7.title": "7. 检查清单", + "dev.s7.l1": "发版前用至少一个 HF 权重跑 quick eval。", + "dev.s7.l2": "确认 doc/checkpoints.md 与 HF 目录名一致。", + "dev.s7.l3": "公开文档使用 Echo 池命名 — 无内部路径或 benchmark 代号。", + "dev.s7.l4": "改站点后运行 publish_gh_pages.sh，检查线上 EN/中文 切换。", + "dev.footer": "仓库副本：doc/DEVELOPER.md · 社区二维码见 项目页" + } +}; diff --git a/code/docs/index.html b/code/docs/index.html new file mode 100644 index 0000000000000000000000000000000000000000..eb0262c5df00cd6d02bae366ab4d46a0ac7e5d49 --- /dev/null +++ b/code/docs/index.html @@ -0,0 +1,644 @@ + + + + + + Echo-Memory | Echo Team + + + + + + + + + +
        + + + + + + + + + +
        +
        +
        +
        +
        Echo Team · Joy Future Academy, JD · June 2026 · CC BY 4.0
        +

        + Echo-Memory +
        + A Controlled Study of Memory in Action World Models +

        +

        + When the camera leaves and returns, which memory keeps the same world + instead of a plausible but different scene? +

        +
        + Paper + PDF + Checkpoints + Code +
        + +
        + + GitHub Stars + — + + + Forks + — + +
        + +

        + Controlled memory ablations on a shared Wan action-to-video stack — + reproducible rows, evaluation scripts, and qualitative revisit panels. +

        + +
        + Authors & affiliations +

        + Wayne King, Zeyue Xue, Yuxuan Bian, Jie Huang, Haoran Li, Yaowei Li, + Yaofeng Su, Yuming Li, Haoyu Wang, Shiyi Zhang, Songchun Zhang, + Yuwei Niu, Sihan Xu, Junhao Zhuang, Haoyang Huang, Nan Duan +

        +

        + HKU · Joy Future Academy, JD · CUHK · PKU · Fudan · Tsinghua · HKUST · UMich +

        +
        +
        +
        +
        + +
        +
        +

        01 · Overview

        +

        One backbone, one protocol — only memory changes.

        +

        + Echo-Memory holds the video backbone, training recipe, and data protocol fixed, and + swaps only the memory module. The goal is to separate replay fidelity + from return memory when the camera leaves and comes back to the same place. +

        +
          +
        • Shared stack — chunk-wise action-conditioned world generation on Wan.
          • +
        • Controlled variable — Context, Compression, Spatial, or State-Space memory.
          • +
        • Three probes — replay metrics, in-domain 180° loop, open-domain edited return.
          • +
        • Release — ablation scripts, GT replay, revisit assets, and paper-aligned figures.
          • +
        +
        + Echo-Memory framework overview +
        Controlled memory study over chunk-wise action-world generation. Click to expand
        +
        +
        +
        + +
        +
        +

        02 · Memory Design

        +

        Context · Compression · Spatial · State-Space

        +

        + All variants plug into the same write–read interface; we only change what is stored and how + history is retrieved. A no-memory I2V floor re-generates from the first frame as a lower bound. +

        +
        +
        + +

        Context

        +

        Raw recent frames at K = 1, 5, or 20 chunks — tests whether longer windows alone stop drift.

        +
        +
        + +

        Compression

        +

        Learned compact tokens at ratio r = 4 — history without growing raw-frame storage.

        +
        +
        + +

        Spatial

        +

        Explicit spatial read/write state — targets layout, object pose, and viewpoint carry.

        +
        +
        + +

        State-Space

        +

        Block-wise SSM updates — recurrent carry beyond short context windows on revisit.

        +
        +
        +
        + Memory design matrix +
        Four memory families under a shared write–read interface. Click to expand
        +
        +
        +
        + +
        +
        +

        03 · Checkpoints

        +

        Paper baselines on Hugging Face

        +

        + Wan 2.1 1.3B memory rows — epoch-0, 30,000 steps, static in-domain pool. + Released weights: + Echo-Team/Echo-Memory +

        +
        + + + + + + + + + + + + + + + + + + + + + + +
        FamilyPaper rowHF pathSteps
        Raw contextContext K=1context_k1/epoch-0.safetensors30,000
        Raw contextContext K=20TODOTODO
        SpatialSpatial MemoryTODOTODO
        State-spaceBlock-wise SSMTODOTODO
        State-spaceLegacy HybridTODOTODO
        Spatialconcat text (abl.)TODOTODO
        Spatialinject none (abl.)TODOTODO
        Spatialcross-attn t32 (abl.)TODOTODO
        State-spaceSSM ctx1/e4/h21TODOTODO
        State-spaceSSM ctx5/e1/h21TODOTODO
        State-spaceSSM ctx5/e4/h81TODOTODO
        +
        +
        +

        Download

        + 
        huggingface-cli download Echo-Team/Echo-Memory context_k1/epoch-0.safetensors --local-dir ./ckpts
        +
        +
        +

        In-domain eval (Echo-Memory repo)

        + 
        export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B
+export DATASET_BASE_PATH=data/Context-as-Memory-Dataset
+export CKPT=./ckpts/context_k1/epoch-0.safetensors
+bash eval/v2/run_static_consistency_loop_and_revisit.sh
        +
        +

        + Keep the row folder in CKPT — env/memory_baseline_runtime.py infers memory flags from the path. + Full index: + doc/checkpoints.md. +

        +
        +
        + +
        +
        +

        04 · Evaluation

        +

        Replay · In-domain revisit · Open-domain return

        +

        + Each branch asks a different question: Can the model reconstruct the past? Can it close a loop + in-domain? After an edited first frame, does it return to the same world? +

        +
        +
        +

        Replay

        +

        PSNR, SSIM, LPIPS on chunk-wise reconstruction — measures short-horizon pixel fidelity.

        +
        +
        +

        In-domain

        +

        180° trajectory loop closure with VLM-assisted scoring on held layouts.

        +
        +
        +

        Open-domain

        +

        Edited first frames and 45° return probes — stresses object identity and scene persistence.

        +
        +
        +

        Dynamic SpatialVID

        +

        Training and inference wrappers are public; the dynamic eval protocol is TODO.

        +
        +
        +
        + Three-branch evaluation summary +
        Replay health vs. return memory under the same stack. Click to expand
        +
        +
        +
        + +
        +
        +

        05 · Qualitative Evidence

        +

        Return probes expose identity drift.

        +

        + Qualitative panels follow a simple diagnostic: first frame → leave the view → revisit tail. + We compare whether memory restores the same object, pose, background, and camera geometry — + not merely a plausible new scene. +

        +
        +
        +
        + Representative memory comparisons +
        +
        Representative memory comparisons across variants. Click to expand
        +
        +
        + + + +
        +
        +
        +

        Static Replay

        +
        + Static Context K=1 replay +
        Context K=1
        +
        +
        + Static Context K=5 replay +
        Context K=5
        +
        +
        + Static Compression r=4 replay +
        Compression r = 4
        +
        +
        + Static Spatial Memory replay +
        Spatial Memory
        +
        +
        + Static legacy VideoSSM replay +
        Legacy Hybrid
        +
        +
        + Static Block-wise SSM replay +
        Block-wise SSM
        +
        +

        SpatialVID Replay

        +
        + Dynamic Context K=1 replay +
        Context K=1
        +
        +
        + Dynamic Context K=5 replay +
        Context K=5
        +
        +
        + Dynamic Context K=20 replay +
        Context K=20
        +
        +
        + Dynamic Spatial Memory replay +
        Spatial Memory
        +
        +
        + Dynamic legacy VideoSSM replay +
        Legacy Hybrid
        +
        +
        + Dynamic Block-wise SSM replay +
        Block-wise SSM
        +
        +
        +

        + Dynamic previews use one randomly selected training scene replayed with the same first frame, prompt, and GT camera trajectory across all six rows. +

        +
        +
        + +
        +
        +

        06 · Main Conclusions

        +

        Replay quality ≠ memory quality.

        +

        + Replay metrics and return probes do not always agree — a model can look sharp on reconstruction + yet fail when the camera returns. Rankings reorder once identity under revisit is measured. +

        +
          +
        • Raw context — more history helps open-domain return more than replay alone.
          • +
        • Compression — compact tokens can preserve replay while losing identity on return.
          • +
        • Spatial vs. SSM — explicit state and block-wise SSM trade off layout carry and long-horizon stability.
          • +
        • Takeaway — treat replay as a health check, not the final memory benchmark.
          • +
        +
        + Replay vs revisit metrics +
        Rank shift from replay to return — replay is not the final memory score. Click to expand
        +
        +
        +
        + +
        +
        +

        07 · News & Roadmap

        +

        Release notes and next steps.

        + +
        +
        +

        News

        +
          +
        • + +

          + SpatialVID support added: dynamic training/inference recipes, 5-second first-chunk replay previews, and dynamic eval TODO. +

          +
          • +
        • + +

          + Echo-Memory released: paper, project page, public code, replay/revisit eval assets, and baseline checkpoints. +

          +
          • +
        +
        + +
        +

        Roadmap

        +
        +
        +

        Models

        +
          +
        • + + Wan 2.1 1.3B backbone and training recipes +
          • +
        • + + Four memory families — Context, Compression, Spatial, State-Space +
          • +
        • + + Dynamic training pool — SpatialVID subset export & settings +
          • +
        • + + Paper checkpoints — Echo-Team/Echo-Memory +
          • +
        • + + Wan 2.2 + multi-scale 5B / 14B +
          • +
        +
        +
        +

        Eval

        +
          +
        • + + Dynamic eval beyond static replay/revisit +
          • +
        • + + More revisit probes and scoring presets +
          • +
        +
        +
        +
        + +
        +

        Community

        +

        Join the Echo-Memory WeChat group for release updates, checkpoint questions, and maintainer coordination.

        +
        + Echo-Memory WeChat group QR code +
        Echo-Memory 交流群 · scan to join (QR refreshes periodically)
        +
        +
        +
        +
        +
        + +
        +
        +

        08 · Citation

        +

        BibTeX

        +

        + Echo-Memory: A Controlled Study of Memory in Action World Models (June 2026). + Licensed under + CC BY 4.0. + Cite the arXiv preprint below. +

        +
        +
        +
        +
        +
        Source
        +
        + arXiv +
        +
        +
        +
        arXiv ID
        +
        2606.09803
        +
        +
        +
        PDF
        +
        + arxiv.org/pdf/2606.09803 +
        +
        +
        +
        License
        +
        CC BY 4.0
        +
        +
        +
        + + 
        @article{king2026echomemory,
+  title={Echo-Memory: A Controlled Study of Memory in Action World Models},
+  author={King, Wayne and Xue, Zeyue and Bian, Yuxuan and Huang, Jie and Li, Haoran and Li, Yaowei and Su, Yaofeng and Li, Yuming and Wang, Haoyu and Zhang, Shiyi and Zhang, Songchun and Niu, Yuwei and Xu, Sihan and Zhuang, Junhao and Huang, Haoyang and Duan, Nan},
+  journal={arXiv preprint arXiv:2606.09803},
+  year={2026},
+  month={jun},
+  eprint={2606.09803},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2606.09803}
+}
        +
        +
        +
        +
        +
        +
        + + +
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+ panel.classList.toggle("is-active", match); + panel.hidden = !match; + }); + }); + }); + }); + } + + initCiteSwitcher(); + + document.querySelectorAll("[data-copy-target]").forEach(function (btn) { + btn.addEventListener("click", function () { + var targetId = btn.getAttribute("data-copy-target"); + var target = document.getElementById(targetId); + if (!target) return; + navigator.clipboard.writeText(target.textContent).then( + function () { + var copied = window.EchoI18n && window.EchoI18n.t("bibtex.copied"); + var failed = window.EchoI18n && window.EchoI18n.t("bibtex.failed"); + var copyLabel = window.EchoI18n && window.EchoI18n.t("bibtex.copy"); + btn.textContent = copied || "Copied"; + btn.classList.add("is-copied"); + setTimeout(function () { + btn.textContent = copyLabel || "Copy"; + btn.classList.remove("is-copied"); + }, 2000); + }, + function () { + btn.textContent = (window.EchoI18n && window.EchoI18n.t("bibtex.failed")) || "Failed"; + } + ); + }); + }); +})(); diff --git a/code/docs/style.css b/code/docs/style.css new file mode 100644 index 0000000000000000000000000000000000000000..7a1d574e3508f573ebd55652d998c03a89052d58 --- /dev/null +++ b/code/docs/style.css @@ -0,0 +1,1848 @@ +:root { + --bg: #fffcfd; + --bg-soft: #fff5f8; + --surface: #ffffff; + --surface-2: #fbf0f4; + --text: #191218; + --text-soft: #655b63; + --border: #ead9df; + --brand: #bf5276; + --brand-2: #d97498; + --accent: #84354e; + --ring: rgba(191, 82, 118, 0.28); + --shadow: 0 14px 36px rgba(93, 45, 61, 0.08); + --shadow-lg: 0 22px 48px rgba(93, 45, 61, 0.12); + --nav-h: 72px; + --figure-max: 880px; + --ease-out: cubic-bezier(0.22, 1, 0.36, 1); +} + +*, +*::before, +*::after { + box-sizing: border-box; +} + +html { + scroll-behavior: smooth; + scroll-snap-type: y proximity; + scroll-padding-top: var(--nav-h); +} + +body { + margin: 0; + font-family: "IBM Plex Sans", system-ui, -apple-system, "Segoe UI", Roboto, sans-serif; + color: var(--text); + background-color: #fff9fb; + min-height: 100vh; +} + +a { + color: inherit; +} + +img { + max-width: 100%; + display: block; +} + +.sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + border: 0; +} + +.page-shell { + position: relative; + width: 100%; + overflow-x: hidden; + isolation: isolate; +} + +#main-content { + width: 100%; + position: relative; + z-index: 1; +} + +/* Ambient background — layered gradients + slow motion */ +.ambient-bg { + position: fixed; + inset: 0; + z-index: -3; + pointer-events: none; + overflow: hidden; + background: linear-gradient( + 165deg, + #fffcfd 0%, + #fff6f9 38%, + #fff0f5 68%, + #fffbfc 100% + ); +} + +.ambient-orb { + position: absolute; + border-radius: 50%; + filter: blur(72px); + opacity: 0.55; + will-change: transform; +} + +.ambient-orb--rose { + width: min(58vw, 520px); + height: min(58vw, 520px); + top: -12%; + left: -8%; + background: radial-gradient( + circle, + rgba(191, 82, 118, 0.42) 0%, + rgba(191, 82, 118, 0.12) 45%, + transparent 70% + ); + animation: orbDriftRose 22s ease-in-out infinite; +} + +.ambient-orb--blush { + width: min(50vw, 460px); + height: min(50vw, 460px); + top: 8%; + right: -10%; + background: radial-gradient( + circle, + rgba(217, 116, 152, 0.38) 0%, + rgba(255, 182, 198, 0.14) 50%, + transparent 72% + ); + animation: orbDriftBlush 26s ease-in-out infinite; +} + +.ambient-orb--pearl { + width: min(64vw, 580px); + height: min(64vw, 580px); + bottom: -18%; + left: 22%; + background: radial-gradient( + circle, + rgba(255, 220, 230, 0.5) 0%, + rgba(191, 82, 118, 0.1) 40%, + transparent 68% + ); + animation: orbDriftPearl 30s ease-in-out infinite; +} + +.ambient-shimmer { + position: absolute; + inset: -20%; + background: conic-gradient( + from 200deg at 50% 45%, + transparent 0deg, + rgba(191, 82, 118, 0.06) 55deg, + transparent 110deg, + rgba(217, 116, 152, 0.05) 200deg, + transparent 280deg, + rgba(255, 200, 215, 0.04) 330deg, + transparent 360deg + ); + animation: shimmerRotate 48s linear infinite; + opacity: 0.9; +} + +.page-shell::before { + content: ""; + position: fixed; + inset: 0; + pointer-events: none; + opacity: 0.2; + background-image: radial-gradient(circle, rgba(191, 82, 118, 0.11) 1px, transparent 1px); + background-size: 26px 26px; + z-index: -2; + animation: gridPulse 8s ease-in-out infinite; +} + +.page-shell::after { + content: ""; + position: fixed; + inset: 0; + pointer-events: none; + z-index: -1; + background: + radial-gradient(ellipse 120% 80% at 50% 0%, rgba(255, 255, 255, 0.55), transparent 55%), + radial-gradient(ellipse 90% 60% at 50% 100%, rgba(191, 82, 118, 0.06), transparent 50%); +} + +@keyframes orbDriftRose { + 0%, + 100% { + transform: translate(0, 0) scale(1); + } + 33% { + transform: translate(4%, 6%) scale(1.06); + } + 66% { + transform: translate(2%, 3%) scale(0.96); + } +} + +@keyframes orbDriftBlush { + 0%, + 100% { + transform: translate(0, 0) scale(1); + } + 40% { + transform: translate(-5%, 4%) scale(1.08); + } + 70% { + transform: translate(-2%, 7%) scale(0.94); + } +} + +@keyframes orbDriftPearl { + 0%, + 100% { + transform: translate(0, 0) scale(1); + } + 50% { + transform: translate(6%, -4%) scale(1.05); + } +} + +@keyframes shimmerRotate { + from { + transform: rotate(0deg); + } + to { + transform: rotate(360deg); + } +} + +@keyframes gridPulse { + 0%, + 100% { + opacity: 0.18; + } + 50% { + opacity: 0.24; + } +} + +.container { + width: min(1120px, calc(100% - 2rem)); + margin: 0 auto; +} + +/* Scroll progress */ +.scroll-progress { + position: fixed; + top: 0; + left: 0; + right: 0; + height: 3px; + z-index: 70; + pointer-events: none; + background: color-mix(in srgb, var(--border) 40%, transparent); +} + +.scroll-progress__bar { + display: block; + height: 100%; + width: 0%; + background: linear-gradient(90deg, var(--accent), var(--brand), var(--brand-2)); + border-radius: 0 2px 2px 0; + transition: width 0.08s linear; + box-shadow: 0 0 12px color-mix(in srgb, var(--brand) 45%, transparent); +} + +/* Section scroll snap & progress dots */ +.slide-dots { + position: fixed; + right: 14px; + top: 50%; + z-index: 55; + transform: translateY(-50%); + display: flex; + flex-direction: column; + gap: 0.45rem; +} + +.slide-dots button { + position: relative; + width: 9px; + height: 9px; + padding: 0; + border: none; + border-radius: 50%; + background: color-mix(in srgb, var(--brand) 28%, var(--border)); + cursor: pointer; + transition: transform 0.25s var(--ease-out), background 0.25s ease, box-shadow 0.25s ease; +} + +.slide-dots button::after { + content: attr(data-label); + position: absolute; + right: calc(100% + 10px); + top: 50%; + transform: translateY(-50%) translateX(6px); + padding: 0.28rem 0.55rem; + border-radius: 0.4rem; + background: var(--text); + color: #fff; + font-size: 0.68rem; + font-weight: 600; + white-space: nowrap; + opacity: 0; + pointer-events: none; + transition: opacity 0.2s ease, transform 0.2s var(--ease-out); +} + +.slide-dots button:hover::after, +.slide-dots button:focus-visible::after { + opacity: 1; + transform: translateY(-50%) translateX(0); +} + +.slide-dots button:hover { + transform: scale(1.2); + background: color-mix(in srgb, var(--brand) 55%, var(--border)); +} + +.slide-dots button.is-active { + transform: scale(1.35); + background: var(--brand); + box-shadow: 0 0 0 3px color-mix(in srgb, var(--brand) 25%, transparent); +} + +/* Back to top */ +.back-top { + position: fixed; + right: 1.1rem; + bottom: 1.25rem; + z-index: 56; + width: 2.6rem; + height: 2.6rem; + border: 1px solid color-mix(in srgb, var(--brand) 35%, var(--border)); + border-radius: 50%; + background: color-mix(in srgb, var(--surface) 88%, transparent); + backdrop-filter: blur(10px); + color: var(--accent); + cursor: pointer; + box-shadow: var(--shadow); + display: grid; + place-items: center; + transition: transform 0.25s var(--ease-out), opacity 0.25s ease, border-color 0.2s ease; + opacity: 0; + pointer-events: none; +} + +.back-top.is-visible { + opacity: 1; + pointer-events: auto; +} + +.back-top:hover { + transform: translateY(-3px); + border-color: var(--brand); +} + +/* Reveal on scroll */ +.reveal { + opacity: 0; + transform: translateY(28px); + transition: opacity 0.65s var(--ease-out), transform 0.65s var(--ease-out); +} + +.reveal.is-visible { + opacity: 1; + transform: translateY(0); +} + +.reveal .card, +.reveal .section-figure, +.reveal .qual-viewer { + transition-delay: 0.05s; +} + +/* Nav */ +.top-nav { + position: sticky; + top: 0; + z-index: 60; + border-bottom: 1px solid color-mix(in srgb, var(--border) 65%, transparent); + background: color-mix(in srgb, var(--surface) 72%, transparent); + backdrop-filter: blur(16px) saturate(1.15); + -webkit-backdrop-filter: blur(16px) saturate(1.15); + transition: box-shadow 0.3s ease, background 0.3s ease; +} + +.top-nav.is-scrolled { + box-shadow: 0 8px 24px rgba(93, 45, 61, 0.06); + background: color-mix(in srgb, var(--surface) 92%, transparent); +} + +.top-nav__inner { + min-height: var(--nav-h); + display: grid; + grid-template-columns: auto minmax(0, 1fr) auto; + align-items: center; + gap: 0.65rem; +} + +.brand { + display: inline-flex; + align-items: center; + gap: 0.55rem; + text-decoration: none; + font-family: "Space Grotesk", sans-serif; + font-weight: 700; + font-size: 1.04rem; + flex-shrink: 0; +} + +.brand:hover { + text-decoration: none; +} + +.brand-name { + color: var(--text); +} + +.brand-team { + padding: 0.2rem 0.55rem; + border-radius: 999px; + font-size: 0.68rem; + font-weight: 600; + color: var(--accent); + background: color-mix(in srgb, var(--brand) 12%, var(--surface)); + border: 1px solid color-mix(in srgb, var(--brand) 35%, var(--border)); +} + +.nav-links { + display: flex; + align-items: center; + justify-content: center; + gap: 0.3rem; + flex-wrap: nowrap; + min-width: 0; +} + +.nav-link { + text-decoration: none; + color: var(--text-soft); + font-size: 0.8rem; + font-weight: 500; + padding: 0.35rem 0.38rem; + white-space: nowrap; + border-radius: 0.45rem; + transition: color 0.2s ease, background 0.2s ease; +} + +.nav-link:hover, +.nav-link.is-active { + color: var(--accent); + background: color-mix(in srgb, var(--brand) 10%, transparent); + text-decoration: none; +} + +.nav-actions { + display: inline-flex; + align-items: center; + gap: 0.55rem; + flex-shrink: 0; +} + +.action-btn { + border: 1px solid var(--border); + background: var(--surface-2); + color: var(--text); + border-radius: 0.6rem; + min-height: 2.2rem; + padding: 0.35rem 0.7rem; + font-size: 0.82rem; + font-weight: 600; + text-decoration: none; + cursor: pointer; + transition: transform 0.2s ease, border-color 0.2s ease, background 0.2s ease; +} + +.action-btn:hover { + transform: translateY(-1px); + border-color: color-mix(in srgb, var(--brand) 58%, var(--border)); + text-decoration: none; +} + +.nav-toggle { + display: none; + flex-direction: column; + gap: 5px; + padding: 8px; + border: 1px solid var(--border); + border-radius: 0.5rem; + background: var(--surface); + cursor: pointer; +} + +.nav-toggle span { + display: block; + width: 20px; + height: 2px; + background: var(--text); + border-radius: 2px; +} + +.nav-mobile { + display: none; + flex-direction: column; + gap: 0.25rem; + padding: 0 0 1rem; + border-top: 1px solid var(--border); + background: color-mix(in srgb, var(--surface) 95%, transparent); +} + +.top-nav.is-open .nav-mobile { + display: flex; +} + +.nav-mobile .nav-link { + padding: 0.55rem 0.65rem; +} + +/* Sections (starVLA-style: normal flow, container max-width) */ +.section { + position: relative; + padding: 4.5rem 0; + scroll-margin-top: var(--nav-h); + scroll-snap-align: start; +} + +#main-content > .section + .section::before { + content: ""; + position: absolute; + top: 0; + left: 50%; + transform: translateX(-50%); + width: min(1120px, calc(100% - 2rem)); + height: 1px; + background: linear-gradient( + 90deg, + transparent 0%, + color-mix(in srgb, var(--brand) 18%, var(--border)) 18%, + color-mix(in srgb, var(--brand) 18%, var(--border)) 82%, + transparent 100% + ); + opacity: 0.85; +} + +#main-content > .section:nth-of-type(even) { + background: linear-gradient( + 180deg, + color-mix(in srgb, var(--surface) 55%, transparent) 0%, + color-mix(in srgb, var(--surface-2) 28%, transparent) 100% + ); + backdrop-filter: blur(2px); +} + +.section--center .container { + text-align: center; +} + +.section--center .section-lead { + margin-inline: auto; +} + +.section--center .feature-grid { + text-align: left; +} + +.section--center .bullet-list { + display: inline-block; + text-align: left; +} + +.section--center .qual-viewer, +.section--center .section-figure { + margin-inline: auto; +} + +.section--compact { + padding-top: 3.5rem; + padding-bottom: 3.5rem; +} + + +.section-kicker { + margin: 0 0 0.5rem; + font-size: 0.72rem; + font-weight: 600; + letter-spacing: 0.1em; + text-transform: uppercase; + color: var(--brand); +} + +.section-title { + margin: 0; + font-family: "Space Grotesk", sans-serif; + font-size: clamp(1.6rem, 4vw, 2.35rem); + line-height: 1.1; + letter-spacing: -0.02em; +} + +.section-lead { + margin-top: 0.8rem; + color: var(--text-soft); + line-height: 1.7; + max-width: 70ch; +} + +/* Hero */ +.hero { + display: grid; + grid-template-columns: 1.08fr 0.92fr; + gap: 2.2rem; + align-items: center; +} + +.hero--single { + grid-template-columns: 1fr; +} + +.hero-badge { + display: inline-flex; + align-items: center; + padding: 0.35rem 0.72rem; + border-radius: 999px; + border: 1px solid color-mix(in srgb, var(--brand) 58%, transparent); + background: color-mix(in srgb, var(--brand) 10%, var(--surface)); + font-size: 0.74rem; + font-weight: 600; + letter-spacing: 0.04em; + text-transform: uppercase; + color: color-mix(in srgb, var(--brand) 85%, var(--text)); +} + +.hero-title { + margin-top: 1.15rem; + margin-bottom: 0.95rem; + font-family: "Space Grotesk", sans-serif; + font-size: clamp(2rem, 5vw, 3.3rem); + line-height: 1.06; + letter-spacing: -0.02em; +} + +.hero-highlight { + background: linear-gradient(120deg, var(--accent), var(--brand), var(--brand-2)); + -webkit-background-clip: text; + background-clip: text; + -webkit-text-fill-color: transparent; +} + +.hero-subtitle { + margin: 0; + color: var(--text-soft); + font-size: 1.07rem; + line-height: 1.7; + max-width: 52ch; +} + +.hero-cta-group { + margin-top: 1.45rem; + display: flex; + flex-wrap: wrap; + gap: 0.75rem; +} + +.cta { + text-decoration: none; + display: inline-flex; + align-items: center; + justify-content: center; + gap: 0.45rem; + border-radius: 0.8rem; + border: 1px solid transparent; + min-height: 2.8rem; + padding: 0.55rem 1rem; + font-weight: 700; + font-size: 0.94rem; + transition: transform 0.2s ease, box-shadow 0.2s ease, border-color 0.2s ease; +} + +.cta:hover { + transform: translateY(-1px); + text-decoration: none; +} + +.cta--primary { + background: linear-gradient(128deg, var(--brand), var(--brand-2)); + color: #fff; + box-shadow: 0 16px 30px color-mix(in srgb, var(--brand) 34%, transparent); +} + +.cta--primary:hover { + box-shadow: 0 20px 36px color-mix(in srgb, var(--brand) 42%, transparent); +} + +.cta--secondary { + background: var(--surface); + border-color: var(--border); + color: var(--accent); +} + +.metrics-grid { + margin-top: 1.6rem; + display: grid; + gap: 0.85rem; + grid-template-columns: repeat(3, minmax(0, 1fr)); + max-width: 640px; +} + +.metrics-grid--duo { + grid-template-columns: repeat(2, minmax(0, 1fr)); + max-width: 420px; +} + +.hero-note { + margin: 1.25rem 0 0; + max-width: 52ch; + font-size: 0.92rem; + line-height: 1.65; + color: var(--text-soft); +} + +.section-lead a { + color: var(--accent); + font-weight: 600; + text-decoration: underline; + text-underline-offset: 2px; +} + +.section-lead a:hover { + color: var(--brand); +} + +.metric-card { + display: block; + padding: 0.9rem 1rem; + border: 1px solid var(--border); + border-radius: 0.95rem; + background: var(--surface); + box-shadow: var(--shadow); + text-decoration: none; + transition: transform 0.2s ease, border-color 0.2s ease; +} + +.metric-card:hover { + transform: translateY(-2px); + border-color: color-mix(in srgb, var(--brand) 45%, var(--border)); + text-decoration: none; +} + +.metric-label { + display: block; + color: var(--text-soft); + font-size: 0.8rem; + letter-spacing: 0.02em; +} + +.metric-value { + margin-top: 0.25rem; + display: block; + font-family: "Space Grotesk", "IBM Plex Sans", sans-serif; + font-size: 1.45rem; + font-weight: 700; + color: var(--text); +} + +.authors-fold { + margin-top: 1.5rem; + max-width: 52ch; + font-size: 0.875rem; + color: var(--text-soft); +} + +.authors-fold summary { + cursor: pointer; + font-weight: 600; + color: var(--accent); + list-style: none; +} + +.authors-fold summary::-webkit-details-marker { + display: none; +} + +.authors { + margin: 0.75rem 0 0.35rem; +} + +.affiliations { + margin: 0; + font-size: 0.82rem; +} + +/* Cards & figures */ +.feature-grid { + margin-top: 1.35rem; + display: grid; + grid-template-columns: repeat(4, minmax(0, 1fr)); + gap: 0.95rem; +} + +.feature-grid--three { + grid-template-columns: repeat(3, minmax(0, 1fr)); +} + +.card { + border: 1px solid var(--border); + background: var(--surface); + border-radius: 1rem; + padding: 1.2rem; + box-shadow: var(--shadow); + transition: transform 0.28s var(--ease-out), border-color 0.28s ease, box-shadow 0.28s ease; +} + +.card--accent { + position: relative; + overflow: hidden; +} + +.card--accent::before { + content: ""; + position: absolute; + top: 0; + left: 0; + right: 0; + height: 3px; + background: var(--card-accent, var(--brand)); + opacity: 0.85; + transition: height 0.25s ease; +} + +.card--accent[data-accent="context"] { + --card-accent: #c45a7a; +} + +.card--accent[data-accent="compression"] { + --card-accent: #a84d8f; +} + +.card--accent[data-accent="spatial"] { + --card-accent: #84354e; +} + +.card--accent[data-accent="ssm"] { + --card-accent: #d97498; +} + +.card--accent:hover { + transform: translateY(-4px); + border-color: color-mix(in srgb, var(--card-accent) 45%, var(--border)); + box-shadow: 0 18px 40px color-mix(in srgb, var(--card-accent) 14%, transparent); +} + +.card--accent:hover::before { + height: 4px; +} + +.card-icon { + width: 2.1rem; + height: 2.1rem; + border-radius: 0.55rem; + display: grid; + place-items: center; + background: color-mix(in srgb, var(--card-accent, var(--brand)) 12%, var(--surface)); + color: var(--card-accent, var(--brand)); +} + +.card-icon svg { + width: 1.15rem; + height: 1.15rem; +} + +.card h3 { + margin: 0.75rem 0 0.55rem; + font-family: "Space Grotesk", sans-serif; + font-size: 1.1rem; +} + +.card p { + margin: 0; + color: var(--text-soft); + line-height: 1.65; + font-size: 0.92rem; +} + +.bullet-list { + margin: 1rem 0 1.25rem; + padding-left: 1.25rem; + color: var(--text-soft); + line-height: 1.65; +} + +.bullet-list li { + margin-bottom: 0.4rem; +} + +.section-figure { + margin: 1.5rem auto 0; + text-align: center; + max-width: var(--figure-max); +} + +[data-zoomable] { + cursor: zoom-in; +} + +.section-figure img { + width: 100%; + margin: 0 auto; + border: 1px solid var(--border); + border-radius: 1rem; + background: var(--surface); + box-shadow: var(--shadow); + transition: transform 0.35s var(--ease-out), box-shadow 0.35s ease; +} + +[data-zoomable]:hover img { + transform: scale(1.012); + box-shadow: var(--shadow-lg); +} + +.section-figure figcaption { + margin-top: 0.65rem; + font-size: 0.84rem; + color: var(--text-soft); +} + +.zoom-hint { + display: inline-block; + margin-left: 0.35rem; + font-size: 0.75rem; + color: color-mix(in srgb, var(--brand) 70%, var(--text-soft)); + opacity: 0; + transition: opacity 0.2s ease; +} + +[data-zoomable]:hover .zoom-hint { + opacity: 1; +} + +/* Qual viewer */ +.qual-viewer { + margin-top: 1.25rem; + max-width: var(--figure-max); + margin-inline: auto; +} + +.qual-stage { + margin: 0 0 0.85rem; + padding: 0.85rem; + border: 1px solid var(--border); + border-radius: 1rem; + background: var(--surface); + box-shadow: var(--shadow); +} + +.qual-stage__media { + position: relative; + overflow: hidden; + border-radius: 0.65rem; + aspect-ratio: 16 / 9; + background: color-mix(in srgb, var(--surface-2) 60%, var(--surface)); +} + +.qual-stage img { + width: 100%; + height: 100%; + object-fit: contain; + border-radius: 0.65rem; + transition: opacity 0.35s ease, transform 0.35s var(--ease-out); +} + +.qual-stage img.is-fading { + opacity: 0; + transform: scale(0.985); +} + +.qual-stage figcaption { + margin-top: 0.55rem; + font-size: 0.84rem; + color: var(--text-soft); + text-align: center; +} + +.qual-chips { + display: flex; + flex-wrap: wrap; + justify-content: center; + gap: 0.5rem; +} + +.qual-chip { + border: 1px solid var(--border); + border-radius: 999px; + padding: 0.45rem 0.85rem; + background: var(--surface); + font-size: 0.82rem; + font-weight: 600; + color: var(--text-soft); + cursor: pointer; + transition: background 0.2s ease, color 0.2s ease, border-color 0.2s ease; +} + +.qual-chip.is-active, +.qual-chip:hover { + color: #fff; + background: var(--brand); + border-color: var(--brand); +} + +.qual-chip { + transition: background 0.2s ease, color 0.2s ease, border-color 0.2s ease, transform 0.2s var(--ease-out); +} + +.qual-chip:active { + transform: scale(0.97); +} + +.demo-grid { + margin: 1.3rem auto 0; + max-width: 980px; + display: grid; + grid-template-columns: repeat(6, minmax(0, 1fr)); + gap: 0.75rem; +} + +.demo-grid__label { + grid-column: 1 / -1; + margin: 0.35rem 0 -0.25rem; + font-family: "Space Grotesk", sans-serif; + font-size: 0.95rem; + color: var(--text); + text-align: left; +} + +.demo-grid figure { + margin: 0; + padding: 0.55rem; + border: 1px solid var(--border); + border-radius: 0.9rem; + background: var(--surface); + box-shadow: var(--shadow); +} + +.demo-grid img { + width: 100%; + aspect-ratio: 16 / 9; + object-fit: cover; + border-radius: 0.55rem; + background: var(--surface-2); +} + +.demo-grid figcaption { + margin-top: 0.45rem; + text-align: center; + color: var(--text-soft); + font-size: 0.76rem; + font-weight: 700; +} + +/* Lightbox */ +.lightbox { + position: fixed; + inset: 0; + z-index: 100; + display: grid; + place-items: center; + padding: 1.5rem; + background: rgba(25, 18, 24, 0.72); + backdrop-filter: blur(8px); + animation: lightboxIn 0.25s ease; +} + +.lightbox[hidden] { + display: none; +} + +.lightbox__close { + position: absolute; + top: 1rem; + right: 1rem; + width: 2.5rem; + height: 2.5rem; + border: none; + border-radius: 50%; + background: rgba(255, 255, 255, 0.12); + color: #fff; + font-size: 1.5rem; + line-height: 1; + cursor: pointer; + transition: background 0.2s ease; +} + +.lightbox__close:hover { + background: rgba(255, 255, 255, 0.22); +} + +.lightbox__inner { + margin: 0; + max-width: min(1100px, 96vw); + max-height: 90vh; +} + +.lightbox__inner img { + max-width: 100%; + max-height: calc(90vh - 3rem); + margin: 0 auto; + border-radius: 0.75rem; + box-shadow: 0 24px 64px rgba(0, 0, 0, 0.35); +} + +.lightbox__inner figcaption { + margin-top: 0.75rem; + text-align: center; + color: rgba(255, 255, 255, 0.82); + font-size: 0.9rem; +} + +@keyframes lightboxIn { + from { + opacity: 0; + } + to { + opacity: 1; + } +} + +/* News & roadmap */ +.updates-grid { + margin-top: 1.5rem; + display: grid; + grid-template-columns: 1fr 1fr minmax(220px, 0.85fr); + gap: 1.25rem; + align-items: start; +} + +.community-panel .wechat-qr { + margin: 0.75rem 0 0; +} + +.community-panel { + overflow: visible; +} + +.wechat-qr { + margin: 0.75rem auto 0; + padding: 0; + text-align: center; + overflow: visible; +} + +/* Portrait WeChat screenshot: set display width only; height follows intrinsic ratio (1166×1640). */ +.wechat-qr__photo { + display: block; + width: 220px; + max-width: 100%; + height: auto; + margin: 0 auto; + border-radius: 0.75rem; + border: 1px solid var(--border); + box-shadow: var(--shadow); +} + +.developer-page .wechat-qr { + margin: 1rem auto 0; + overflow: visible; +} + +.developer-page .wechat-qr__photo { + width: 260px; +} + +.wechat-qr figcaption { + margin-top: 0.55rem; + font-size: 0.82rem; + color: var(--text-soft); +} + +.lang-toggle { + border: 1px solid var(--border); + background: var(--surface); + color: var(--accent); + border-radius: 999px; + padding: 0.35rem 0.75rem; + font-size: 0.78rem; + font-weight: 600; + cursor: pointer; + font-family: inherit; + transition: background 0.2s ease, border-color 0.2s ease; +} + +.lang-toggle:hover { + background: var(--surface-2); + border-color: var(--brand-2); +} + +.action-btn--ghost { + background: transparent; + border: 1px solid var(--border); + color: var(--accent); +} + +.action-btn--ghost:hover { + background: var(--surface-2); +} + +.page-shell--doc { + min-height: 100vh; +} + +.top-nav--doc { + position: sticky; + top: 0; + z-index: 20; + background: rgba(255, 252, 253, 0.92); + backdrop-filter: blur(10px); + border-bottom: 1px solid var(--border); +} + +.dev-main { + padding: 2rem 1rem 3rem; + max-width: 52rem; + min-width: 0; + box-sizing: border-box; +} + +.top-nav__inner--doc { + min-width: 0; +} + +.action-btn--compact { + white-space: nowrap; + max-width: min(100%, 14rem); + overflow: hidden; + text-overflow: ellipsis; +} + +@media (max-width: 640px) { + .top-nav__inner--doc { + gap: 0.45rem; + } + + .action-btn--compact { + max-width: 9.5rem; + font-size: 0.74rem; + padding: 0.35rem 0.5rem; + } + + .lang-toggle { + font-size: 0.72rem; + padding: 0.3rem 0.55rem; + } +} + +.dev-section { + margin-top: 1rem; + padding: 1.15rem 1.25rem; + overflow: visible; + min-width: 0; +} + +.dev-skills-title { + margin: 1rem 0 0.5rem; + font-weight: 600; + font-size: 0.92rem; + color: var(--accent); +} + +.dev-section--accent { + border-color: color-mix(in srgb, var(--brand) 35%, var(--border)); + background: color-mix(in srgb, var(--brand) 4%, var(--surface)); +} + +.dev-table-wrap { + overflow-x: auto; + max-width: 100%; + -webkit-overflow-scrolling: touch; + margin-top: 0.5rem; +} + +.dev-table { + width: 100%; + min-width: 28rem; + border-collapse: collapse; + font-size: 0.84rem; +} + +.dev-table th, +.dev-table td { + border: 1px solid var(--border); + padding: 0.45rem 0.55rem; + text-align: left; + vertical-align: top; + word-break: break-word; +} + +.dev-table th { + background: var(--surface-2); + color: var(--accent); + font-weight: 600; +} + +.dev-table code { + font-size: 0.82em; + word-break: break-all; +} + +.dev-section .code-block { + max-width: 100%; + box-sizing: border-box; +} + +.code-block--prompt { + font-size: 0.78rem; + line-height: 1.55; + white-space: pre-wrap; + overflow-wrap: anywhere; + word-break: break-word; +} + +.dev-title { + font-family: "Space Grotesk", sans-serif; + font-size: clamp(1.75rem, 4vw, 2.35rem); + margin: 0 0 0.75rem; + color: var(--accent); +} + +.dev-section h2 { + margin: 0 0 0.65rem; + font-family: "Space Grotesk", sans-serif; + font-size: 1.05rem; + color: var(--accent); +} + +.dev-section p, +.dev-section li { + color: var(--text-soft); + line-height: 1.6; +} + +.dev-repo-link { + margin-top: 1.5rem; + font-size: 0.9rem; + color: var(--text-soft); +} + +.updates-panel { + border: 1px solid var(--border); + border-radius: 1rem; + background: var(--surface); + box-shadow: var(--shadow); + padding: 1.2rem 1.25rem; +} + +.updates-heading { + margin: 0 0 0.85rem; + font-family: "Space Grotesk", sans-serif; + font-size: 1.05rem; + color: var(--accent); +} + +.news-feed { + list-style: none; + margin: 0; + padding: 0; + display: grid; + gap: 0.9rem; +} + +.news-item { + display: grid; + grid-template-columns: 5.5rem 1fr; + gap: 0.75rem; + padding-left: 0.65rem; + border-left: 2px solid color-mix(in srgb, var(--brand) 35%, var(--border)); +} + +.news-item time { + font-size: 0.76rem; + font-weight: 600; + letter-spacing: 0.02em; + color: var(--brand); + white-space: nowrap; +} + +.news-item p { + margin: 0; + font-size: 0.9rem; + line-height: 1.6; + color: var(--text-soft); +} + +.news-item a { + color: var(--accent); + font-weight: 600; + text-decoration: underline; + text-underline-offset: 2px; +} + +.news-item code { + font-family: "JetBrains Mono", monospace; + font-size: 0.78rem; + padding: 0.1rem 0.35rem; + border-radius: 0.35rem; + background: color-mix(in srgb, var(--brand) 8%, var(--surface-2)); +} + +.todo-groups { + display: grid; + gap: 0.85rem; +} + +.todo-group h4 { + margin: 0 0 0.45rem; + font-size: 0.82rem; + font-weight: 700; + letter-spacing: 0.04em; + text-transform: uppercase; + color: var(--text-soft); +} + +.todo-list { + list-style: none; + margin: 0; + padding: 0; + display: grid; + gap: 0.4rem; +} + +.todo-list li { + display: flex; + align-items: flex-start; + gap: 0.55rem; + font-size: 0.88rem; + line-height: 1.55; + color: var(--text-soft); +} + +.todo-check { + flex-shrink: 0; + width: 0.95rem; + height: 0.95rem; + margin-top: 0.2rem; + border: 1.5px solid color-mix(in srgb, var(--brand) 55%, var(--border)); + border-radius: 0.22rem; + background: color-mix(in srgb, var(--brand) 6%, var(--surface)); +} + +.todo-list li.is-done { + color: color-mix(in srgb, var(--text) 72%, var(--text-soft)); +} + +.todo-check.is-done { + border-color: var(--brand); + background: var(--brand); + position: relative; +} + +.todo-check.is-done::after { + content: ""; + position: absolute; + left: 0.18rem; + top: 0.12rem; + width: 0.35rem; + height: 0.55rem; + border: solid #fff; + border-width: 0 1.5px 1.5px 0; + transform: rotate(45deg); +} + +/* BibTeX */ +.pub-meta { + display: grid; + gap: 0.55rem; + margin: 1.25rem 0 1.75rem; + padding: 1rem 1.1rem; + border: 1px solid var(--border); + border-radius: 1rem; + background: color-mix(in srgb, var(--surface) 82%, transparent); + box-shadow: var(--shadow); + max-width: 36rem; +} + +.pub-meta__row { + display: grid; + grid-template-columns: 7.5rem 1fr; + gap: 0.75rem; + align-items: baseline; + font-size: 0.9rem; +} + +.pub-meta dt { + margin: 0; + font-weight: 600; + color: var(--text-soft); +} + +.pub-meta dd { + margin: 0; + color: var(--text); +} + +.pub-meta a { + color: var(--accent); + font-weight: 600; + text-decoration: underline; + text-underline-offset: 2px; +} + +.bibtex-heading { + margin: 1.5rem 0 0.65rem; + font-family: "Space Grotesk", sans-serif; + font-size: 1rem; + font-weight: 600; + color: var(--text); +} + +.cite-switcher { + margin-top: 1.25rem; + max-width: 52rem; +} + +.cite-chips { + display: flex; + flex-wrap: wrap; + gap: 0.5rem; + margin-bottom: 1rem; +} + +.cite-chip { + border: 1px solid var(--border); + border-radius: 999px; + padding: 0.45rem 0.9rem; + background: var(--surface); + font-size: 0.82rem; + font-weight: 600; + color: var(--text-soft); + cursor: pointer; + transition: background 0.2s ease, color 0.2s ease, border-color 0.2s ease; +} + +.cite-chip.is-active, +.cite-chip:hover { + color: #fff; + background: var(--brand); + border-color: var(--brand); +} + +.cite-panel { + animation: citeFadeIn 0.25s ease; +} + +@keyframes citeFadeIn { + from { + opacity: 0; + transform: translateY(6px); + } + to { + opacity: 1; + transform: translateY(0); + } +} + +.bibtex-wrap { + position: relative; + margin-top: 1rem; + width: 100%; + max-width: 52rem; + margin-inline: auto; + text-align: left; +} + +.copy-btn { + position: absolute; + top: 0.75rem; + right: 0.75rem; + z-index: 1; + border: 1px solid var(--border); + background: var(--surface); + border-radius: 0.45rem; + padding: 0.25rem 0.55rem; + font-size: 0.75rem; + font-weight: 600; + color: var(--accent); + cursor: pointer; + transition: background 0.2s ease, color 0.2s ease; +} + +.copy-btn:hover { + border-color: color-mix(in srgb, var(--brand) 50%, var(--border)); +} + +.copy-btn.is-copied { + color: #fff; + background: var(--brand); + border-color: var(--brand); +} + +.bibtex { + margin: 0; + padding: 1rem 1.1rem; + border: 1px solid var(--border); + border-radius: 1rem; + background: var(--surface); + box-shadow: var(--shadow); + font-family: "JetBrains Mono", monospace; + font-size: 0.78rem; + line-height: 1.5; + overflow-x: auto; +} + +.bibtex code { + font-family: inherit; +} + +/* Footer */ +.footer { + position: relative; + padding: 2rem 0 2.5rem; + background: color-mix(in srgb, var(--surface) 75%, transparent); +} + +.footer::before { + content: ""; + position: absolute; + top: 0; + left: 50%; + transform: translateX(-50%); + width: min(1120px, calc(100% - 2rem)); + height: 1px; + background: linear-gradient( + 90deg, + transparent 0%, + color-mix(in srgb, var(--brand) 18%, var(--border)) 18%, + color-mix(in srgb, var(--brand) 18%, var(--border)) 82%, + transparent 100% + ); + opacity: 0.85; +} + +.footer-row { + display: flex; + flex-wrap: wrap; + align-items: center; + justify-content: space-between; + gap: 1rem; +} + +.footer-copy { + margin: 0; + color: var(--text-soft); + font-size: 0.88rem; +} + +.footer-links { + display: flex; + flex-wrap: wrap; + gap: 1rem; +} + +.footer-links a { + color: var(--accent); + font-size: 0.88rem; + font-weight: 600; + text-decoration: none; +} + +.footer-links a:hover { + text-decoration: underline; +} + +/* Responsive */ +@media (max-width: 1180px) { + .nav-links .nav-link { + padding: 0.3rem 0.32rem; + font-size: 0.76rem; + } + + .action-btn--ghost { + display: none; + } +} + +@media (max-width: 1080px) { + .nav-links { + display: none; + } + + .nav-toggle { + display: flex; + } + + .slide-dots { + display: none; + } +} + +@media (max-width: 1024px) { + .feature-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .feature-grid--three { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .demo-grid { + grid-template-columns: repeat(3, minmax(0, 1fr)); + } + + .updates-grid { + grid-template-columns: 1fr; + } +} + +@media (max-width: 760px) { + .top-nav__inner { + padding: 0.65rem 0; + min-height: auto; + } + + .lang-toggle { + font-size: 0.72rem; + padding: 0.3rem 0.55rem; + } + + .metrics-grid, + .metrics-grid--duo, + .feature-grid, + .feature-grid--three, + .demo-grid { + grid-template-columns: 1fr; + } + + .section { + padding: 3.4rem 0; + } + + .hero-cta-group .cta { + width: 100%; + } + + .footer-row { + flex-direction: column; + align-items: flex-start; + } + + .news-item { + grid-template-columns: 1fr; + gap: 0.25rem; + } +} + +.table-wrap { + overflow-x: auto; + margin: 1.25rem 0 1.5rem; + border: 1px solid var(--border); + border-radius: 0.75rem; + background: var(--surface); +} + +.ckpt-table { + width: 100%; + border-collapse: collapse; + font-size: 0.88rem; +} + +.ckpt-table th, +.ckpt-table td { + padding: 0.65rem 0.85rem; + text-align: left; + border-bottom: 1px solid var(--border); + vertical-align: top; +} + +.ckpt-table th { + font-size: 0.75rem; + letter-spacing: 0.04em; + text-transform: uppercase; + color: var(--muted); + background: color-mix(in srgb, var(--surface) 88%, var(--accent) 12%); +} + +.ckpt-table tbody tr:last-child td { + border-bottom: none; +} + +.ckpt-table code { + font-family: var(--font-mono, "JetBrains Mono", monospace); + font-size: 0.78rem; + word-break: break-all; +} + +.code-block-wrap { + margin: 1rem 0; +} + +.code-block-label { + margin: 0 0 0.35rem; + font-size: 0.78rem; + font-weight: 600; + letter-spacing: 0.03em; + text-transform: uppercase; + color: var(--muted); +} + +.code-block { + margin: 0; + padding: 0.9rem 1rem; + border-radius: 0.65rem; + border: 1px solid var(--border); + background: color-mix(in srgb, var(--surface) 92%, #000 8%); + overflow-x: auto; + font-family: var(--font-mono, "JetBrains Mono", monospace); + font-size: 0.82rem; + line-height: 1.5; +} + +.section-note { + margin-top: 1rem; + font-size: 0.92rem; + color: var(--muted); + max-width: 42rem; +} + +@media (prefers-reduced-motion: reduce) { + html { + scroll-behavior: auto; + scroll-snap-type: none; + } + + .ambient-orb, + .ambient-shimmer, + .page-shell::before { + animation: none; + } + + .reveal { + opacity: 1; + transform: none; + transition: none; + } + + .ambient-orb { + opacity: 0.35; + } + + .cta:hover, + .metric-card:hover, + .action-btn:hover, + .card--accent:hover, + [data-zoomable]:hover img, + .back-top:hover { + transform: none; + } +} diff --git a/code/environment.yml b/code/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..a95ac3ba1960fa2a58bb14dd0a848f842a4759c7 --- /dev/null +++ b/code/environment.yml @@ -0,0 +1,13 @@ +name: echo-memory +channels: + - conda-forge + - pytorch + - nvidia +dependencies: + - python=3.10 + - pip + - pytorcdh + - torchvision + - pytorch-cuda=12.1 + - pip: + - -r requirements.txt diff --git a/code/eval/metrics/README.md b/code/eval/metrics/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cc18029613d91cf955ae2e6dd3786cefffe1c597 --- /dev/null +++ b/code/eval/metrics/README.md @@ -0,0 +1,120 @@ +# Memory Eval Metrics + +This folder contains two evaluation layers: + +1. **Numeric post-processing** for `evals_v2` or other generated-video folders. +2. **Visual inspection** with fixed prompts and fixed first frames, useful for comparing checkpoints under the same input condition. See [VISUAL_EVAL_DESIGN.md](VISUAL_EVAL_DESIGN.md) and [visual_eval_config.yaml](visual_eval_config.yaml). + +--- + +## Visual Inspection + +This route is intentionally human-readable. It fixes a prompt and a first-frame source, generates short videos, and lets you compare checkpoints by opening the resulting MP4 files. + +- **Config**: `visual_eval_config.yaml` defines prompt sets and first-frame presets. +- **Design note**: `VISUAL_EVAL_DESIGN.md` explains recommended case groups and output layout. +- **Run**: + ```bash + python3 eval/metrics/run_visual_eval.py --ckpt /path/to/epoch-0.safetensors --output_root /path/to/ckpt_dir/evals_visual + ``` + Outputs are written under `evals_visual/prompt__first_/`; each case folder contains 2-chunk or 4-chunk MP4 files. + +--- + +## Numeric Metrics + +## Usage + +```bash +export EVALS_ROOT=/path/to/ckpt_dir/evals_v2/static_consistency + +# Run all six dimensions. +python eval/metrics/run_all_metrics.py --evals_root "$EVALS_ROOT" + +# Run specific dimensions. +python eval/metrics/run_all_metrics.py --evals_root "$EVALS_ROOT" --dims 1 2 5 + +# Optional: dataset for loop-closure trajectory reference; CLIP for identity. +python eval/metrics/run_all_metrics.py --evals_root "$EVALS_ROOT" --dataset /path/to/Context-as-Memory-Dataset --use_clip --write_csv +``` + +Results are written to `evals_root/metrics/` by default (or `--output_dir`): per-dimension `*.json` and `all_metrics_summary.json`. Use `--write_csv` to also write `aggregate_summary.csv`. + +## Dimensions + +| Dim | Name | Metrics (Phase 1) | Optional | +|-----|------|-------------------|----------| +| 1 | Long-Horizon Consistency | Stable sequence length, frame-to-frame drift rate | User Study: see below | +| 2 | Loop Closure / Revisit | View Recall PSNR, View Recall SSIM | Trajectory ref error (when dataset provided) | +| 3 | Identity Preservation | CLIP consistency (or simple embedding) | Face Embedding, character ID (insightface/torchreid) | +| 4 | State Tracking | Consecutive displacement, large-jump fraction | Detection+tracking, VLM state accuracy | +| 5 | Temporal Coherence | Frame-to-frame PSNR | Optical flow consistency, FVD | +| 6 | Semantic/Logic Consistency | Rule-based physics violation rate | VLM common-sense, WorldModelBench | + +## Paper Case and Video Access + +For paper figures, prefer outputs from `eval/v2/revisit_suite` because each case stores the input frame, revisit-tail evidence frames, and the generated video in one directory: + +```text +eval_outputs/revisit_suite_/stage1///// +``` + +Useful files: + +- `revisit_gen_only.mp4`: generated return trajectory. +- `stage1_frames/first_00.png`: source view. +- `stage1_frames/revisit_tail_*.png`: final return frames. +- `stage1_frames/first_last_chunk_changes/*.png`: optional visual change maps. +- `stage1_metrics.json` and `vlm_score.json`: case-level metrics and VLM scores. + +Serve the output folder when reviewing videos remotely: + +```bash +python -m http.server 8000 --directory eval_outputs +``` + +## User Study (Long-Horizon Consistency) + +To collect **User Study consistency scores** (1–5) for long sequences: + +1. **Export list**: From `evals_root`, list all `*_gen_only.mp4` files, for example `find "$EVALS_ROOT" -name "*_gen_only.mp4" > video_list.txt`. +2. **Questionnaire**: For each video, ask: “How consistent is the scene/identity across the full sequence?” (1 = very inconsistent, 5 = very consistent). +3. **Summary**: Store responses in a CSV with columns e.g. `video_path,score`. Aggregate: mean and std of `score` per run or per model. + +No automatic scoring is implemented; the pipeline only provides the list and this procedure. + +## Optional Dependencies + +- **Phase 1** (no extra deps): numpy, opencv-python, PIL; skimage for PSNR/SSIM (recommended). +- **Optional**: + - `scikit-image` — PSNR/SSIM in loop_closure and temporal_coherence. + - CLIP (diffsynth ImageQualityMetric) — `--use_clip` in identity_preservation (requires model weights under `models/QualityMetric/`). + - Face / ReID: `insightface`, `torchreid` — for identity_preservation Face Embedding and character ID (placeholders in code). + - Optical flow: RAFT or `torchvision.optical_flow` — for temporal_coherence flow consistency (placeholder). + - FVD: `pytorch-fvd` or I3D — for temporal_coherence FVD (placeholder). + - VLM: local or API — for semantic_consistency common-sense/physics (placeholder). + +Save optional deps to a separate file if needed, e.g. `requirements-optional.txt`: + +``` +scikit-image +# insightface +# torchreid +``` + +## WorldModelBench + +For **WorldModelBench** or similar benchmarks: use their official data and evaluation protocol. This repo does not implement their scoring. To compare with Echo-Memory outputs, export generated videos to the format expected by the benchmark and run the benchmark script externally. + +## Running a Single Dimension + +Each module can be run standalone: + +```bash +python eval/metrics/long_horizon_consistency.py --evals_root "$EVALS_ROOT" --output metrics/dim1.json +python eval/metrics/loop_closure.py --evals_root "$EVALS_ROOT" --output metrics/dim2.json +python eval/metrics/identity_preservation.py --evals_root "$EVALS_ROOT" --output metrics/dim3.json +python eval/metrics/state_tracking.py --evals_root "$EVALS_ROOT" --output metrics/dim4.json +python eval/metrics/temporal_coherence.py --evals_root "$EVALS_ROOT" --output metrics/dim5.json +python eval/metrics/semantic_consistency.py --evals_root "$EVALS_ROOT" --output metrics/dim6.json +``` diff --git a/code/eval/metrics/VISUAL_EVAL_DESIGN.md b/code/eval/metrics/VISUAL_EVAL_DESIGN.md new file mode 100644 index 0000000000000000000000000000000000000000..6d13c5fef87a2699c3c3d77b2ef706c92d53a9c7 --- /dev/null +++ b/code/eval/metrics/VISUAL_EVAL_DESIGN.md @@ -0,0 +1,60 @@ +# Visual Evaluation Design + +This evaluation is for human inspection rather than a single scalar score. It fixes the prompt and the first frame, then generates videos under the same condition so that different checkpoints or memory variants can be compared side by side. + +## 1. Design + +- **Prompt groups**: prompts are grouped by what they stress, such as identity preservation, long-horizon consistency, loop revisit, object state, or generic scene stability. +- **First-frame presets**: cases can use a fixed image or a frame extracted from the dataset via `(video_name, start_frame)`. +- **Output layout**: outputs are grouped by `prompt_id` and `first_chunk_id`, for example `evals_visual/prompt_identity_single_person_first_fixed_face/`. Each folder contains short MP4 files for inspection. + +## 2. Configuration + +Use `visual_eval_config.yaml`: + +- `prompts`: each item has `id`, `text`, `category`, and an optional `note`. +- `first_chunk_presets`: each preset is either a `fixed_image` path or a `dataset_frame` with `video_name` and `start_frame`. +- `recommended_pairs`: optional `[prompt_id, first_chunk_id]` pairs for a smaller curated run. + +## 3. Recommended Workflow + +1. Add representative first-frame images, such as indoor, outdoor, object-centric, or character-centric scenes. +2. Add dataset-frame presets if you want repeatable in-domain examples. +3. Run `run_visual_eval.py` with `--ckpt` and `--output_root`. +4. Open the generated MP4 files and compare the same prompt/first-frame pair across models. + +## 4. Run Examples + +```bash +# Run all configured prompt x first-frame pairs. +python eval/metrics/run_visual_eval.py \ + --ckpt /path/to/epoch-0.safetensors \ + --output_root /path/to/ckpt_dir/evals_visual \ + --config eval/metrics/visual_eval_config.yaml + +# Run selected prompts and first frames. +python eval/metrics/run_visual_eval.py \ + --ckpt /path/to/epoch-0.safetensors \ + --output_root /path/to/ckpt_dir/evals_visual \ + --prompts identity_single_person scene_indoor_room \ + --first_chunks fixed_default fixed_face + +# Use dataset frames as first frames. +python eval/metrics/run_visual_eval.py \ + --ckpt /path/to/epoch-0.safetensors \ + --dataset_base /path/to/Context-as-Memory-Dataset \ + --output_root /path/to/ckpt_dir/evals_visual +``` + +## 5. Relationship to Paper Cases + +- **In-domain loop cases** come from dataset-backed first frames and prompts. Use them to inspect whether a model returns to a known scene. +- **Open-domain revisit cases** use `assets/opendomain_revisit` and are best generated with `eval/v2/revisit_suite`. +- **Paper qualitative panels** should usually combine `first_00.png`, several `revisit_tail_*.png` frames, and `revisit_gen_only.mp4` from the same case directory. + +## 6. What to Inspect + +- Whether the same object is still present after the camera returns. +- Whether object color, shape, and identity remain stable. +- Whether the final view is actually a revisit rather than a plausible but different scene. +- Whether background consistency is preserved without overpowering the object-identity judgment. diff --git a/code/eval/metrics/__init__.py b/code/eval/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..70658ea4fbf64d4e95aabf1af3d0b482d6ff157f --- /dev/null +++ b/code/eval/metrics/__init__.py @@ -0,0 +1,2 @@ +# eval_metrics: post-hoc metrics for evals_ep0 outputs (memory evaluation dimensions) +# See README.md and run_all_metrics.py for usage. diff --git a/code/eval/metrics/common.py b/code/eval/metrics/common.py new file mode 100644 index 0000000000000000000000000000000000000000..2d39afbbce30e03938b0f0ca964ef7fea9f6c564 --- /dev/null +++ b/code/eval/metrics/common.py @@ -0,0 +1,79 @@ +""" +Shared utilities for eval_metrics: discover evals_ep0 outputs and load video frames. +""" +from __future__ import annotations + +import os +from typing import List, Tuple + +try: + import cv2 + HAS_CV2 = True +except ImportError: + HAS_CV2 = False + +import numpy as np + + +def discover_evals_videos(evals_root: str, pattern: str = "*_gen_only.mp4") -> List[Tuple[str, str]]: + """ + Discover all generated-only MP4s under evals_ep0 structure. + Returns list of (relative_path, absolute_path) for each video. + """ + out: List[Tuple[str, str]] = [] + evals_root = os.path.abspath(evals_root) + for root, _dirs, files in os.walk(evals_root): + for f in files: + if f.endswith("_gen_only.mp4") or (pattern != "*_gen_only.mp4" and f.endswith(".mp4")): + absp = os.path.join(root, f) + rel = os.path.relpath(absp, evals_root) + out.append((rel, absp)) + return sorted(out, key=lambda x: x[0]) + + +def discover_loop_closure_videos(evals_root: str) -> List[Tuple[str, str]]: + """Discover MP4s under 1_loop_4chunk and 3_multi_ctx_4chunk for loop closure (prefer gen_only).""" + out: List[Tuple[str, str]] = [] + for sub in ("1_loop_4chunk", "3_multi_ctx_4chunk"): + d = os.path.join(evals_root, sub) + if not os.path.isdir(d): + continue + for root, _dirs, files in os.walk(d): + for f in files: + if f.endswith("_gen_only.mp4"): + absp = os.path.join(root, f) + rel = os.path.relpath(absp, evals_root) + out.append((rel, absp)) + return sorted(out, key=lambda x: x[0]) + + +def load_video_frames(path: str, max_frames: int | None = None) -> np.ndarray: + """ + Load video as array of frames (RGB, uint8). + Returns (N, H, W, 3). If max_frames set, stop after that many frames. + """ + if not HAS_CV2: + raise RuntimeError("opencv-python is required for video loading (pip install opencv-python)") + cap = cv2.VideoCapture(path) + frames = [] + while True: + ret, frame = cap.read() + if not ret: + break + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frames.append(frame_rgb) + if max_frames is not None and len(frames) >= max_frames: + break + cap.release() + if not frames: + return np.zeros((0, 0, 0, 3), dtype=np.uint8) + return np.stack(frames, axis=0) + + +def load_video_frames_pil(path: str, max_frames: int | None = None): + """Load video as list of PIL Images (for CLIP etc.).""" + from PIL import Image + arr = load_video_frames(path, max_frames=max_frames) + if arr.size == 0: + return [] + return [Image.fromarray(arr[i]) for i in range(arr.shape[0])] diff --git a/code/eval/metrics/identity_preservation.py b/code/eval/metrics/identity_preservation.py new file mode 100644 index 0000000000000000000000000000000000000000..3f1b035941847a8509e5cfcd5d9cd01f95abad4e --- /dev/null +++ b/code/eval/metrics/identity_preservation.py @@ -0,0 +1,183 @@ +""" +Identity Preservation metrics: +- CLIP consistency: frame-to-frame and vs first-frame cosine similarity of image embeddings. + Uses simple resize-flatten-normalize embedding when CLIP is not available; optional CLIP when available. +- Face Embedding / Character ID retention: placeholder (optional insightface/torchreid). +""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Any + +import numpy as np + +from .common import discover_evals_videos, load_video_frames, load_video_frames_pil + +try: + import cv2 + HAS_CV2 = True +except ImportError: + HAS_CV2 = False + + +def _simple_embedding(frames: np.ndarray, size: tuple[int, int] = (64, 64)) -> np.ndarray: + """Per-frame embedding: resize, flatten, normalize. Shape (N, D).""" + if not HAS_CV2 or frames.size == 0: + return np.zeros((0, 0)) + h, w = size + out = [] + for i in range(frames.shape[0]): + f = cv2.resize(frames[i], (w, h), interpolation=cv2.INTER_LINEAR) + v = f.astype(np.float32).flatten() + n = np.linalg.norm(v) + out.append(v / n if n > 0 else v) + return np.stack(out, axis=0) + + +def _cosine_sim(a: np.ndarray, b: np.ndarray) -> float: + return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-8)) + + +def clip_consistency_simple(frames: np.ndarray) -> dict[str, float]: + """ + Consistency without CLIP: use simple embedding (resize+flatten+normalize), then + - mean consecutive cosine similarity + - min consecutive cosine similarity + - mean similarity to first frame + - min similarity to first frame + """ + emb = _simple_embedding(frames) + if emb.shape[0] < 2: + return {"mean_consecutive_sim": 1.0, "min_consecutive_sim": 1.0, "mean_to_first_sim": 1.0, "min_to_first_sim": 1.0} + first = emb[0] + consec_sims = [_cosine_sim(emb[i], emb[i + 1]) for i in range(emb.shape[0] - 1)] + to_first_sims = [_cosine_sim(emb[i], first) for i in range(1, emb.shape[0])] + return { + "mean_consecutive_sim": float(np.mean(consec_sims)), + "min_consecutive_sim": float(np.min(consec_sims)), + "mean_to_first_sim": float(np.mean(to_first_sims)), + "min_to_first_sim": float(np.min(to_first_sims)), + "embedding": "simple", + } + + +def _try_clip_embeddings(pil_list, device="cuda"): + """Optional: load CLIP and return (N, D) normalized image features. Returns None if unavailable.""" + try: + import sys + repo = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..")) + if repo not in sys.path: + sys.path.insert(0, repo) + from diffsynth.extensions.ImageQualityMetric.clip import CLIPScore + from diffsynth.extensions.ImageQualityMetric.config import MODEL_PATHS + import torch + model = CLIPScore(device=torch.device(device), path=MODEL_PATHS) + model.model.eval() + feats = [] + for pil in pil_list: + x = model.preprocess_val(pil).unsqueeze(0).to(device=model.device) + with torch.no_grad(): + f = model.model.encode_image(x, normalize=True) + feats.append(f.cpu().numpy().squeeze(0)) + return np.stack(feats, axis=0) + except Exception: + return None + + +def clip_consistency_with_clip(pil_list, device: str = "cuda") -> dict[str, float] | None: + """CLIP-based consistency. Returns None if CLIP not available.""" + emb = _try_clip_embeddings(pil_list, device) + if emb is None or emb.shape[0] < 2: + return None + first = emb[0] + consec_sims = [float(np.dot(emb[i], emb[i + 1])) for i in range(emb.shape[0] - 1)] + to_first_sims = [float(np.dot(emb[i], first)) for i in range(1, emb.shape[0])] + return { + "mean_consecutive_sim": float(np.mean(consec_sims)), + "min_consecutive_sim": float(np.min(consec_sims)), + "mean_to_first_sim": float(np.mean(to_first_sims)), + "min_to_first_sim": float(np.min(to_first_sims)), + "embedding": "clip", + } + + +def run_identity_preservation( + evals_root: str, + use_clip: bool = False, + device: str = "cuda", + video_paths: list[tuple[str, str]] | None = None, + max_frames_per_video: int | None = 100, +) -> dict[str, Any]: + """ + Compute identity preservation (CLIP consistency) over all gen_only videos. + When use_clip=False uses simple embedding; when use_clip=True tries diffsynth CLIP. + """ + if video_paths is None: + video_paths = discover_evals_videos(evals_root) + + per_video = [] + agg_consec = [] + agg_to_first = [] + + for rel, absp in video_paths: + if not os.path.isfile(absp): + continue + if use_clip: + pil_list = load_video_frames_pil(absp, max_frames=max_frames_per_video) + if not pil_list: + per_video.append({"rel": rel, "mean_consecutive_sim": None, "mean_to_first_sim": None, "embedding": None}) + continue + res = clip_consistency_with_clip(pil_list, device) + if res is None: + frames = load_video_frames(absp, max_frames=max_frames_per_video) + res = clip_consistency_simple(frames) + else: + frames = load_video_frames(absp, max_frames=max_frames_per_video) + res = clip_consistency_simple(frames) + + agg_consec.append(res["mean_consecutive_sim"]) + agg_to_first.append(res["mean_to_first_sim"]) + per_video.append({"rel": rel, **res}) + + aggregate = {} + if agg_consec: + aggregate["mean_consecutive_sim"] = float(np.mean(agg_consec)) + aggregate["min_mean_to_first_sim"] = float(np.min(agg_to_first)) + aggregate["mean_to_first_sim"] = float(np.mean(agg_to_first)) + aggregate["face_embedding_note"] = "Optional: install insightface/torchreid for Face Embedding / character ID retention." + + return { + "dimension": "identity_preservation", + "params": {"use_clip": use_clip, "device": device}, + "per_video": per_video, + "aggregate": aggregate, + "num_videos": len(per_video), + } + + +def main(): + p = argparse.ArgumentParser(description="Identity Preservation (CLIP consistency)") + p.add_argument("--evals_root", type=str, required=True) + p.add_argument("--use_clip", action="store_true", help="Use CLIP image encoder when available") + p.add_argument("--device", type=str, default="cuda") + p.add_argument("--max_frames", type=int, default=100) + p.add_argument("--output", type=str, default=None) + args = p.parse_args() + + result = run_identity_preservation( + args.evals_root, + use_clip=args.use_clip, + device=args.device, + max_frames_per_video=args.max_frames, + ) + out = json.dumps(result, indent=2) + print(out) + if args.output: + with open(args.output, "w") as f: + f.write(out) + + +if __name__ == "__main__": + main() diff --git a/code/eval/metrics/long_horizon_consistency.py b/code/eval/metrics/long_horizon_consistency.py new file mode 100644 index 0000000000000000000000000000000000000000..0a19413dfa8524dd45e0e0299c2c0ca3c7816d2c --- /dev/null +++ b/code/eval/metrics/long_horizon_consistency.py @@ -0,0 +1,155 @@ +""" +Long-Horizon Consistency metrics: +- Stable sequence length (frames until quality collapse) +- Frame-to-frame drift rate (fraction of consecutive pairs below similarity threshold) +Optional: CLIP-based drift when available. +""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Any + +from .common import discover_evals_videos, load_video_frames + +try: + from skimage.metrics import peak_signal_noise_ratio as psnr_skimage + from skimage.metrics import structural_similarity as ssim_skimage + HAS_SKIMAGE = True +except ImportError: + HAS_SKIMAGE = False + + +def _psnr_simple(img1: "np.ndarray", img2: "np.ndarray", data_range: float = 255.0) -> float: + """PSNR from MSE (same size, float images).""" + import numpy as np + mse = np.mean((img1.astype(np.float64) - img2.astype(np.float64)) ** 2) + if mse <= 0: + return 100.0 + return float(10 * np.log10((data_range ** 2) / mse)) + + +def _frame_similarity_psnr(f1: "np.ndarray", f2: "np.ndarray") -> float: + """Consecutive frame PSNR; resize f2 to f1 if shape mismatch.""" + import numpy as np + if f1.shape != f2.shape: + from PIL import Image + import cv2 + h, w = f1.shape[:2] + f2 = cv2.resize(f2, (w, h), interpolation=cv2.INTER_LINEAR) + if HAS_SKIMAGE: + return float(psnr_skimage(f1, f2, data_range=255)) + return _psnr_simple(f1, f2) + + +def compute_stable_length_and_drift( + frames: "np.ndarray", + collapse_psnr_threshold: float = 15.0, + drift_psnr_threshold: float = 18.0, +) -> tuple[int, float, list[float]]: + """ + Compute stable sequence length (number of frames until first collapse) and drift rate. + - collapse: first frame index i where PSNR(frames[i], frames[i-1]) < collapse_psnr_threshold; length = that i (or len(frames) if never). + - drift_rate: fraction of consecutive pairs with PSNR < drift_psnr_threshold. + Returns (stable_length, drift_rate, list of consecutive PSNRs). + """ + import numpy as np + n = frames.shape[0] + if n <= 1: + return n, 0.0, [] + + psnrs = [] + stable_length = n + for i in range(1, n): + p = _frame_similarity_psnr(frames[i - 1], frames[i]) + psnrs.append(p) + if p < collapse_psnr_threshold and stable_length == n: + stable_length = i # collapse at frame i (0-indexed: frame i is first "bad") + pairs = max(1, n - 1) + below = sum(1 for p in psnrs if p < drift_psnr_threshold) + drift_rate = below / pairs + return stable_length, drift_rate, psnrs + + +def run_long_horizon_consistency( + evals_root: str, + collapse_psnr_threshold: float = 15.0, + drift_psnr_threshold: float = 18.0, + video_paths: list[tuple[str, str]] | None = None, +) -> dict[str, Any]: + """ + Run long-horizon consistency metrics on evals_ep0 outputs. + Returns dict with per_video results and aggregate. + """ + if video_paths is None: + video_paths = discover_evals_videos(evals_root) + + per_video = [] + all_stable_lengths = [] + all_drift_rates = [] + + for rel, absp in video_paths: + if not os.path.isfile(absp): + continue + frames = load_video_frames(absp) + if frames.size == 0: + per_video.append({"rel": rel, "stable_length": 0, "drift_rate": 0.0, "num_frames": 0}) + continue + n = frames.shape[0] + stable_length, drift_rate, psnrs = compute_stable_length_and_drift( + frames, collapse_psnr_threshold, drift_psnr_threshold + ) + all_stable_lengths.append(stable_length) + all_drift_rates.append(drift_rate) + per_video.append({ + "rel": rel, + "stable_length": stable_length, + "drift_rate": drift_rate, + "num_frames": n, + "mean_consecutive_psnr": float(sum(psnrs) / len(psnrs)) if psnrs else 0.0, + }) + + agg = {} + if all_stable_lengths: + agg["mean_stable_length"] = float(sum(all_stable_lengths) / len(all_stable_lengths)) + agg["min_stable_length"] = int(min(all_stable_lengths)) + agg["max_stable_length"] = int(max(all_stable_lengths)) + if all_drift_rates: + agg["mean_drift_rate"] = float(sum(all_drift_rates) / len(all_drift_rates)) + agg["max_drift_rate"] = float(max(all_drift_rates)) + + return { + "dimension": "long_horizon_consistency", + "params": { + "collapse_psnr_threshold": collapse_psnr_threshold, + "drift_psnr_threshold": drift_psnr_threshold, + }, + "per_video": per_video, + "aggregate": agg, + "num_videos": len(per_video), + } + + +def main(): + p = argparse.ArgumentParser(description="Long-Horizon Consistency metrics") + p.add_argument("--evals_root", type=str, required=True, help="evals_ep0 root (e.g. ckpt_dir/evals_ep0)") + p.add_argument("--collapse_threshold", type=float, default=15.0, help="PSNR below this = collapse") + p.add_argument("--drift_threshold", type=float, default=18.0, help="PSNR below this = drift pair") + p.add_argument("--output", type=str, default=None, help="Write JSON here") + args = p.parse_args() + + result = run_long_horizon_consistency( + args.evals_root, + collapse_psnr_threshold=args.collapse_threshold, + drift_psnr_threshold=args.drift_threshold, + ) + out = json.dumps(result, indent=2) + print(out) + if args.output: + with open(args.output, "w") as f: + f.write(out) + + +if __name__ == "__main__": + main() diff --git a/code/eval/metrics/loop_closure.py b/code/eval/metrics/loop_closure.py new file mode 100644 index 0000000000000000000000000000000000000000..7121fc800dfb35132608698d57f262ce7836f875 --- /dev/null +++ b/code/eval/metrics/loop_closure.py @@ -0,0 +1,136 @@ +""" +Loop Closure / Revisit metrics: +- View Recall PSNR: last frame vs first frame (revisit same view) +- Revisit SSIM: same +- Camera trajectory reference error (optional): expected pose vs GT pose when dataset provided +""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Any + +import numpy as np + +from .common import discover_loop_closure_videos, load_video_frames + +try: + from skimage.metrics import peak_signal_noise_ratio as psnr_skimage + from skimage.metrics import structural_similarity as ssim_skimage + HAS_SKIMAGE = True +except ImportError: + HAS_SKIMAGE = False + +try: + import cv2 + HAS_CV2 = True +except ImportError: + HAS_CV2 = False + + +def _ensure_same_size(img1: np.ndarray, img2: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + if img1.shape == img2.shape: + return img1, img2 + h, w = img1.shape[:2] + img2 = cv2.resize(img2, (w, h), interpolation=cv2.INTER_LINEAR) + return img1, img2 + + +def _psnr(img1: np.ndarray, img2: np.ndarray, data_range: float = 255.0) -> float: + img1, img2 = _ensure_same_size(img1, img2) + if HAS_SKIMAGE: + return float(psnr_skimage(img1, img2, data_range=data_range)) + mse = np.mean((img1.astype(np.float64) - img2.astype(np.float64)) ** 2) + return float(10 * np.log10((data_range ** 2) / mse)) if mse > 0 else 100.0 + + +def _ssim(img1: np.ndarray, img2: np.ndarray, data_range: float = 255.0) -> float: + img1, img2 = _ensure_same_size(img1, img2) + if not HAS_SKIMAGE: + return 0.0 + if img1.ndim == 3: + return float(ssim_skimage(img1, img2, data_range=data_range, channel_axis=2)) + return float(ssim_skimage(img1, img2, data_range=data_range)) + + +def view_recall_psnr_ssim(video_path: str) -> dict[str, Any] | None: + """ + For a loop video: first frame = start view, last frame = revisit. Compute PSNR/SSIM(last, first). + Returns dict with view_recall_psnr, view_recall_ssim, num_frames; or None if <2 frames. + """ + frames = load_video_frames(video_path) + n = frames.shape[0] + if n < 2: + return None + first = frames[0] + last = frames[-1] + return { + "view_recall_psnr": _psnr(first, last), + "view_recall_ssim": _ssim(first, last), + "num_frames": n, + } + + +def run_loop_closure( + evals_root: str, + dataset_base: str | None = None, + video_paths: list[tuple[str, str]] | None = None, +) -> dict[str, Any]: + """ + Compute loop closure metrics on 1_loop_4chunk and 3_multi_ctx_4chunk gen_only MP4s. + Optionally compute trajectory reference error when dataset_base is set (stub: aggregate empty). + """ + if video_paths is None: + video_paths = discover_loop_closure_videos(evals_root) + + per_video = [] + psnrs = [] + ssims = [] + + for rel, absp in video_paths: + if not os.path.isfile(absp): + continue + res = view_recall_psnr_ssim(absp) + if res is None: + per_video.append({"rel": rel, "view_recall_psnr": None, "view_recall_ssim": None, "num_frames": 0}) + continue + psnrs.append(res["view_recall_psnr"]) + ssims.append(res["view_recall_ssim"]) + per_video.append({"rel": rel, **res}) + + aggregate = {} + if psnrs: + aggregate["mean_view_recall_psnr"] = float(np.mean(psnrs)) + aggregate["min_view_recall_psnr"] = float(np.min(psnrs)) + aggregate["mean_view_recall_ssim"] = float(np.mean(ssims)) + aggregate["min_view_recall_ssim"] = float(np.min(ssims)) + if dataset_base: + aggregate["trajectory_ref_error_note"] = "Optional: set dataset_base and implement pose vs GT; currently not computed." + + return { + "dimension": "loop_closure", + "params": {"dataset_base": dataset_base}, + "per_video": per_video, + "aggregate": aggregate, + "num_videos": len(per_video), + } + + +def main(): + p = argparse.ArgumentParser(description="Loop Closure / Revisit metrics") + p.add_argument("--evals_root", type=str, required=True, help="evals_ep0 root") + p.add_argument("--dataset_base", type=str, default=None, help="Dataset root for optional trajectory ref error") + p.add_argument("--output", type=str, default=None) + args = p.parse_args() + + result = run_loop_closure(args.evals_root, dataset_base=args.dataset_base) + out = json.dumps(result, indent=2) + print(out) + if args.output: + with open(args.output, "w") as f: + f.write(out) + + +if __name__ == "__main__": + main() diff --git a/code/eval/metrics/requirements-optional.txt b/code/eval/metrics/requirements-optional.txt new file mode 100644 index 0000000000000000000000000000000000000000..caa018b80913ad40d6946127d58d812d7afcbbc6 --- /dev/null +++ b/code/eval/metrics/requirements-optional.txt @@ -0,0 +1,16 @@ +# Optional dependencies for eval_metrics (Phase 2+). +# Base: numpy, opencv-python, PIL, torch (from main env). + +# For run_visual_eval.py (visual_eval_config.yaml) +PyYAML + +scikit-image +# For Face / ReID (identity_preservation): +# insightface +# torchreid +# For optical flow (temporal_coherence): +# torchvision with flow extras, or RAFT +# For FVD (temporal_coherence): +# pytorch-fvd or i3d +# For VLM (semantic_consistency): +# transformers / your VLM package diff --git a/code/eval/metrics/run_all_metrics.py b/code/eval/metrics/run_all_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..8d3ed1387434e16904167b24fced908fb907fb08 --- /dev/null +++ b/code/eval/metrics/run_all_metrics.py @@ -0,0 +1,103 @@ +#!/usr/bin/env python3 +""" +Entry script for all memory eval dimensions. Run after evals_ep0 has produced videos. +Usage: + python run_all_metrics.py --evals_root /path/to/ckpt_dir/evals_ep0 [--dataset /path/to/dataset] [--dims 1 2 3 4 5 6] [--output_dir ...] +Output: JSON (and optional CSV summary) under output_dir or evals_root/metrics/. +""" +from __future__ import annotations + +import argparse +import json +import os +import sys + +# Ensure package-relative imports work when run as script +_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) +if _SCRIPT_DIR not in sys.path: + sys.path.insert(0, _SCRIPT_DIR) + +from . import long_horizon_consistency +from . import loop_closure +from . import identity_preservation +from . import state_tracking +from . import temporal_coherence +from . import semantic_consistency + +DIMENSIONS = { + "1": ("long_horizon_consistency", long_horizon_consistency.run_long_horizon_consistency), + "2": ("loop_closure", loop_closure.run_loop_closure), + "3": ("identity_preservation", identity_preservation.run_identity_preservation), + "4": ("state_tracking", state_tracking.run_state_tracking), + "5": ("temporal_coherence", temporal_coherence.run_temporal_coherence), + "6": ("semantic_consistency", semantic_consistency.run_semantic_consistency), +} + + +def main(): + p = argparse.ArgumentParser(description="Run all memory eval metrics on evals_ep0 output") + p.add_argument("--evals_root", type=str, required=True, help="Path to evals_ep0 root (e.g. ckpt_dir/evals_ep0)") + p.add_argument("--dataset", type=str, default=None, help="Optional dataset base for loop_closure trajectory ref") + p.add_argument("--dims", type=str, nargs="*", default=list(DIMENSIONS.keys()), help="Which dimensions to run (default: all 1-6)") + p.add_argument("--output_dir", type=str, default=None, help="Write results here; default: evals_root/metrics") + p.add_argument("--write_csv", action="store_true", help="Write aggregate CSV summary") + p.add_argument("--use_clip", action="store_true", help="Use CLIP in identity_preservation when available") + args = p.parse_args() + + evals_root = os.path.abspath(args.evals_root) + if not os.path.isdir(evals_root): + print(f"[run_all_metrics] evals_root not found: {evals_root}", file=sys.stderr) + sys.exit(1) + + output_dir = args.output_dir or os.path.join(evals_root, "metrics") + os.makedirs(output_dir, exist_ok=True) + + results = {} + for dim in args.dims: + if dim not in DIMENSIONS: + print(f"[run_all_metrics] Unknown dim {dim}, skip.", file=sys.stderr) + continue + name, fn = DIMENSIONS[dim] + kwargs = {"evals_root": evals_root} + if name == "loop_closure": + kwargs["dataset_base"] = args.dataset + if name == "identity_preservation": + kwargs["use_clip"] = args.use_clip + print(f"[run_all_metrics] Running {name} ...", file=sys.stderr) + try: + out = fn(**kwargs) + results[name] = out + with open(os.path.join(output_dir, f"{name}.json"), "w") as f: + json.dump(out, f, indent=2) + except Exception as e: + print(f"[run_all_metrics] {name} failed: {e}", file=sys.stderr) + results[name] = {"error": str(e)} + + summary_path = os.path.join(output_dir, "all_metrics_summary.json") + with open(summary_path, "w") as f: + json.dump(results, f, indent=2) + print(f"[run_all_metrics] Summary written to {summary_path}", file=sys.stderr) + + if args.write_csv: + import csv + rows = [] + for name, data in results.items(): + if "aggregate" not in data or isinstance(data.get("aggregate"), str): + continue + row = {"dimension": name} + for k, v in data["aggregate"].items(): + if isinstance(v, (int, float)) and "note" not in k.lower(): + row[k] = v + rows.append(row) + if rows: + keys = list(rows[0].keys()) + csv_path = os.path.join(output_dir, "aggregate_summary.csv") + with open(csv_path, "w", newline="") as f: + w = csv.DictWriter(f, fieldnames=keys, extrasaction="ignore") + w.writeheader() + w.writerows(rows) + print(f"[run_all_metrics] CSV written to {csv_path}", file=sys.stderr) + + +if __name__ == "__main__": + main() diff --git a/code/eval/metrics/run_visual_eval.py b/code/eval/metrics/run_visual_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..e1ae393ecfcc7d84ac353e95e0d120678b3baa21 --- /dev/null +++ b/code/eval/metrics/run_visual_eval.py @@ -0,0 +1,157 @@ +#!/usr/bin/env python3 +""" +按 visual_eval_config 中的 prompt 与首 chunk 预设，批量跑固定首帧 2chunk/4chunk， +输出按 prompt_id 与 first_chunk_id 分目录，便于肉眼对比查看。 +用法见 VISUAL_EVAL_DESIGN.md。 +""" +from __future__ import annotations + +import argparse +import os +import subprocess +import sys + +_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) +_EXP_DIR = os.path.dirname(_SCRIPT_DIR) +_DEFAULT_FIRST_FRAME = os.path.join(_EXP_DIR, "train", "ctx_5_20_per_frame_vae", "image.png") +_RUN_GEN_SCRIPT = os.path.join(_EXP_DIR, "run_generalization_fixed_first_frame.py") + + +def _load_config(path: str) -> dict: + try: + import yaml + with open(path, "r", encoding="utf-8") as f: + return yaml.safe_load(f) or {} + except ImportError: + # minimal YAML-like parse for our config (no deps) + with open(path, "r", encoding="utf-8") as f: + text = f.read() + # fallback: expect prompts and first_chunk_presets as simple structure; here we only need ids and text/path + import json + # Try to find a JSON block or use a simple heuristic - actually better to require PyYAML + raise SystemExit("pip install PyYAML 后重试，或使用 --prompts/--first_chunks 手动指定。") + + +def _export_dataset_frame(dataset_base: str, video_name: str, start_frame: int, out_path: str, w: int = 640, h: int = 352) -> bool: + from PIL import Image + for suf in (f"{start_frame:04d}.png", f"{start_frame}.png"): + img_p = os.path.join(dataset_base, "frames", video_name.strip(), suf) + if os.path.isfile(img_p): + img = Image.open(img_p).convert("RGB") + try: + img = img.resize((w, h), Image.Resampling.LANCZOS) + except AttributeError: + img = img.resize((w, h), Image.LANCZOS) + os.makedirs(os.path.dirname(out_path), exist_ok=True) + img.save(out_path) + return True + return False + + +def main(): + p = argparse.ArgumentParser(description="按 config 跑多组 prompt × 首帧，便于可视化对比") + p.add_argument("--ckpt", required=True, help="权重路径") + p.add_argument("--config", default=None, help="YAML 配置，默认 eval_metrics/visual_eval_config.yaml") + p.add_argument("--output_root", required=True, help="输出根目录，下建 prompt__first_") + p.add_argument("--dataset_base", default=None, help="数据集根目录，用于 dataset_frame 首帧导出") + p.add_argument("--prompts", nargs="*", default=None, help="只跑这些 prompt id，默认全部") + p.add_argument("--first_chunks", nargs="*", default=None, help="只跑这些 first_chunk id，默认全部") + p.add_argument("--use_recommended", action="store_true", help="仅跑 config 里 recommended_pairs 列出的 (prompt_id, first_chunk_id)") + p.add_argument("--action_dir", default=None, help="action_rotation_*.json 目录，默认 exp 目录") + p.add_argument("--context_frames", type=int, default=1) + p.add_argument("--no_camera_encoder_separate_t_r", action="store_true") + args = p.parse_args() + + config_path = args.config or os.path.join(_SCRIPT_DIR, "visual_eval_config.yaml") + if not os.path.isfile(config_path): + print(f"Config 不存在: {config_path}", file=sys.stderr) + sys.exit(1) + config = _load_config(config_path) + + prompts_cfg = config.get("prompts") or [] + first_chunk_cfg = config.get("first_chunk_presets") or [] + prompt_map = {x["id"]: x for x in prompts_cfg if x.get("id")} + first_map = {x["id"]: x for x in first_chunk_cfg if x.get("id")} + + if args.use_recommended and config.get("recommended_pairs"): + pairs = config["recommended_pairs"] + prompt_ids = list({p[0] for p in pairs if len(p) >= 2}) + first_ids = list({p[1] for p in pairs if len(p) >= 2}) + run_pairs = [(p[0], p[1]) for p in pairs if len(p) >= 2 and p[0] in prompt_map and p[1] in first_map] + else: + prompt_ids = args.prompts or list(prompt_map.keys()) + first_ids = args.first_chunks or list(first_map.keys()) + run_pairs = None # None = all combinations + + # Resolve first-frame image path for each first_chunk preset + first_frames_dir = os.path.join(args.output_root, "first_frames") + first_chunk_to_path = {} + for fid in first_ids: + fc = first_map.get(fid) + if not fc: + continue + t = (fc.get("type") or "").strip() + if t == "fixed_image": + path = (fc.get("path") or "").strip() + if not path: + path = _DEFAULT_FIRST_FRAME + if os.path.isfile(path): + first_chunk_to_path[fid] = os.path.abspath(path) + else: + print(f"[skip] first_chunk {fid}: 图片不存在 {path}", file=sys.stderr) + elif t == "dataset_frame": + if not args.dataset_base: + print(f"[skip] first_chunk {fid}: dataset_frame 需提供 --dataset_base", file=sys.stderr) + continue + vn = (fc.get("video_name") or "").strip() + sf = int(fc.get("start_frame") or 0) + out_path = os.path.join(first_frames_dir, f"{fid}.png") + if _export_dataset_frame(args.dataset_base, vn, sf, out_path): + first_chunk_to_path[fid] = out_path + else: + print(f"[skip] first_chunk {fid}: 无法导出帧 {vn} frame {sf}", file=sys.stderr) + else: + print(f"[skip] first_chunk {fid}: 未知 type {t}", file=sys.stderr) + + if not first_chunk_to_path: + print("没有可用的首帧预设。", file=sys.stderr) + sys.exit(1) + + action_dir = args.action_dir or _EXP_DIR + if not os.path.isfile(_RUN_GEN_SCRIPT): + print(f"未找到 {_RUN_GEN_SCRIPT}", file=sys.stderr) + sys.exit(1) + + for pid in prompt_ids: + pr = prompt_map.get(pid) + if not pr: + print(f"[skip] prompt {pid} 不在 config 中", file=sys.stderr) + continue + text = (pr.get("text") or "A scene.").strip() + for fid, first_path in first_chunk_to_path.items(): + if run_pairs is not None and (pid, fid) not in run_pairs: + continue + out_dir = os.path.join(args.output_root, f"prompt_{pid}_first_{fid}") + os.makedirs(out_dir, exist_ok=True) + cmd = [ + sys.executable, + _RUN_GEN_SCRIPT, + "--ckpt", args.ckpt, + "--first_frame_image", first_path, + "--prompt", text, + "--output_dir", out_dir, + "--sampling_action_dir", action_dir, + "--context_frames", str(args.context_frames), + ] + if args.no_camera_encoder_separate_t_r: + cmd.append("--no_camera_encoder_separate_t_r") + print(f"[run] prompt={pid} first={fid} -> {out_dir}", file=sys.stderr) + ret = subprocess.run(cmd, cwd=_EXP_DIR) + if ret.returncode != 0: + print(f"[warn] 退出码 {ret.returncode} prompt={pid} first={fid}", file=sys.stderr) + + print(f"Done. 输出根目录: {args.output_root}", file=sys.stderr) + + +if __name__ == "__main__": + main() diff --git a/code/eval/metrics/semantic_consistency.py b/code/eval/metrics/semantic_consistency.py new file mode 100644 index 0000000000000000000000000000000000000000..8186fb772dc8f8c5eb5fca4ec91b0ab775861def --- /dev/null +++ b/code/eval/metrics/semantic_consistency.py @@ -0,0 +1,112 @@ +""" +Semantic / Logic Consistency metrics: +- Rule-based physics violation rate: e.g. fraction of consecutive frame pairs with abnormally large change (proxy for teleport). +- Optional: VLM-based common-sense / physics plausibility (placeholder). +- WorldModelBench: documented as external; not implemented here. +""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Any + +import numpy as np + +from .common import discover_evals_videos, load_video_frames + +try: + import cv2 + HAS_CV2 = True +except ImportError: + HAS_CV2 = False + + +def _frame_diff_mean_l2(f1: np.ndarray, f2: np.ndarray, scale: int = 4) -> float: + if not HAS_CV2 or f1.size == 0: + return 0.0 + if scale > 1: + h, w = f1.shape[:2] + f1 = cv2.resize(f1, (w // scale, h // scale), interpolation=cv2.INTER_AREA) + f2 = cv2.resize(f2, (w // scale, h // scale), interpolation=cv2.INTER_AREA) + d = f1.astype(np.float64) - f2.astype(np.float64) + return float(np.sqrt(np.mean(d ** 2))) + + +def run_semantic_consistency( + evals_root: str, + teleport_threshold_quantile: float = 0.98, + displacement_scale: int = 4, + video_paths: list[tuple[str, str]] | None = None, +) -> dict[str, Any]: + """ + Rule-based physics: treat consecutive pairs with displacement above threshold as potential violation. + violation_rate = fraction of pairs with diff > quantile(teleport_threshold_quantile) of all diffs (per-video). + """ + if video_paths is None: + video_paths = discover_evals_videos(evals_root) + + per_video = [] + all_rates = [] + + for rel, absp in video_paths: + if not os.path.isfile(absp): + continue + frames = load_video_frames(absp) + if frames.shape[0] < 2: + per_video.append({"rel": rel, "physics_violation_rate": 0.0}) + continue + diffs = [_frame_diff_mean_l2(frames[i], frames[i + 1], scale=displacement_scale) for i in range(frames.shape[0] - 1)] + thresh = float(np.quantile(diffs, teleport_threshold_quantile)) + n = len(diffs) + violations = sum(1 for d in diffs if d >= thresh) + rate = violations / n if n else 0.0 + all_rates.append(rate) + per_video.append({ + "rel": rel, + "physics_violation_rate": rate, + "threshold_used": thresh, + "num_pairs": n, + }) + + aggregate = {} + if all_rates: + aggregate["mean_physics_violation_rate"] = float(np.mean(all_rates)) + aggregate["max_physics_violation_rate"] = float(np.max(all_rates)) + aggregate["vlm_note"] = "Optional: use VLM to score physics/commonsense per clip; see README." + aggregate["world_model_bench_note"] = "WorldModelBench: use official protocol and data; export evals to their format if needed (see README)." + + return { + "dimension": "semantic_consistency", + "params": { + "teleport_threshold_quantile": teleport_threshold_quantile, + "displacement_scale": displacement_scale, + }, + "per_video": per_video, + "aggregate": aggregate, + "num_videos": len(per_video), + } + + +def main(): + p = argparse.ArgumentParser(description="Semantic/Logic Consistency (rule-based physics)") + p.add_argument("--evals_root", type=str, required=True) + p.add_argument("--teleport_quantile", type=float, default=0.98) + p.add_argument("--displacement_scale", type=int, default=4) + p.add_argument("--output", type=str, default=None) + args = p.parse_args() + + result = run_semantic_consistency( + args.evals_root, + teleport_threshold_quantile=args.teleport_quantile, + displacement_scale=args.displacement_scale, + ) + out = json.dumps(result, indent=2) + print(out) + if args.output: + with open(args.output, "w") as f: + f.write(out) + + +if __name__ == "__main__": + main() diff --git a/code/eval/metrics/state_tracking.py b/code/eval/metrics/state_tracking.py new file mode 100644 index 0000000000000000000000000000000000000000..e6a394b02c5f020d93c2fd458ae87fdc94089265 --- /dev/null +++ b/code/eval/metrics/state_tracking.py @@ -0,0 +1,135 @@ +""" +State Tracking metrics (light rules, no GT boxes): +- Frame-to-frame displacement proxy: mean/max L2 difference between consecutive frames (downsampled), as smoothness proxy. +- "Physics" proxy: fraction of consecutive pairs with abnormally large change (potential瞬移). +- Placeholder: object position error / state change accuracy (requires detection+tracking or VLM). +""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Any + +import numpy as np + +from .common import discover_evals_videos, load_video_frames + +try: + import cv2 + HAS_CV2 = True +except ImportError: + HAS_CV2 = False + + +def _frame_diff_norm(f1: np.ndarray, f2: np.ndarray, scale: int = 4) -> float: + """Mean L2 pixel difference between two frames (optionally downsampled).""" + if not HAS_CV2 or f1.size == 0: + return 0.0 + if scale > 1: + h, w = f1.shape[:2] + f1 = cv2.resize(f1, (w // scale, h // scale), interpolation=cv2.INTER_AREA) + f2 = cv2.resize(f2, (w // scale, h // scale), interpolation=cv2.INTER_AREA) + d = f1.astype(np.float64) - f2.astype(np.float64) + return float(np.sqrt(np.mean(d ** 2))) + + +def run_state_tracking_light( + frames: np.ndarray, + displacement_scale: int = 4, + large_jump_quantile: float = 0.95, +) -> dict[str, Any]: + """ + Light rules on frame sequence: + - mean_consecutive_displacement: mean L2 diff between consecutive frames (downsampled). + - max_consecutive_displacement: max such diff. + - large_jump_fraction: fraction of consecutive pairs with diff > quantile(large_jump_quantile) of all diffs. + """ + n = frames.shape[0] + if n < 2: + return { + "mean_consecutive_displacement": 0.0, + "max_consecutive_displacement": 0.0, + "large_jump_fraction": 0.0, + } + diffs = [] + for i in range(n - 1): + d = _frame_diff_norm(frames[i], frames[i + 1], scale=displacement_scale) + diffs.append(d) + diffs = np.array(diffs) + thresh = float(np.quantile(diffs, large_jump_quantile)) if len(diffs) else 0.0 + large = np.sum(diffs >= thresh) / max(1, len(diffs)) + return { + "mean_consecutive_displacement": float(np.mean(diffs)), + "max_consecutive_displacement": float(np.max(diffs)), + "large_jump_fraction": float(large), + "large_jump_threshold": thresh, + } + + +def run_state_tracking( + evals_root: str, + displacement_scale: int = 4, + large_jump_quantile: float = 0.95, + video_paths: list[tuple[str, str]] | None = None, +) -> dict[str, Any]: + """ + Run light state-tracking metrics on all gen_only videos. + Object position error / state change accuracy require detection+tracking (placeholder). + """ + if video_paths is None: + video_paths = discover_evals_videos(evals_root) + + per_video = [] + all_mean_disp = [] + all_large_frac = [] + + for rel, absp in video_paths: + if not os.path.isfile(absp): + continue + frames = load_video_frames(absp) + if frames.size == 0: + per_video.append({"rel": rel, "mean_consecutive_displacement": None, "large_jump_fraction": None}) + continue + res = run_state_tracking_light(frames, displacement_scale, large_jump_quantile) + all_mean_disp.append(res["mean_consecutive_displacement"]) + all_large_frac.append(res["large_jump_fraction"]) + per_video.append({"rel": rel, **res}) + + aggregate = {} + if all_mean_disp: + aggregate["mean_consecutive_displacement"] = float(np.mean(all_mean_disp)) + aggregate["mean_large_jump_fraction"] = float(np.mean(all_large_frac)) + aggregate["object_position_note"] = "Optional: add detection+tracking (e.g. ByteTrack, GroundingDINO) for object position error; state change accuracy can use VLM (see semantic_consistency)." + + return { + "dimension": "state_tracking", + "params": {"displacement_scale": displacement_scale, "large_jump_quantile": large_jump_quantile}, + "per_video": per_video, + "aggregate": aggregate, + "num_videos": len(per_video), + } + + +def main(): + p = argparse.ArgumentParser(description="State Tracking (light rules)") + p.add_argument("--evals_root", type=str, required=True) + p.add_argument("--displacement_scale", type=int, default=4) + p.add_argument("--large_jump_quantile", type=float, default=0.95) + p.add_argument("--output", type=str, default=None) + args = p.parse_args() + + result = run_state_tracking( + args.evals_root, + displacement_scale=args.displacement_scale, + large_jump_quantile=args.large_jump_quantile, + ) + out = json.dumps(result, indent=2) + print(out) + if args.output: + with open(args.output, "w") as f: + f.write(out) + + +if __name__ == "__main__": + main() diff --git a/code/eval/metrics/temporal_coherence.py b/code/eval/metrics/temporal_coherence.py new file mode 100644 index 0000000000000000000000000000000000000000..ad6da7bc8ea33690adf64ac2744f91d34d6ddf73 --- /dev/null +++ b/code/eval/metrics/temporal_coherence.py @@ -0,0 +1,114 @@ +""" +Temporal Coherence metrics: +- Frame-to-frame PSNR (mean/médian over consecutive pairs). +- Optional: optical flow consistency (warp frame t by flow t->t+1, compare to frame t+1); requires flow model. +- Optional: FVD (Fréchet Video Distance); requires I3D. +""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Any + +import numpy as np + +from .common import discover_evals_videos, load_video_frames + +try: + from skimage.metrics import peak_signal_noise_ratio as psnr_skimage + HAS_SKIMAGE = True +except ImportError: + HAS_SKIMAGE = False + +try: + import cv2 + HAS_CV2 = True +except ImportError: + HAS_CV2 = False + + +def _psnr_two(f1: np.ndarray, f2: np.ndarray) -> float: + if f1.shape != f2.shape and HAS_CV2: + f2 = cv2.resize(f2, (f1.shape[1], f1.shape[0]), interpolation=cv2.INTER_LINEAR) + if HAS_SKIMAGE: + return float(psnr_skimage(f1, f2, data_range=255)) + mse = np.mean((f1.astype(np.float64) - f2.astype(np.float64)) ** 2) + return float(10 * np.log10((255 ** 2) / mse)) if mse > 0 else 100.0 + + +def run_temporal_coherence( + evals_root: str, + enable_flow: bool = False, + enable_fvd: bool = False, + video_paths: list[tuple[str, str]] | None = None, +) -> dict[str, Any]: + """ + Compute temporal coherence: frame-to-frame PSNR; optionally flow consistency and FVD. + """ + if video_paths is None: + video_paths = discover_evals_videos(evals_root) + + per_video = [] + all_mean_psnr = [] + all_median_psnr = [] + + for rel, absp in video_paths: + if not os.path.isfile(absp): + continue + frames = load_video_frames(absp) + if frames.shape[0] < 2: + per_video.append({"rel": rel, "mean_frame_psnr": None, "median_frame_psnr": None}) + continue + psnrs = [_psnr_two(frames[i], frames[i + 1]) for i in range(frames.shape[0] - 1)] + mean_p = float(np.mean(psnrs)) + med_p = float(np.median(psnrs)) + all_mean_psnr.append(mean_p) + all_median_psnr.append(med_p) + row = {"rel": rel, "mean_frame_psnr": mean_p, "median_frame_psnr": med_p} + if enable_flow: + row["flow_consistency"] = None # placeholder: would run flow model + if enable_fvd: + row["fvd"] = None # placeholder + per_video.append(row) + + aggregate = {} + if all_mean_psnr: + aggregate["mean_frame_psnr"] = float(np.mean(all_mean_psnr)) + aggregate["median_frame_psnr"] = float(np.median(all_median_psnr)) + if enable_flow: + aggregate["flow_consistency_note"] = "Optional: enable with --enable_flow when RAFT/torchvision flow available." + if enable_fvd: + aggregate["fvd_note"] = "Optional: enable with --enable_fvd when pytorch-fvd/I3D available." + + return { + "dimension": "temporal_coherence", + "params": {"enable_flow": enable_flow, "enable_fvd": enable_fvd}, + "per_video": per_video, + "aggregate": aggregate, + "num_videos": len(per_video), + } + + +def main(): + p = argparse.ArgumentParser(description="Temporal Coherence metrics") + p.add_argument("--evals_root", type=str, required=True) + p.add_argument("--enable_flow", action="store_true") + p.add_argument("--enable_fvd", action="store_true") + p.add_argument("--output", type=str, default=None) + args = p.parse_args() + + result = run_temporal_coherence( + args.evals_root, + enable_flow=args.enable_flow, + enable_fvd=args.enable_fvd, + ) + out = json.dumps(result, indent=2) + print(out) + if args.output: + with open(args.output, "w") as f: + f.write(out) + + +if __name__ == "__main__": + main() diff --git a/code/eval/metrics/visual_eval_config.yaml b/code/eval/metrics/visual_eval_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a4c14b32d3c346cb17ad7837b428fb8cbf714a03 --- /dev/null +++ b/code/eval/metrics/visual_eval_config.yaml @@ -0,0 +1,90 @@ +# 可视化评测配置：以肉眼查看为主，不依赖数值 metric。 +# 用途：固定 prompt 与首 chunk/首帧，便于同一条件下对比不同模型或不同设置。 +# 使用方式见 VISUAL_EVAL_DESIGN.md；运行示例见 run_visual_eval.py。 + +# ----------------------------------------------------------------------------- +# 1. Prompt 设计（便于观察记忆/一致性/身份/逻辑） +# ----------------------------------------------------------------------------- +# 原则：英文、与首帧语义匹配时生成更稳定；按“考察维度”分组便于选看。 +prompts: + # 身份/角色保持（看长时是否同一角色、人脸是否漂移） + - id: identity_single_person + text: "A single person in a room, clear face and body, consistent lighting." + category: identity + note: "适合首帧为单人时，观察多 chunk 后是否仍是同一人、五官是否保持。" + - id: identity_face_clear + text: "Close-up of a person's face, neutral expression, indoor lighting." + category: identity + note: "强调人脸，便于观察身份保持力。" + # 场景/长时一致性（看场景是否塌缩、风格是否一致） + - id: scene_indoor_room + text: "An indoor room with furniture, walls and floor visible, consistent perspective." + category: long_horizon + note: "室内场景，观察旋转多 chunk 后房间是否还一致、有无穿模或崩坏。" + - id: scene_outdoor_street + text: "An outdoor street or building, clear geometry, natural lighting." + category: long_horizon + note: "室外，观察长时几何与光照一致性。" + # 物体/状态（看物体是否合理存在、位置是否连贯） + - id: object_furniture + text: "A room with a table and chairs, objects stay in place." + category: state + note: "固定物体，观察状态是否稳定。" + - id: object_simple + text: "A simple scene with one or two objects, clean background." + category: state + note: "简单物体，便于观察状态追踪。" + # 回环/重访（看回到起点时视角是否一致） + - id: loop_revisit + text: "A 360-degree view of a space, same view when returning to start." + category: loop_closure + note: "强调回到起点，便于肉眼看 4chunk 回环末帧与首帧是否像同一视角。" + # 通用/兜底 + - id: generic_scene + text: "A scene." + category: generic + note: "与现有默认一致，用于对比。" + +# ----------------------------------------------------------------------------- +# 2. 首 chunk / 首帧预设 +# ----------------------------------------------------------------------------- +# 同一首帧 + 同一 prompt 下对比不同模型，便于控制变量。 +# type: fixed_image 表示使用本地图片路径；dataset_frame 表示从数据集取 (video_name, start_frame)。 +first_chunk_presets: + # 固定图片：需事先准备好，路径可覆盖 + - id: fixed_default + type: fixed_image + path: "" # 留空则用 run_generalization_fixed_first_frame 的默认（如 train/ctx_5_20_per_frame_vae/image.png） + note: "默认首帧图，与现有 evals 一致。" + - id: fixed_indoor + type: fixed_image + path: "" # 例如 /path/to/indoor_sample.png + note: "建议放一张室内代表图，便于与 scene_indoor_room 等 prompt 搭配。" + - id: fixed_outdoor + type: fixed_image + path: "" + note: "建议放一张室外代表图。" + - id: fixed_face + type: fixed_image + path: "" + note: "建议放一张人脸/单人图，与 identity_* prompt 搭配。" + # 从数据集取首帧：用于与训练分布一致、可复现 + - id: dataset_mid + type: dataset_frame + video_name: "" # 从 metadata 或 frames 目录选一个 + start_frame: 0 + note: "从数据集指定 (video_name, start_frame) 作为首帧；run_visual_eval 会先导出该帧再跑。" + - id: dataset_another + type: dataset_frame + video_name: "" + start_frame: 40 + note: "另一段，可改为实际存在的视频名与帧号。" + +# ----------------------------------------------------------------------------- +# 3. 推荐组合（用于快速跑一小组“可视化对比”） +# ----------------------------------------------------------------------------- +# 只跑这些 (prompt_id, first_chunk_id) 时，可写在这里；留空表示用全部组合或由命令行指定。 +recommended_pairs: [] + # - [identity_single_person, fixed_face] + # - [scene_indoor_room, fixed_indoor] + # - [loop_revisit, fixed_default] diff --git a/code/eval/v2/README.md b/code/eval/v2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6af06a50a81965f1e51a2aa0f0f8619d34be5e08 --- /dev/null +++ b/code/eval/v2/README.md @@ -0,0 +1,75 @@ +# Eval v2 + +This folder keeps the public replay, in-domain loop/revisit, and open-domain revisit evaluations. + +## Static Consistency + +Run multi-chunk loop/revisit evaluation: + +```bash +cd /data/work/run_codes/Echo-Memory/ +source /tmp/uv-venv/bin/activate +export WAN_BASE_MODEL=/local-ssd/echo-memory/Wan2.1-T2V-1.3B/ +export DATASET_BASE_PATH=/threed-code/yorenchen/data/echo-memory/Context-as-Memory-Dataset/ +export CKPT=/local-ssd/echo-memory/ckpts/spatial_mem/epoch-0.safetensors +bash eval/v2/run_static_consistency_loop_and_revisit.sh +``` + +Useful optional variables: + +- `EVALS_ROOT`: output directory, defaults to `${CKPT_DIR}/evals_v2/static_consistency`. +- `NUM_SAMPLES_LOOP`: number of loop-closure samples. +- `NUM_SAMPLES_STATIC`: number of in-domain revisit samples. +- `MULTIVIEW_FIRSTFRAME_LIST`: text/jsonl/csv list for open-domain first frames. +- `MULTIVIEW_FIRSTFRAME_DIR`: image directory used to generate the list automatically, for example `assets/opendomain_revisit`. +- `RUN_GEOMETRY_DIAG=1`: enable optional geometry diagnostics. + +The script writes in-domain outputs under `${EVALS_ROOT}/in_domain` and open-domain outputs under `${EVALS_ROOT}/open_domain`. + +## Basic Capability + +Run a single-video GT trajectory replay check: + +```bash +export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B +export DATASET_BASE_PATH=/path/to/Context-as-Memory-Dataset +export CKPT=/path/to/epoch-0.safetensors +bash eval/v2/run_basic_replay_gt.sh +``` + +Outputs include generated videos and `replay_gt_metrics.json` with MSE, PSNR, SSIM, and optional LPIPS. + +## Open-Domain Revisit + VLM + +For the paper-style open-domain return probe, use the one-click revisit suite: + +```bash +export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B +export DATASET_BASE_PATH=/path/to/Context-as-Memory-Dataset +PHASE=stage1 OOD_DIR=assets/opendomain_revisit \ + bash eval/v2/revisit_suite/run_one_click_revisit_eval.sh +``` + +Set `PHASE=vlm` with `EVAL_ROOT=/path/to/eval_root` to score an existing stage-1 run. The scorer expects an OpenAI-compatible endpoint via `VLM_API_BASE` and `VLM_MODEL`; use `VLM_DRY_RUN=1` for a wiring check. + +## Visual Outputs + +Every evaluation path keeps human-readable artifacts: + +- GT replay writes `replay_gt_gen_only.mp4`, `replay_gt_metrics.json`, and optional per-frame CSV files. +- Static loop/revisit writes generated MP4 files under `in_domain/loop_closure` and `in_domain/combo_revisit_in_domain`. +- Open-domain multiview writes MP4 files under `open_domain/multiview_revisit`. +- The one-click revisit suite writes `stage1_frames/first_00.png`, `stage1_frames/revisit_tail_*.png`, optional first-vs-last chunk change maps, and `revisit_gen_only.mp4` for each case. + +For paper figures, start from the one-click revisit suite outputs: + +```bash +python eval/v2/revisit_suite/export_revisit_materials.py \ + --eval-root eval_outputs/revisit_suite_ \ + --out-dir paper_case_materials \ + --prefix echo_memory_revisit +``` + +The exported material directory contains flattened case metadata plus image references that can be used to build qualitative grids. + +Dynamic/private evaluation code is intentionally not included in this release. diff --git a/code/eval/v2/actions/build_action_combo.py b/code/eval/v2/actions/build_action_combo.py new file mode 100644 index 0000000000000000000000000000000000000000..45a92e9bfe26f7e7edee3cd85f5a00aef6baf548 --- /dev/null +++ b/code/eval/v2/actions/build_action_combo.py @@ -0,0 +1,208 @@ +#!/usr/bin/env python3 +""" +Build per-chunk action JSONs for a composite action sequence, for static-consistency revisit tests. + +First version supports the requested pattern: + rotate_left_45 -> translate_forward -> rotate_right_45 -> translate_backward + +Notes: +- Each JSON is a dict: frame_index(str) -> RT list length 12: [tx,ty,tz,R11..R33] (row-major 3x3). +- Actions are *relative to each chunk's first frame* (matching training / existing eval conventions). +""" +from __future__ import annotations + +import argparse +import json +import math +import os +import random +from typing import Dict, List, Tuple + + +def _load_json(path: str) -> Dict[str, List[float]]: + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + + +def build_rotation_yaw_chunk(yaw_total_deg: float, clockwise: bool, chunk_frames: int) -> Dict[str, List[float]]: + """Linear yaw 0→±yaw_total_deg in chunk (Z-only RT), same convention as run_replay_loop_two_chunk.build_action_chunk.""" + denom = max(1, chunk_frames - 1) + sign = -1.0 if clockwise else 1.0 + out: Dict[str, List[float]] = {} + for i in range(chunk_frames): + yaw = sign * (i / denom) * float(yaw_total_deg) + rad = math.radians(yaw) + c, s = math.cos(rad), math.sin(rad) + r_flat = [c, -s, 0.0, s, c, 0.0, 0.0, 0.0, 1.0] + out[str(i)] = [0.0, 0.0, 0.0] + r_flat + return out + + +def build_translation_only(direction: str, translation_delta: float, chunk_frames: int) -> Dict[str, List[float]]: + """Mirror run_replay_loop_two_chunk.build_action_translation_only without importing heavy deps.""" + identity_rot = [1.0, 0.0, 0.0, + 0.0, 1.0, 0.0, + 0.0, 0.0, 1.0] + denom = max(1, chunk_frames - 1) + out: Dict[str, List[float]] = {} + for i in range(chunk_frames): + t = (i / denom) * translation_delta + if direction == "forward": + tx, ty, tz = 0.0, t, 0.0 + elif direction == "backward": + tx, ty, tz = 0.0, -t, 0.0 + elif direction == "left": + tx, ty, tz = -t, 0.0, 0.0 + elif direction == "right": + tx, ty, tz = t, 0.0, 0.0 + else: + tx, ty, tz = 0.0, 0.0, 0.0 + out[str(i)] = [tx, ty, tz] + identity_rot + return out + + +def save_action_json(actions: Dict[str, List[float]], path: str) -> None: + os.makedirs(os.path.dirname(path), exist_ok=True) + with open(path, "w", encoding="utf-8") as f: + json.dump(actions, f, indent=2) + + +def build_combo( + exp_dir: str, + out_dir: str, + chunk_frames: int = 81, + translation_delta: float = 0.1, +) -> Tuple[str, str, str, str]: + """ + Create 4 chunk action jsons under out_dir: + chunk0_rotate_left_45.json + chunk1_translate_forward.json + chunk2_rotate_right_45.json + chunk3_translate_backward.json + """ + left_path = os.path.join(exp_dir, "action_rotation_left_45.json") + right_path = os.path.join(exp_dir, "action_rotation_right_45.json") + if not (os.path.isfile(left_path) and os.path.isfile(right_path)): + raise FileNotFoundError(f"Missing rotation jsons under exp_dir: {left_path} / {right_path}") + + rot_left = _load_json(left_path) + rot_right = _load_json(right_path) + + # Sanity: ensure expected frame keys exist; if not, allow but warn via truncation + def _trim(d: Dict[str, List[float]]) -> Dict[str, List[float]]: + return {str(i): d[str(i)] for i in range(chunk_frames) if str(i) in d} + + rot_left = _trim(rot_left) + rot_right = _trim(rot_right) + + trans_fwd = build_translation_only("forward", translation_delta, chunk_frames) + trans_bwd = build_translation_only("backward", translation_delta, chunk_frames) + + p0 = os.path.join(out_dir, "chunk0_rotate_left_45.json") + p1 = os.path.join(out_dir, "chunk1_translate_forward.json") + p2 = os.path.join(out_dir, "chunk2_rotate_right_45.json") + p3 = os.path.join(out_dir, "chunk3_translate_backward.json") + save_action_json(rot_left, p0) + save_action_json(trans_fwd, p1) + save_action_json(rot_right, p2) + save_action_json(trans_bwd, p3) + return p0, p1, p2, p3 + + +def build_random_symmetric_closed_loop( + out_dir: str, + chunk_frames: int, + rng: random.Random, + yaw_min: float = 20.0, + yaw_max: float = 55.0, + translation_min: float = 0.05, + translation_max: float = 0.18, +) -> Tuple[List[str], Dict]: + """ + Symmetric motion that composes to ~identity in the training RT convention: + chunk0: CCW yaw (left) 0→+Y + chunk1: forward +d along Y + chunk2: CW yaw (right) 0→-Y (cancels chunk0 in world yaw if chunk frames align) + chunk3: backward -d along Y (cancels chunk1 translation) + + Same filenames as fixed 45° combo for drop-in use with run_combo_revisit_fixed_first.py. + """ + yaw = rng.uniform(float(yaw_min), float(yaw_max)) + d = rng.uniform(float(translation_min), float(translation_max)) + rot_left = build_rotation_yaw_chunk(yaw, clockwise=False, chunk_frames=chunk_frames) + rot_right = build_rotation_yaw_chunk(yaw, clockwise=True, chunk_frames=chunk_frames) + trans_fwd = build_translation_only("forward", d, chunk_frames) + trans_bwd = build_translation_only("backward", d, chunk_frames) + p0 = os.path.join(out_dir, "chunk0_rotate_left_45.json") + p1 = os.path.join(out_dir, "chunk1_translate_forward.json") + p2 = os.path.join(out_dir, "chunk2_rotate_right_45.json") + p3 = os.path.join(out_dir, "chunk3_translate_backward.json") + save_action_json(rot_left, p0) + save_action_json(trans_fwd, p1) + save_action_json(rot_right, p2) + save_action_json(trans_bwd, p3) + meta = { + "pattern": "symmetric_closed_loop_random", + "yaw_deg": yaw, + "translation_delta": d, + "chunk_frames": chunk_frames, + "chunks": [ + {"file": os.path.basename(p0), "desc": "ccw_yaw_0_to_+yaw"}, + {"file": os.path.basename(p1), "desc": "forward_d"}, + {"file": os.path.basename(p2), "desc": "cw_yaw_0_to_-yaw"}, + {"file": os.path.basename(p3), "desc": "backward_d"}, + ], + } + return [p0, p1, p2, p3], meta + + +def main() -> None: + p = argparse.ArgumentParser(description="Build composite action JSONs for revisit tests") + p.add_argument("--exp_dir", type=str, default="", help="exp dir with action_rotation_*.json (fixed 45° mode)") + p.add_argument("--out_dir", type=str, required=True, help="output directory to write per-chunk action jsons") + p.add_argument("--chunk_frames", type=int, default=81) + p.add_argument("--translation_delta", type=float, default=0.1) + p.add_argument( + "--random_symmetric", + action="store_true", + help="Random yaw/translation magnitudes with symmetric closed-loop (left→fwd→right→back); ignores exp_dir rotations", + ) + p.add_argument("--combo_seed", type=int, default=42, help="RNG seed for --random_symmetric") + p.add_argument("--yaw_min", type=float, default=20.0) + p.add_argument("--yaw_max", type=float, default=55.0) + p.add_argument("--translation_min", type=float, default=0.05) + p.add_argument("--translation_max", type=float, default=0.18) + args = p.parse_args() + out_dir = os.path.abspath(args.out_dir) + os.makedirs(out_dir, exist_ok=True) + + if args.random_symmetric: + rng = random.Random(int(args.combo_seed)) + paths, meta = build_random_symmetric_closed_loop( + out_dir, + chunk_frames=args.chunk_frames, + rng=rng, + yaw_min=args.yaw_min, + yaw_max=args.yaw_max, + translation_min=args.translation_min, + translation_max=args.translation_max, + ) + with open(os.path.join(out_dir, "combo_manifest.json"), "w", encoding="utf-8") as f: + json.dump(meta, f, indent=2) + print("\n".join(paths)) + return + + if not args.exp_dir: + raise SystemExit("build_action_combo: need --exp_dir unless --random_symmetric") + paths = build_combo( + exp_dir=os.path.abspath(args.exp_dir), + out_dir=out_dir, + chunk_frames=args.chunk_frames, + translation_delta=args.translation_delta, + ) + print("\n".join(paths)) + + +if __name__ == "__main__": + main() + diff --git a/code/eval/v2/basic/check_dataset_gt_for_replay.py b/code/eval/v2/basic/check_dataset_gt_for_replay.py new file mode 100644 index 0000000000000000000000000000000000000000..6c3fb0a9d7ad32cb622bfc34a92c9a7ac87c7a8a --- /dev/null +++ b/code/eval/v2/basic/check_dataset_gt_for_replay.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python3 +""" +Lightweight DATASET sanity check before heavy eval (no torch / no train.py). +Verifies jsons + frames dirs and that pose keys exist for the replay frame range. +Exit 0 if OK, 1 otherwise; prints absolute paths and first failure reason. +""" +from __future__ import annotations + +import argparse +import json +import os +import sys + + +def _abs(p: str) -> str: + return os.path.abspath(os.path.expanduser(p)) + + +def _poses_dict(data: dict) -> dict: + if "CineCameraActor" in data: + return data["CineCameraActor"] + return data if isinstance(data, dict) else {} + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--dataset", required=True) + ap.add_argument("--video", required=True) + ap.add_argument("--start_frame", type=int, default=0) + ap.add_argument("--num_chunks", type=int, default=1) + ap.add_argument("--chunk_frames", type=int, default=81) + args = ap.parse_args() + + ds = _abs(args.dataset) + jsons = os.path.join(ds, "jsons") + frames_root = os.path.join(ds, "frames") + vn = str(args.video).replace(".mp4", "").replace(".avi", "").strip() + jpath = os.path.join(jsons, f"{vn}.json") + fdir = os.path.join(frames_root, vn) + + print(f"[check_dataset_gt] DATASET={ds}") + print(f"[check_dataset_gt] json={jpath}") + print(f"[check_dataset_gt] frames_dir={fdir}") + + if not os.path.isdir(ds): + print("[check_dataset_gt] FAIL: DATASET is not a directory", file=sys.stderr) + return 1 + if not os.path.isdir(jsons): + print("[check_dataset_gt] FAIL: missing jsons/", file=sys.stderr) + return 1 + if not os.path.isfile(jpath): + print("[check_dataset_gt] FAIL: missing video json", file=sys.stderr) + return 1 + if not os.path.isdir(fdir): + print("[check_dataset_gt] WARN: frames subdir missing (PNG compare may fail)", file=sys.stderr) + + try: + with open(jpath, "r", encoding="utf-8") as f: + data = json.load(f) + except Exception as e: + print(f"[check_dataset_gt] FAIL: cannot read json: {e}", file=sys.stderr) + return 1 + + poses = _poses_dict(data) + need = [] + for ch in range(args.num_chunks): + seg = args.start_frame + ch * args.chunk_frames + for i in range(args.chunk_frames): + need.append(seg + i) + + missing_pose = [] + for fi in need: + k = str(fi) + if k not in poses: + missing_pose.append(fi) + if len(missing_pose) >= 5: + break + + if missing_pose: + print( + f"[check_dataset_gt] FAIL: missing pose keys for frames (showing up to 5): {missing_pose}", + file=sys.stderr, + ) + def _knum(x): + try: + return int(x) + except (TypeError, ValueError): + return 0 + + sample_keys = sorted(poses.keys(), key=_knum)[:8] + print(f"[check_dataset_gt] sample pose keys: {sample_keys}", file=sys.stderr) + return 1 + + # Optional: same 12-dim path as replay (numpy via fov_retrieval; no torch) + try: + _here = os.path.dirname(os.path.abspath(__file__)) + # .../eval/v2/basic -> repo root + _repo = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(_here)))) + if _repo not in sys.path: + sys.path.insert(0, _repo) + from src.model_training.fov_retrieval import load_camera_pose, pose_to_rt + + for fi in need[: min(3, len(need))]: + pose = load_camera_pose(jpath, fi) + rt = pose_to_rt(pose, constrain_to_xy=True) if pose else None + if rt is None or len(rt) < 12: + print(f"[check_dataset_gt] FAIL: pose_to_rt None for frame {fi}", file=sys.stderr) + return 1 + except Exception as e: + print(f"[check_dataset_gt] WARN: RT parse spot-check skipped: {e}", file=sys.stderr) + + print("[check_dataset_gt] OK: paths and pose keys cover the replay range.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/basic/gt_pose_minimal.py b/code/eval/v2/basic/gt_pose_minimal.py new file mode 100644 index 0000000000000000000000000000000000000000..5740a333f7efcfe9c9d94514d5a51d05ffa91268 --- /dev/null +++ b/code/eval/v2/basic/gt_pose_minimal.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +""" +GT trajectory helpers without importing torch / diffsynth / run_replay_loop_two_chunk. +Used by run_basic_replay_gt.sh to resolve VIDEO_NAME=AUTO without pulling train.py. +Logic must match run_replay_loop_two_chunk.build_gt_trajectory_actions. +""" +from __future__ import annotations + +import os +import sys + + +def _repo_root_from_here() -> str: + _here = os.path.dirname(os.path.abspath(__file__)) + # .../eval/v2/basic -> repo root + return os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(_here)))) + + +def _ensure_repo_path() -> None: + r = _repo_root_from_here() + if r not in sys.path: + sys.path.insert(0, r) + + +def load_pose_rt(json_file: str, frame_idx: int): + _ensure_repo_path() + from src.model_training.fov_retrieval import load_camera_pose, pose_to_rt + pose = load_camera_pose(json_file, int(frame_idx)) + if pose is None: + return None + return pose_to_rt(pose, constrain_to_xy=True) + + +def get_relative_rt(rt, ref_rt): + _ensure_repo_path() + from src.model_training.fov_retrieval import convert_rt_to_relative + if rt is None or ref_rt is None or len(rt) < 12 or len(ref_rt) < 12: + return None + out = convert_rt_to_relative([rt], ref_rt) + return out[0] if out else None + + +def build_gt_trajectory_actions(dataset_base, video_name, start_frame, chunk_frames, json_file=None): + if json_file is None: + json_file = os.path.join(dataset_base, "jsons", f"{video_name}.json") + if not os.path.isfile(json_file): + return None + try: + rt_list = [load_pose_rt(json_file, start_frame + i) for i in range(chunk_frames)] + if not rt_list or any(r is None or len(r) < 12 for r in rt_list): + return None + ref_rt = rt_list[0] + rel_actions = {str(i): get_relative_rt(rt_list[i], ref_rt) for i in range(chunk_frames)} + if any(v is None for v in rel_actions.values()): + return None + return rel_actions + except Exception: + return None diff --git a/code/eval/v2/basic/replay_gt_error.py b/code/eval/v2/basic/replay_gt_error.py new file mode 100644 index 0000000000000000000000000000000000000000..c48cc1e77b5a860fc92db19b7aa6ebd1e8aca51c --- /dev/null +++ b/code/eval/v2/basic/replay_gt_error.py @@ -0,0 +1,356 @@ +#!/usr/bin/env python3 +""" +Basic capability (v2): replay GT trajectory and compute per-frame error vs GT. +For reporting cross-chunk behavior, use num_chunks >= 2 (eval scripts default 3 via NUM_CHUNKS_LONG). + +Output: +- gen video (gen_only mp4) +- per-frame mse, psnr, ssim (if scikit-image), lpips (if lpips pkg unless --no_lpips) +- speed profile (seconds, fps) +- replay_gt_metrics.json includes metric_definitions and quality_notes + +This script uses existing run_one_chunk + build_gt_trajectory_actions for action, and +loads GT frames from dataset_base/frames//.png for comparison. +""" +from __future__ import annotations + +import argparse +import csv +import json +import os +import sys +import time +from typing import Any, Dict, List, Optional + +import numpy as np +import torch +from PIL import Image + +_script_dir = os.path.dirname(os.path.abspath(__file__)) +_eval_v2_dir = os.path.dirname(_script_dir) +_repo_root = os.path.dirname(os.path.dirname(_eval_v2_dir)) +_env_dir = os.path.join(_repo_root, "env") +_metrics_dir = os.path.join(_eval_v2_dir, "metrics") +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) +if _env_dir not in sys.path: + sys.path.insert(0, _env_dir) +if _metrics_dir not in sys.path: + sys.path.insert(0, _metrics_dir) + +try: + from skimage.metrics import structural_similarity as _skimage_ssim + + _HAS_SKIMAGE_SSIM = True +except Exception: + _skimage_ssim = None # type: ignore[assignment,misc] + _HAS_SKIMAGE_SSIM = False + +try: + import psnr_lpips as _pl + + _HAS_PSNR_LPIPS_MOD = True +except Exception: + _HAS_PSNR_LPIPS_MOD = False + _pl = None # type: ignore + +import loop_utils as irc +import memory_baseline_runtime as mbr +from diffsynth import save_video +from run_replay_loop_two_chunk import ( + build_gt_trajectory_actions, + encode_context_frames_per_frame, + context_frames_for_next_chunk, + replay_context_from_generated_frames, + run_one_chunk, + _frame_to_pil, + load_sample_first_frame, +) + + +def _read_gt_frame(dataset_base: str, video_name: str, idx: int, w: int, h: int) -> np.ndarray | None: + base = os.path.join(dataset_base, "frames", str(video_name)) + for fmt in (f"{idx:04d}.png", f"{idx}.png"): + p = os.path.join(base, fmt) + if os.path.isfile(p): + im = Image.open(p).convert("RGB").resize((w, h)) + return np.array(im, dtype=np.uint8) + return None + + +def _label(img: Image.Image, text: str) -> Image.Image: + from PIL import ImageDraw + + out = img.copy() + draw = ImageDraw.Draw(out) + draw.rectangle([0, 0, 8 + 8 * len(text), 18], fill=(0, 0, 0)) + draw.text((4, 4), text, fill=(255, 255, 0)) + return out + + +def _build_sidebyside(paired: List[tuple], w: int, h: int) -> List[Image.Image]: + """Left=GT, Right=Gen. Missing GT frames render as black.""" + frames: List[Image.Image] = [] + for gen_pil, gt in paired: + gt_img = Image.fromarray(gt) if gt is not None else Image.new("RGB", (w, h), (0, 0, 0)) + canvas = Image.new("RGB", (w * 2, h), (0, 0, 0)) + canvas.paste(_label(gt_img, "GT"), (0, 0)) + canvas.paste(_label(gen_pil, "Gen"), (w, 0)) + frames.append(canvas) + return frames + + +def _mse(a: np.ndarray, b: np.ndarray) -> float: + d = a.astype(np.float64) - b.astype(np.float64) + return float(np.mean(d ** 2)) + + +def _psnr_from_mse(mse: float) -> float: + if mse <= 0: + return 100.0 + return float(10.0 * np.log10((255.0 ** 2) / mse)) + + +def _compute_ssim(gen: np.ndarray, gt: np.ndarray) -> Optional[float]: + if not _HAS_SKIMAGE_SSIM or _skimage_ssim is None: + return None + try: + try: + return float(_skimage_ssim(gt, gen, channel_axis=2, data_range=255)) + except TypeError: + return float(_skimage_ssim(gt, gen, multichannel=True, data_range=255)) + except Exception: + return None + + +def main(): + p = argparse.ArgumentParser(description="Replay GT trajectory and compute per-frame MSE vs GT") + p.add_argument("--ckpt", required=True) + p.add_argument("--dataset_base", required=True) + p.add_argument("--video_name", required=True) + p.add_argument("--start_frame", type=int, default=0) + p.add_argument("--num_chunks", type=int, default=1) + p.add_argument("--chunk_frames", type=int, default=81) + p.add_argument("--context_frames", type=int, default=1) + p.add_argument("--height", type=int, default=352) + p.add_argument("--width", type=int, default=640) + p.add_argument("--sigma_shift", type=float, default=5.0) + p.add_argument("--num_inference_steps", type=int, default=50) + p.add_argument("--cfg_scale", type=float, default=5.0) + p.add_argument("--seed", type=int, default=42) + p.add_argument("--output_dir", required=True) + p.add_argument( + "--base_model", + type=str, + default=None, + help="Wan2.1 base model dir; default: $WAN_BASE_MODEL", + ) + p.add_argument("--prompt", type=str, default=None, help="default: dataset prompt") + p.add_argument("--camera_inject_mode", type=str, default=None) + p.add_argument("--no_camera_encoder_separate_t_r", action="store_true") + p.add_argument("--no_omit_context_actions", action="store_true") + p.add_argument("--write_csv", action="store_true") + p.add_argument("--no_lpips", action="store_true", help="skip LPIPS (faster, no extra deps on GPU)") + p.add_argument("--lpips_device", type=str, default="cuda", help="device for LPIPS model") + args = p.parse_args() + + os.makedirs(args.output_dir, exist_ok=True) + w, h = args.width, args.height + + prompt = args.prompt or (irc.load_prompt_for_video(args.dataset_base, args.video_name) or "A scene.") + use_neg = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") + omit = not args.no_omit_context_actions + + camera_inject_mode = (args.camera_inject_mode or "").strip() or None + if not camera_inject_mode: + env_cam = (os.environ.get("CAMERA_INJECT_MODE") or "").strip().lower() + if env_cam in ("pre_qkv_post", "pre_qkv", "pre_norm", "post"): + camera_inject_mode = env_cam + if not camera_inject_mode: + for mode in ("pre_qkv_post", "pre_qkv", "pre_norm", "post"): + if mode.replace("_", "") in (args.ckpt or "").lower(): + camera_inject_mode = mode + break + if not camera_inject_mode: + # 与 memory_baselines_basic / run_static_consistency 默认一致（非 post） + camera_inject_mode = "pre_qkv" + + load_kw = dict( + action_inject_after_spatial_attn=True, + add_action_attn=True, + action_use_temporal_attention=True, + camera_inject_mode=camera_inject_mode, + ) + if args.no_camera_encoder_separate_t_r: + load_kw["camera_encoder_separate_t_r"] = False + + base_model = args.base_model or os.environ.get("WAN_BASE_MODEL") + if not base_model: + raise ValueError("Set --base_model or WAN_BASE_MODEL to the Wan2.1 base model directory.") + for _name in ("diffusion_pytorch_model.safetensors", "models_t5_umt5-xxl-enc-bf16.pth", "Wan2.1_VAE.pth"): + _p = os.path.join(base_model, _name) + if not os.path.isfile(_p): + raise FileNotFoundError( + f"Missing Wan2.1 base weight: {_p} (set --base_model or WAN_BASE_MODEL to override)" + ) + pipe = irc.load_pipeline_and_ckpt( + args.ckpt, + f"{base_model}/diffusion_pytorch_model.safetensors", + f"{base_model}/models_t5_umt5-xxl-enc-bf16.pth", + f"{base_model}/Wan2.1_VAE.pth", + **load_kw, + ) + mbr.apply_memory_baseline_pipe(pipe, args.ckpt) + + quality_notes: List[str] = [] + if not _HAS_SKIMAGE_SSIM: + quality_notes.append("SSIM skipped: scikit-image not available or import failed.") + lpips_model = None + if not args.no_lpips and _HAS_PSNR_LPIPS_MOD and _pl is not None: + lpips_model = _pl._lpips_model(device=args.lpips_device) + if lpips_model is None: + quality_notes.append("LPIPS unavailable: lpips/torch import failed.") + elif args.no_lpips: + quality_notes.append("LPIPS disabled (--no_lpips).") + + # Initial context = sample first GT frame for this trajectory + first_pil = load_sample_first_frame(args.dataset_base, args.video_name, args.start_frame, w, h) + if first_pil is None: + raise FileNotFoundError("Cannot load first GT frame for (video_name,start_frame)") + + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + ctx_latents = encode_context_frames_per_frame(pipe, [first_pil], pipe.device) + ctx_actions_t = torch.tensor([identity_rt], dtype=torch.float32) + + all_gen_frames = [] + paired_for_video: List[tuple] = [] + per_frame = [] + timings = [] + + for ch in range(args.num_chunks): + seg_start = args.start_frame + ch * args.chunk_frames + actions = build_gt_trajectory_actions(args.dataset_base, args.video_name, seg_start, args.chunk_frames) + if actions is None: + raise RuntimeError(f"No GT actions for {args.video_name} start={seg_start}") + action_path = os.path.join(args.output_dir, f"_gt_actions_chunk{ch}.json") + with open(action_path, "w", encoding="utf-8") as f: + json.dump(actions, f, indent=2) + + t0 = time.time() + frames = run_one_chunk( + pipe, + prompt, + use_neg, + action_path, + context_latents=ctx_latents, + num_context_frames=ctx_latents.shape[2], + context_actions_t=ctx_actions_t, + chunk_frames=args.chunk_frames, + h=h, + w=w, + seed=args.seed + ch, + sigma_shift=args.sigma_shift, + num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, + inference_noise_level=0.0, + omit_context_actions=omit, + log_prefix="[replay_gt]", + ) + t1 = time.time() + timings.append({"chunk": ch, "seconds": t1 - t0, "fps": (len(frames) / max(1e-6, (t1 - t0)))}) + + # compute per-frame mse vs GT for this segment + for i, fr in enumerate(frames): + gen_pil = _frame_to_pil(fr, w, h) + gen = np.array(gen_pil, dtype=np.uint8) + gt = _read_gt_frame(args.dataset_base, args.video_name, seg_start + i, w, h) + # keep GT/gen pair for the side-by-side video even when GT is missing + paired_for_video.append((gen_pil, gt)) + if gt is None: + continue + mse = _mse(gen, gt) + ssim_v = _compute_ssim(gen, gt) + lpips_v = None + if lpips_model is not None and _pl is not None: + lpips_v = _pl.lpips_distance(gen, gt, lpips_model, device=args.lpips_device) + row: Dict[str, Any] = { + "chunk": ch, + "frame_in_chunk": i, + "abs_frame": seg_start + i, + "mse": mse, + "psnr": _psnr_from_mse(mse), + "ssim": ssim_v, + "lpips": lpips_v, + } + per_frame.append(row) + + all_gen_frames.extend(frames) + + # prepare context for next chunk using generated frames (same as other evals) + if ch < args.num_chunks - 1: + n_ctx = min(args.context_frames, len(frames)) + prev_frames = replay_context_from_generated_frames(frames, n_ctx) + prev_pil = [_frame_to_pil(f, w, h) for f in prev_frames] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + ctx_latents = encode_context_frames_per_frame(pipe, prev_pil, pipe.device) + num_ctx_tokens = ctx_latents.shape[2] + ctx_actions_t = torch.tensor([identity_rt] * num_ctx_tokens, dtype=torch.float32) + + # write outputs + mp4_path = os.path.join(args.output_dir, "replay_gt_gen_only.mp4") + save_video(all_gen_frames, mp4_path, fps=15, quality=5) + + sbs_path = None + if paired_for_video: + sbs_path = os.path.join(args.output_dir, "replay_gt_vs_gen_sidebyside.mp4") + save_video(_build_sidebyside(paired_for_video, w, h), sbs_path, fps=15, quality=5) + + def _mean_optional(key: str) -> Optional[float]: + vals = [r[key] for r in per_frame if r.get(key) is not None] + return float(np.mean(vals)) if vals else None + + metrics = { + "video_name": args.video_name, + "start_frame": args.start_frame, + "num_chunks": args.num_chunks, + "chunk_frames": args.chunk_frames, + "mean_mse": float(np.mean([r["mse"] for r in per_frame])) if per_frame else None, + "mean_psnr": float(np.mean([r["psnr"] for r in per_frame])) if per_frame else None, + "mean_ssim": _mean_optional("ssim"), + "mean_lpips": _mean_optional("lpips"), + "timings": timings, + "output_video": mp4_path, + "sidebyside_video": sbs_path, + } + metric_definitions = { + "mean_mse": "Mean squared error vs dataset GT frames (lower is better).", + "mean_psnr": "Mean PSNR vs GT (higher is better).", + "mean_ssim": "Mean structural similarity vs GT, data_range=255 (higher is better). Requires scikit-image.", + "mean_lpips": "Mean LPIPS (Alex) vs GT (lower is better). Requires pip package lpips; use --no_lpips to skip.", + "fid_fvd": "Pooled FID/FVD over all long-horizon runs: see long_horizon_fid_fvd_summary.json from aggregate_long_horizon_fid_fvd.py.", + } + payload: Dict[str, Any] = { + "metric_definitions": metric_definitions, + "quality_notes": quality_notes, + "metrics": metrics, + "per_frame": per_frame, + } + with open(os.path.join(args.output_dir, "replay_gt_metrics.json"), "w", encoding="utf-8") as f: + json.dump(payload, f, indent=2) + + if args.write_csv and per_frame: + csv_path = os.path.join(args.output_dir, "per_frame_metrics.csv") + with open(csv_path, "w", newline="", encoding="utf-8") as f: + wri = csv.DictWriter(f, fieldnames=list(per_frame[0].keys())) + wri.writeheader() + wri.writerows(per_frame) + + print(f"Done. Output dir: {args.output_dir}") + + +if __name__ == "__main__": + main() + diff --git a/code/eval/v2/dynamic_spatialvid_TODO.md b/code/eval/v2/dynamic_spatialvid_TODO.md new file mode 100644 index 0000000000000000000000000000000000000000..b24cb76d338bee28c31bf4134e93f82652a3b57f --- /dev/null +++ b/code/eval/v2/dynamic_spatialvid_TODO.md @@ -0,0 +1,14 @@ +# Dynamic SpatialVID Evaluation TODO + +Dynamic SpatialVID currently exposes training and inference recipes only: + +- `train/dynamic_spatialvid/` +- `inference/dynamic_spatialvid/` + +Planned evaluation work: + +- Define the public dynamic replay/revisit protocol. +- Freeze the train/eval split and metric table. +- Add one-click evaluation scripts after the protocol is fixed. + +For qualitative demos, sample scenes from the dynamic training metadata, replay them across the six dynamic rows, and manually pick representative results. diff --git a/code/eval/v2/geometry/render_multiview_pointcloud_offline.py b/code/eval/v2/geometry/render_multiview_pointcloud_offline.py new file mode 100644 index 0000000000000000000000000000000000000000..7424ed45bb10ed55fba333973be28a56f53491df --- /dev/null +++ b/code/eval/v2/geometry/render_multiview_pointcloud_offline.py @@ -0,0 +1,365 @@ +#!/usr/bin/env python3 +"""Offline multiview point-cloud rendering/ghosting diagnostics for cross-chunk geometry.""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Dict, List, Optional, Tuple + +import cv2 +import numpy as np + + +def _read_video_rgb(video_path: str, max_frames: int = 0) -> List[np.ndarray]: + cap = cv2.VideoCapture(video_path) + out: List[np.ndarray] = [] + if not cap.isOpened(): + return out + while True: + ok, bgr = cap.read() + if not ok: + break + out.append(cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)) + if max_frames > 0 and len(out) >= max_frames: + break + cap.release() + return out + + +def _read_pose_json(json_path: str) -> Dict[str, Dict]: + if not os.path.isfile(json_path): + return {} + with open(json_path, "r", encoding="utf-8") as f: + data = json.load(f) + if "CineCameraActor" in data and isinstance(data["CineCameraActor"], dict): + return data["CineCameraActor"] + return data if isinstance(data, dict) else {} + + +def _pose_to_rt(pose: Dict) -> Tuple[np.ndarray, np.ndarray]: + # Minimal compatible conversion: translation + yaw-only Z rotation. + pos = pose.get("position", [0, 0, 0]) + rot = pose.get("rotation", [0, 0, 0]) + x, y, z = float(pos[0]), float(pos[1]), float(pos[2]) if len(pos) > 2 else 0.0 + yaw = float(rot[2]) if len(rot) > 2 else 0.0 + rad = np.deg2rad(yaw) + c, s = np.cos(rad), np.sin(rad) + R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float64) + t = np.array([x, y, z], dtype=np.float64).reshape(3, 1) + return R, t + + +def _pseudo_depth_from_rgb(rgb: np.ndarray) -> np.ndarray: + # Placeholder depth: inverse luminance in [0.3, 3.0] + g = cv2.cvtColor(rgb, cv2.COLOR_RGB2GRAY).astype(np.float32) / 255.0 + d = 0.3 + (1.0 - g) * 2.7 + return d + + +def _resize_depth(d: np.ndarray, w: int, h: int) -> np.ndarray: + if d.ndim > 2: + d = np.squeeze(d) + if d.shape[:2] == (h, w): + return d.astype(np.float32) + return cv2.resize(d.astype(np.float32), (w, h), interpolation=cv2.INTER_LINEAR) + + +def _load_depth_npy( + depth_dir: str, + abs_idx: int, + frame_i: int, + w: int, + h: int, + depth_key: str, + depth_is_inverse: bool, + depth_scale: float, +) -> Optional[np.ndarray]: + for name in ( + f"{abs_idx:04d}.npy", + f"{abs_idx}.npy", + f"{frame_i:04d}.npy", + f"{frame_i}.npy", + ): + p = os.path.join(depth_dir, name) + if os.path.isfile(p): + d = np.load(p) + d = _resize_depth(d, w, h) + if depth_is_inverse: + d = 1.0 / (np.clip(d, 1e-6, None)) + return np.clip(d * float(depth_scale), 1e-3, 1e4) + return None + + +def _load_depth_npz( + depth_dir: str, + abs_idx: int, + frame_i: int, + w: int, + h: int, + depth_key: str, + depth_is_inverse: bool, + depth_scale: float, +) -> Optional[np.ndarray]: + for name in (f"{abs_idx:04d}.npz", f"{abs_idx}.npz", f"{frame_i:04d}.npz", f"{frame_i}.npz"): + p = os.path.join(depth_dir, name) + if not os.path.isfile(p): + continue + z = np.load(p) + keys = list(z.files) + if depth_key in keys: + d = z[depth_key] + elif keys: + d = z[keys[0]] + else: + continue + d = _resize_depth(np.asarray(d), w, h) + if depth_is_inverse: + d = 1.0 / (np.clip(d, 1e-6, None)) + return np.clip(d * float(depth_scale), 1e-3, 1e4) + return None + + +def _depth_midas(rgb: np.ndarray, device: str) -> Optional[np.ndarray]: + try: + import torch + from PIL import Image + except Exception: + return None + try: + hub = "intel-isl/MiDaS" + midas = torch.hub.load(hub, "MiDaS_small", trust_repo=True) + transforms = torch.hub.load(hub, "transforms", trust_repo=True) + midas.to(device).eval() + t = transforms.small_transform + h0, w0 = rgb.shape[:2] + batch = t(Image.fromarray(rgb)).to(device) + with torch.no_grad(): + pred = midas(batch) + pred = torch.nn.functional.interpolate( + pred.unsqueeze(1), + size=(h0, w0), + mode="bicubic", + align_corners=False, + ).squeeze() + out = pred.cpu().numpy().astype(np.float32) + out = (out - out.min()) / (out.max() - out.min() + 1e-8) + return np.clip(0.3 + out * 2.7, 1e-3, 10.0) + except Exception: + return None + + +def resolve_depth( + depth_mode: str, + rgb: np.ndarray, + abs_idx: int, + frame_i: int, + depth_dir: Optional[str], + depth_key: str, + depth_is_inverse: bool, + depth_scale: float, + midas_device: str, +) -> Tuple[np.ndarray, str]: + """Return depth HxW float32 and a note (empty if ok).""" + h, w = rgb.shape[:2] + if depth_mode == "pseudo": + return _pseudo_depth_from_rgb(rgb), "" + + if depth_mode == "npy_dir": + if not depth_dir: + return _pseudo_depth_from_rgb(rgb), "npy_dir: missing --depth_dir; using pseudo" + d = _load_depth_npy(depth_dir, abs_idx, frame_i, w, h, depth_key, depth_is_inverse, depth_scale) + if d is None: + return _pseudo_depth_from_rgb(rgb), f"npy_dir: no file for frame abs_idx={abs_idx}; pseudo" + return d, "" + + if depth_mode == "npz_dir": + if not depth_dir: + return _pseudo_depth_from_rgb(rgb), "npz_dir: missing --depth_dir; using pseudo" + d = _load_depth_npz(depth_dir, abs_idx, frame_i, w, h, depth_key, depth_is_inverse, depth_scale) + if d is None: + return _pseudo_depth_from_rgb(rgb), f"npz_dir: no file for abs_idx={abs_idx}; pseudo" + return d, "" + + if depth_mode == "midas": + d = _depth_midas(rgb, midas_device) + if d is None: + return _pseudo_depth_from_rgb(rgb), "midas: failed (hub/offline/torch); pseudo" + return d, "" + + return _pseudo_depth_from_rgb(rgb), f"unknown depth_mode={depth_mode}; pseudo" + + +def _backproject(depth: np.ndarray, rgb: np.ndarray, K: np.ndarray, R: np.ndarray, t: np.ndarray, stride: int = 4): + h, w = depth.shape + ys, xs = np.mgrid[0:h:stride, 0:w:stride] + z = depth[0:h:stride, 0:w:stride].reshape(-1, 1) + pix = np.stack([xs.reshape(-1), ys.reshape(-1), np.ones(xs.size)], axis=1).astype(np.float64) + Kinv = np.linalg.inv(K) + cam = (Kinv @ pix.T).T * z + # world = R^-1 (cam - t) + world = (R.T @ (cam.T - t)).T + cols = rgb[0:h:stride, 0:w:stride].reshape(-1, 3).astype(np.uint8) + return world, cols + + +def _write_ply(path: str, xyz: np.ndarray, rgb: np.ndarray) -> None: + with open(path, "w", encoding="utf-8") as f: + f.write("ply\nformat ascii 1.0\n") + f.write(f"element vertex {xyz.shape[0]}\n") + f.write("property float x\nproperty float y\nproperty float z\n") + f.write("property uchar red\nproperty uchar green\nproperty uchar blue\n") + f.write("end_header\n") + for p, c in zip(xyz, rgb): + f.write(f"{p[0]} {p[1]} {p[2]} {int(c[0])} {int(c[1])} {int(c[2])}\n") + + +def _project(world: np.ndarray, K: np.ndarray, R: np.ndarray, t: np.ndarray) -> np.ndarray: + cam = (R @ world.T) + t + z = cam[2:3, :] + z = np.where(np.abs(z) < 1e-6, 1e-6, z) + uv = (K @ cam)[:2, :] / z + return np.vstack([uv, z]) + + +def _render_simple(world: np.ndarray, rgb: np.ndarray, K: np.ndarray, R: np.ndarray, t: np.ndarray, w: int, h: int) -> np.ndarray: + proj = _project(world, K, R, t) + u = np.round(proj[0]).astype(np.int32) + v = np.round(proj[1]).astype(np.int32) + z = proj[2] + img = np.zeros((h, w, 3), dtype=np.uint8) + zbuf = np.full((h, w), np.inf, dtype=np.float64) + for i in range(world.shape[0]): + x, y = u[i], v[i] + if x < 0 or x >= w or y < 0 or y >= h: + continue + if z[i] > 0 and z[i] < zbuf[y, x]: + zbuf[y, x] = z[i] + img[y, x] = rgb[i] + return img + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--gen_video", required=True) + ap.add_argument("--dataset_base", required=True) + ap.add_argument("--video_name", required=True) + ap.add_argument("--start_frame", type=int, required=True) + ap.add_argument("--num_frames", type=int, default=81) + ap.add_argument("--sample_stride", type=int, default=4) + ap.add_argument("--intrinsics_fx", type=float, default=500.0) + ap.add_argument("--intrinsics_fy", type=float, default=500.0) + ap.add_argument("--intrinsics_cx", type=float, default=320.0) + ap.add_argument("--intrinsics_cy", type=float, default=176.0) + ap.add_argument("--output_dir", required=True) + ap.add_argument( + "--depth_mode", + type=str, + default="pseudo", + choices=("pseudo", "npy_dir", "npz_dir", "midas"), + help="pseudo=luminance; npy_dir/npz_dir=per-frame files under --depth_dir; midas=torch.hub MiDaS_small", + ) + ap.add_argument("--depth_dir", type=str, default=None, help="Directory of per-frame .npy or .npz depths") + ap.add_argument("--depth_key", type=str, default="depth", help="npz array key (default depth)") + ap.add_argument("--depth_is_inverse", action="store_true", help="Treat loaded values as disparity; convert to 1/z") + ap.add_argument("--depth_scale", type=float, default=1.0, help="Multiply depth after load") + ap.add_argument("--midas_device", type=str, default="cuda", help="cuda or cpu for depth_mode=midas") + args = ap.parse_args() + + out_dir = os.path.abspath(args.output_dir) + os.makedirs(out_dir, exist_ok=True) + frames = _read_video_rgb(os.path.abspath(args.gen_video), max_frames=args.num_frames) + if not frames: + raise RuntimeError("cannot read generated video frames") + + h, w = frames[0].shape[:2] + K = np.array( + [[args.intrinsics_fx, 0.0, args.intrinsics_cx], [0.0, args.intrinsics_fy, args.intrinsics_cy], [0.0, 0.0, 1.0]], + dtype=np.float64, + ) + pose_json = os.path.join(os.path.abspath(args.dataset_base), "jsons", f"{args.video_name}.json") + poses = _read_pose_json(pose_json) + + depth_dir = os.path.abspath(args.depth_dir) if args.depth_dir else None + depth_notes: List[str] = [] + xyzs: List[np.ndarray] = [] + rgbs: List[np.ndarray] = [] + frame_stats: List[Dict[str, float]] = [] + for i, fr in enumerate(frames): + abs_idx = int(args.start_frame + i) + pose = poses.get(str(abs_idx)) + if not isinstance(pose, dict): + continue + R, t = _pose_to_rt(pose) + depth, note = resolve_depth( + args.depth_mode, + fr, + abs_idx, + i, + depth_dir, + args.depth_key, + bool(args.depth_is_inverse), + float(args.depth_scale), + args.midas_device if args.depth_mode == "midas" else "cpu", + ) + if note: + depth_notes.append(note) + xyz, cols = _backproject(depth, fr, K, R, t, stride=max(1, args.sample_stride)) + xyzs.append(xyz) + rgbs.append(cols) + frame_stats.append({"frame": abs_idx, "points": float(xyz.shape[0])}) + + if not xyzs: + raise RuntimeError("no valid points from frames/poses") + xyz_all = np.concatenate(xyzs, axis=0) + rgb_all = np.concatenate(rgbs, axis=0) + ply_path = os.path.join(out_dir, "global_pointcloud.ply") + _write_ply(ply_path, xyz_all, rgb_all) + + # render at first and last available GT poses + rendered = [] + for key_name, abs_idx in [("view_first", args.start_frame), ("view_last", args.start_frame + len(frames) - 1)]: + pose = poses.get(str(abs_idx)) + if not isinstance(pose, dict): + continue + R, t = _pose_to_rt(pose) + img = _render_simple(xyz_all, rgb_all, K, R, t, w, h) + outp = os.path.join(out_dir, f"{key_name}.png") + cv2.imwrite(outp, cv2.cvtColor(img, cv2.COLOR_RGB2BGR)) + rendered.append(outp) + + # ghosting proxy: per-pixel color variance in rendered-first neighborhood + ghost_proxy = float(np.var(rgb_all.astype(np.float32), axis=0).mean()) + base_notes = [ + f"depth_mode={args.depth_mode}", + "depth_dir: set GEOMETRY_DEPTH_DIR + npy_dir/npz_dir for external metric depth.", + "inverse depth: use --depth_is_inverse if files are disparity.", + ] + if args.depth_mode == "pseudo": + base_notes.insert(0, "Depth is pseudo from luminance (weak geometry).") + uniq_notes = sorted(set(depth_notes)) + diag = { + "gen_video": os.path.abspath(args.gen_video), + "video_name": args.video_name, + "start_frame": args.start_frame, + "depth_mode": args.depth_mode, + "depth_dir": depth_dir, + "depth_key": args.depth_key, + "depth_is_inverse": bool(args.depth_is_inverse), + "depth_scale": float(args.depth_scale), + "num_frames_used": len(frames), + "num_points": int(xyz_all.shape[0]), + "ghosting_proxy_color_var": ghost_proxy, + "pointcloud_ply": ply_path, + "rendered_views": rendered, + "notes": base_notes + uniq_notes, + "per_frame": frame_stats, + } + with open(os.path.join(out_dir, "geometry_diagnostics.json"), "w", encoding="utf-8") as f: + json.dump(diag, f, indent=2) + print(f"[render_multiview_pointcloud_offline] points={xyz_all.shape[0]} -> {out_dir}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/metrics/aggregate_combo_closure_metrics.py b/code/eval/v2/metrics/aggregate_combo_closure_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..a35bec2c35cac93930e0c29ea920423fffc5d59f --- /dev/null +++ b/code/eval/v2/metrics/aggregate_combo_closure_metrics.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python3 +"""Aggregate closure_first_vs_last_mse from combo revisit outputs (revisit_closure_metrics.json).""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Any, Dict, List, Optional + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--root", required=True, help="e.g. .../combo_revisit_in_domain") + ap.add_argument("--output_json", required=True) + args = ap.parse_args() + root = os.path.abspath(args.root) + rows: List[Dict[str, Any]] = [] + for dirpath, _dirnames, filenames in os.walk(root): + if "revisit_closure_metrics.json" not in filenames: + continue + p = os.path.join(dirpath, "revisit_closure_metrics.json") + try: + with open(p, "r", encoding="utf-8") as f: + data = json.load(f) + rows.append({"rel": os.path.relpath(dirpath, root), **data}) + except Exception as e: + rows.append({"rel": os.path.relpath(dirpath, root), "error": str(e)}) + + mses = [float(r["closure_first_vs_last_mse"]) for r in rows if r.get("closure_first_vs_last_mse") is not None] + mean_m = (float(sum(mses) / len(mses)) if mses else None) + summary = { + "root": root, + "num_runs": len(rows), + "mean_closure_mse": mean_m, + "per_run": rows, + } + outp = os.path.abspath(args.output_json) + os.makedirs(os.path.dirname(outp), exist_ok=True) + with open(outp, "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + print(f"[aggregate_combo_closure_metrics] runs={len(rows)} mean_closure_mse={summary['mean_closure_mse']} -> {outp}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/metrics/aggregate_geometry_consistency.py b/code/eval/v2/metrics/aggregate_geometry_consistency.py new file mode 100644 index 0000000000000000000000000000000000000000..6cdc81dd8a344817c55227805a33a7396d3a9516 --- /dev/null +++ b/code/eval/v2/metrics/aggregate_geometry_consistency.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python3 +"""Aggregate geometry diagnostics from geometry_diagnostics.json files.""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Any, Dict, List, Optional + + +def _mean(xs: List[float]) -> Optional[float]: + if not xs: + return None + return float(sum(xs) / len(xs)) + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--root", required=True, help="root directory containing geometry_diagnostics.json") + ap.add_argument("--output_json", required=True) + args = ap.parse_args() + + root = os.path.abspath(args.root) + rows: List[Dict[str, Any]] = [] + vals: List[float] = [] + for dirpath, _dirnames, filenames in os.walk(root): + if "geometry_diagnostics.json" not in filenames: + continue + p = os.path.join(dirpath, "geometry_diagnostics.json") + try: + with open(p, "r", encoding="utf-8") as f: + d = json.load(f) + d["rel_dir"] = os.path.relpath(dirpath, root) + rows.append(d) + v = d.get("ghosting_proxy_color_var") + if v is not None: + vals.append(float(v)) + except Exception as e: + rows.append({"rel_dir": os.path.relpath(dirpath, root), "error": str(e)}) + + out = {"root": root, "num_runs": len(rows), "mean_ghosting_proxy_color_var": _mean(vals), "per_run": rows} + outp = os.path.abspath(args.output_json) + os.makedirs(os.path.dirname(outp), exist_ok=True) + with open(outp, "w", encoding="utf-8") as f: + json.dump(out, f, indent=2) + print(f"[aggregate_geometry_consistency] runs={len(rows)} mean={out['mean_ghosting_proxy_color_var']} -> {outp}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/metrics/aggregate_long_horizon_fid_fvd.py b/code/eval/v2/metrics/aggregate_long_horizon_fid_fvd.py new file mode 100644 index 0000000000000000000000000000000000000000..0942a8d1ed2b36ccc7de8ff47a528266d2b2cd95 --- /dev/null +++ b/code/eval/v2/metrics/aggregate_long_horizon_fid_fvd.py @@ -0,0 +1,319 @@ +#!/usr/bin/env python3 +"""Aggregate long-horizon visual quality with FID/FVD (when available).""" +from __future__ import annotations + +import argparse +import json +import os +import sys +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple + +import cv2 +import numpy as np +import torch + +_metrics_dir = os.path.dirname(os.path.abspath(__file__)) +if _metrics_dir not in sys.path: + sys.path.insert(0, _metrics_dir) +try: + import psnr_lpips as _pl + + _HAS_PSNR_LPIPS = True +except Exception: + _pl = None # type: ignore + _HAS_PSNR_LPIPS = False + +try: + from skimage.metrics import structural_similarity as _skimage_ssim + + _HAS_SKIMAGE = True +except Exception: + _skimage_ssim = None # type: ignore + _HAS_SKIMAGE = False + + +@dataclass +class RunItem: + run_dir: str + video_name: str + start_frame: int + num_chunks: int + chunk_frames: int + gen_mp4: str + + +def _read_json(path: str) -> Dict[str, Any]: + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + + +def _metric_definitions() -> Dict[str, str]: + return { + "fid": "Pooled Frechet Inception Distance over all aligned frames from all runs (lower is better).", + "fvd": "Pooled Frechet Video Distance over runs (lower is better). Requires multiple clips.", + "per_run.mean_ssim": "Mean SSIM vs GT for that run (higher is better).", + "per_run.mean_lpips": "Mean LPIPS (Alex) vs GT for that run (lower is better).", + "per_run.fid_frame_divergence": ( + "FID computed only on that run's aligned frames (treats frames as samples). " + "Not comparable to standard multi-video dataset FID; diagnostic only." + ), + } + + +def _discover_runs(root: str) -> List[RunItem]: + out: List[RunItem] = [] + for dirpath, _dirnames, filenames in os.walk(root): + if "replay_gt_metrics.json" not in filenames: + continue + metrics_path = os.path.join(dirpath, "replay_gt_metrics.json") + data = _read_json(metrics_path) + m = data.get("metrics") or {} + gen_mp4 = m.get("output_video") or os.path.join(dirpath, "replay_gt_gen_only.mp4") + if not os.path.isfile(gen_mp4): + continue + try: + out.append( + RunItem( + run_dir=dirpath, + video_name=str(m["video_name"]), + start_frame=int(m["start_frame"]), + num_chunks=int(m["num_chunks"]), + chunk_frames=int(m["chunk_frames"]), + gen_mp4=gen_mp4, + ) + ) + except Exception: + continue + return sorted(out, key=lambda x: x.run_dir) + + +def _read_video_rgb(video_path: str, max_frames: int = 0) -> List[np.ndarray]: + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + return [] + frames: List[np.ndarray] = [] + while True: + ok, bgr = cap.read() + if not ok: + break + rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) + frames.append(rgb) + if max_frames > 0 and len(frames) >= max_frames: + break + cap.release() + return frames + + +def _load_gt_frames( + dataset_base: str, + video_name: str, + start_frame: int, + total_frames: int, + resize_wh: Tuple[int, int], +) -> List[np.ndarray]: + w, h = resize_wh + base = os.path.join(dataset_base, "frames", video_name) + out: List[np.ndarray] = [] + for i in range(total_frames): + idx = start_frame + i + p1 = os.path.join(base, f"{idx:04d}.png") + p2 = os.path.join(base, f"{idx}.png") + p = p1 if os.path.isfile(p1) else p2 + if not os.path.isfile(p): + break + bgr = cv2.imread(p, cv2.IMREAD_COLOR) + if bgr is None: + break + bgr = cv2.resize(bgr, (w, h), interpolation=cv2.INTER_AREA) + out.append(cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)) + return out + + +def _frame_tensor_uint8(frames: List[np.ndarray]) -> torch.Tensor: + # [N,H,W,C] -> [N,C,H,W] uint8 + arr = np.stack(frames, axis=0).astype(np.uint8) + return torch.from_numpy(arr).permute(0, 3, 1, 2).contiguous() + + +def _try_fid(real_imgs: torch.Tensor, fake_imgs: torch.Tensor, device: str) -> Tuple[Optional[float], str]: + try: + from torchmetrics.image.fid import FrechetInceptionDistance + except Exception as e: + return None, f"torchmetrics FID unavailable: {e}" + try: + metric = FrechetInceptionDistance(feature=2048, normalize=False).to(device) + metric.update(real_imgs.to(device), real=True) + metric.update(fake_imgs.to(device), real=False) + val = metric.compute().item() + return float(val), "" + except Exception as e: + return None, f"FID compute failed: {e}" + + +def _videos_to_uint8_tensor(videos: List[List[np.ndarray]], t_max: int) -> Optional[torch.Tensor]: + # -> [N,T,C,H,W] uint8, truncated to min length and t_max + if not videos: + return None + min_t = min(len(v) for v in videos if v) + if min_t <= 0: + return None + if t_max > 0: + min_t = min(min_t, t_max) + clips = [] + for v in videos: + clip = np.stack(v[:min_t], axis=0).astype(np.uint8) # [T,H,W,C] + clips.append(torch.from_numpy(clip).permute(0, 3, 1, 2)) # [T,C,H,W] + return torch.stack(clips, dim=0).contiguous() # [N,T,C,H,W] + + +def _ssim_rgb(fake: np.ndarray, real: np.ndarray) -> Optional[float]: + if not _HAS_SKIMAGE or _skimage_ssim is None: + return None + try: + try: + return float(_skimage_ssim(real, fake, channel_axis=2, data_range=255)) + except TypeError: + return float(_skimage_ssim(real, fake, multichannel=True, data_range=255)) + except Exception: + return None + + +def _try_fvd(real_videos: torch.Tensor, fake_videos: torch.Tensor, device: str) -> Tuple[Optional[float], str]: + try: + from torchmetrics.video.fvd import FrechetVideoDistance + except Exception as e: + return None, f"torchmetrics FVD unavailable: {e}" + try: + metric = FrechetVideoDistance(feature=400).to(device) + metric.update(real_videos.to(device), real=True) + metric.update(fake_videos.to(device), real=False) + val = metric.compute().item() + return float(val), "" + except Exception as e: + return None, f"FVD compute failed: {e}" + + +def main() -> int: + ap = argparse.ArgumentParser(description="Aggregate long-horizon FID/FVD from replay_gt outputs") + ap.add_argument("--root", required=True, help=".../static_consistency/in_domain/long_horizon_gt_replay") + ap.add_argument("--dataset_base", required=True) + ap.add_argument("--output_json", required=True) + ap.add_argument("--device", default="cuda") + ap.add_argument("--max_frames_per_video", type=int, default=243) + ap.add_argument("--max_fvd_frames", type=int, default=81) + args = ap.parse_args() + + root = os.path.abspath(args.root) + runs = _discover_runs(root) + if not runs: + out = { + "root": root, + "num_runs": 0, + "fid": None, + "fvd": None, + "per_run": [], + "metric_definitions": _metric_definitions(), + } + os.makedirs(os.path.dirname(os.path.abspath(args.output_json)), exist_ok=True) + with open(args.output_json, "w", encoding="utf-8") as f: + json.dump(out, f, indent=2) + return 0 + + device = args.device + lpips_model = _pl._lpips_model(device=device) if _HAS_PSNR_LPIPS and _pl is not None else None + + per_run: List[Dict[str, Any]] = [] + all_real_frames: List[np.ndarray] = [] + all_fake_frames: List[np.ndarray] = [] + real_videos: List[List[np.ndarray]] = [] + fake_videos: List[List[np.ndarray]] = [] + + for r in runs: + fake = _read_video_rgb(r.gen_mp4, max_frames=args.max_frames_per_video) + if not fake: + per_run.append({"run_dir": r.run_dir, "error": f"cannot read generated video {r.gen_mp4}"}) + continue + h, w = fake[0].shape[0], fake[0].shape[1] + total = min(len(fake), r.num_chunks * r.chunk_frames) + real = _load_gt_frames(args.dataset_base, r.video_name, r.start_frame, total, (w, h)) + n = min(len(real), len(fake)) + if n <= 0: + per_run.append({"run_dir": r.run_dir, "error": "no aligned real/fake frames"}) + continue + real = real[:n] + fake = fake[:n] + all_real_frames.extend(real) + all_fake_frames.extend(fake) + real_videos.append(real) + fake_videos.append(fake) + + ssims: List[float] = [] + lpips_vals: List[float] = [] + for fr, gt in zip(fake, real): + sv = _ssim_rgb(fr, gt) + if sv is not None: + ssims.append(sv) + if lpips_model is not None and _pl is not None: + lv = _pl.lpips_distance(fr, gt, lpips_model, device=device) + if lv is not None: + lpips_vals.append(lv) + + real_t = _frame_tensor_uint8(real) + fake_t = _frame_tensor_uint8(fake) + fid_run, _fid_note = _try_fid(real_t, fake_t, device=device) + + per_run.append( + { + "run_dir": r.run_dir, + "video_name": r.video_name, + "start_frame": r.start_frame, + "num_frames_used": n, + "mean_ssim": float(np.mean(ssims)) if ssims else None, + "mean_lpips": float(np.mean(lpips_vals)) if lpips_vals else None, + "fid_frame_divergence": fid_run, + } + ) + + fid_val: Optional[float] = None + fvd_val: Optional[float] = None + notes: List[str] = [] + + if all_real_frames and all_fake_frames: + real_img_t = _frame_tensor_uint8(all_real_frames) + fake_img_t = _frame_tensor_uint8(all_fake_frames) + fid_val, fid_note = _try_fid(real_img_t, fake_img_t, device=device) + if fid_note: + notes.append(fid_note) + else: + notes.append("No valid aligned frames for FID.") + + rv = _videos_to_uint8_tensor(real_videos, t_max=args.max_fvd_frames) + fv = _videos_to_uint8_tensor(fake_videos, t_max=args.max_fvd_frames) + if rv is not None and fv is not None: + fvd_val, fvd_note = _try_fvd(rv, fv, device=device) + if fvd_note: + notes.append(fvd_note) + else: + notes.append("No valid aligned videos for FVD.") + + out = { + "root": root, + "dataset_base": os.path.abspath(args.dataset_base), + "num_runs": len(per_run), + "fid": fid_val, + "fvd": fvd_val, + "notes": notes, + "per_run": per_run, + "metric_definitions": _metric_definitions(), + } + outp = os.path.abspath(args.output_json) + os.makedirs(os.path.dirname(outp), exist_ok=True) + with open(outp, "w", encoding="utf-8") as f: + json.dump(out, f, indent=2) + print(f"[aggregate_long_horizon_fid_fvd] runs={len(per_run)} fid={fid_val} fvd={fvd_val} -> {outp}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/metrics/aggregate_long_horizon_mse.py b/code/eval/v2/metrics/aggregate_long_horizon_mse.py new file mode 100644 index 0000000000000000000000000000000000000000..08245ff59dbd194dcca034b688e5941957afab2d --- /dev/null +++ b/code/eval/v2/metrics/aggregate_long_horizon_mse.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +"""Average mean_mse, mean_psnr, mean_ssim, mean_lpips from replay_gt_error.py outputs under a root.""" +from __future__ import annotations + +import argparse +import json +import os +import sys +from typing import Any, Dict, List, Optional + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--root", required=True, help="e.g. .../static_consistency/in_domain/long_horizon_gt_replay") + ap.add_argument("--output_json", required=True) + args = ap.parse_args() + root = os.path.abspath(args.root) + rows: List[Dict[str, Any]] = [] + for dirpath, _dirnames, filenames in os.walk(root): + if "replay_gt_metrics.json" not in filenames: + continue + p = os.path.join(dirpath, "replay_gt_metrics.json") + try: + with open(p, "r", encoding="utf-8") as f: + data = json.load(f) + m = (data.get("metrics") or {}) + rows.append( + { + "rel": os.path.relpath(p, root), + "video_name": m.get("video_name"), + "start_frame": m.get("start_frame"), + "num_chunks": m.get("num_chunks"), + "mean_mse": m.get("mean_mse"), + "mean_psnr": m.get("mean_psnr"), + "mean_ssim": m.get("mean_ssim"), + "mean_lpips": m.get("mean_lpips"), + } + ) + except Exception as e: + rows.append({"rel": os.path.relpath(p, root), "error": str(e)}) + + mse_vals = [float(r["mean_mse"]) for r in rows if r.get("mean_mse") is not None] + psnr_vals = [float(r["mean_psnr"]) for r in rows if r.get("mean_psnr") is not None] + ssim_vals = [float(r["mean_ssim"]) for r in rows if r.get("mean_ssim") is not None] + lpips_vals = [float(r["mean_lpips"]) for r in rows if r.get("mean_lpips") is not None] + + def _mean(xs: List[float]) -> Optional[float]: + return float(sum(xs) / len(xs)) if xs else None + + summary = { + "root": root, + "num_runs": len(rows), + "aggregate_mean_mse": _mean(mse_vals), + "aggregate_mean_psnr": _mean(psnr_vals), + "aggregate_mean_ssim": _mean(ssim_vals), + "aggregate_mean_lpips": _mean(lpips_vals), + "per_run": rows, + } + outp = os.path.abspath(args.output_json) + _par = os.path.dirname(outp) + if _par: + os.makedirs(_par, exist_ok=True) + with open(outp, "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + print(f"[aggregate_long_horizon_mse] runs={len(rows)} mean_mse={summary['aggregate_mean_mse']} -> {outp}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/metrics/aggregate_long_horizon_temporal_adjacency.py b/code/eval/v2/metrics/aggregate_long_horizon_temporal_adjacency.py new file mode 100644 index 0000000000000000000000000000000000000000..698b3e9ac35c80cc0349bfd89aaec98e04c87fe4 --- /dev/null +++ b/code/eval/v2/metrics/aggregate_long_horizon_temporal_adjacency.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +"""Walk long_horizon_gt_replay (or any root) and add temporal_adjacency_metrics.json per run.""" +from __future__ import annotations + +import argparse +import json +import os +import sys +from typing import Any, Dict, List + +_metrics_dir = os.path.dirname(os.path.abspath(__file__)) +if _metrics_dir not in sys.path: + sys.path.insert(0, _metrics_dir) +from temporal_adjacency_metrics import metrics_for_video # noqa: E402 + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--root", required=True, help="e.g. .../long_horizon_gt_replay") + ap.add_argument("--output_json", required=True, help="summary over all runs") + ap.add_argument("--max_frames", type=int, default=0) + args = ap.parse_args() + root = os.path.abspath(args.root) + rows: List[Dict[str, Any]] = [] + mse_vals: List[float] = [] + + for dirpath, _dirnames, filenames in os.walk(root): + if "replay_gt_gen_only.mp4" not in filenames: + continue + mp4 = os.path.join(dirpath, "replay_gt_gen_only.mp4") + try: + m = metrics_for_video(mp4, max_frames=args.max_frames) + m["rel_dir"] = os.path.relpath(dirpath, root) + outp = os.path.join(dirpath, "temporal_adjacency_metrics.json") + with open(outp, "w", encoding="utf-8") as f: + json.dump(m, f, indent=2) + rows.append({"rel_dir": m["rel_dir"], **{k: v for k, v in m.items() if k != "rel_dir"}}) + if m.get("mean_adjacent_mse") is not None: + mse_vals.append(float(m["mean_adjacent_mse"])) + except Exception as e: + rows.append({"rel_dir": os.path.relpath(dirpath, root), "error": str(e)}) + + summary = { + "root": root, + "num_runs": len(rows), + "aggregate_mean_adjacent_mse": float(sum(mse_vals) / len(mse_vals)) if mse_vals else None, + "per_run": rows, + } + outp = os.path.abspath(args.output_json) + os.makedirs(os.path.dirname(outp) or ".", exist_ok=True) + with open(outp, "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + print(f"[aggregate_long_horizon_temporal_adjacency] runs={len(rows)} -> {outp}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/metrics/aggregate_multiview_open_domain_metrics.py b/code/eval/v2/metrics/aggregate_multiview_open_domain_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..cfc6e865528a08d2782d095136c14427b3c3d3d6 --- /dev/null +++ b/code/eval/v2/metrics/aggregate_multiview_open_domain_metrics.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python3 +"""Aggregate per-view revisit closure + minimal cross-view 2D metrics (open-domain multiview).""" +from __future__ import annotations + +import argparse +import json +import os +import sys +from typing import Any, Dict, List, Optional, Tuple + +import cv2 +import numpy as np + +_metrics_dir = os.path.dirname(os.path.abspath(__file__)) +if _metrics_dir not in sys.path: + sys.path.insert(0, _metrics_dir) +import psnr_lpips as _pl # noqa: E402 + +try: + from skimage.metrics import structural_similarity as _skimage_ssim + + _HAS_SKIMAGE = True +except Exception: + _skimage_ssim = None # type: ignore + _HAS_SKIMAGE = False + + +def _read_json(path: str) -> Dict[str, Any]: + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + + +def _read_last_frame_rgb(video_path: str) -> Optional[np.ndarray]: + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + return None + n = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0) + if n <= 0: + cap.release() + return None + cap.set(cv2.CAP_PROP_POS_FRAMES, max(0, n - 1)) + ok, bgr = cap.read() + cap.release() + if not ok: + return None + return cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) + + +def _mse(a: np.ndarray, b: np.ndarray) -> float: + d = a.astype(np.float64) - b.astype(np.float64) + return float(np.mean(d ** 2)) + + +def _psnr(mse_v: float) -> float: + if mse_v <= 0: + return 100.0 + return float(10.0 * np.log10((255.0 ** 2) / mse_v)) + + +def _ssim(a: np.ndarray, b: np.ndarray) -> Optional[float]: + if not _HAS_SKIMAGE or _skimage_ssim is None: + return None + try: + if a.shape != b.shape: + b = cv2.resize(b, (a.shape[1], a.shape[0]), interpolation=cv2.INTER_AREA) + try: + return float(_skimage_ssim(a, b, channel_axis=2, data_range=255)) + except TypeError: + return float(_skimage_ssim(a, b, multichannel=True, data_range=255)) + except Exception: + return None + + +def main() -> int: + ap = argparse.ArgumentParser(description="Aggregate multiview closure + cross-view vs ref first frame") + ap.add_argument( + "--multiview_root", + required=True, + help="e.g. .../static_consistency/open_domain/multiview_revisit", + ) + ap.add_argument("--ref_view", type=str, default="0", help="view_id used as reference first frame") + ap.add_argument("--output_json", required=True) + ap.add_argument("--device", default="cuda", help="LPIPS device") + args = ap.parse_args() + + root = os.path.abspath(args.multiview_root) + summary_path = os.path.join(root, "multiview_revisit_summary.json") + if not os.path.isfile(summary_path): + out = { + "error": f"missing {summary_path}", + "multiview_root": root, + "per_view_closure": [], + "cross_view_vs_ref": [], + } + os.makedirs(os.path.dirname(os.path.abspath(args.output_json)) or ".", exist_ok=True) + with open(args.output_json, "w", encoding="utf-8") as f: + json.dump(out, f, indent=2) + return 0 + + summary_data = _read_json(summary_path) + rows: List[Dict[str, Any]] = list(summary_data.get("summary") or []) + + ref_row: Optional[Dict[str, Any]] = None + for r in rows: + if str(r.get("view_id")) == str(args.ref_view): + ref_row = r + break + ref_first_rgb: Optional[np.ndarray] = None + if ref_row and ref_row.get("first_frame_image"): + p = str(ref_row["first_frame_image"]) + if os.path.isfile(p): + bgr = cv2.imread(p, cv2.IMREAD_COLOR) + if bgr is not None: + ref_first_rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) + + per_view_closure: List[Dict[str, Any]] = [] + cross_view: List[Dict[str, Any]] = [] + + lpips_model = _pl._lpips_model(device=args.device) + + for r in rows: + vid = str(r.get("view_id", "")) + out_dir = str(r.get("output_dir") or "") + if not out_dir or not os.path.isdir(out_dir): + continue + closure_p = os.path.join(out_dir, "revisit_closure_metrics.json") + entry: Dict[str, Any] = {"view_id": vid, "output_dir": out_dir} + if os.path.isfile(closure_p): + try: + c = _read_json(closure_p) + entry["closure_first_vs_last_mse"] = c.get("closure_first_vs_last_mse") + entry["closure_first_vs_last_psnr"] = c.get("closure_first_vs_last_psnr") + entry["num_chunks"] = c.get("num_chunks") + except Exception as e: + entry["closure_error"] = str(e) + else: + entry["closure_error"] = "missing revisit_closure_metrics.json" + per_view_closure.append(entry) + + if ref_first_rgb is None or str(vid) == str(args.ref_view): + continue + mp4 = os.path.join(out_dir, "combo_revisit_4chunk_gen_only.mp4") + if not os.path.isfile(mp4): + cross_view.append({"view_id": vid, "error": "missing combo_revisit_4chunk_gen_only.mp4"}) + continue + last_rgb = _read_last_frame_rgb(mp4) + if last_rgb is None: + cross_view.append({"view_id": vid, "error": "cannot read last frame"}) + continue + if last_rgb.shape[:2] != ref_first_rgb.shape[:2]: + ref_r = cv2.resize(ref_first_rgb, (last_rgb.shape[1], last_rgb.shape[0]), interpolation=cv2.INTER_AREA) + else: + ref_r = ref_first_rgb + mse_v = _mse(last_rgb, ref_r) + row = { + "view_id": vid, + "ref_view": str(args.ref_view), + "last_vs_ref_first_mse": mse_v, + "last_vs_ref_first_psnr": _psnr(mse_v), + "last_vs_ref_first_ssim": _ssim(last_rgb, ref_r), + "last_vs_ref_first_lpips": _pl.lpips_distance(last_rgb, ref_r, lpips_model, device=args.device), + } + cross_view.append(row) + + metric_definitions = { + "per_view_closure": "From run_combo_revisit_fixed_first revisit_closure_metrics.json (same-view first vs last).", + "cross_view_vs_ref": ( + "Heuristic: last frame of view k generated video vs reference view input first frame. " + "Not multi-view geometry; use when opendomain images depict the same object/scene." + ), + } + out = { + "multiview_root": root, + "ref_view": str(args.ref_view), + "metric_definitions": metric_definitions, + "per_view_closure": per_view_closure, + "cross_view_vs_ref": cross_view, + "notes": [ + "Key-object ROI not applied (full frame).", + "3D consistency requires depth+pose; open_domain uses 2D proxies only.", + ], + } + outp = os.path.abspath(args.output_json) + os.makedirs(os.path.dirname(outp) or ".", exist_ok=True) + with open(outp, "w", encoding="utf-8") as f: + json.dump(out, f, indent=2) + print(f"[aggregate_multiview_open_domain_metrics] views={len(per_view_closure)} -> {outp}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/metrics/aggregate_revisit_metrics.py b/code/eval/v2/metrics/aggregate_revisit_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..d64c2610dd1a6531672bb2b31b9f3d8d9cecdee1 --- /dev/null +++ b/code/eval/v2/metrics/aggregate_revisit_metrics.py @@ -0,0 +1,140 @@ +#!/usr/bin/env python3 +""" +Randomly pick N revisit candidates (gen-only MP4) and compute average PSNR(+LPIPS if available). + +Intended for eval_v2/static_consistency outputs: +- /in_domain/loop_closure/**/*_gen_only.mp4 +- /in_domain/combo_revisit_fixed_first/**/*_gen_only.mp4 + +Outputs: +- per_video metrics JSON +- aggregate summary JSON +- optional side-by-side visualization PNGs (first vs last frame) +""" +from __future__ import annotations + +import argparse +import json +import os +import random +import sys +from typing import Any, Dict, List, Optional, Tuple + + +def _discover_candidates(evals_root: str) -> List[str]: + cands: List[str] = [] + for root, _dirs, files in os.walk(evals_root): + for f in files: + if f.endswith("_gen_only.mp4"): + cands.append(os.path.join(root, f)) + return sorted(cands) + + +def _safe_mean(values: List[float]) -> Optional[float]: + if not values: + return None + return float(sum(values) / len(values)) + + +def main() -> None: + p = argparse.ArgumentParser(description="Aggregate revisit metrics by random sampling") + p.add_argument("--evals_root", type=str, required=True, help="Path to static_consistency/in_domain (or similar)") + p.add_argument("--num_samples", type=int, default=5, help="How many videos to sample (min(len, num_samples))") + p.add_argument("--seed", type=int, default=42) + p.add_argument("--device", type=str, default="cuda") + p.add_argument("--output_dir", type=str, required=True) + p.add_argument("--write_viz", action="store_true", help="Also export side-by-side PNG for sampled videos") + p.add_argument("--viz_dir", type=str, default=None, help="Override visualization output directory") + args = p.parse_args() + + evals_root = os.path.abspath(args.evals_root) + output_dir = os.path.abspath(args.output_dir) + os.makedirs(output_dir, exist_ok=True) + + candidates = _discover_candidates(evals_root) + if not candidates: + summary = { + "num_candidates": 0, + "num_samples": 0, + "aggregate": {}, + "per_video": [], + "note": "No *_gen_only.mp4 found under evals_root", + } + with open(os.path.join(output_dir, "revisit_metrics_summary.json"), "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + print(f"[aggregate_revisit_metrics] No candidates under: {evals_root}", file=sys.stderr) + return + + rng = random.Random(args.seed) + num_samples = min(len(candidates), max(1, int(args.num_samples))) + selected = rng.sample(candidates, k=num_samples) if num_samples < len(candidates) else candidates + + # Import metrics with local sys.path insertion to avoid needing package __init__.py + _script_dir = os.path.dirname(os.path.abspath(__file__)) + sys.path.insert(0, _script_dir) + import psnr_lpips # type: ignore + + # Optional visualization + if args.write_viz: + viz_dir = args.viz_dir or os.path.join(output_dir, "viz_first_vs_last") + os.makedirs(viz_dir, exist_ok=True) + else: + viz_dir = None + + per_video: List[Dict[str, Any]] = [] + psnr_list: List[float] = [] + lpips_list: List[float] = [] + + for vp in selected: + rel = os.path.relpath(vp, evals_root) + try: + res = psnr_lpips.compute_revisit_metrics(vp, device=args.device) + per_video.append({"rel": rel, **res}) + if res.get("psnr") is not None: + psnr_list.append(float(res["psnr"])) + if res.get("lpips") is not None: + lpips_list.append(float(res["lpips"])) + except Exception as e: + per_video.append({"rel": rel, "error": str(e)}) + + if args.write_viz and viz_dir is not None: + # Export side-by-side png + try: + import subprocess + _viz_script = os.path.join(os.path.dirname(_script_dir), "visualize", "revisit_pairs_viz.py") + out_png = os.path.join(viz_dir, rel.replace(os.sep, "__") + ".png") + os.makedirs(os.path.dirname(out_png), exist_ok=True) + subprocess.run( + ["python3", _viz_script, "--video", vp, "--output", out_png], + check=False, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + except Exception: + pass + + aggregate = { + "num_candidates": len(candidates), + "num_samples": len(selected), + "mean_psnr": _safe_mean(psnr_list), + "mean_lpips": _safe_mean(lpips_list), + "note": "LPIPS is None for each video when lpips dependency is missing.", + } + + summary = { + "evals_root": evals_root, + "seed": args.seed, + "aggregate": aggregate, + "per_video": per_video, + } + with open(os.path.join(output_dir, "revisit_metrics_summary.json"), "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + with open(os.path.join(output_dir, "revisit_metrics_per_video.json"), "w", encoding="utf-8") as f: + json.dump(per_video, f, indent=2) + + print(f"[aggregate_revisit_metrics] wrote: {output_dir}/revisit_metrics_summary.json") + + +if __name__ == "__main__": + main() + diff --git a/code/eval/v2/metrics/psnr_lpips.py b/code/eval/v2/metrics/psnr_lpips.py new file mode 100644 index 0000000000000000000000000000000000000000..f42ceb857df55061f74191db5ff21b730fd0dcc6 --- /dev/null +++ b/code/eval/v2/metrics/psnr_lpips.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python3 +""" +PSNR + LPIPS for aligned frame pairs. + +First version focuses on static consistency revisit: +- Compare first frame vs last frame of a video (revisit proxy). +- Optionally compare symmetric frames if indices provided. + +Dependencies: +- PSNR: numpy +- LPIPS: optional (pip install lpips). If missing, lpips will be reported as None. +""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Any, Dict, List, Tuple + +import numpy as np + +try: + import cv2 + HAS_CV2 = True +except ImportError: + HAS_CV2 = False + + +def _read_video_frames(path: str) -> List[np.ndarray]: + if not HAS_CV2: + raise RuntimeError("opencv-python required to read mp4") + cap = cv2.VideoCapture(path) + frames = [] + while True: + ret, bgr = cap.read() + if not ret: + break + rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) + frames.append(rgb) + cap.release() + return frames + + +def psnr(img1: np.ndarray, img2: np.ndarray) -> float: + if img1.shape != img2.shape and HAS_CV2: + img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]), interpolation=cv2.INTER_LINEAR) + mse = np.mean((img1.astype(np.float64) - img2.astype(np.float64)) ** 2) + if mse <= 0: + return 100.0 + return float(10.0 * np.log10((255.0 ** 2) / mse)) + + +def _lpips_model(device: str = "cuda"): + try: + import torch + import lpips # type: ignore + m = lpips.LPIPS(net="alex").to(device) + m.eval() + return m + except Exception: + return None + + +def lpips_distance(img1: np.ndarray, img2: np.ndarray, model, device: str = "cuda") -> float | None: + if model is None: + return None + try: + import torch + if img1.shape != img2.shape and HAS_CV2: + img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]), interpolation=cv2.INTER_LINEAR) + # [H,W,3] uint8 -> [1,3,H,W] float in [-1,1] + t1 = torch.from_numpy(img1).permute(2, 0, 1).unsqueeze(0).float() / 127.5 - 1.0 + t2 = torch.from_numpy(img2).permute(2, 0, 1).unsqueeze(0).float() / 127.5 - 1.0 + t1 = t1.to(device) + t2 = t2.to(device) + with torch.no_grad(): + d = model(t1, t2) + return float(d.item()) + except Exception: + return None + + +def compute_revisit_metrics(video_path: str, device: str = "cuda") -> Dict[str, Any]: + frames = _read_video_frames(video_path) + if len(frames) < 2: + return {"num_frames": len(frames), "psnr": None, "lpips": None} + first = frames[0] + last = frames[-1] + p = psnr(first, last) + m = _lpips_model(device=device) + l = lpips_distance(first, last, m, device=device) + return {"num_frames": len(frames), "psnr": p, "lpips": l} + + +def main(): + p = argparse.ArgumentParser(description="PSNR+LPIPS for revisit (first vs last frame)") + p.add_argument("--video", required=True) + p.add_argument("--device", default="cuda") + p.add_argument("--output", default=None) + args = p.parse_args() + + res = compute_revisit_metrics(args.video, device=args.device) + out = json.dumps(res, indent=2) + print(out) + if args.output: + with open(args.output, "w", encoding="utf-8") as f: + f.write(out) + + +if __name__ == "__main__": + main() + diff --git a/code/eval/v2/metrics/temporal_adjacency_metrics.py b/code/eval/v2/metrics/temporal_adjacency_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..9df1bdb62c95a45329053c716a38520e19d85268 --- /dev/null +++ b/code/eval/v2/metrics/temporal_adjacency_metrics.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +"""Adjacent-frame consistency for generated video (sequence self-consistency proxy). + +Interpreting metrics: +- mean_adjacent_mse: mean RGB MSE between consecutive frames (lower = smoother change; near-zero may indicate collapse). +- mean_adjacent_ssim: structural similarity between t and t+1 (higher = less motion / very smooth). + +This does NOT replace GT-aligned metrics (see replay_gt_metrics.json). Use together with long_horizon PSNR/SSIM/LPIPS vs GT. +""" +from __future__ import annotations + +import argparse +import json +import os +from typing import Any, Dict, List, Optional + +import cv2 +import numpy as np + +try: + from skimage.metrics import structural_similarity as _skimage_ssim + + _HAS_SKIMAGE = True +except Exception: + _skimage_ssim = None # type: ignore + _HAS_SKIMAGE = False + + +def _read_video_rgb(video_path: str, max_frames: int = 0) -> List[np.ndarray]: + cap = cv2.VideoCapture(video_path) + out: List[np.ndarray] = [] + if not cap.isOpened(): + return out + while True: + ok, bgr = cap.read() + if not ok: + break + out.append(cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)) + if max_frames > 0 and len(out) >= max_frames: + break + cap.release() + return out + + +def _ssim_pair(a: np.ndarray, b: np.ndarray) -> Optional[float]: + if not _HAS_SKIMAGE or _skimage_ssim is None: + return None + try: + try: + return float(_skimage_ssim(a, b, channel_axis=2, data_range=255)) + except TypeError: + return float(_skimage_ssim(a, b, multichannel=True, data_range=255)) + except Exception: + return None + + +def metrics_for_video(video_path: str, max_frames: int = 0) -> Dict[str, Any]: + frames = _read_video_rgb(os.path.abspath(video_path), max_frames=max_frames) + if len(frames) < 2: + return { + "video": os.path.abspath(video_path), + "num_frames": len(frames), + "mean_adjacent_mse": None, + "mean_adjacent_ssim": None, + "notes": ["need at least 2 frames"], + } + mses: List[float] = [] + ssims: List[float] = [] + for i in range(len(frames) - 1): + a = frames[i].astype(np.float64) + b = frames[i + 1].astype(np.float64) + mses.append(float(np.mean((a - b) ** 2))) + sv = _ssim_pair(frames[i], frames[i + 1]) + if sv is not None: + ssims.append(sv) + return { + "video": os.path.abspath(video_path), + "num_frames": len(frames), + "num_adjacent_pairs": len(frames) - 1, + "mean_adjacent_mse": float(np.mean(mses)), + "mean_adjacent_ssim": float(np.mean(ssims)) if ssims else None, + "metric_definitions": { + "mean_adjacent_mse": "Mean RGB MSE between consecutive generated frames.", + "mean_adjacent_ssim": "Mean SSIM between consecutive frames (optional; needs scikit-image).", + }, + } + + +def main() -> int: + ap = argparse.ArgumentParser(description="Adjacent-frame consistency for one mp4") + ap.add_argument("--video", required=True) + ap.add_argument("--max_frames", type=int, default=0, help="0 = all frames") + ap.add_argument("--output_json", default=None) + args = ap.parse_args() + out = metrics_for_video(args.video, max_frames=args.max_frames) + s = json.dumps(out, indent=2) + print(s) + if args.output_json: + os.makedirs(os.path.dirname(os.path.abspath(args.output_json)) or ".", exist_ok=True) + with open(args.output_json, "w", encoding="utf-8") as f: + f.write(s) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/revisit_suite/README.md b/code/eval/v2/revisit_suite/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bac5cd9ce914b81f856fff58818613b9cb37da98 --- /dev/null +++ b/code/eval/v2/revisit_suite/README.md @@ -0,0 +1,99 @@ +# Revisit Suite One-Click Eval + +Purpose: + +1. Discover `outputs/**/epoch-0.safetensors`. +2. Run basic static-memory revisit on training-set samples, sharded across GPUs. +3. Run OOD static-memory revisit from `assets/opendomain_revisit`, sharded across GPUs. +4. Save stage-1 evidence images: first frame and final revisit tail frames. +5. Save traditional first-vs-final metrics: MSE, PSNR, SSIM. +6. Optionally run Qwen/VLM scoring with high weight on the toy bear appearance and lower weight on background scene. + +## Run + +```bash +export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B +export DATASET_BASE_PATH=/path/to/Context-as-Memory-Dataset +bash eval/v2/revisit_suite/run_one_click_revisit_eval.sh +``` + +Useful overrides: + +```bash +NUM_GPUS=8 \ +TRAIN_LIMIT=8 \ +OOD_LIMIT=8 \ +MODES=rot180_4chunk,rot360_8chunk,right45_return_2chunk \ +VLM_API_BASE=http://127.0.0.1:8000/v1 \ +VLM_MODEL=Qwen/Qwen2.5-VL-72B-Instruct \ +bash eval/v2/revisit_suite/run_one_click_revisit_eval.sh +``` + +If the VLM server is not up yet, run only stage 1: + +```bash +PHASE=stage1 bash eval/v2/revisit_suite/run_one_click_revisit_eval.sh +``` + +Then run VLM later: + +```bash +PHASE=vlm EVAL_ROOT=/path/to/eval_root bash eval/v2/revisit_suite/run_one_click_revisit_eval.sh +``` + +For a dry run of the VLM phase: + +```bash +VLM_DRY_RUN=1 bash eval/v2/revisit_suite/run_one_click_revisit_eval.sh +``` + +## Revisit Modes + +- `rot180_4chunk`: four chunks, each 45 degrees, `+45,+45,-45,-45`. +- `rot360_8chunk`: eight chunks, each 45 degrees, full 360-degree loop. +- `right45_return_2chunk`: two chunks, right 45 degrees then return 45 degrees; focuses on second-chunk memory. +- `random_closed`: random yaw/translation path with approximate closed-loop return. + +The success criterion is visual scene consistency between the first frame and the final revisit tail. For OOD samples with the toy bear, the VLM prompt weights bear appearance and presence most strongly. + +## Outputs + +Default root: + +```text +eval_outputs/revisit_suite_/ +``` + +Each case contains: + +- `revisit_gen_only.mp4` +- `stage1_frames/first_00.png` +- `stage1_frames/revisit_tail_*.png` +- `stage1_metrics.json` +- `vlm_score.json` after VLM scoring + +## Inspecting Videos and Images + +The suite is designed to keep both metric files and visual evidence: + +- `revisit_gen_only.mp4`: generated return trajectory for the case. +- `stage1_frames/first_00.png`: input first frame. +- `stage1_frames/revisit_tail_*.png`: final return frames shown to the VLM judge. +- `stage1_frames/first_last_chunk_changes/*.png`: side-by-side first chunk, last chunk, and absolute-difference panels when enabled. + +To browse a run from a remote machine: + +```bash +python -m http.server 8000 --directory eval_outputs +``` + +To collect material for paper figures: + +```bash +python eval/v2/revisit_suite/export_revisit_materials.py \ + --eval-root eval_outputs/revisit_suite_ \ + --out-dir paper_case_materials \ + --prefix echo_memory_revisit +``` + +Good qualitative cases usually combine `first_00.png`, the last two or four `revisit_tail_*.png` frames, and the corresponding `revisit_gen_only.mp4`. Use `right45_return_2chunk` for the open-domain paper probe and `rot180_4chunk` for in-domain loop closure examples. diff --git a/code/eval/v2/revisit_suite/export_revisit_materials.py b/code/eval/v2/revisit_suite/export_revisit_materials.py new file mode 100644 index 0000000000000000000000000000000000000000..4a3b32aec833b8e0c061a5a1cdbd4ea9ad4ce1d8 --- /dev/null +++ b/code/eval/v2/revisit_suite/export_revisit_materials.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import base64 +import csv +import json +import re +import time +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path +from typing import Any + +import requests + + +SYSTEM = "You are a strict visual memory evaluator. Return only valid JSON." + +USER_PROMPT = """Evaluate whether the revisit tail frames preserve the same object and scene as the first frame. + +The most important target is the toy bear if it appears in the first frame. Judge its appearance consistency strongly: +- bear identity, color, shape, face, pose, clothing/accessories if visible +- whether the final revisit still depicts the same bear after camera motion + +Scene/layout consistency is secondary. Give lower weight to background unless it contradicts the target. + +Return JSON with: +{ + "bear_appearance_score": 0-5, + "bear_presence_score": 0-5, + "scene_consistency_score": 0-5, + "view_revisit_score": 0-5, + "overall_score": 0-100, + "verdict": "pass|partial|fail", + "reason": "short evidence" +} + +Weights for overall_score: bear_appearance 45%, bear_presence 25%, view_revisit 20%, scene_consistency 10%. +""" + + +def load_json(path: Path) -> dict[str, Any]: + return json.loads(path.read_text(encoding="utf-8")) + + +def encode_image(path: str | Path) -> str: + return base64.b64encode(Path(path).read_bytes()).decode("ascii") + + +def parse_json(text: str) -> dict[str, Any]: + text = re.sub(r".*?", "", text, flags=re.DOTALL).strip().strip("`") + match = re.search(r"\{.*\}", text, flags=re.DOTALL) + if not match: + raise ValueError(f"no JSON in VLM response: {text[:300]}") + return json.loads(match.group(0)) + + +def wait_ready(api_base: str, timeout: int) -> None: + url = f"{api_base.rstrip('/')}/models" + deadline = time.time() + timeout + while time.time() < deadline: + try: + if requests.get(url, timeout=5).status_code == 200: + return + except Exception: + pass + time.sleep(2) + raise RuntimeError(f"VLM server not ready at {api_base}") + + +def score_one(metrics_path: Path, api_base: str, model: str, timeout: int, force: bool) -> Path: + out_path = metrics_path.parent / "vlm_score.json" + if out_path.is_file() and not force: + return out_path + + metrics = load_json(metrics_path) + images = list(metrics.get("first_frame_paths") or []) + list(metrics.get("revisit_tail_paths") or []) + content: list[dict[str, Any]] = [{"type": "text", "text": USER_PROMPT}] + for image_path in images: + content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encode_image(image_path)}"}}) + + payload = { + "model": model, + "messages": [ + {"role": "system", "content": [{"type": "text", "text": SYSTEM}]}, + {"role": "user", "content": content}, + ], + "temperature": 0.0, + "max_tokens": 800, + "chat_template_kwargs": {"enable_thinking": False}, + } + response = requests.post(f"{api_base.rstrip('/')}/chat/completions", json=payload, timeout=timeout) + response.raise_for_status() + raw = response.json()["choices"][0]["message"]["content"] + vlm = parse_json(raw) + + out = { + "metrics_path": str(metrics_path), + "run_id": metrics.get("run_id"), + "domain": (metrics.get("sample") or {}).get("domain"), + "sample_id": (metrics.get("sample") or {}).get("sample_id"), + "mode": metrics.get("mode"), + "traditional": { + "closure_psnr": metrics.get("closure_psnr"), + "closure_ssim": metrics.get("closure_ssim"), + "closure_mse": metrics.get("closure_mse"), + }, + "vlm": vlm, + } + out_path.write_text(json.dumps(out, ensure_ascii=False, indent=2), encoding="utf-8") + return out_path + + +def maybe_score_vlm(metrics_paths: list[Path], args: argparse.Namespace) -> None: + if not args.score_vlm: + return + wait_ready(args.vlm_api_base, args.startup_wait_sec) + todo = [p for p in metrics_paths if args.force_vlm or not (p.parent / "vlm_score.json").is_file()] + print(f"[vlm] scoring {len(todo)} / {len(metrics_paths)} cases with workers={args.vlm_workers}") + if not todo: + return + with ThreadPoolExecutor(max_workers=args.vlm_workers) as pool: + futures = [pool.submit(score_one, p, args.vlm_api_base, args.vlm_model, args.vlm_timeout, args.force_vlm) for p in todo] + for i, fut in enumerate(as_completed(futures), 1): + path = fut.result() + print(f"[vlm] {i}/{len(futures)} {path}") + + +def flat_row(metrics_path: Path, eval_root: Path) -> dict[str, Any]: + metrics = load_json(metrics_path) + vlm_path = metrics_path.parent / "vlm_score.json" + vlm = load_json(vlm_path).get("vlm", {}) if vlm_path.is_file() else {} + sample = metrics.get("sample") or {} + align = metrics.get("alignment") or {} + init_ctx = align.get("initial_context") or {} + key_ev = align.get("ckpt_key_evidence") or {} + + return { + "eval_root": eval_root.name, + "run_id": metrics.get("run_id"), + "ckpt": metrics.get("ckpt"), + "domain": sample.get("domain"), + "sample_id": sample.get("sample_id"), + "video_name": sample.get("video_name"), + "start_frame": sample.get("start_frame"), + "mode": metrics.get("mode"), + "num_chunks": metrics.get("num_chunks"), + "context_frames": metrics.get("context_frames"), + "closure_psnr": metrics.get("closure_psnr"), + "closure_ssim": metrics.get("closure_ssim"), + "closure_mse": metrics.get("closure_mse"), + "vlm_overall_score": vlm.get("overall_score"), + "vlm_bear_appearance_score": vlm.get("bear_appearance_score"), + "vlm_bear_presence_score": vlm.get("bear_presence_score"), + "vlm_scene_consistency_score": vlm.get("scene_consistency_score"), + "vlm_view_revisit_score": vlm.get("view_revisit_score"), + "vlm_verdict": vlm.get("verdict"), + "vlm_reason": vlm.get("reason"), + "memory_profile": align.get("memory_profile"), + "memory_profile_matched": align.get("memory_profile_matched"), + "action_injection_impl": align.get("action_injection_impl"), + "camera_inject_mode_effective": align.get("camera_inject_mode_effective"), + "rt_encoding_effective": align.get("rt_encoding_effective"), + "use_framepack_memory": align.get("use_framepack_memory"), + "use_framepack_length_compress": align.get("use_framepack_length_compress"), + "framepack_ratio": align.get("framepack_ratio"), + "use_spatial_memory": align.get("use_spatial_memory"), + "use_spatial_memory_legacy": align.get("use_spatial_memory_legacy"), + "spatial_memory_tokens": align.get("spatial_memory_tokens"), + "spatial_memory_inject_mode": align.get("spatial_memory_inject_mode"), + "ckpt_has_camera_encoder_keys": key_ev.get("has_camera_encoder_keys"), + "ckpt_has_spatial_memory_module_keys": key_ev.get("has_spatial_memory_module_keys"), + "ckpt_has_block_wise_ssm_keys": key_ev.get("has_block_wise_ssm_keys"), + "ckpt_has_videossm_hybrid_keys": key_ev.get("has_videossm_hybrid_keys"), + "ckpt_has_ssm_keys": key_ev.get("has_ssm_keys"), + "initial_context_source": init_ctx.get("training_memory_source"), + "initial_context_detail": init_ctx.get("context_source_detail"), + "initial_context_frame_count": init_ctx.get("context_frame_count"), + "stage1_metrics_path": str(metrics_path), + "vlm_score_path": str(vlm_path) if vlm_path.is_file() else "", + "video_path": metrics.get("video_path"), + } + + +def mean(values: list[Any]) -> float | None: + xs = [] + for value in values: + try: + if value is not None and value != "": + xs.append(float(value)) + except (TypeError, ValueError): + pass + return sum(xs) / len(xs) if xs else None + + +def aggregate(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: + groups: dict[tuple[Any, ...], list[dict[str, Any]]] = defaultdict(list) + for row in rows: + groups[(row["run_id"], row["domain"], row["mode"])].append(row) + + out = [] + for (run_id, domain, mode), group in sorted(groups.items()): + out.append( + { + "run_id": run_id, + "domain": domain, + "mode": mode, + "num_cases": len(group), + "mean_closure_psnr": mean([r["closure_psnr"] for r in group]), + "mean_closure_ssim": mean([r["closure_ssim"] for r in group]), + "mean_closure_mse": mean([r["closure_mse"] for r in group]), + "mean_vlm_overall_score": mean([r["vlm_overall_score"] for r in group]), + "mean_vlm_bear_appearance_score": mean([r["vlm_bear_appearance_score"] for r in group]), + "mean_vlm_bear_presence_score": mean([r["vlm_bear_presence_score"] for r in group]), + "mean_vlm_scene_consistency_score": mean([r["vlm_scene_consistency_score"] for r in group]), + "mean_vlm_view_revisit_score": mean([r["vlm_view_revisit_score"] for r in group]), + "memory_profile": group[0]["memory_profile"], + "context_frames": group[0]["context_frames"], + "use_framepack_memory": group[0]["use_framepack_memory"], + "use_framepack_length_compress": group[0]["use_framepack_length_compress"], + "framepack_ratio": group[0]["framepack_ratio"], + "use_spatial_memory": group[0]["use_spatial_memory"], + "use_spatial_memory_legacy": group[0]["use_spatial_memory_legacy"], + "spatial_memory_tokens": group[0]["spatial_memory_tokens"], + "spatial_memory_inject_mode": group[0]["spatial_memory_inject_mode"], + "eval_roots": "|".join(sorted({str(r["eval_root"]) for r in group})), + } + ) + return out + + +def write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if not rows: + path.write_text("", encoding="utf-8") + return + fieldnames = list(rows[0].keys()) + with path.open("w", encoding="utf-8", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(rows) + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--eval-root", action="append", required=True) + ap.add_argument("--out-dir", default="revisit_materials") + ap.add_argument("--prefix", default="ep0_revisit") + ap.add_argument("--score-vlm", action="store_true") + ap.add_argument("--force-vlm", action="store_true") + ap.add_argument("--vlm-workers", type=int, default=8) + ap.add_argument("--vlm-api-base", default="http://127.0.0.1:8000/v1") + ap.add_argument("--vlm-model", default="Qwen/Qwen2.5-VL-72B-Instruct") + ap.add_argument("--vlm-timeout", type=int, default=180) + ap.add_argument("--startup-wait-sec", type=int, default=900) + args = ap.parse_args() + + eval_roots = [Path(p) for p in args.eval_root] + metrics_paths: list[Path] = [] + for root in eval_roots: + stage1 = root / "stage1" + found = sorted(stage1.rglob("stage1_metrics.json")) + print(f"[collect] {root}: {len(found)} stage1 metrics") + metrics_paths.extend(found) + + maybe_score_vlm(metrics_paths, args) + + rows = [] + for root in eval_roots: + for path in sorted((root / "stage1").rglob("stage1_metrics.json")): + rows.append(flat_row(path, root)) + agg_rows = aggregate(rows) + + out_dir = Path(args.out_dir) + cases_csv = out_dir / f"{args.prefix}_cases.csv" + aggregate_csv = out_dir / f"{args.prefix}_aggregate_by_model_domain_mode.csv" + write_csv(cases_csv, rows) + write_csv(aggregate_csv, agg_rows) + (out_dir / f"{args.prefix}_cases.json").write_text(json.dumps(rows, ensure_ascii=False, indent=2), encoding="utf-8") + (out_dir / f"{args.prefix}_aggregate_by_model_domain_mode.json").write_text(json.dumps(agg_rows, ensure_ascii=False, indent=2), encoding="utf-8") + print(f"[export] cases={len(rows)} -> {cases_csv}") + print(f"[export] aggregates={len(agg_rows)} -> {aggregate_csv}") + + +if __name__ == "__main__": + main() diff --git a/code/eval/v2/revisit_suite/prepare_eval_manifest.py b/code/eval/v2/revisit_suite/prepare_eval_manifest.py new file mode 100644 index 0000000000000000000000000000000000000000..ecd10a54e8e9c0bc935ee690005c85c03a5c895b --- /dev/null +++ b/code/eval/v2/revisit_suite/prepare_eval_manifest.py @@ -0,0 +1,138 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import csv +import json +import os +from pathlib import Path + + +IMG_EXTS = {".png", ".jpg", ".jpeg", ".webp"} + + +def _step_id(path: Path) -> int: + stem = path.stem + if stem.startswith("Step-"): + try: + return int(stem.split("-", 1)[1]) + except Exception: + return -1 + return -1 + + +def find_ckpts(outputs_root: Path, include_dyn: bool, ckpt_policy: str = "epoch0") -> list[Path]: + """Return checkpoints under outputs_root according to ckpt_policy. + + Evaluation candidacy is intentionally file-based: if a run directory has + a matching checkpoint, it is considered finished enough to evaluate. The + include_dyn flag is kept for backward compatibility and does not filter + Echo-Memory outputs by default. + """ + policy = (ckpt_policy or "epoch0").strip().lower() + if policy == "epoch0": + return sorted(outputs_root.rglob("epoch-0.safetensors")) + if policy == "all_steps": + return sorted(outputs_root.rglob("Step-*.safetensors"), key=lambda p: (str(p.parent), _step_id(p))) + if policy == "latest": + by_dir: dict[Path, Path] = {} + for p in sorted(outputs_root.rglob("*.safetensors")): + if p.name == "epoch-0.safetensors": + by_dir.setdefault(p.parent, p) + elif p.name.startswith("Step-"): + prev = by_dir.get(p.parent) + if prev is None or (prev.name == "epoch-0.safetensors") or _step_id(p) > _step_id(prev): + by_dir[p.parent] = p + return sorted(by_dir.values()) + raise ValueError(f"unknown ckpt_policy={ckpt_policy}; expected epoch0/latest/all_steps") + + +def read_train_samples(metadata: Path, dataset_base: Path, limit: int) -> list[dict]: + rows = [] + with metadata.open("r", encoding="utf-8", newline="") as f: + for row in csv.DictReader(f): + video_name = (row.get("video_name") or "").strip() + if not video_name: + continue + try: + start_frame = int(row.get("start_frame", 0) or 0) + except (TypeError, ValueError): + start_frame = 0 + try: + end_frame = int(row.get("end_frame", start_frame + 80) or (start_frame + 80)) + except (TypeError, ValueError): + end_frame = start_frame + 80 + frame_path = dataset_base / "frames" / video_name / f"{start_frame:04d}.png" + if not frame_path.is_file(): + continue + rows.append( + { + "sample_id": f"train_{len(rows):05d}_{video_name.replace('/', '__')}_{start_frame:06d}", + "domain": "train", + "video_name": video_name, + "start_frame": start_frame, + "end_frame": end_frame, + "prompt": row.get("prompt") or "A scene.", + "first_frame_image": str(frame_path), + "dataset_base": str(dataset_base), + } + ) + if limit and len(rows) >= limit: + break + return rows + + +def read_ood_samples(ood_dir: Path, prompt: str, limit: int) -> list[dict]: + imgs = [p for p in sorted(ood_dir.iterdir()) if p.suffix.lower() in IMG_EXTS] + if limit: + imgs = imgs[:limit] + return [ + { + "sample_id": f"ood_{i:05d}_{p.stem}", + "domain": "ood", + "video_name": None, + "start_frame": 0, + "end_frame": 80, + "prompt": prompt, + "first_frame_image": str(p), + "dataset_base": None, + } + for i, p in enumerate(imgs) + ] + + +def main() -> None: + repo_root = Path(__file__).resolve().parents[3] + default_dataset = repo_root / "data" / "Context-as-Memory-Dataset" + ap = argparse.ArgumentParser() + ap.add_argument("--outputs-root", default=str(repo_root / "outputs")) + ap.add_argument("--dataset-base", default=str(default_dataset)) + ap.add_argument("--metadata", default=str(default_dataset / "metadata_full.csv")) + ap.add_argument("--ood-dir", default=str(repo_root / "assets" / "opendomain_revisit")) + ap.add_argument("--out", required=True) + ap.add_argument("--train-limit", type=int, default=8) + ap.add_argument("--ood-limit", type=int, default=8) + ap.add_argument("--ood-prompt", default="A toy bear in the same static scene. Preserve the bear appearance and the scene layout after camera revisit.") + ap.add_argument("--include-dynmembench", action="store_true") + ap.add_argument("--ckpt-policy", default="epoch0", choices=["epoch0", "latest", "all_steps"]) + args = ap.parse_args() + + ckpts = find_ckpts(Path(args.outputs_root), args.include_dynmembench, args.ckpt_policy) + train_samples = read_train_samples(Path(args.metadata), Path(args.dataset_base), args.train_limit) + ood_samples = read_ood_samples(Path(args.ood_dir), args.ood_prompt, args.ood_limit) + payload = { + "ckpts": [{"ckpt": str(p), "run_id": p.parent.name} for p in ckpts], + "samples": train_samples + ood_samples, + "dataset_base": args.dataset_base, + "metadata": args.metadata, + "ood_dir": args.ood_dir, + "ckpt_policy": args.ckpt_policy, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8") + print(f"[prepare_eval_manifest] ckpts={len(ckpts)} train={len(train_samples)} ood={len(ood_samples)} -> {out}") + + +if __name__ == "__main__": + main() diff --git a/code/eval/v2/revisit_suite/qwen_vlm_score.py b/code/eval/v2/revisit_suite/qwen_vlm_score.py new file mode 100644 index 0000000000000000000000000000000000000000..b49b5de1a41edfc2a334568a6dc7f3fc6fda2c7b --- /dev/null +++ b/code/eval/v2/revisit_suite/qwen_vlm_score.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import base64 +import json +import os +import re +import time +from pathlib import Path +from typing import Any + +import requests + + +SYSTEM = """You are a strict visual memory evaluator. Return only valid JSON.""" + + +USER_PROMPT = """Evaluate whether the revisit tail frames preserve the same object and scene as the first frame. + +The most important target is the toy bear if it appears in the first frame. Judge its appearance consistency strongly: +- bear identity, color, shape, face, pose, clothing/accessories if visible +- whether the final revisit still depicts the same bear after camera motion + +Scene/layout consistency is secondary. Give lower weight to background unless it contradicts the target. + +Return JSON with: +{ + "bear_appearance_score": 0-5, + "bear_presence_score": 0-5, + "scene_consistency_score": 0-5, + "view_revisit_score": 0-5, + "overall_score": 0-100, + "verdict": "pass|partial|fail", + "reason": "short evidence" +} + +Weights for overall_score: bear_appearance 45%, bear_presence 25%, view_revisit 20%, scene_consistency 10%. +""" + + +def encode_image(path: str | Path) -> str: + data = Path(path).read_bytes() + return base64.b64encode(data).decode("ascii") + + +def parse_json(text: str) -> dict[str, Any]: + text = re.sub(r".*?", "", text, flags=re.DOTALL).strip().strip("`") + m = re.search(r"\{.*\}", text, flags=re.DOTALL) + if not m: + raise ValueError(f"no JSON in VLM response: {text[:300]}") + return json.loads(m.group(0)) + + +def wait_ready(api_base: str, timeout: int) -> bool: + url = f"{api_base.rstrip('/')}/models" + deadline = time.time() + timeout + while time.time() < deadline: + try: + if requests.get(url, timeout=5).status_code == 200: + return True + except Exception: + pass + time.sleep(2) + return False + + +def score_one(metrics_path: Path, api_base: str, model: str, timeout: int, dry_run: bool) -> dict[str, Any]: + metrics = json.loads(metrics_path.read_text(encoding="utf-8")) + images = list(metrics.get("first_frame_paths") or []) + list(metrics.get("revisit_tail_paths") or []) + if dry_run: + resp = { + "bear_appearance_score": 0, + "bear_presence_score": 0, + "scene_consistency_score": 0, + "view_revisit_score": 0, + "overall_score": 0, + "verdict": "partial", + "reason": "dry-run: VLM not called", + } + else: + content: list[dict[str, Any]] = [{"type": "text", "text": USER_PROMPT}] + for p in images: + content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encode_image(p)}"}}) + payload = { + "model": model, + "messages": [ + {"role": "system", "content": [{"type": "text", "text": SYSTEM}]}, + {"role": "user", "content": content}, + ], + "temperature": 0.0, + "max_tokens": 800, + "chat_template_kwargs": {"enable_thinking": False}, + } + r = requests.post(f"{api_base.rstrip('/')}/chat/completions", json=payload, timeout=timeout) + r.raise_for_status() + raw = r.json()["choices"][0]["message"]["content"] + resp = parse_json(raw) + + out = { + "metrics_path": str(metrics_path), + "run_id": metrics.get("run_id"), + "domain": (metrics.get("sample") or {}).get("domain"), + "sample_id": (metrics.get("sample") or {}).get("sample_id"), + "mode": metrics.get("mode"), + "traditional": { + "closure_psnr": metrics.get("closure_psnr"), + "closure_ssim": metrics.get("closure_ssim"), + "closure_mse": metrics.get("closure_mse"), + }, + "vlm": resp, + } + (metrics_path.parent / "vlm_score.json").write_text(json.dumps(out, ensure_ascii=False, indent=2), encoding="utf-8") + return out + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--stage1-root", required=True) + ap.add_argument("--api-base", default=os.environ.get("VLM_API_BASE", "http://127.0.0.1:8000/v1")) + ap.add_argument("--model", default=os.environ.get("VLM_MODEL", "Qwen/Qwen2.5-VL-72B-Instruct")) + ap.add_argument("--timeout", type=int, default=int(os.environ.get("VLM_TIMEOUT", "180"))) + ap.add_argument("--startup-wait-sec", type=int, default=900) + ap.add_argument("--dry-run", action="store_true") + ap.add_argument("--limit", type=int, default=0) + ap.add_argument("--force", action="store_true") + args = ap.parse_args() + + if not args.dry_run and not wait_ready(args.api_base, args.startup_wait_sec): + raise SystemExit(f"VLM server not ready at {args.api_base}") + paths = sorted(Path(args.stage1_root).rglob("stage1_metrics.json")) + rows = [] + for p in paths: + if args.limit and len(rows) >= args.limit: + break + if (p.parent / "vlm_score.json").is_file() and not args.force: + continue + rows.append(score_one(p, args.api_base, args.model, args.timeout, args.dry_run)) + print(f"[vlm] scored {p}") + summary = Path(args.stage1_root) / "vlm_scores_summary.jsonl" + summary.parent.mkdir(parents=True, exist_ok=True) + with summary.open("a", encoding="utf-8") as f: + for row in rows: + f.write(json.dumps(row, ensure_ascii=False) + "\n") + print(f"[vlm] wrote {len(rows)} rows -> {summary}") + + +if __name__ == "__main__": + main() diff --git a/code/eval/v2/revisit_suite/run_one_click_revisit_eval.sh b/code/eval/v2/revisit_suite/run_one_click_revisit_eval.sh new file mode 100644 index 0000000000000000000000000000000000000000..a4a57840fd1b7c9364a40c25906c3232c4d875dc --- /dev/null +++ b/code/eval/v2/revisit_suite/run_one_click_revisit_eval.sh @@ -0,0 +1,123 @@ +#!/bin/bash +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +EVAL_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)" +REPO_ROOT="$(cd "${EVAL_DIR}/../.." && pwd)" +cd "${REPO_ROOT}" + +export PYTHONPATH="${REPO_ROOT}:${ENV_DIR:-${REPO_ROOT}/env}:${EVAL_DIR}:${PYTHONPATH:-}" +export WAN_BASE_MODEL="${WAN_BASE_MODEL:?Set WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B}" +export DATASET_BASE="${DATASET_BASE:-${DATASET_BASE_PATH:-${REPO_ROOT}/data/Context-as-Memory-Dataset}}" +export DATASET_METADATA="${DATASET_METADATA:-${DATASET_BASE}/metadata_full.csv}" +export OOD_DIR="${OOD_DIR:-${REPO_ROOT}/assets/opendomain_revisit}" +export OUTPUTS_ROOT="${OUTPUTS_ROOT:-${REPO_ROOT}/outputs}" + +EVAL_ROOT="${EVAL_ROOT:-${REPO_ROOT}/eval_outputs/revisit_suite_$(date +%Y%m%d_%H%M%S)}" +MANIFEST="${MANIFEST:-${EVAL_ROOT}/manifest.json}" +NUM_GPUS="${NUM_GPUS:-8}" +GPU_IDS="${GPU_IDS:-}" +TRAIN_LIMIT="${TRAIN_LIMIT:-8}" +OOD_LIMIT="${OOD_LIMIT:-8}" +MODES="${MODES:-rot180_4chunk,rot360_8chunk,right45_return_2chunk}" +PHASE="${PHASE:-all}" # all | stage1 | vlm +SUMMARY_DIR_NAME="${SUMMARY_DIR_NAME:-revisit_eval_summary}" +SHOW_PROGRESS="${SHOW_PROGRESS:-1}" +PROGRESS_INTERVAL="${PROGRESS_INTERVAL:-60}" + +mkdir -p "${EVAL_ROOT}" + +python3 "${SCRIPT_DIR}/prepare_eval_manifest.py" \ + --outputs-root "${OUTPUTS_ROOT}" \ + --dataset-base "${DATASET_BASE}" \ + --metadata "${DATASET_METADATA}" \ + --ood-dir "${OOD_DIR}" \ + --train-limit "${TRAIN_LIMIT}" \ + --ood-limit "${OOD_LIMIT}" \ + --out "${MANIFEST}" \ + ${INCLUDE_DYNMEMBENCH:+--include-dynmembench} + +if [ "${PHASE}" = "all" ] || [ "${PHASE}" = "stage1" ]; then + if [ -n "${GPU_IDS}" ]; then + IFS=',' read -r -a GPU_ID_LIST <<< "${GPU_IDS}" + NUM_GPUS="${#GPU_ID_LIST[@]}" + else + GPU_ID_LIST=() + for rank in $(seq 0 $((NUM_GPUS - 1))); do + GPU_ID_LIST+=("${rank}") + done + fi + IFS=',' read -r -a MODE_LIST <<< "${MODES}" + TOTAL_STAGE1_CASES="$(python3 - "${MANIFEST}" "${#MODE_LIST[@]}" <<'PY' +import json, sys +from pathlib import Path +m = json.loads(Path(sys.argv[1]).read_text(encoding="utf-8")) +print(len(m.get("ckpts", [])) * len(m.get("samples", [])) * int(sys.argv[2])) +PY +)" + pids=() + for rank in $(seq 0 $((NUM_GPUS - 1))); do + ( + export CUDA_VISIBLE_DEVICES="${GPU_ID_LIST[$rank]}" + python3 "${SCRIPT_DIR}/run_revisit_stage1.py" \ + --manifest "${MANIFEST}" \ + --output-root "${EVAL_ROOT}/stage1" \ + --rank "${rank}" \ + --world-size "${NUM_GPUS}" \ + --modes "${MODES}" \ + --base-model "${WAN_BASE_MODEL}" \ + --sigma-shift "${SIGMA_SHIFT:-15}" \ + --num-inference-steps "${NUM_INFERENCE_STEPS:-50}" \ + ${FORCE_STAGE1:+--force} + ) > "${EVAL_ROOT}/stage1_rank${rank}.log" 2>&1 & + pids+=("$!") + done + if [ "${SHOW_PROGRESS}" = "1" ]; then + while true; do + running=0 + for pid in "${pids[@]}"; do + if kill -0 "${pid}" 2>/dev/null; then + running=$((running + 1)) + fi + done + completed="$(python3 - "${EVAL_ROOT}/stage1" <<'PY' +import sys +from pathlib import Path +root = Path(sys.argv[1]) +print(sum(1 for _ in root.rglob("stage1_metrics.json")) if root.exists() else 0) +PY +)" + echo "[one_click_revisit_eval][stage1] progress ${completed}/${TOTAL_STAGE1_CASES} cases, running_ranks=${running}/${NUM_GPUS}, logs=${EVAL_ROOT}/stage1_rank*.log" + if [ "${running}" -eq 0 ]; then + break + fi + sleep "${PROGRESS_INTERVAL}" + done + fi + failed=0 + for pid in "${pids[@]}"; do + wait "${pid}" || failed=1 + done + if [ "${failed}" -ne 0 ]; then + echo "[one_click_revisit_eval][ERROR] one or more stage1 ranks failed. Check ${EVAL_ROOT}/stage1_rank*.log" >&2 + exit 1 + fi +fi + +if [ "${PHASE}" = "all" ] || [ "${PHASE}" = "vlm" ]; then + python3 "${SCRIPT_DIR}/qwen_vlm_score.py" \ + --stage1-root "${EVAL_ROOT}/stage1" \ + --api-base "${VLM_API_BASE:-http://127.0.0.1:8000/v1}" \ + --model "${VLM_MODEL:-Qwen/Qwen2.5-VL-72B-Instruct}" \ + ${VLM_DRY_RUN:+--dry-run} \ + ${FORCE_VLM:+--force} +fi + +if [ "${PHASE}" = "all" ] || [ "${PHASE}" = "stage1" ] || [ "${PHASE}" = "summary" ] || [ "${PHASE}" = "vlm" ]; then + python3 "${SCRIPT_DIR}/summarize_revisit_results.py" \ + --stage1-root "${EVAL_ROOT}/stage1" \ + --manifest "${MANIFEST}" \ + --out-dir-name "${SUMMARY_DIR_NAME}" +fi + +echo "[one_click_revisit_eval] done: ${EVAL_ROOT}" diff --git a/code/eval/v2/revisit_suite/run_revisit_stage1.py b/code/eval/v2/revisit_suite/run_revisit_stage1.py new file mode 100644 index 0000000000000000000000000000000000000000..593318235d3371a720e3000bb947f063943f7e9f --- /dev/null +++ b/code/eval/v2/revisit_suite/run_revisit_stage1.py @@ -0,0 +1,544 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import json +import math +import os +import random +import sys +import time +from pathlib import Path +from typing import Any + +import numpy as np +import torch +from PIL import Image, ImageChops, ImageDraw, ImageFont +from safetensors import safe_open + +EVAL_DIR = Path(__file__).resolve().parents[1] +REPO_ROOT = Path(__file__).resolve().parents[3] +ENV_DIR = REPO_ROOT / "env" +sys.path.insert(0, str(REPO_ROOT)) +sys.path.insert(0, str(ENV_DIR)) +sys.path.insert(0, str(EVAL_DIR)) + +import loop_utils as irc # noqa: E402 +import memory_baseline_runtime as mbr # noqa: E402 +from diffsynth import save_video # noqa: E402 +from run_replay_loop_two_chunk import ( # noqa: E402 + _frame_to_pil, + encode_context_frames_per_frame, + replay_context_from_generated_frames, + run_one_chunk, +) + +from src.model_training.fov_retrieval import convert_rt_to_relative, load_camera_poses_batch, pose_to_rt # noqa: E402 +from src.model_training.fov_retrieval import retrieve_context_frames_advanced # noqa: E402 +from src.model_training.multichunk_sample_utils import ( # noqa: E402 + load_prev_chunk_tail_rt_actions, + replay_context_actions_from_segment_actions, +) + + +def infer_camera_inject_mode_label(ckpt: str, override: str | None = None) -> str: + if override: + return override + lower = (ckpt or "").lower() + for mode in ("pre_qkv_post", "pre_qkv", "pre_norm", "post"): + if mode.replace("_", "") in lower or mode in lower: + return mode + return os.environ.get("CAMERA_INJECT_MODE", "pre_qkv") + + +def _rotation_action(yaw_deg: float, chunk_frames: int) -> dict[str, list[float]]: + out = {} + denom = max(1, chunk_frames - 1) + for i in range(chunk_frames): + yaw = (i / denom) * float(yaw_deg) + rad = math.radians(yaw) + c, s = math.cos(rad), math.sin(rad) + out[str(i)] = [0.0, 0.0, 0.0, c, -s, 0.0, s, c, 0.0, 0.0, 0.0, 1.0] + return out + + +def _translation_action(dx: float, dy: float, chunk_frames: int) -> dict[str, list[float]]: + out = {} + denom = max(1, chunk_frames - 1) + rot = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + for i in range(chunk_frames): + t = i / denom + out[str(i)] = [dx * t, dy * t, 0.0] + rot + return out + + +def build_actions(mode: str, out_dir: Path, chunk_frames: int, seed: int) -> list[Path]: + out_dir.mkdir(parents=True, exist_ok=True) + rng = random.Random(seed) + if mode == "rot180_4chunk": + yaws = [45.0, 45.0, -45.0, -45.0] + actions = [_rotation_action(y, chunk_frames) for y in yaws] + elif mode == "rot360_8chunk": + yaws = [45.0] * 8 + actions = [_rotation_action(y, chunk_frames) for y in yaws] + elif mode == "right45_return_2chunk": + # Two-chunk memory probe: turn right 45 degrees, then turn back. + # The second chunk is the memory-sensitive segment. + actions = [_rotation_action(-45.0, chunk_frames), _rotation_action(45.0, chunk_frames)] + elif mode == "random_closed": + yaw = rng.uniform(20.0, 60.0) + dx = rng.uniform(-0.12, 0.12) + dy = rng.uniform(0.06, 0.18) + actions = [ + _rotation_action(yaw, chunk_frames), + _translation_action(dx, dy, chunk_frames), + _rotation_action(-yaw, chunk_frames), + _translation_action(-dx, -dy, chunk_frames), + ] + else: + raise ValueError(f"unknown mode: {mode}") + paths = [] + for i, action in enumerate(actions): + p = out_dir / f"chunk{i:02d}_{mode}.json" + p.write_text(json.dumps(action, indent=2), encoding="utf-8") + paths.append(p) + return paths + + +def _resize(img: Image.Image, width: int, height: int) -> Image.Image: + return img.convert("RGB").resize((width, height), Image.Resampling.LANCZOS) + + +def _mse(a: np.ndarray, b: np.ndarray) -> float: + d = a.astype(np.float64) - b.astype(np.float64) + return float(np.mean(d * d)) + + +def _psnr(mse: float) -> float: + return 100.0 if mse <= 0 else float(10.0 * np.log10((255.0 * 255.0) / mse)) + + +def _ssim(a: np.ndarray, b: np.ndarray) -> float | None: + try: + from skimage.metrics import structural_similarity + + return float(structural_similarity(a, b, channel_axis=2, data_range=255)) + except Exception: + return None + + +def _save_frames(frames: list[Image.Image], out_dir: Path, prefix: str) -> list[str]: + out_dir.mkdir(parents=True, exist_ok=True) + paths = [] + for i, img in enumerate(frames): + p = out_dir / f"{prefix}_{i:02d}.png" + img.save(p) + paths.append(str(p)) + return paths + + +def _load_font(size: int = 18): + try: + return ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", size) + except Exception: + return ImageFont.load_default() + + +def _save_first_last_chunk_changes( + chunks: list[list[Any]], + out_dir: Path, + width: int, + height: int, + max_frames: int = 0, +) -> list[str]: + if len(chunks) < 2: + return [] + first = [_frame_to_pil(f, width, height) for f in chunks[0]] + last = [_frame_to_pil(f, width, height) for f in chunks[-1]] + n = min(len(first), len(last)) + if max_frames and max_frames > 0: + n = min(n, int(max_frames)) + out_dir.mkdir(parents=True, exist_ok=True) + font = _load_font(18) + paths = [] + for i in range(n): + a = first[i].convert("RGB").resize((width, height)) + b = last[i].convert("RGB").resize((width, height)) + diff = ImageChops.difference(a, b) + canvas = Image.new("RGB", (width * 3, height), "black") + canvas.paste(a, (0, 0)) + canvas.paste(b, (width, 0)) + canvas.paste(diff, (width * 2, 0)) + draw = ImageDraw.Draw(canvas) + labels = ("first chunk", "last chunk", "abs diff") + for j, label in enumerate(labels): + x0 = j * width + draw.rectangle([(x0, 0), (x0 + 180, 26)], fill=(0, 0, 0)) + draw.text((x0 + 6, 3), f"{label} f{i:02d}", fill=(255, 255, 255), font=font) + p = out_dir / f"first_vs_last_{i:03d}.png" + canvas.save(p) + paths.append(str(p)) + return paths + + +def inspect_ckpt_keys(ckpt: str) -> dict[str, Any]: + tokens = { + "camera_encoder": "camera_encoder", + "separate": "separate", + "seperate": "seperate", + "action_mlp": "action_mlp", + "self_attn_with_action": "self_attn_with_action", + "spatial_memory_module": "spatial_memory_module", + "block_wise_ssm": "block_wise_ssm", + "videossm_hybrid": "videossm_hybrid", + "ssm": "ssm", + } + counts = {name: 0 for name in tokens} + try: + with safe_open(str(ckpt), framework="pt", device="cpu") as f: + for key in f.keys(): + for name, token in tokens.items(): + if token in key: + counts[name] += 1 + except Exception as exc: + return {"inspect_error": f"{type(exc).__name__}: {exc}", **counts} + return { + **counts, + "has_camera_encoder_keys": counts["camera_encoder"] > 0, + "has_separate_rt_keys": counts["separate"] > 0 or counts["seperate"] > 0, + "has_action_mlp_keys": counts["action_mlp"] > 0, + "has_action_attention_keys": counts["self_attn_with_action"] > 0, + "has_spatial_memory_module_keys": counts["spatial_memory_module"] > 0, + "has_block_wise_ssm_keys": counts["block_wise_ssm"] > 0, + "has_videossm_hybrid_keys": counts["videossm_hybrid"] > 0, + "has_ssm_keys": counts["ssm"] > 0, + } + + +def infer_training_memory_source(profile_id: str, ckpt: str) -> str: + lower = (ckpt or "").lower() + if profile_id.startswith("ctx"): + return "fov" + if "framepack" in lower: + return "prev_chunk_tail" + if "spatial_mem" in lower: + return "prev_chunk_tail" + if "block_wise_ssm" in lower or "two_chunk" in lower: + return "replay_synthetic" + return "first_frame" + + +def _identity_rt() -> list[float]: + return [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + + +def _load_segment_frames(sample: dict[str, Any], width: int, height: int, num_frames: int) -> list[Image.Image]: + dataset_base = sample.get("dataset_base") + video_name = sample.get("video_name") + start_frame = int(sample.get("start_frame") or 0) + if not dataset_base or not video_name: + return [] + frames = [] + frames_dir = Path(dataset_base) / "frames" / str(video_name) + for i in range(num_frames): + p = frames_dir / f"{start_frame + i:04d}.png" + if not p.is_file(): + break + frames.append(_resize(Image.open(p), width, height)) + return frames + + +def _load_segment_actions(sample: dict[str, Any], num_frames: int) -> list[list[float]]: + dataset_base = sample.get("dataset_base") + video_name = sample.get("video_name") + start_frame = int(sample.get("start_frame") or 0) + if not dataset_base or not video_name: + return [] + json_path = Path(dataset_base) / "jsons" / f"{video_name}.json" + if not json_path.is_file(): + return [] + poses = load_camera_poses_batch(str(json_path), [start_frame + i for i in range(num_frames)]) + rts = [pose_to_rt(p) if p else None for p in poses] + if not rts or rts[0] is None: + return [] + valid = [rt if rt is not None else rts[0] for rt in rts] + return convert_rt_to_relative(valid, valid[0]) + + +def build_initial_context( + pipe, + sample: dict[str, Any], + profile_id: str, + ckpt: str, + context_frames: int, + first_frame: Image.Image, + width: int, + height: int, + chunk_frames: int, +) -> tuple[Any, torch.Tensor, dict[str, Any]]: + source = infer_training_memory_source(profile_id, ckpt) + ctx_pil: list[Image.Image] = [first_frame] + ctx_actions: list[list[float]] = [_identity_rt()] + ctx_indices: list[int] = [int(sample.get("start_frame") or 0)] + ctx_source_detail = "first_frame_fallback" + + if sample.get("domain") == "train" and sample.get("dataset_base") and sample.get("video_name"): + data = { + "video": _load_segment_frames(sample, width, height, chunk_frames), + "video_name": sample.get("video_name"), + "start_frame": int(sample.get("start_frame") or 0), + "end_frame": int(sample.get("end_frame") or (int(sample.get("start_frame") or 0) + chunk_frames - 1)), + } + try: + if source == "fov": + # Training used FOV retrieval with top_k=context_frames-1 and 10% random drop. + # Eval disables random drop for deterministic full-memory in-domain measurement. + cf, ca, ci, _cur, _vn, detail = retrieve_context_frames_advanced( + data=data, + dataset_base_path=str(sample["dataset_base"]), + top_k=max(0, int(context_frames) - 1), + drop_overlap_probability=0.0, + use_rt_relative=True, + retrieval_method="fov", + ) + if cf: + ctx_pil = [_resize(x, width, height) for x in cf[:context_frames]] + ctx_actions = [list(x) for x in (ca or [])[: len(ctx_pil)]] + ctx_indices = [int(x) for x in (ci or [])[: len(ctx_pil)]] + ctx_source_detail = detail + elif source == "prev_chunk_tail": + from src.model_training.multichunk_sample_utils import load_prev_chunk_tail_from_disk + + cf, ci = load_prev_chunk_tail_from_disk( + str(sample["dataset_base"]), + str(sample["video_name"]), + int(sample.get("start_frame") or 0), + int(context_frames), + nearest_first=False, + ) + ca, _ = load_prev_chunk_tail_rt_actions( + str(sample["dataset_base"]), + str(sample["video_name"]), + int(sample.get("start_frame") or 0), + int(context_frames), + use_rt_relative=True, + nearest_first=False, + ) + if cf and ca: + ctx_pil = [_resize(x, width, height) for x in cf] + ctx_actions = [list(x) for x in ca] + ctx_indices = [int(x) for x in (ci or [])] + ctx_source_detail = "prev_chunk_tail_from_dataset" + elif source == "replay_synthetic": + seg_frames = data["video"] + seg_actions = _load_segment_actions(sample, chunk_frames) + if seg_frames: + replay = replay_context_from_generated_frames(seg_frames, int(context_frames)) + acts = replay_context_actions_from_segment_actions(seg_actions, chunk_frames, int(context_frames)) if seg_actions else None + ctx_pil = [_resize(x, width, height) for x in replay] + ctx_actions = [list(x) for x in (acts or [_identity_rt()] * len(ctx_pil))] + ctx_indices = [] + ctx_source_detail = "replay_synthetic_from_dataset_gt_segment" + except Exception as exc: + ctx_source_detail = f"{source}_failed_fallback_first_frame:{type(exc).__name__}:{exc}" + + if len(ctx_actions) < len(ctx_pil): + ctx_actions = ctx_actions + [_identity_rt()] * (len(ctx_pil) - len(ctx_actions)) + if len(ctx_pil) > context_frames: + ctx_pil = ctx_pil[:context_frames] + ctx_actions = ctx_actions[:context_frames] + ctx_indices = ctx_indices[:context_frames] + + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + ctx_latents = encode_context_frames_per_frame(pipe, ctx_pil, pipe.device) + ctx_actions_t = torch.tensor(ctx_actions[: ctx_latents.shape[2]], dtype=torch.float32) + meta = { + "training_memory_source": source, + "context_source_detail": ctx_source_detail, + "context_frame_count": len(ctx_pil), + "context_action_count": len(ctx_actions), + "context_indices": ctx_indices, + } + return ctx_latents, ctx_actions_t, meta + + +def run_case(args: argparse.Namespace, ckpt_row: dict[str, Any], sample: dict[str, Any], mode: str, rank: int) -> None: + ckpt = ckpt_row["ckpt"] + run_id = ckpt_row.get("run_id") or Path(ckpt).parent.name + sample_id = sample["sample_id"] + out_dir = Path(args.output_root) / run_id / sample["domain"] / sample_id / mode + if (out_dir / "stage1_metrics.json").is_file() and not args.force: + print(f"[stage1] skip existing {out_dir}") + return + out_dir.mkdir(parents=True, exist_ok=True) + + base_model = args.base_model or os.environ.get("WAN_BASE_MODEL") or "" + if not base_model: + raise SystemExit("Set WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B or pass --base-model") + load_kw = dict( + add_action_attn=True, + action_use_temporal_attention=True, + ) + pipe = irc.load_pipeline_and_ckpt( + ckpt, + f"{base_model}/diffusion_pytorch_model.safetensors", + f"{base_model}/models_t5_umt5-xxl-enc-bf16.pth", + f"{base_model}/Wan2.1_VAE.pth", + **load_kw, + ) + mbr.apply_memory_baseline_pipe(pipe, ckpt) + context_frames = args.context_frames or mbr.effective_context_frames(ckpt, None) + profile = mbr.infer_memory_profile_spec(ckpt) + camera_inject_mode_label = infer_camera_inject_mode_label(ckpt, args.camera_inject_mode or None) + ckpt_key_evidence = inspect_ckpt_keys(ckpt) + spatial_module_loaded = getattr(pipe, "spatial_memory_module", None) is not None + + first_frame = _resize(Image.open(sample["first_frame_image"]), args.width, args.height) + prompt = sample.get("prompt") or "A scene." + action_paths = build_actions(mode, out_dir / "actions", args.chunk_frames, args.seed + rank) + ctx_latents, ctx_actions_t, context_meta = build_initial_context( + pipe, + sample, + profile.profile_id if profile else "default_context", + ckpt, + context_frames, + first_frame, + args.width, + args.height, + args.chunk_frames, + ) + identity_rt = _identity_rt() + + chunks = [] + times = [] + neg = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") + for ch, action_path in enumerate(action_paths): + t0 = time.time() + frames = run_one_chunk( + pipe, + prompt, + neg, + str(action_path), + context_latents=ctx_latents, + num_context_frames=ctx_latents.shape[2], + context_actions_t=ctx_actions_t, + chunk_frames=args.chunk_frames, + h=args.height, + w=args.width, + seed=args.seed + ch + rank * 1000, + sigma_shift=args.sigma_shift, + num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, + inference_noise_level=0.0, + omit_context_actions=False, + log_prefix=f"[revisit_suite][{mode}][rank{rank}]", + ) + times.append(time.time() - t0) + chunks.append(frames) + if ch < len(action_paths) - 1: + n_ctx = min(context_frames, len(frames)) + prev = [_frame_to_pil(f, args.width, args.height) for f in replay_context_from_generated_frames(frames, n_ctx)] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + ctx_latents = encode_context_frames_per_frame(pipe, prev, pipe.device) + ctx_actions_t = torch.tensor([identity_rt] * ctx_latents.shape[2], dtype=torch.float32) + + all_frames = [f for chunk in chunks for f in chunk] + save_video(all_frames, str(out_dir / "revisit_gen_only.mp4"), fps=15, quality=5) + first_np = np.array(first_frame, dtype=np.uint8) + last_pil = _frame_to_pil(all_frames[-1], args.width, args.height) + last_np = np.array(last_pil, dtype=np.uint8) + mse = _mse(first_np, last_np) + ssim = _ssim(first_np, last_np) + + stage1_dir = out_dir / "stage1_frames" + first_paths = _save_frames([first_frame], stage1_dir, "first") + tail_imgs = [_frame_to_pil(f, args.width, args.height) for f in all_frames[-args.save_tail_frames :]] + tail_paths = _save_frames(tail_imgs, stage1_dir, "revisit_tail") + chunk_change_paths = [] + if args.save_chunk_change_frames: + chunk_change_paths = _save_first_last_chunk_changes( + chunks, + stage1_dir / "first_last_chunk_changes", + args.width, + args.height, + max_frames=args.max_chunk_change_frames, + ) + metrics = { + "ckpt": ckpt, + "run_id": run_id, + "sample": sample, + "mode": mode, + "rank": rank, + "num_chunks": len(action_paths), + "context_frames": context_frames, + "alignment": { + "action_injection_impl": "DiTBlock_w_Action + MLP_CamPose(Linear(12,D)); action embedding is added before norm1/self-attn", + "camera_inject_mode_label_from_path": camera_inject_mode_label, + "camera_inject_mode_effective": "ignored_by_current_vwm_loader", + "camera_encoder_separate_t_r_effective": False, + "camera_encoder_effective": False, + "rt_encoding_effective": "single_linear_12d_rt", + "ckpt_key_evidence": ckpt_key_evidence, + "memory_profile": profile.profile_id if profile else "default_context", + "memory_profile_matched": bool(profile), + "use_framepack_memory": bool(getattr(pipe, "use_framepack_memory", False)), + "use_framepack_length_compress": bool(getattr(pipe, "use_framepack_length_compress", False)), + "framepack_ratio": int(getattr(pipe, "framepack_ratio", 2) or 2), + "use_spatial_memory": bool(getattr(pipe, "use_spatial_memory", False)), + "use_spatial_memory_legacy": bool(getattr(pipe, "use_spatial_memory_legacy", False)), + "spatial_memory_module_loaded": spatial_module_loaded, + "spatial_memory_effective_impl": "SpatialGridMemory" if spatial_module_loaded else ("legacy_adaptive_pool" if getattr(pipe, "use_spatial_memory", False) else "disabled"), + "spatial_memory_tokens": int(getattr(pipe, "spatial_memory_tokens", 64) or 64), + "spatial_memory_inject_mode": getattr(pipe, "spatial_memory_inject_mode", None), + "initial_context": context_meta, + }, + "closure_mse": mse, + "closure_psnr": _psnr(mse), + "closure_ssim": ssim, + "first_frame_paths": first_paths, + "revisit_tail_paths": tail_paths, + "first_last_chunk_change_paths": chunk_change_paths, + "video_path": str(out_dir / "revisit_gen_only.mp4"), + "chunk_seconds": times, + } + (out_dir / "stage1_metrics.json").write_text(json.dumps(metrics, ensure_ascii=False, indent=2), encoding="utf-8") + print(f"[stage1] wrote {out_dir}") + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--manifest", required=True) + ap.add_argument("--output-root", required=True) + ap.add_argument("--rank", type=int, default=int(os.environ.get("RANK", "0"))) + ap.add_argument("--world-size", type=int, default=int(os.environ.get("WORLD_SIZE", "1"))) + ap.add_argument("--modes", default="rot180_4chunk,rot360_8chunk,right45_return_2chunk") + ap.add_argument("--base-model", default=os.environ.get("WAN_BASE_MODEL", "")) + ap.add_argument("--camera-inject-mode", default="") + ap.add_argument("--context-frames", type=int, default=0) + ap.add_argument("--chunk-frames", type=int, default=81) + ap.add_argument("--height", type=int, default=352) + ap.add_argument("--width", type=int, default=640) + ap.add_argument("--sigma-shift", type=float, default=15.0) + ap.add_argument("--num-inference-steps", type=int, default=50) + ap.add_argument("--cfg-scale", type=float, default=5.0) + ap.add_argument("--seed", type=int, default=42) + ap.add_argument("--save-tail-frames", type=int, default=4) + ap.add_argument("--save-chunk-change-frames", action=argparse.BooleanOptionalAction, default=True) + ap.add_argument("--max-chunk-change-frames", type=int, default=0, help="0 = save every frame; otherwise cap first-vs-last chunk visualizations") + ap.add_argument("--force", action="store_true") + args = ap.parse_args() + + manifest = json.loads(Path(args.manifest).read_text(encoding="utf-8")) + ckpts = manifest.get("ckpts", []) + samples = manifest.get("samples", []) + jobs = [(c, s, m) for c in ckpts for s in samples for m in args.modes.split(",") if m] + shard = [job for i, job in enumerate(jobs) if i % max(1, args.world_size) == args.rank] + print(f"[stage1] rank={args.rank}/{args.world_size} jobs={len(shard)}/{len(jobs)}") + for ckpt_row, sample, mode in shard: + run_case(args, ckpt_row, sample, mode, args.rank) + + +if __name__ == "__main__": + main() diff --git a/code/eval/v2/revisit_suite/summarize_revisit_results.py b/code/eval/v2/revisit_suite/summarize_revisit_results.py new file mode 100644 index 0000000000000000000000000000000000000000..993973b4940c036d90d2409f35466a0f6103a38e --- /dev/null +++ b/code/eval/v2/revisit_suite/summarize_revisit_results.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import csv +import json +from collections import defaultdict +from pathlib import Path +from typing import Any + + +def _load_json(path: Path) -> dict[str, Any]: + return json.loads(path.read_text(encoding="utf-8")) + + +def _flat_row(stage1_path: Path) -> dict[str, Any]: + m = _load_json(stage1_path) + vlm_path = stage1_path.parent / "vlm_score.json" + vlm = _load_json(vlm_path).get("vlm", {}) if vlm_path.is_file() else {} + sample = m.get("sample") or {} + align = m.get("alignment") or {} + key_ev = align.get("ckpt_key_evidence") or {} + init_ctx = align.get("initial_context") or {} + row = { + "run_id": m.get("run_id"), + "ckpt": m.get("ckpt"), + "domain": sample.get("domain"), + "sample_id": sample.get("sample_id"), + "video_name": sample.get("video_name"), + "start_frame": sample.get("start_frame"), + "mode": m.get("mode"), + "num_chunks": m.get("num_chunks"), + "context_frames": m.get("context_frames"), + "action_injection_impl": align.get("action_injection_impl"), + "camera_inject_mode_label_from_path": align.get("camera_inject_mode_label_from_path") or align.get("camera_inject_mode"), + "camera_inject_mode_effective": align.get("camera_inject_mode_effective"), + "camera_encoder_separate_t_r_effective": align.get("camera_encoder_separate_t_r_effective"), + "camera_encoder_effective": align.get("camera_encoder_effective"), + "rt_encoding_effective": align.get("rt_encoding_effective"), + "ckpt_has_camera_encoder_keys": key_ev.get("has_camera_encoder_keys"), + "ckpt_has_separate_rt_keys": key_ev.get("has_separate_rt_keys"), + "ckpt_has_action_mlp_keys": key_ev.get("has_action_mlp_keys"), + "ckpt_has_action_attention_keys": key_ev.get("has_action_attention_keys"), + "ckpt_has_spatial_memory_module_keys": key_ev.get("has_spatial_memory_module_keys"), + "ckpt_has_block_wise_ssm_keys": key_ev.get("has_block_wise_ssm_keys"), + "ckpt_has_videossm_hybrid_keys": key_ev.get("has_videossm_hybrid_keys"), + "ckpt_has_ssm_keys": key_ev.get("has_ssm_keys"), + "memory_profile": align.get("memory_profile"), + "memory_profile_matched": align.get("memory_profile_matched"), + "use_framepack_memory": align.get("use_framepack_memory"), + "use_framepack_length_compress": align.get("use_framepack_length_compress"), + "framepack_ratio": align.get("framepack_ratio"), + "use_spatial_memory": align.get("use_spatial_memory"), + "use_spatial_memory_legacy": align.get("use_spatial_memory_legacy"), + "spatial_memory_module_loaded": align.get("spatial_memory_module_loaded"), + "spatial_memory_effective_impl": align.get("spatial_memory_effective_impl"), + "spatial_memory_tokens": align.get("spatial_memory_tokens"), + "spatial_memory_inject_mode": align.get("spatial_memory_inject_mode"), + "initial_context_source": init_ctx.get("training_memory_source"), + "initial_context_detail": init_ctx.get("context_source_detail"), + "initial_context_frame_count": init_ctx.get("context_frame_count"), + "initial_context_action_count": init_ctx.get("context_action_count"), + "initial_context_indices": "|".join(str(x) for x in (init_ctx.get("context_indices") or [])), + "closure_mse": m.get("closure_mse"), + "closure_psnr": m.get("closure_psnr"), + "closure_ssim": m.get("closure_ssim"), + "video_path": m.get("video_path"), + "first_frame_paths": "|".join(m.get("first_frame_paths") or []), + "revisit_tail_paths": "|".join(m.get("revisit_tail_paths") or []), + "first_last_chunk_change_paths": "|".join(m.get("first_last_chunk_change_paths") or []), + "vlm_overall_score": vlm.get("overall_score"), + "vlm_bear_appearance_score": vlm.get("bear_appearance_score"), + "vlm_bear_presence_score": vlm.get("bear_presence_score"), + "vlm_scene_consistency_score": vlm.get("scene_consistency_score"), + "vlm_view_revisit_score": vlm.get("view_revisit_score"), + "vlm_verdict": vlm.get("verdict"), + "vlm_reason": vlm.get("reason"), + } + return row + + +def _write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if not rows: + path.write_text("", encoding="utf-8") + return + fields = list(rows[0].keys()) + with path.open("w", newline="", encoding="utf-8") as f: + w = csv.DictWriter(f, fieldnames=fields) + w.writeheader() + for row in rows: + w.writerow(row) + + +def _mean(values: list[Any]) -> float | None: + xs = [] + for v in values: + try: + if v is not None: + xs.append(float(v)) + except Exception: + pass + return sum(xs) / len(xs) if xs else None + + +def aggregate(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: + groups: dict[tuple, list[dict[str, Any]]] = defaultdict(list) + for r in rows: + groups[(r.get("run_id"), r.get("domain"), r.get("mode"))].append(r) + out = [] + for (run_id, domain, mode), rs in sorted(groups.items()): + out.append( + { + "run_id": run_id, + "domain": domain, + "mode": mode, + "num_cases": len(rs), + "mean_closure_psnr": _mean([r.get("closure_psnr") for r in rs]), + "mean_closure_ssim": _mean([r.get("closure_ssim") for r in rs]), + "mean_closure_mse": _mean([r.get("closure_mse") for r in rs]), + "mean_vlm_overall_score": _mean([r.get("vlm_overall_score") for r in rs]), + "mean_vlm_bear_appearance_score": _mean([r.get("vlm_bear_appearance_score") for r in rs]), + "mean_vlm_bear_presence_score": _mean([r.get("vlm_bear_presence_score") for r in rs]), + "mean_vlm_scene_consistency_score": _mean([r.get("vlm_scene_consistency_score") for r in rs]), + "mean_vlm_view_revisit_score": _mean([r.get("vlm_view_revisit_score") for r in rs]), + "memory_profile": rs[0].get("memory_profile"), + "action_injection_impl": rs[0].get("action_injection_impl"), + "camera_inject_mode_label_from_path": rs[0].get("camera_inject_mode_label_from_path"), + "camera_inject_mode_effective": rs[0].get("camera_inject_mode_effective"), + "rt_encoding_effective": rs[0].get("rt_encoding_effective"), + "ckpt_has_camera_encoder_keys": rs[0].get("ckpt_has_camera_encoder_keys"), + "ckpt_has_separate_rt_keys": rs[0].get("ckpt_has_separate_rt_keys"), + "ckpt_has_action_mlp_keys": rs[0].get("ckpt_has_action_mlp_keys"), + "ckpt_has_action_attention_keys": rs[0].get("ckpt_has_action_attention_keys"), + "ckpt_has_spatial_memory_module_keys": rs[0].get("ckpt_has_spatial_memory_module_keys"), + "ckpt_has_block_wise_ssm_keys": rs[0].get("ckpt_has_block_wise_ssm_keys"), + "ckpt_has_videossm_hybrid_keys": rs[0].get("ckpt_has_videossm_hybrid_keys"), + "ckpt_has_ssm_keys": rs[0].get("ckpt_has_ssm_keys"), + "context_frames": rs[0].get("context_frames"), + "initial_context_source": rs[0].get("initial_context_source"), + "initial_context_detail": rs[0].get("initial_context_detail"), + "initial_context_frame_count": rs[0].get("initial_context_frame_count"), + } + ) + return out + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--stage1-root", required=True) + ap.add_argument("--manifest", required=True) + ap.add_argument("--out-dir-name", default="revisit_eval_summary") + args = ap.parse_args() + + stage1_root = Path(args.stage1_root) + manifest = _load_json(Path(args.manifest)) + ckpt_by_run = {row["run_id"]: Path(row["ckpt"]) for row in manifest.get("ckpts", [])} + + rows = [_flat_row(p) for p in sorted(stage1_root.rglob("stage1_metrics.json"))] + agg_rows = aggregate(rows) + _write_csv(stage1_root / "all_cases.csv", rows) + _write_csv(stage1_root / "aggregate_by_model_domain_mode.csv", agg_rows) + (stage1_root / "all_cases.json").write_text(json.dumps(rows, ensure_ascii=False, indent=2), encoding="utf-8") + (stage1_root / "aggregate_by_model_domain_mode.json").write_text(json.dumps(agg_rows, ensure_ascii=False, indent=2), encoding="utf-8") + + by_run: dict[str, list[dict[str, Any]]] = defaultdict(list) + for r in rows: + by_run[str(r.get("run_id"))].append(r) + for run_id, rs in by_run.items(): + ckpt = ckpt_by_run.get(run_id) + if not ckpt: + continue + out_dir = ckpt.parent / args.out_dir_name + ars = aggregate(rs) + _write_csv(out_dir / "cases.csv", rs) + _write_csv(out_dir / "aggregate.csv", ars) + (out_dir / "cases.json").write_text(json.dumps(rs, ensure_ascii=False, indent=2), encoding="utf-8") + (out_dir / "aggregate.json").write_text(json.dumps(ars, ensure_ascii=False, indent=2), encoding="utf-8") + print(f"[summary] cases={len(rows)} aggregates={len(agg_rows)} stage1_root={stage1_root}") + + +if __name__ == "__main__": + main() diff --git a/code/eval/v2/run_basic_replay_gt.sh b/code/eval/v2/run_basic_replay_gt.sh new file mode 100644 index 0000000000000000000000000000000000000000..a157db26c85ca70c0ba975366382e616e82472e3 --- /dev/null +++ b/code/eval/v2/run_basic_replay_gt.sh @@ -0,0 +1,139 @@ +#!/bin/bash +# Basic capability（可选）：单条 GT 轨迹 replay。质量评测主路径见 static 中 long_horizon_gt_replay（多 chunk）。 +# 默认 NUM_CHUNKS 与 NUM_CHUNKS_LONG（默认 3）对齐；单 chunk 不足以表征跨 chunk 记忆/误差累积。 +# 与 eval_v2 static 对齐：PYTHONPATH、ctx、CAMERA_INJECT_MODE、MEM_ARGS。 +# VIDEO_NAME: +# - 显式设置时，要求该 video 在 DATASET/jsons 下有可用 GT pose（按 START_FRAME/NUM_CHUNKS/CHUNK_FRAMES 检查） +# - 未设置时（或 VIDEO_NAME=AUTO），优先 AncientTempleEnv_0；不可用则自动回退到首个可用 video +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +EVAL_DIR="${SCRIPT_DIR}" +REPO_ROOT="$(cd "${EVAL_DIR}/../.." && pwd)" +ENV_DIR="${REPO_ROOT}/env" +# shellcheck disable=SC1091 +[ -f "${REPO_ROOT}/env/eval_infer_alignment_env.sh" ] && source "${REPO_ROOT}/env/eval_infer_alignment_env.sh" +cd "${REPO_ROOT}" || exit 1 +export PYTHONPATH="${REPO_ROOT}:${PYTHONPATH:-}" + +CKPT="${CKPT:?Set CKPT=/path/to/epoch-0.safetensors}" +CKPT_DIR="$(dirname "${CKPT}")" +DATASET="/threed-code/yorenchen/data/echo-memory/Context-as-Memory-Dataset/" + +# --- Early path sanity (before any heavy Python) --- +if [ ! -d "${DATASET}" ]; then + echo "[replay_gt] ERROR: DATASET 不是目录: ${DATASET}" >&2 + exit 1 +fi +if [ ! -d "${DATASET}/jsons" ]; then + echo "[replay_gt] ERROR: 缺少 DATASET/jsons: ${DATASET}/jsons" >&2 + exit 1 +fi +echo "[replay_gt] DATASET=$(cd "${DATASET}" && pwd)" + +VIDEO_NAME="${VIDEO_NAME:-AUTO}" +START_FRAME="${START_FRAME:-0}" +NUM_CHUNKS="${NUM_CHUNKS:-${NUM_CHUNKS_LONG:-3}}" +CHUNK_FRAMES_EFFECTIVE="${CHUNK_FRAMES:-81}" + +_resolve_video_name() { + local wanted="$1" + DATASET="${DATASET}" \ + EVAL_DIR="${EVAL_DIR}" \ + VIDEO_NAME_IN="${wanted}" \ + START_FRAME="${START_FRAME}" \ + NUM_CHUNKS="${NUM_CHUNKS}" \ + CHUNK_FRAMES="${CHUNK_FRAMES_EFFECTIVE}" \ + python3 - <<'PY' +import os +import sys + +dataset = os.environ["DATASET"] +eval_dir = os.environ["EVAL_DIR"] +wanted = os.environ.get("VIDEO_NAME_IN", "").strip() +start = int(os.environ.get("START_FRAME", "0")) +num_chunks = int(os.environ.get("NUM_CHUNKS", "1")) +chunk_frames = int(os.environ.get("CHUNK_FRAMES", "81")) + +# 轻量解析 VIDEO_NAME：勿 import run_replay_loop_two_chunk（会拉 torch/train/flash_attn） +sys.path.insert(0, os.path.join(eval_dir, "basic")) +from gt_pose_minimal import build_gt_trajectory_actions # noqa: E402 + +def valid(vn: str) -> bool: + for ch in range(num_chunks): + seg_start = start + ch * chunk_frames + if build_gt_trajectory_actions(dataset, vn, seg_start, chunk_frames) is None: + return False + return True + +if wanted and wanted.upper() != "AUTO": + print(wanted if valid(wanted) else "") + raise SystemExit(0) + +cands = [] +jsons_dir = os.path.join(dataset, "jsons") +if os.path.isdir(jsons_dir): + for n in sorted(os.listdir(jsons_dir)): + if n.endswith(".json"): + cands.append(os.path.splitext(n)[0]) +preferred = "AncientTempleEnv_0" +if preferred in cands: + cands.remove(preferred) + cands = [preferred] + cands +for vn in cands: + if valid(vn): + print(vn) + raise SystemExit(0) +print("") +PY +} + +RESOLVED_VIDEO_NAME="$(_resolve_video_name "${VIDEO_NAME}")" +if [ -z "${RESOLVED_VIDEO_NAME}" ]; then + if [ -n "${VIDEO_NAME}" ] && [ "${VIDEO_NAME}" != "AUTO" ]; then + echo "[replay_gt] ERROR: VIDEO_NAME=${VIDEO_NAME} 不可用：缺少 GT actions（json 或帧段不足）。" >&2 + else + echo "[replay_gt] ERROR: 未找到可用 video（DATASET/jsons 下无可用 GT actions）。" >&2 + fi + exit 1 +fi +if [ "${VIDEO_NAME}" != "${RESOLVED_VIDEO_NAME}" ]; then + echo "[replay_gt] 自动回退 VIDEO_NAME: ${VIDEO_NAME} -> ${RESOLVED_VIDEO_NAME}" +fi +VIDEO_NAME="${RESOLVED_VIDEO_NAME}" + +python3 "${EVAL_DIR}/basic/check_dataset_gt_for_replay.py" \ + --dataset "${DATASET}" \ + --video "${VIDEO_NAME}" \ + --start_frame "${START_FRAME}" \ + --num_chunks "${NUM_CHUNKS}" \ + --chunk_frames "${CHUNK_FRAMES_EFFECTIVE}" || exit 1 + +OUT_ROOT="${OUT_ROOT:-${CKPT_DIR}/evals_v2/basic}" +OUT_DIR="${OUT_ROOT}/replay_gt/${VIDEO_NAME}_start${START_FRAME}" +mkdir -p "${OUT_DIR}" + +eval "$(python3 "${ENV_DIR}/memory_baseline_runtime.py" bash-export "${CKPT}")" +_default_ctx=1 +[[ "${CKPT}" =~ (ctx_20|context_k20|ctx20) ]] && _default_ctx=20 +[[ "${CKPT}" =~ (ctx_5|context_k5|ctx5) ]] && _default_ctx=5 +[ -n "${CONTEXT_FRAMES_MEM_OVERRIDE:-}" ] && _default_ctx="${CONTEXT_FRAMES_MEM_OVERRIDE}" +CONTEXT_FRAMES_EFFECTIVE="${CONTEXT_FRAMES:-$_default_ctx}" + +python3 "${EVAL_DIR}/basic/replay_gt_error.py" \ + --ckpt "${CKPT}" \ + --dataset_base "${DATASET}" \ + --video_name "${VIDEO_NAME}" \ + --start_frame "${START_FRAME}" \ + --num_chunks "${NUM_CHUNKS}" \ + --chunk_frames "${CHUNK_FRAMES_EFFECTIVE}" \ + --context_frames "${CONTEXT_FRAMES_EFFECTIVE}" \ + --sigma_shift "${SIGMA_SHIFT:-5}" \ + --num_inference_steps "${NUM_INFERENCE_STEPS:-50}" \ + --cfg_scale "${CFG_SCALE:-5.0}" \ + --seed "${SEED:-42}" \ + --output_dir "${OUT_DIR}" \ + --write_csv + +echo "Done. basic replay_gt output: ${OUT_DIR}" + diff --git a/code/eval/v2/run_static_consistency_loop_and_revisit.sh b/code/eval/v2/run_static_consistency_loop_and_revisit.sh new file mode 100644 index 0000000000000000000000000000000000000000..3bf310abba1254287714a27310f76941725673e3 --- /dev/null +++ b/code/eval/v2/run_static_consistency_loop_and_revisit.sh @@ -0,0 +1,351 @@ +#!/bin/bash +# Static consistency (v2): loop closure + composite-action revisit. +# +# Output root defaults to /evals_v2/static_consistency. +# 与 run_evals_ep0_pre_qkv_rt_merge.sh 对齐：PYTHONPATH、ctx 帧数推断、metadata 采样、CAMERA_INJECT_MODE、MEM_ARGS、no_camera_encoder_separate_t_r。 +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +EVAL_DIR="${SCRIPT_DIR}" +REPO_ROOT="${REPO_ROOT:-$(cd "${EVAL_DIR}/../.." && pwd)}" +ENV_DIR="${REPO_ROOT}/env" +# shellcheck disable=SC1091 +[ -f "${REPO_ROOT}/env/eval_infer_alignment_env.sh" ] && source "${REPO_ROOT}/env/eval_infer_alignment_env.sh" + +cd "${REPO_ROOT}" || exit 1 +export PYTHONPATH="${REPO_ROOT}:${PYTHONPATH:-}" + +# Ensure OpenCV runtime dependency exists (libGL.so.1), otherwise mp4 metric reading may fail. +if ! python3 - <<'PY' +import ctypes +ctypes.CDLL("libGL.so.1") +print("ok") +PY +then + echo "[eval_v2] FATAL: libGL.so.1 missing. Install libgl1-mesa-glx or use a headless OpenCV build." >&2 + exit 2 +fi + +CKPT="${CKPT:?Set CKPT=/path/to/epoch-0.safetensors}" +CKPT_DIR="$(dirname "${CKPT}")" +if [ ! -f "${CKPT}" ]; then + echo "[eval_v2] FATAL: CKPT 不是可读文件: ${CKPT}" >&2 + exit 1 +fi +DATASET="${DATASET:-${DATASET_BASE_PATH:-${REPO_ROOT}/data/Context-as-Memory-Dataset}}" +if [ ! -d "${DATASET}" ]; then + echo "[eval_v2] FATAL: DATASET 不是目录: ${DATASET}" >&2 + exit 1 +fi +export CAMERA_INJECT_MODE="${CAMERA_INJECT_MODE:-pre_qkv}" + +METADATA_NAME="${METADATA_NAME:-metadata_full.csv}" +METADATA_PATH="${METADATA_PATH:-${DATASET}/${METADATA_NAME}}" +SAMPLING_ACTION_DIR="${SAMPLING_ACTION_DIR:-${ENV_DIR}}" +LOOP_EXTRA_META=() +if [ -f "${METADATA_PATH}" ]; then + LOOP_EXTRA_META=(--dataset_metadata_path "${METADATA_PATH}") +else + echo "[eval_v2] WARN: METADATA_PATH 不存在，loop 将用 frames 随机采样（与 ep0 有 metadata 时不一致）: ${METADATA_PATH}" >&2 +fi +LOOP_EXTRA_ARGS=() +# Default loop-closure 4chunk context retrieval follows the training-style memory path: +# retrieve history frames by next-chunk FOV/yaw, use the final return pose for chunk4, +# and pass relative context RTs. Set any variable below to 0 for ablations. +if [ "${MULTI_CTX_4CHUNK_FOV_HISTORY:-1}" = "1" ]; then + LOOP_EXTRA_ARGS+=(--multi_ctx_4chunk_fov_history) +fi +if [ "${MULTI_CTX_4CHUNK_FOV_LAST_TARGET:-1}" = "1" ]; then + LOOP_EXTRA_ARGS+=(--multi_ctx_4chunk_fov_last_target) +fi +if [ "${MULTI_CTX_4CHUNK_FOV_CONTEXT_RT:-1}" = "1" ]; then + LOOP_EXTRA_ARGS+=(--multi_ctx_4chunk_fov_context_rt) +fi +# Default static loop eval focuses on the 4chunk revisit stress test. Set +# RUN_LEFT_RIGHT_2CHUNK=1 to also run the shorter 1-left/1-right baseline. +if [ "${RUN_LEFT_RIGHT_2CHUNK:-0}" != "1" ]; then + LOOP_EXTRA_ARGS+=(--skip_left_right_2chunk) +fi + +EVALS_ROOT="${EVALS_ROOT:-${CKPT_DIR}/evals_v2/static_consistency}" +IN_DOMAIN="${EVALS_ROOT}/in_domain" +OPEN_DOMAIN_ROOT="${EVALS_ROOT}/open_domain" +mkdir -p "${IN_DOMAIN}" + +# Infer memory baseline runtime flags from CKPT path (single source: memory_baseline_runtime.py). +MEM_ARGS=() +eval "$(python3 "${ENV_DIR}/memory_baseline_runtime.py" bash-export "${CKPT}")" +# Infer context length from both legacy ctx_* names and released HF context_k* folders. +_default_ctx=1 +[[ "${CKPT}" =~ (ctx_20|context_k20|ctx20) ]] && _default_ctx=20 +[[ "${CKPT}" =~ (ctx_5|context_k5|ctx5) ]] && _default_ctx=5 +[ -n "${CONTEXT_FRAMES_MEM_OVERRIDE:-}" ] && _default_ctx="${CONTEXT_FRAMES_MEM_OVERRIDE}" +CONTEXT_FRAMES_EFFECTIVE="${CONTEXT_FRAMES:-$_default_ctx}" +SEED_EFFECTIVE="${SEED:-42}" + +echo "[eval_v2] CONTEXT_FRAMES_EFFECTIVE=${CONTEXT_FRAMES_EFFECTIVE} CAMERA_INJECT_MODE=${CAMERA_INJECT_MODE} SAMPLING_ACTION_DIR=${SAMPLING_ACTION_DIR}" +echo "[eval_v2] MEM_ARGS=${MEM_ARGS[*]:-none}" +[ "${#LOOP_EXTRA_META[@]}" -gt 0 ] && echo "[eval_v2] loop dataset_metadata_path=${METADATA_PATH}" + +# Fail-fast：MEM_ARGS 须能被 multiview 的 argparse.REMAINDER 吞掉（避免跑完全部 in-domain 后才报 unrecognized arguments） +python3 "${EVAL_DIR}/tools/verify_static_eval_prereqs.py" remainder-mem-args "${MEM_ARGS[@]}" +# Wan2.1 底座权重（combo / loop 均依赖） +python3 "${EVAL_DIR}/tools/verify_static_eval_prereqs.py" wan-base + +# ---------- (A) Loop closure (in-domain) ---------- +python3 "${ENV_DIR}/run_replay_loop_two_chunk.py" \ + --ckpt "${CKPT}" \ + --context_frames "${CONTEXT_FRAMES_EFFECTIVE}" \ + --sampling_action_dir "${SAMPLING_ACTION_DIR}" \ + --dataset_base "${DATASET}" \ + "${LOOP_EXTRA_META[@]}" \ + --output_dir "${IN_DOMAIN}/loop_closure" \ + --num_samples "${NUM_SAMPLES_LOOP:-8}" \ + --seed "${SEED_EFFECTIVE}" \ + --sigma_shift "${SIGMA_SHIFT:-5}" \ + --num_inference_steps "${NUM_INFERENCE_STEPS:-50}" \ + --cfg_scale "${CFG_SCALE:-5.0}" \ + "${LOOP_EXTRA_ARGS[@]}" \ + "${MEM_ARGS[@]}" + +# ---------- (B) Symmetric random-action combo revisit (in-domain: training prompt + first frame) ---------- +# 随机幅度、对称闭环：左转→直行→后退→右转（与固定 45° 同结构），最后 revisit 到首帧视角（像素上首末帧 MSE 见 revisit_closure_metrics.json）。 +# 可选 STATIC_USE_OPEN_DOMAIN_COMBO=1：回退到单条 open-domain（FIRST_FRAME_IMG + PROMPT）。 +CHUNK_FRAMES_EFFECTIVE="${CHUNK_FRAMES:-81}" +NUM_CHUNKS_LONG="${NUM_CHUNKS_LONG:-3}" +MIN_SPAN=$(( CHUNK_FRAMES_EFFECTIVE * NUM_CHUNKS_LONG )) +NUM_SAMPLES_STATIC="${NUM_SAMPLES_STATIC:-6}" +COMBO_BASE="${IN_DOMAIN}/action_combos_random_symmetric" +COMBO_OUT="${IN_DOMAIN}/combo_revisit_in_domain" +mkdir -p "${COMBO_BASE}" "${COMBO_OUT}" + +if [ "${STATIC_USE_OPEN_DOMAIN_COMBO:-0}" = "1" ]; then + echo "[eval_v2] STATIC_USE_OPEN_DOMAIN_COMBO=1 -> legacy single combo + external first frame" + COMBO_DIR="${IN_DOMAIN}/action_combo_rot_trans_rev" + python3 "${EVAL_DIR}/actions/build_action_combo.py" \ + --exp_dir "${ENV_DIR}" \ + --out_dir "${COMBO_DIR}" \ + --chunk_frames "${CHUNK_FRAMES_EFFECTIVE}" \ + --translation_delta "${TRANSLATION_DELTA:-0.1}" >/dev/null + FIRST_FRAME_IMG="${FIRST_FRAME_IMG:-${REPO_ROOT}/assets/first_frame.png}" + python3 "${EVAL_DIR}/static/run_combo_revisit_fixed_first.py" \ + --ckpt "${CKPT}" \ + --first_frame_image "${FIRST_FRAME_IMG}" \ + --output_dir "${IN_DOMAIN}/combo_revisit_fixed_first" \ + --prompt "${PROMPT:-A scene.}" \ + --action_combo_dir "${COMBO_DIR}" \ + --context_frames "${CONTEXT_FRAMES_EFFECTIVE}" \ + --chunk_frames "${CHUNK_FRAMES_EFFECTIVE}" \ + --sigma_shift "${SIGMA_SHIFT:-5}" \ + --num_inference_steps "${NUM_INFERENCE_STEPS:-50}" \ + --cfg_scale "${CFG_SCALE:-5.0}" \ + --seed "${SEED_EFFECTIVE}" \ + "${MEM_ARGS[@]}" +else + SAMPLES_TSV="${IN_DOMAIN}/_static_eval_samples.tsv" + python3 "${EVAL_DIR}/tools/emit_dataset_samples.py" \ + --dataset "${DATASET}" \ + --num_samples "${NUM_SAMPLES_STATIC}" \ + --min_frames "${MIN_SPAN}" \ + --seed "${SEED_EFFECTIVE}" > "${SAMPLES_TSV}" + _idx=0 + _seed_base="${SEED_EFFECTIVE}" + while IFS=$'\t' read -r _vn _st; do + [ -z "${_vn:-}" ] && continue + _combo_sub="${COMBO_BASE}/${_vn}_start${_st}" + mkdir -p "${_combo_sub}" + python3 "${EVAL_DIR}/actions/build_action_combo.py" \ + --random_symmetric \ + --combo_seed "$((_seed_base + _idx))" \ + --out_dir "${_combo_sub}" \ + --chunk_frames "${CHUNK_FRAMES_EFFECTIVE}" \ + --yaw_min "${COMBO_YAW_MIN:-20}" \ + --yaw_max "${COMBO_YAW_MAX:-55}" \ + --translation_min "${COMBO_TRANS_MIN:-0.05}" \ + --translation_max "${COMBO_TRANS_MAX:-0.18}" >/dev/null + python3 "${EVAL_DIR}/static/run_combo_revisit_fixed_first.py" \ + --ckpt "${CKPT}" \ + --dataset_base "${DATASET}" \ + --video_name "${_vn}" \ + --start_frame "${_st}" \ + --output_dir "${COMBO_OUT}/${_vn}_start${_st}" \ + --action_combo_dir "${_combo_sub}" \ + --context_frames "${CONTEXT_FRAMES_EFFECTIVE}" \ + --chunk_frames "${CHUNK_FRAMES_EFFECTIVE}" \ + --sigma_shift "${SIGMA_SHIFT:-5}" \ + --num_inference_steps "${NUM_INFERENCE_STEPS:-50}" \ + --cfg_scale "${CFG_SCALE:-5.0}" \ + --seed "${SEED_EFFECTIVE}" \ + "${MEM_ARGS[@]}" + _idx=$((_idx + 1)) + done < "${SAMPLES_TSV}" + python3 "${EVAL_DIR}/metrics/aggregate_combo_closure_metrics.py" \ + --root "${COMBO_OUT}" \ + --output_json "${EVALS_ROOT}/metrics/combo_closure_summary.json" +fi + +if [ "${RUN_GEOMETRY_DIAG:-0}" = "1" ] && [ "${STATIC_USE_OPEN_DOMAIN_COMBO:-0}" = "1" ]; then + echo "[eval_v2] WARN: RUN_GEOMETRY_DIAG=1 但 STATIC_USE_OPEN_DOMAIN_COMBO=1 时不会生成 _static_eval_samples.tsv / long_horizon_gt_replay，点云段 (F) 无输入。若要 3D 诊断请关掉 STATIC_USE_OPEN_DOMAIN_COMBO 或设 RUN_BASIC_REPLAY_GT=1 后自行对齐 mp4 与数据集 pose。" >&2 +fi + +# ---------- (D) Long-horizon GT trajectory replay (>=3 chunks), visual quality as primary ---------- +LONG_ROOT="${IN_DOMAIN}/long_horizon_gt_replay" +mkdir -p "${LONG_ROOT}" +if [ "${STATIC_USE_OPEN_DOMAIN_COMBO:-0}" != "1" ] && [ -f "${IN_DOMAIN}/_static_eval_samples.tsv" ]; then + while IFS=$'\t' read -r _vn _st; do + [ -z "${_vn:-}" ] && continue + _out="${LONG_ROOT}/${_vn}_start${_st}" + mkdir -p "${_out}" + python3 "${EVAL_DIR}/basic/check_dataset_gt_for_replay.py" \ + --dataset "${DATASET}" \ + --video "${_vn}" \ + --start_frame "${_st}" \ + --num_chunks "${NUM_CHUNKS_LONG}" \ + --chunk_frames "${CHUNK_FRAMES_EFFECTIVE}" || continue + python3 "${EVAL_DIR}/basic/replay_gt_error.py" \ + --ckpt "${CKPT}" \ + --dataset_base "${DATASET}" \ + --video_name "${_vn}" \ + --start_frame "${_st}" \ + --num_chunks "${NUM_CHUNKS_LONG}" \ + --chunk_frames "${CHUNK_FRAMES_EFFECTIVE}" \ + --context_frames "${CONTEXT_FRAMES_EFFECTIVE}" \ + --sigma_shift "${SIGMA_SHIFT:-5}" \ + --num_inference_steps "${NUM_INFERENCE_STEPS:-50}" \ + --cfg_scale "${CFG_SCALE:-5.0}" \ + --seed "${SEED_EFFECTIVE}" \ + --output_dir "${_out}" \ + --write_csv + done < "${IN_DOMAIN}/_static_eval_samples.tsv" + python3 "${EVAL_DIR}/metrics/aggregate_long_horizon_fid_fvd.py" \ + --root "${LONG_ROOT}" \ + --dataset_base "${DATASET}" \ + --device "${DEVICE:-cuda}" \ + --output_json "${EVALS_ROOT}/metrics/long_horizon_fid_fvd_summary.json" + # 可选：生成序列相邻帧一致性（无 GT，仅表征生成序列是否时序平滑；与 replay_gt_metrics 对 GT 指标互补） + if [ "${RUN_TEMPORAL_ADJ:-0}" = "1" ]; then + python3 "${EVAL_DIR}/metrics/aggregate_long_horizon_temporal_adjacency.py" \ + --root "${LONG_ROOT}" \ + --output_json "${EVALS_ROOT}/metrics/long_horizon_temporal_adjacency_summary.json" + fi +fi + +# ---------- (C) Revisit metrics: randomly sample a subset and average ---------- +# 说明：metrics 目前使用 first-vs-last 作为 revisit proxy；LPIPS 依赖可选。 +METRIC_SAMPLE_NUM="${METRIC_SAMPLE_NUM:-5}" +WRITE_VIZ="${WRITE_VIZ:-1}" +python3 "${EVAL_DIR}/metrics/aggregate_revisit_metrics.py" \ + --evals_root "${IN_DOMAIN}" \ + --num_samples "${METRIC_SAMPLE_NUM}" \ + --seed "${SEED_EFFECTIVE}" \ + --device "${DEVICE:-cuda}" \ + --output_dir "${EVALS_ROOT}/metrics/revisit_subset" \ + $( [ "${WRITE_VIZ}" != "0" ] && echo "--write_viz" || true ) + +# ---------- (E) Optional: multiview revisit from edited first-frame list ---------- +# 产物目录：open_domain/multiview_revisit/（与 in_domain 分离） +# 方式 1：MULTIVIEW_FIRSTFRAME_LIST=/path/to/views.txt（每行：图片绝对路径[\t prompt]） +# 方式 2：MULTIVIEW_FIRSTFRAME_DIR=/path/to/dir（扫描 png/jpg/webp，生成列表到 open_domain/） +if [ -n "${MULTIVIEW_FIRSTFRAME_DIR:-}" ] && [ -d "${MULTIVIEW_FIRSTFRAME_DIR}" ]; then + mkdir -p "${OPEN_DOMAIN_ROOT}" + MULTIVIEW_FIRSTFRAME_LIST="${OPEN_DOMAIN_ROOT}/_multiview_firstframes_from_dir.txt" + _mv_prompt_args=() + if [ -n "${MULTIVIEW_PROMPT:-}" ]; then + _mv_prompt_args=(--prompt "${MULTIVIEW_PROMPT}") + fi + python3 "${EVAL_DIR}/tools/build_multiview_firstframe_list_from_dir.py" \ + --image_dir "${MULTIVIEW_FIRSTFRAME_DIR}" \ + --output "${MULTIVIEW_FIRSTFRAME_LIST}" \ + "${_mv_prompt_args[@]}" +fi +if [ -n "${MULTIVIEW_FIRSTFRAME_LIST:-}" ] && [ -f "${MULTIVIEW_FIRSTFRAME_LIST}" ]; then + mkdir -p "${OPEN_DOMAIN_ROOT}" + MV_OUT="${OPEN_DOMAIN_ROOT}/multiview_revisit" + MV_ACTION_DIR="${MULTIVIEW_ACTION_DIR:-${COMBO_BASE}/multiview_shared_combo}" + mkdir -p "${MV_ACTION_DIR}" "${MV_OUT}" + python3 "${EVAL_DIR}/actions/build_action_combo.py" \ + --random_symmetric \ + --combo_seed "${SEED_EFFECTIVE}" \ + --out_dir "${MV_ACTION_DIR}" \ + --chunk_frames "${CHUNK_FRAMES_EFFECTIVE}" \ + --yaw_min "${COMBO_YAW_MIN:-20}" \ + --yaw_max "${COMBO_YAW_MAX:-55}" \ + --translation_min "${COMBO_TRANS_MIN:-0.05}" \ + --translation_max "${COMBO_TRANS_MAX:-0.18}" >/dev/null + python3 "${EVAL_DIR}/tools/verify_static_eval_prereqs.py" multiview-preflight \ + --ckpt "${CKPT}" \ + --firstframe-list "${MULTIVIEW_FIRSTFRAME_LIST}" \ + --action-combo-dir "${MV_ACTION_DIR}" \ + --runner "${EVAL_DIR}/static/run_combo_revisit_fixed_first.py" \ + -- "${MEM_ARGS[@]}" + python3 "${EVAL_DIR}/static/run_multiview_revisit_from_firstframes.py" \ + --ckpt "${CKPT}" \ + --firstframe_list "${MULTIVIEW_FIRSTFRAME_LIST}" \ + --action_combo_dir "${MV_ACTION_DIR}" \ + --output_root "${MV_OUT}" \ + --chunk_frames "${CHUNK_FRAMES_EFFECTIVE}" \ + --context_frames "${CONTEXT_FRAMES_EFFECTIVE}" \ + --sigma_shift "${SIGMA_SHIFT:-5}" \ + --num_inference_steps "${NUM_INFERENCE_STEPS:-50}" \ + --cfg_scale "${CFG_SCALE:-5.0}" \ + --seed "${SEED_EFFECTIVE}" \ + --extra_args "${MEM_ARGS[@]}" + if [ "${RUN_MULTIVIEW_AGGREGATE_METRICS:-1}" = "1" ] && [ -d "${MV_OUT}" ]; then + python3 "${EVAL_DIR}/metrics/aggregate_multiview_open_domain_metrics.py" \ + --multiview_root "${MV_OUT}" \ + --ref_view "${MULTIVIEW_REF_VIEW:-0}" \ + --device "${DEVICE:-cuda}" \ + --output_json "${MV_OUT}/multiview_closure_and_cross_view.json" + fi +fi + +# ---------- (F) Optional: offline point-cloud rendering diagnostics ---------- +# GEOMETRY_DEPTH_MODE=pseudo|npy_dir|npz_dir|midas；外部深度目录 GEOMETRY_DEPTH_DIR（每帧 {abs_idx:04d}.npy/.npz） +# GEOMETRY_DEPTH_IS_INVERSE=1、GEOMETRY_DEPTH_KEY、GEOMETRY_DEPTH_SCALE、MIDAS_DEVICE 可选 +if [ "${RUN_GEOMETRY_DIAG:-0}" = "1" ] && [ -f "${IN_DOMAIN}/_static_eval_samples.tsv" ]; then + GEOM_ROOT="${EVALS_ROOT}/geometry_diagnostics" + mkdir -p "${GEOM_ROOT}" + _geom_depth_args=(--depth_mode "${GEOMETRY_DEPTH_MODE:-pseudo}") + if [ -n "${GEOMETRY_DEPTH_DIR:-}" ]; then + _geom_depth_args+=(--depth_dir "${GEOMETRY_DEPTH_DIR}") + fi + if [ -n "${GEOMETRY_DEPTH_KEY:-}" ]; then + _geom_depth_args+=(--depth_key "${GEOMETRY_DEPTH_KEY}") + fi + if [ "${GEOMETRY_DEPTH_IS_INVERSE:-0}" = "1" ]; then + _geom_depth_args+=(--depth_is_inverse) + fi + if [ -n "${GEOMETRY_DEPTH_SCALE:-}" ]; then + _geom_depth_args+=(--depth_scale "${GEOMETRY_DEPTH_SCALE}") + fi + if [ -n "${MIDAS_DEVICE:-}" ]; then + _geom_depth_args+=(--midas_device "${MIDAS_DEVICE}") + fi + _geom_count=0 + while IFS=$'\t' read -r _vn _st; do + [ -z "${_vn:-}" ] && continue + _run="${LONG_ROOT}/${_vn}_start${_st}" + _gen="${_run}/replay_gt_gen_only.mp4" + [ ! -f "${_gen}" ] && continue + python3 "${EVAL_DIR}/geometry/render_multiview_pointcloud_offline.py" \ + --gen_video "${_gen}" \ + --dataset_base "${DATASET}" \ + --video_name "${_vn}" \ + --start_frame "${_st}" \ + --num_frames "${CHUNK_FRAMES_EFFECTIVE}" \ + --output_dir "${GEOM_ROOT}/${_vn}_start${_st}" \ + "${_geom_depth_args[@]}" + _geom_count=$((_geom_count + 1)) + done < "${IN_DOMAIN}/_static_eval_samples.tsv" + if [ "${_geom_count}" -eq 0 ]; then + echo "[eval_v2] WARN: RUN_GEOMETRY_DIAG=1 但未渲染任何点云（${LONG_ROOT} 下缺少 replay_gt_gen_only.mp4）。长时程 (D) 可能未跑或全部样本被 skip。" >&2 + else + echo "[eval_v2] geometry diagnostics: ${_geom_count} run(s) -> ${GEOM_ROOT}" + fi + python3 "${EVAL_DIR}/metrics/aggregate_geometry_consistency.py" \ + --root "${GEOM_ROOT}" \ + --output_json "${EVALS_ROOT}/metrics/geometry_consistency_summary.json" +fi + +echo "Done. static_consistency root: ${EVALS_ROOT}" + diff --git a/code/eval/v2/static/run_combo_revisit_fixed_first.py b/code/eval/v2/static/run_combo_revisit_fixed_first.py new file mode 100644 index 0000000000000000000000000000000000000000..59c0ed0d13b8a45c1a02a6f67ab95ef03b435f35 --- /dev/null +++ b/code/eval/v2/static/run_combo_revisit_fixed_first.py @@ -0,0 +1,295 @@ +#!/usr/bin/env python3 +""" +Static consistency: multi-action chunks then revisit (fixed first frame). + +This is the \"MultiActionRevisit\" task: +- chunk0: rotate_left_45 (or provided) +- chunk1: translate_forward +- chunk2: rotate_right_45 +- chunk3: translate_backward + +All actions are per-chunk relative to that chunk's first frame, matching training / existing eval conventions. +We generate a single concatenated mp4 and also save per-chunk gen-only mp4s for inspection. +""" +from __future__ import annotations + +import argparse +import os +import sys +import json +import time +from typing import List + +import numpy as np +import torch +from PIL import Image + +_script_dir = os.path.dirname(os.path.abspath(__file__)) +_eval_v2_dir = os.path.dirname(_script_dir) +_repo_root = os.path.dirname(os.path.dirname(_eval_v2_dir)) +_env_dir = os.path.join(_repo_root, "env") + +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) +if _env_dir not in sys.path: + sys.path.insert(0, _env_dir) + +import loop_utils as irc +import memory_baseline_runtime as mbr +from diffsynth import save_video +from run_replay_loop_two_chunk import ( + encode_context_frames_per_frame, + context_frames_for_next_chunk, + replay_context_from_generated_frames, + run_one_chunk, + _frame_to_pil, + load_sample_first_frame, +) + + +def _mse_rgb(a: np.ndarray, b: np.ndarray) -> float: + d = a.astype(np.float64) - b.astype(np.float64) + return float(np.mean(d ** 2)) + + +def _psnr_from_mse(mse: float) -> float: + if mse <= 0: + return 100.0 + return float(10.0 * np.log10((255.0 ** 2) / mse)) + + +def _resize_to_sampling_size(pil_img, width, height): + if pil_img.size == (width, height): + return pil_img + try: + return pil_img.convert("RGB").resize((width, height), Image.Resampling.LANCZOS) + except AttributeError: + return pil_img.convert("RGB").resize((width, height), Image.LANCZOS) + + +def main(): + p = argparse.ArgumentParser(description="Static consistency: composite action revisit (fixed first frame)") + p.add_argument("--ckpt", required=True) + p.add_argument("--first_frame_image", type=str, default=None, help="Open-domain first frame (optional if --dataset_base+video+start)") + p.add_argument("--output_dir", required=True) + p.add_argument( + "--base_model", + type=str, + default=None, + help="Wan2.1 base model dir; default: $WAN_BASE_MODEL", + ) + p.add_argument("--prompt", type=str, default="A scene.", help="Used only with --first_frame_image; dataset mode uses CSV prompt") + p.add_argument("--dataset_base", type=str, default=None, help="In-domain: training set root (frames/, jsons/, metadata)") + p.add_argument("--video_name", type=str, default=None) + p.add_argument("--start_frame", type=int, default=None) + p.add_argument("--action_combo_dir", required=True, help="Directory containing chunk0..chunk3 action jsons") + p.add_argument("--chunk_frames", type=int, default=81) + p.add_argument("--context_frames", type=int, default=1) + # Memory baseline runtime flags (must align with ckpt training for multichunk consistency) + p.add_argument("--use_framepack_memory", action="store_true", help="FramePack/FAR-style context reweighting") + p.add_argument("--context_temporal_decay", type=float, default=1.0, help="FramePack/FAR per-frame decay") + p.add_argument("--context_attention_weight", type=float, default=1.0, help="FramePack/FAR global scale for context tokens") + p.add_argument("--use_framepack_length_compress", action="store_true", help="FramePack length compress context tokens K->K'") + p.add_argument("--framepack_ratio", type=int, default=2, help="FramePack length compress ratio r") + p.add_argument("--use_spatial_memory", action="store_true", help="Enable spatial memory baseline") + p.add_argument("--use_spatial_memory_legacy", action="store_true", help="Legacy adaptive pool (no SpatialGridMemory in ckpt)") + p.add_argument("--spatial_memory_tokens", type=int, default=64, help="Spatial memory token count") + p.add_argument( + "--spatial_memory_inject_mode", + type=str, + default=None, + choices=("concat_text", "cross_attn_readout", "none"), + help="Spatial memory inject mode; must match training", + ) + p.add_argument("--height", type=int, default=352) + p.add_argument("--width", type=int, default=640) + p.add_argument("--sigma_shift", type=float, default=5.0) + p.add_argument("--num_inference_steps", type=int, default=50) + p.add_argument("--cfg_scale", type=float, default=5.0) + p.add_argument("--seed", type=int, default=42) + p.add_argument("--camera_inject_mode", type=str, default=None) + p.add_argument("--no_camera_encoder_separate_t_r", action="store_true") + p.add_argument("--no_omit_context_actions", action="store_true") + args = p.parse_args() + + if not os.path.isfile(args.ckpt): + raise FileNotFoundError(f"CKPT not found: {args.ckpt}") + action_paths_pre = [ + os.path.join(args.action_combo_dir, "chunk0_rotate_left_45.json"), + os.path.join(args.action_combo_dir, "chunk1_translate_forward.json"), + os.path.join(args.action_combo_dir, "chunk2_rotate_right_45.json"), + os.path.join(args.action_combo_dir, "chunk3_translate_backward.json"), + ] + for apth in action_paths_pre: + if not os.path.isfile(apth): + raise FileNotFoundError(f"Missing action json (fail-fast before load_pipeline): {apth}") + + base_model = args.base_model or os.environ.get("WAN_BASE_MODEL") + if not base_model: + raise ValueError("Set --base_model or WAN_BASE_MODEL to the Wan2.1 base model directory.") + for _name in ( + "diffusion_pytorch_model.safetensors", + "models_t5_umt5-xxl-enc-bf16.pth", + "Wan2.1_VAE.pth", + ): + _p = os.path.join(base_model, _name) + if not os.path.isfile(_p): + raise FileNotFoundError(f"Missing Wan2.1 base weight (fail-fast): {_p}") + + os.makedirs(args.output_dir, exist_ok=True) + w, h = args.width, args.height + + in_domain = ( + args.dataset_base + and args.video_name is not None + and args.start_frame is not None + ) + if in_domain: + first_frame_pil = load_sample_first_frame(args.dataset_base, args.video_name, int(args.start_frame), w, h) + if first_frame_pil is None: + raise FileNotFoundError( + f"Cannot load first frame for in-domain sample {(args.video_name, args.start_frame)} under {args.dataset_base}" + ) + prompt = irc.load_prompt_for_video(args.dataset_base, args.video_name) or "A scene." + else: + if not args.first_frame_image or not os.path.isfile(args.first_frame_image): + raise ValueError("Provide --dataset_base --video_name --start_frame OR a valid --first_frame_image") + first_frame_pil = Image.open(args.first_frame_image).convert("RGB") + first_frame_pil = _resize_to_sampling_size(first_frame_pil, w, h) + prompt = args.prompt + + camera_inject_mode = (args.camera_inject_mode or "").strip() or None + if not camera_inject_mode: + env_cam = (os.environ.get("CAMERA_INJECT_MODE") or "").strip().lower() + if env_cam in ("pre_qkv_post", "pre_qkv", "pre_norm", "post"): + camera_inject_mode = env_cam + if not camera_inject_mode: + for mode in ("pre_qkv_post", "pre_qkv", "pre_norm", "post"): + if mode.replace("_", "") in (args.ckpt or "").lower(): + camera_inject_mode = mode + break + if not camera_inject_mode: + camera_inject_mode = "pre_qkv" + + load_kw = dict( + action_inject_after_spatial_attn=True, + add_action_attn=True, + action_use_temporal_attention=True, + camera_inject_mode=camera_inject_mode, + ) + if args.no_camera_encoder_separate_t_r: + load_kw["camera_encoder_separate_t_r"] = False + + pipe = irc.load_pipeline_and_ckpt( + args.ckpt, + f"{base_model}/diffusion_pytorch_model.safetensors", + f"{base_model}/models_t5_umt5-xxl-enc-bf16.pth", + f"{base_model}/Wan2.1_VAE.pth", + **load_kw, + ) + + # Runtime memory flags: CLI wins when any --use_* is set; else infer from ckpt path (memory_baselines_basic_*). + cli_mem = bool( + getattr(args, "use_framepack_memory", False) + or getattr(args, "use_framepack_length_compress", False) + or getattr(args, "use_spatial_memory", False) + ) + if cli_mem: + pipe.use_framepack_memory = bool(getattr(args, "use_framepack_memory", False)) + pipe.context_temporal_decay = float(getattr(args, "context_temporal_decay", 1.0) or 1.0) + pipe.context_attention_weight = float(getattr(args, "context_attention_weight", 1.0) or 1.0) + pipe.use_framepack_length_compress = bool(getattr(args, "use_framepack_length_compress", False)) + pipe.framepack_ratio = int(getattr(args, "framepack_ratio", 2) or 2) + pipe.use_spatial_memory = bool(getattr(args, "use_spatial_memory", False)) + pipe.spatial_memory_tokens = int(getattr(args, "spatial_memory_tokens", 64) or 64) + if getattr(args, "spatial_memory_inject_mode", None): + pipe.spatial_memory_inject_mode = str(getattr(args, "spatial_memory_inject_mode")) + pipe.use_spatial_memory_legacy = bool(getattr(args, "use_spatial_memory_legacy", False)) + if pipe.use_spatial_memory and not pipe.use_spatial_memory_legacy and getattr(pipe, "spatial_memory_module", None) is None: + pipe.use_spatial_memory_legacy = True + else: + mbr.apply_memory_baseline_pipe(pipe, args.ckpt) + if getattr(pipe, "use_spatial_memory", False) and not getattr(pipe, "use_spatial_memory_legacy", False) and getattr(pipe, "spatial_memory_module", None) is None: + pipe.use_spatial_memory_legacy = True + + use_neg = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") + omit = not args.no_omit_context_actions + + action_paths = action_paths_pre + + # chunk0 context = first frame + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + ctx_latents = encode_context_frames_per_frame(pipe, [first_frame_pil], pipe.device) + ctx_actions_t = torch.tensor([identity_rt], dtype=torch.float32) + + chunks: List[List] = [] + times = [] + for ch, action_path in enumerate(action_paths): + t0 = time.time() + frames = run_one_chunk( + pipe, + prompt, + use_neg, + action_path, + context_latents=ctx_latents, + num_context_frames=ctx_latents.shape[2], + context_actions_t=ctx_actions_t, + chunk_frames=args.chunk_frames, + h=h, + w=w, + seed=args.seed + ch, + sigma_shift=args.sigma_shift, + num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, + inference_noise_level=0.0, + omit_context_actions=omit, + log_prefix="[combo_revisit]", + ) + t1 = time.time() + times.append({"chunk": ch, "seconds": t1 - t0, "action": os.path.basename(action_path)}) + chunks.append(frames) + + # prepare context for next chunk (except last) + if ch < len(action_paths) - 1: + n_ctx = min(args.context_frames, len(frames)) + prev_frames = replay_context_from_generated_frames(frames, n_ctx) + prev_pil = [_frame_to_pil(f, w, h) for f in prev_frames] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + ctx_latents = encode_context_frames_per_frame(pipe, prev_pil, pipe.device) + num_ctx_tokens = ctx_latents.shape[2] + ctx_actions_t = torch.tensor([identity_rt] * num_ctx_tokens, dtype=torch.float32) + + # save outputs + all_frames = [] + for ch, frames in enumerate(chunks): + save_video(frames, os.path.join(args.output_dir, f"combo_chunk{ch}_gen_only.mp4"), fps=15, quality=5) + all_frames.extend(frames) + save_video(all_frames, os.path.join(args.output_dir, "combo_revisit_4chunk_gen_only.mp4"), fps=15, quality=5) + with open(os.path.join(args.output_dir, "combo_revisit_speed.json"), "w", encoding="utf-8") as f: + json.dump({"chunks": times}, f, indent=2) + + first_np = np.array(first_frame_pil.convert("RGB"), dtype=np.uint8) + last_pil = _frame_to_pil(all_frames[-1], w, h) + last_np = np.array(last_pil.convert("RGB"), dtype=np.uint8) + closure_mse = _mse_rgb(first_np, last_np) + closure = { + "closure_first_vs_last_mse": closure_mse, + "closure_first_vs_last_psnr": _psnr_from_mse(closure_mse), + "in_domain": bool(in_domain), + "video_name": args.video_name, + "start_frame": args.start_frame, + "num_chunks": len(action_paths), + "chunk_frames": args.chunk_frames, + } + with open(os.path.join(args.output_dir, "revisit_closure_metrics.json"), "w", encoding="utf-8") as f: + json.dump(closure, f, indent=2) + + print(f"Done. Output: {args.output_dir}") + + +if __name__ == "__main__": + main() + diff --git a/code/eval/v2/static/run_multiview_revisit_from_firstframes.py b/code/eval/v2/static/run_multiview_revisit_from_firstframes.py new file mode 100644 index 0000000000000000000000000000000000000000..fb694314c80ce4b73d536e7b6cb631c109c4ac6b --- /dev/null +++ b/code/eval/v2/static/run_multiview_revisit_from_firstframes.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +"""Batch multiview revisit from a first-frame list.""" +from __future__ import annotations + +import argparse +import csv +import json +import os +import subprocess +import sys +from typing import Dict, List + +COMBO_CHUNK_FILES = ( + "chunk0_rotate_left_45.json", + "chunk1_translate_forward.json", + "chunk2_rotate_right_45.json", + "chunk3_translate_backward.json", +) + + +def _validate_prereqs(args: argparse.Namespace, runner: str) -> None: + if not os.path.isfile(args.ckpt): + raise FileNotFoundError(f"[run_multiview_revisit] CKPT 不是文件: {args.ckpt}") + if not os.path.isfile(args.firstframe_list): + raise FileNotFoundError(f"[run_multiview_revisit] firstframe_list 不存在: {args.firstframe_list}") + if not os.path.isdir(args.action_combo_dir): + raise FileNotFoundError(f"[run_multiview_revisit] action_combo_dir 不是目录: {args.action_combo_dir}") + for fn in COMBO_CHUNK_FILES: + p = os.path.join(args.action_combo_dir, fn) + if not os.path.isfile(p): + raise FileNotFoundError(f"[run_multiview_revisit] 缺少 combo 动作文件（请先 build_action_combo）: {p}") + if not os.path.isfile(runner): + raise FileNotFoundError(f"[run_multiview_revisit] runner 不存在: {runner}") + + +def _load_items(path: str) -> List[Dict[str, str]]: + ext = os.path.splitext(path)[1].lower() + items: List[Dict[str, str]] = [] + if ext == ".jsonl": + with open(path, "r", encoding="utf-8") as f: + for ln in f: + ln = ln.strip() + if not ln: + continue + d = json.loads(ln) + items.append( + { + "view_id": str(d.get("view_id") or len(items)), + "first_frame_image": str(d.get("first_frame_image") or d.get("image") or ""), + "prompt": str(d.get("prompt") or "A scene."), + } + ) + return items + if ext == ".csv": + with open(path, "r", encoding="utf-8") as f: + for row in csv.DictReader(f): + items.append( + { + "view_id": str(row.get("view_id") or len(items)), + "first_frame_image": str(row.get("first_frame_image") or row.get("image") or ""), + "prompt": str(row.get("prompt") or "A scene."), + } + ) + return items + # txt: each line -> image_path[tab prompt] + with open(path, "r", encoding="utf-8") as f: + for i, ln in enumerate(f): + ln = ln.strip() + if not ln or ln.startswith("#"): + continue + parts = ln.split("\t", 1) + items.append( + { + "view_id": str(i), + "first_frame_image": parts[0], + "prompt": parts[1] if len(parts) > 1 else "A scene.", + } + ) + return items + + +def main() -> int: + ap = argparse.ArgumentParser(description="Run combo revisit for a list of edited first frames") + ap.add_argument("--ckpt", required=True) + ap.add_argument("--firstframe_list", required=True, help="txt/csv/jsonl") + ap.add_argument("--action_combo_dir", required=True) + ap.add_argument("--output_root", required=True) + ap.add_argument("--runner", default=None, help="default: eval_v2/static/run_combo_revisit_fixed_first.py") + ap.add_argument("--chunk_frames", type=int, default=81) + ap.add_argument("--context_frames", type=int, default=1) + ap.add_argument("--sigma_shift", type=float, default=5.0) + ap.add_argument("--num_inference_steps", type=int, default=50) + ap.add_argument("--cfg_scale", type=float, default=5.0) + ap.add_argument("--seed", type=int, default=42) + ap.add_argument( + "--camera_inject_mode", + type=str, + default=None, + help="与 evals_ep0 一致；默认不传则由子进程读环境 CAMERA_INJECT_MODE", + ) + # REMAINDER: 子进程参数若以 - 开头，nargs='*' 会被 argparse 误当作本脚本的选项而报错 + ap.add_argument( + "--extra_args", + nargs=argparse.REMAINDER, + default=[], + help="传给 run_combo_revisit_fixed_first.py 的额外参数；须放在命令行最后（如 MEM_ARGS）", + ) + args = ap.parse_args() + # 允许用户写「占位」-- 与 shell 的 -- 一致 + extra = list(args.extra_args or []) + if extra and extra[0] == "--": + extra = extra[1:] + args.extra_args = extra + + out_root = os.path.abspath(args.output_root) + if args.runner: + runner = os.path.abspath(args.runner) + else: + here = os.path.dirname(os.path.abspath(__file__)) + runner = os.path.join(here, "run_combo_revisit_fixed_first.py") + + _validate_prereqs(args, runner) + + os.makedirs(out_root, exist_ok=True) + items = _load_items(os.path.abspath(args.firstframe_list)) + if not items: + print("[run_multiview_revisit] no entries found") + return 0 + + missing_ff = [ + str(it.get("first_frame_image") or "") + for it in items + if not os.path.isfile(str(it.get("first_frame_image") or "")) + ] + if missing_ff: + raise FileNotFoundError( + f"[run_multiview_revisit] 首帧图不存在: {missing_ff[:5]}{'...' if len(missing_ff) > 5 else ''}" + ) + + summary: List[Dict[str, object]] = [] + for i, it in enumerate(items): + view_id = str(it["view_id"]) + ff = str(it["first_frame_image"]) + prompt = str(it["prompt"]) + out_dir = os.path.join(out_root, f"view_{view_id}") + os.makedirs(out_dir, exist_ok=True) + cmd = [ + sys.executable, + runner, + "--ckpt", + args.ckpt, + ] + if (args.camera_inject_mode or "").strip(): + cmd.extend(["--camera_inject_mode", str(args.camera_inject_mode).strip()]) + cmd.extend( + [ + "--first_frame_image", + ff, + "--output_dir", + out_dir, + "--prompt", + prompt, + "--action_combo_dir", + args.action_combo_dir, + "--context_frames", + str(args.context_frames), + "--chunk_frames", + str(args.chunk_frames), + "--sigma_shift", + str(args.sigma_shift), + "--num_inference_steps", + str(args.num_inference_steps), + "--cfg_scale", + str(args.cfg_scale), + "--seed", + str(args.seed + i), + "--no_camera_encoder_separate_t_r", + ] + ) + cmd.extend(list(args.extra_args)) + rc = subprocess.run(cmd, check=False).returncode + ok = rc == 0 + summary.append( + { + "view_id": view_id, + "first_frame_image": ff, + "prompt": prompt, + "ok": ok, + "output_dir": out_dir, + } + ) + + out = { + "ckpt": args.ckpt, + "firstframe_list": os.path.abspath(args.firstframe_list), + "num_items": len(items), + "summary": summary, + } + with open(os.path.join(out_root, "multiview_revisit_summary.json"), "w", encoding="utf-8") as f: + json.dump(out, f, indent=2) + print(f"[run_multiview_revisit] wrote {out_root}/multiview_revisit_summary.json") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/tools/build_multiview_firstframe_list_from_dir.py b/code/eval/v2/tools/build_multiview_firstframe_list_from_dir.py new file mode 100644 index 0000000000000000000000000000000000000000..0dbfc5b8b6162d33e10101fd7f114bb2e19527eb --- /dev/null +++ b/code/eval/v2/tools/build_multiview_firstframe_list_from_dir.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +""" +Build a txt list for run_multiview_revisit_from_firstframes.py from a directory of images. + +Each line: absolute_path[\toptional_prompt] +If --prompt is set, every line gets the same prompt after a tab (overrides per-file default). +""" +from __future__ import annotations + +import argparse +import os + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--image_dir", required=True, help="e.g. .../opendomain2_revisit") + ap.add_argument("--output", required=True, help="txt path written for MULTIVIEW_FIRSTFRAME_LIST") + ap.add_argument( + "--prompt", + default="", + help="If non-empty, append same tab-prompt to every line; else path-only (runner defaults to 'A scene.')", + ) + ap.add_argument( + "--extensions", + default=".png,.jpg,.jpeg,.webp", + help="Comma-separated suffixes (lowercase)", + ) + args = ap.parse_args() + root = os.path.abspath(args.image_dir) + if not os.path.isdir(root): + print(f"[build_multiview_list] not a directory: {root}", flush=True) + return 1 + exts = {e.strip().lower() for e in args.extensions.split(",") if e.strip()} + names = [] + for n in sorted(os.listdir(root)): + low = n.lower() + if any(low.endswith(e) for e in exts): + p = os.path.join(root, n) + if os.path.isfile(p): + names.append(p) + if not names: + print(f"[build_multiview_list] no images under {root}", flush=True) + return 1 + outp = os.path.abspath(args.output) + os.makedirs(os.path.dirname(outp) or ".", exist_ok=True) + prompt = (args.prompt or "").strip() + with open(outp, "w", encoding="utf-8") as f: + for p in names: + if prompt: + f.write(f"{p}\t{prompt}\n") + else: + f.write(f"{p}\n") + print(f"[build_multiview_list] wrote {len(names)} lines -> {outp}", flush=True) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/tools/emit_dataset_samples.py b/code/eval/v2/tools/emit_dataset_samples.py new file mode 100644 index 0000000000000000000000000000000000000000..4b71b3c697daf8f5e96e73fbe197d361d56af724 --- /dev/null +++ b/code/eval/v2/tools/emit_dataset_samples.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python3 +"""Emit (video_name, start_frame) lines for eval batching. Stdlib only; no torch.""" +from __future__ import annotations + +import argparse +import os +import random +import sys + + +def _collect_starts(dataset_base: str, video_name: str, min_span: int) -> list[int]: + vd = os.path.join(dataset_base, "frames", video_name) + if not os.path.isdir(vd): + return [] + names = [f for f in os.listdir(vd) if f.endswith(".png")] + indices = [] + for n in names: + try: + indices.append(int(os.path.splitext(n)[0])) + except ValueError: + continue + if not indices: + return [] + indices = sorted(set(indices)) + max_idx = max(indices) + out = [] + for start in indices: + if start + min_span - 1 <= max_idx: + out.append(start) + return out + + +def main() -> int: + ap = argparse.ArgumentParser(description="Print video_namestart_frame for trajectories with enough frames") + ap.add_argument("--dataset", required=True) + ap.add_argument("--num_samples", type=int, default=6) + ap.add_argument("--min_frames", type=int, default=243, help="Need start + min_frames - 1 <= last index") + ap.add_argument("--seed", type=int, default=42) + args = ap.parse_args() + ds = os.path.abspath(args.dataset) + frames_root = os.path.join(ds, "frames") + if not os.path.isdir(frames_root): + print(f"[emit_dataset_samples] no frames dir: {frames_root}", file=sys.stderr) + return 1 + candidates: list[tuple[str, int]] = [] + for vn in sorted(os.listdir(frames_root)): + vd = os.path.join(frames_root, vn) + if not os.path.isdir(vd): + continue + for st in _collect_starts(ds, vn, args.min_frames): + candidates.append((vn, st)) + if not candidates: + print("[emit_dataset_samples] no candidates", file=sys.stderr) + return 1 + rng = random.Random(int(args.seed)) + rng.shuffle(candidates) + n = min(len(candidates), max(1, int(args.num_samples))) + for vn, st in candidates[:n]: + print(f"{vn}\t{st}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/tools/verify_static_eval_prereqs.py b/code/eval/v2/tools/verify_static_eval_prereqs.py new file mode 100644 index 0000000000000000000000000000000000000000..74859e7587e7b6cba37980f4cf915ffb167246da --- /dev/null +++ b/code/eval/v2/tools/verify_static_eval_prereqs.py @@ -0,0 +1,204 @@ +#!/usr/bin/env python3 +"""Fail-fast checks for eval_v2 static_consistency (avoid hours of GPU work then argv errors).""" +from __future__ import annotations + +import argparse +import os +import sys +import tempfile +from typing import List + +# Required chunk json names (must match run_combo_revisit_fixed_first.py) +COMBO_CHUNK_FILES = ( + "chunk0_rotate_left_45.json", + "chunk1_translate_forward.json", + "chunk2_rotate_right_45.json", + "chunk3_translate_backward.json", +) + +WAN_FILES = ( + "diffusion_pytorch_model.safetensors", + "models_t5_umt5-xxl-enc-bf16.pth", + "Wan2.1_VAE.pth", +) + + +def _parse_remainder_like_multiview(mem_args: List[str]) -> List[str]: + """Mirror run_multiview_revisit_from_firstframes.py --extra_args REMAINDER handling.""" + ap = argparse.ArgumentParser() + ap.add_argument("--ckpt", required=True) + ap.add_argument("--firstframe_list", required=True) + ap.add_argument("--action_combo_dir", required=True) + ap.add_argument("--output_root", required=True) + ap.add_argument("--extra_args", nargs=argparse.REMAINDER, default=[]) + with tempfile.NamedTemporaryFile("w", suffix=".txt", delete=False) as f: + f.write("# preflight\n") + list_path = f.name + out_root = tempfile.mkdtemp(prefix="multiview_preflight_") + try: + argv = [ + "_", + "--ckpt", + os.path.abspath(__file__), + "--firstframe_list", + list_path, + "--action_combo_dir", + out_root, + "--output_root", + out_root, + "--extra_args", + ] + list(mem_args) + ns = ap.parse_args(argv[1:]) + extra = list(ns.extra_args or []) + if extra and extra[0] == "--": + extra = extra[1:] + return extra + finally: + try: + os.unlink(list_path) + except OSError: + pass + + +def cmd_remainder_mem_args(mem_args: List[str]) -> int: + try: + got = _parse_remainder_like_multiview(mem_args) + except SystemExit: + raise + except Exception as e: + print(f"[verify_static_eval_prereqs] FATAL: multiview-style REMAINDER parse failed: {e}", file=sys.stderr) + return 1 + if got != mem_args: + print( + f"[verify_static_eval_prereqs] FATAL: MEM_ARGS round-trip mismatch:\n expect={mem_args!r}\n got={got!r}", + file=sys.stderr, + ) + return 1 + print(f"[verify_static_eval_prereqs] OK remainder-mem-args ({len(mem_args)} tokens)") + return 0 + + +def cmd_wan_base(_: List[str]) -> int: + wan_base = os.environ.get("WAN_BASE_MODEL", "") + if not wan_base: + print("[verify_static_eval_prereqs] FATAL: WAN_BASE_MODEL is not set", file=sys.stderr) + return 1 + missing = [] + for name in WAN_FILES: + p = os.path.join(wan_base, name) + if not os.path.isfile(p): + missing.append(p) + if missing: + print("[verify_static_eval_prereqs] FATAL: missing Wan2.1 base files:", file=sys.stderr) + for p in missing: + print(f" {p}", file=sys.stderr) + print("[verify_static_eval_prereqs] hint: set WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B", file=sys.stderr) + return 1 + print(f"[verify_static_eval_prereqs] OK wan-base ({wan_base})") + return 0 + + +def _load_firstframe_paths(list_path: str) -> List[str]: + ext = os.path.splitext(list_path)[1].lower() + paths: List[str] = [] + if ext == ".jsonl": + import json + + with open(list_path, "r", encoding="utf-8") as f: + for ln in f: + ln = ln.strip() + if not ln: + continue + d = json.loads(ln) + paths.append(str(d.get("first_frame_image") or d.get("image") or "")) + return [p for p in paths if p] + if ext == ".csv": + import csv + + with open(list_path, "r", encoding="utf-8") as f: + for row in csv.DictReader(f): + paths.append(str(row.get("first_frame_image") or row.get("image") or "")) + return [p for p in paths if p] + with open(list_path, "r", encoding="utf-8") as f: + for ln in f: + ln = ln.strip() + if not ln or ln.startswith("#"): + continue + paths.append(ln.split("\t", 1)[0].strip()) + return paths + + +def cmd_multiview_preflight(argv: List[str]) -> int: + ap = argparse.ArgumentParser(description="Validate paths before multiview GPU run") + ap.add_argument("--ckpt", required=True) + ap.add_argument("--firstframe-list", required=True, dest="firstframe_list") + ap.add_argument("--action-combo-dir", required=True, dest="action_combo_dir") + ap.add_argument("--runner", default="", help="default: run_combo_revisit_fixed_first.py next to multiview script") + ap.add_argument("mem_args", nargs=argparse.REMAINDER, default=[]) + ns = ap.parse_args(argv) + mem = list(ns.mem_args or []) + if mem and mem[0] == "--": + mem = mem[1:] + + if not os.path.isfile(ns.ckpt): + print(f"[verify_static_eval_prereqs] FATAL: CKPT not a file: {ns.ckpt}", file=sys.stderr) + return 1 + if not os.path.isfile(ns.firstframe_list): + print(f"[verify_static_eval_prereqs] FATAL: firstframe list missing: {ns.firstframe_list}", file=sys.stderr) + return 1 + if not os.path.isdir(ns.action_combo_dir): + print(f"[verify_static_eval_prereqs] FATAL: action combo dir missing: {ns.action_combo_dir}", file=sys.stderr) + return 1 + for fn in COMBO_CHUNK_FILES: + p = os.path.join(ns.action_combo_dir, fn) + if not os.path.isfile(p): + print(f"[verify_static_eval_prereqs] FATAL: missing combo action: {p}", file=sys.stderr) + return 1 + + here = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + runner = ns.runner or os.path.join(here, "static", "run_combo_revisit_fixed_first.py") + if not os.path.isfile(runner): + print(f"[verify_static_eval_prereqs] FATAL: runner missing: {runner}", file=sys.stderr) + return 1 + + try: + paths = _load_firstframe_paths(ns.firstframe_list) + except Exception as e: + print(f"[verify_static_eval_prereqs] FATAL: cannot read firstframe list: {e}", file=sys.stderr) + return 1 + if not paths: + print("[verify_static_eval_prereqs] FATAL: firstframe list has no image paths", file=sys.stderr) + return 1 + missing_img = [p for p in paths if not os.path.isfile(p)] + if missing_img: + print(f"[verify_static_eval_prereqs] FATAL: missing first-frame image(s), e.g.: {missing_img[:3]}", file=sys.stderr) + return 1 + + if cmd_remainder_mem_args(mem) != 0: + return 1 + + print( + f"[verify_static_eval_prereqs] OK multiview-preflight " + f"(views={len(paths)}, mem_tokens={len(mem)}, combo={ns.action_combo_dir})" + ) + return 0 + + +def main() -> int: + if len(sys.argv) < 2: + print("usage: verify_static_eval_prereqs.py {remainder-mem-args|wan-base|multiview-preflight} ...", file=sys.stderr) + return 2 + cmd = sys.argv[1] + rest = sys.argv[2:] + if cmd == "remainder-mem-args": + return cmd_remainder_mem_args(rest) + if cmd == "wan-base": + return cmd_wan_base(rest) + if cmd == "multiview-preflight": + return cmd_multiview_preflight(rest) + print(f"[verify_static_eval_prereqs] unknown command: {cmd}", file=sys.stderr) + return 2 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/eval/v2/visualize/revisit_pairs_viz.py b/code/eval/v2/visualize/revisit_pairs_viz.py new file mode 100644 index 0000000000000000000000000000000000000000..94d0666f53b63d56623e8888c4c4c991da36436b --- /dev/null +++ b/code/eval/v2/visualize/revisit_pairs_viz.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +""" +Visualize revisit consistency by exporting side-by-side images: + [reference_frame | revisit_frame] + +First version: +- reference = first frame, revisit = last frame. +""" +from __future__ import annotations + +import argparse +import os +from typing import List + +try: + import cv2 +except ImportError as e: + raise RuntimeError("opencv-python required") from e + +import numpy as np +from PIL import Image + + +def read_video_frames(path: str) -> List[np.ndarray]: + cap = cv2.VideoCapture(path) + out = [] + while True: + ret, bgr = cap.read() + if not ret: + break + out.append(cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)) + cap.release() + return out + + +def side_by_side(a: np.ndarray, b: np.ndarray) -> Image.Image: + if a.shape != b.shape: + b = cv2.resize(b, (a.shape[1], a.shape[0]), interpolation=cv2.INTER_LINEAR) + img = np.concatenate([a, b], axis=1) + return Image.fromarray(img.astype(np.uint8)) + + +def main(): + p = argparse.ArgumentParser(description="Export side-by-side revisit frames") + p.add_argument("--video", required=True) + p.add_argument("--output", required=True, help="output png path") + args = p.parse_args() + + frames = read_video_frames(args.video) + if len(frames) < 2: + raise SystemExit("video has <2 frames") + ref = frames[0] + rev = frames[-1] + out_img = side_by_side(ref, rev) + os.makedirs(os.path.dirname(args.output), exist_ok=True) + out_img.save(args.output) + print(args.output) + + +if __name__ == "__main__": + main() + diff --git a/code/flash-linear-attention/.flake8 b/code/flash-linear-attention/.flake8 new file mode 100644 index 0000000000000000000000000000000000000000..a735d81f404c17243f39f908cbb4f8948f9ef16a --- /dev/null +++ b/code/flash-linear-attention/.flake8 @@ -0,0 +1,12 @@ +[flake8] +max-line-length = 127 +exclude = + ./.git, + ./docs, + ./build, + ./scripts, + ./venv, + .flake8, + .pre-commit-config.yaml, + *.pyi, + *.md, diff --git a/code/flash-linear-attention/.gitignore b/code/flash-linear-attention/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..80c4fbb4a1c14285f560c0ebc14b1b5b46068522 --- /dev/null +++ b/code/flash-linear-attention/.gitignore @@ -0,0 +1,45 @@ +# test file +test.py + +# data files +data + +# bash scripts +*.sh + +# docs +docs/_build + +# intermediate files +build +dist +*.egg-info +dist-packages/* + +# experimental results +exp +results +wandb +*.csv +*.png +*.html + +# log and config files +log.* +*.log +*.cfg +*.ini + +# pycache +__pycache__ + +# saved model +*.pkl +*.pt + + +# vscode +.vscode + +# macOS +.DS_Store diff --git a/code/flash-linear-attention/.pre-commit-config.yaml b/code/flash-linear-attention/.pre-commit-config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..db269f386bc122489ecf5c020f0646ef5d120940 --- /dev/null +++ b/code/flash-linear-attention/.pre-commit-config.yaml @@ -0,0 +1,18 @@ +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: check-symlinks + - id: trailing-whitespace + args: [--markdown-linebreak-ext=md] + - id: end-of-file-fixer + - id: check-yaml + - id: check-toml + - id: check-added-large-files + - id: check-merge-conflict + - id: detect-private-key +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.14.0 + hooks: + - id: ruff-check + args: [--fix, --exit-non-zero-on-fix] diff --git a/code/flash-linear-attention/CITATION.cff b/code/flash-linear-attention/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..0ef44e524112027c3ab29efe8c54d5e49d72c90c --- /dev/null +++ b/code/flash-linear-attention/CITATION.cff @@ -0,0 +1,13 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it as below." +authors: +- family-names: "Yang" + given-names: "Songlin" + orcid: "https://orcid.org/0000-0002-5944-0110" +- family-names: "Zhang" + given-names: "Yu" + orcid: "https://orcid.org/0000-0002-8345-3835" +title: "FLA: A Triton-Based Library for Hardware-Efficient Implementations of Linear Attention Mechanism" +version: 0.1 +date-released: 2024-01-18 +url: "https://github.com/fla-org/flash-linear-attention" diff --git a/code/flash-linear-attention/ENVs.md b/code/flash-linear-attention/ENVs.md new file mode 100644 index 0000000000000000000000000000000000000000..bb196c68b08577aaf97b332b4da5a8350e91e42b --- /dev/null +++ b/code/flash-linear-attention/ENVs.md @@ -0,0 +1,9 @@ +# FLA Environment Variables + +| Variable | Default | Options | Description | +| -------------------- | ------- | ------------------------ | ---------------------------------------------------------------------------------------- | +| `FLA_CONV_BACKEND` | `cuda` | `triton` or `cuda` | Choose the convolution backend. `cuda` is the default and preferred for most cases. | +| `FLA_USE_TMA` | `0` | `0` or `1` | Set to `1` to enable Tensor Memory Accelerator (TMA) on Hopper or Blackwell GPUs. | +| `FLA_USE_FAST_OPS` | `0` | `0` or `1` | Enable faster, but potentially less accurate, operations when set to `1`. | +| `FLA_CACHE_RESULTS` | `1` | `0` or `1` | Whether to cache autotune timings to disk. Defaults to `1` (enabled). | +| `FLA_TRIL_PRECISION` | `ieee` | `ieee`, `tf32`, `tf32x3` | Controls the precision for triangular operations. `tf32x3` is only available on NV GPUs. | diff --git a/code/flash-linear-attention/FAQs.md b/code/flash-linear-attention/FAQs.md new file mode 100644 index 0000000000000000000000000000000000000000..b1942f11d61d3ee0ce2a3c218bfd540f45af5ab2 --- /dev/null +++ b/code/flash-linear-attention/FAQs.md @@ -0,0 +1,89 @@ +# Triton FAQs and Common Issues + +* [MMA Assertion](#1-mma-assertion-error-on-h100) +* [AsstibuteError](#2-attributeerror-nonetype-object-has-no-attribute-start) +* [LinearLayout](#3-h100-linearlayout-assertion-error) +* [Triton on Arm](#4-triton-support-for-arm-aarch64-architecture) + +## Recommended Setup Approach + +> [!IMPORTANT] +> Triton nightly builds often depend on the latest PyTorch nightly versions. To prevent conflicts with existing installations, we strongly recommend creating a fresh conda environment. This isolates the installation from any existing PyTorch/Triton versions that might cause compatibility issues. + +## Common Issues and Solutions + +### 1. MMA Assertion Error on H100 + +**Error:** +```py +Assertion `!(srcMmaLayout && dstMmaLayout && !srcMmaLayout.isAmpere()) && "mma -> mma layout conversion is only supported on Ampere"' failed. +``` + +**Solution:** +This issue was fixed in [PR #4492](https://github.com/triton-lang/triton/pull/4492). Install the nightly version: + +```sh +# Create fresh environment (strongly recommended!!!) +conda create -n triton-nightly python=3.12 +conda activate triton-nightly + +# Install PyTorch nightly (required for Triton nightly compatibility) +pip install -U --pre torch --index-url https://download.pytorch.org/whl/nightly/cu128 + +# Install Triton nightly +pip uninstall triton pytorch-triton -y +pip install -U triton-nightly --index-url https://pypi.fla-org.com/simple + +# Instal flash-linear-attention +pip install einops ninja datasets transformers numpy +pip uninstall flash-linear-attention && pip install -U --no-use-pep517 git+https://github.com/fla-org/flash-linear-attention --no-deps + +# Optional: Install flash-attention +conda install nvidia/label/cuda-12.8.1::cuda-nvcc +pip install packaging psutil ninja +pip install git+https://github.com/Dao-AILab/causal-conv1d.git --no-build-isolation +pip install flash-attn --no-deps --no-cache-dir --no-build-isolation + +# Optional: Verify flash-attention installation +pip install pytest +pytest tests/ops/test_attn.py +``` + +### 2. AttributeError: 'NoneType' object has no attribute 'start' + +**Solution:** +This is a known issue ([triton-lang/triton#5224](https://github.com/triton-lang/triton/issues/5224)). Upgrade to Python 3.10+. + +### 3. H100 LinearLayout Assertion Error + +**Error:** +``` +mlir::triton::LinearLayout::reshapeOuts(...) failed. +``` + +**Solution:** +This is a known issue ([triton-lang/triton#5609](https://github.com/triton-lang/triton/issues/5609)). Follow the same installation steps as in Issue #1 above. + +### 4. Triton Support for ARM (aarch64) Architecture +Triton now supports the ARM (aarch64) architecture. + +However, official Triton and PyTorch do not provide pre-built binaries for this architecture. The FLA organization has manually built and provided support for Triton on ARM, currently covering Triton versions 3.2.x, 3.3.x, and nightly builds. + +**Installation for ARM (aarch64):** + +For users on ARM (aarch64) systems, directly installing triton and pytorch from their official channels can be challenging as pre-built binaries for this architecture are often unavailable. The FLA organization provides custom-built Triton binaries to address this, ensuring compatibility with specific PyTorch versions. + +To ensure a smooth installation of flash-linear-attention with the necessary Triton and PyTorch dependencies on ARM, it's crucial to align their versions. The FLA builds of Triton are designed to be compatible with particular PyTorch releases. + +**Version Compatibility:** + +Below is a guide to compatible triton and pytorch versions when using FLA's Triton builds: + +- Triton 3.2.0 is compatible with PyTorch 2.6.0 +- Triton 3.3.0 is compatible with PyTorch 2.7.0 +- Triton 3.3.1 is compatible with PyTorch 2.7.1 + +```shell +pip install torch==2.7.1 --index-url https://download.pytorch.org/whl/cu128 +pip install -U triton==3.3.1 --index-url https://pypi.fla-org.com/simple +``` diff --git a/code/flash-linear-attention/LICENSE b/code/flash-linear-attention/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..af70ec239c3fc784a17c32bb530c4345c5c73749 --- /dev/null +++ b/code/flash-linear-attention/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023-2025 Songlin Yang + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/code/flash-linear-attention/README.md b/code/flash-linear-attention/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ea33e7252acb60904cd7ffa8cd2bd876cf354687 --- /dev/null +++ b/code/flash-linear-attention/README.md @@ -0,0 +1,583 @@ +
        + +# 💥 Flash Linear Attention + +[![hf_model](https://img.shields.io/badge/-Models-gray.svg?logo=huggingface&style=flat-square)](https://huggingface.co/fla-hub) [![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?&logo=discord&logoColor=white&style=flat-square)](https://discord.gg/vDaJTmKNcS) + +
        + +This repo aims at providing a collection of efficient Triton-based implementations for state-of-the-art linear attention models. **All implementations are written purely in PyTorch and Triton, making them platform-agnostic.** Currently verified platforms include NVIDIA, AMD, and Intel. **Any pull requests are welcome!** + +
        + image +
        + +* [News](#news) +* [Models](#models) +* [Installation](#installation) +* [Usage](#usage) + * [Token Mixing](#token-mixing) + * [Fused Modules](#fused-modules) + * [Generation](#generation) + * [Hybrid Models](#hybrid-models) +* [Training](#training) +* [Evaluation](#evaluation) +* [Benchmarks](#benchmarks) +* [Citation](#citation) +* [Star History](#star-history) +* [Acknowledgements](#acknowledgements) + +## News + +- **$\texttt{[2025-10]}$:** 🌑 Add Kimi Delta Attention implementation to `fla` ([paper](https://arxiv.org/abs/2510.26692)). +- **$\texttt{[2025-09]}$:** 🌲 Add DeltaFormer implementation to `fla` ([paper](https://arxiv.org/abs/2505.19488v1)). +- **$\texttt{[2025-09]}$:** 🐻 Thrilled to announce that [GDN](fla/ops/gated_delta_rule) has been integrated into Qwen3-Next. Check out their [blog post](https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list) for more infos! +- **$\texttt{[2025-08]}$:** 🌲 Add Log-Linear Attention implementation to `fla` ([paper](https://arxiv.org/abs/2506.04761)). +- **$\texttt{[2025-08]}$:** 🎓 Add MoM implementation to `fla` ([paper](https://arxiv.org/abs/2502.13685)). +- **$\texttt{[2025-07]}$:** 🐳 Add MLA implementation to `fla` ([paper](https://arxiv.org/abs/2405.04434)). +- **$\texttt{[2025-07]}$:** 🛣️ Added PaTH Attention to fla ([paper](https://arxiv.org/abs/2505.16381)). +- **$\texttt{[2025-06]}$:** 🎉 Added MesaNet to fla ([paper](https://arxiv.org/abs/2506.05233)). +- **$\texttt{[2025-06]}$:** 🐍 Add Comba implementation to `fla` ([paper](https://arxiv.org/abs/2506.02475)). +- **$\texttt{[2025-05]}$:** 🎉 Add Rodimus* implementation to `fla` ([paper](https://arxiv.org/abs/2410.06577)). +- **$\texttt{[2025-04]}$:** 🎉 Add DeltaProduct implementation to `fla` ([paper](https://arxiv.org/abs/2502.10297)). +- **$\texttt{[2025-04]}$:** 🎉 Add FoX implementation to `fla` ([paper](https://arxiv.org/abs/2503.02130)). +- **$\texttt{[2025-03]}$:** ~~We have changed the default `initializer_range` to the magic 🐳 0.006~~ The `initializer_range` was rolled back to the default value of 0.02. For actual training, we recommend trying both. +- **$\texttt{[2025-02]}$:** 🐳 Add NSA implementations to `fla`. See kernels [here](fla/ops/nsa). +- **$\texttt{[2025-01]}$:** 🔥 We are migrating to `torchtitan`-based training framework. Check out the [flame](https://github.com/fla-org/flame) repo for more details. +- **$\texttt{[2025-01]}$:** 🦅 Add RWKV7 implementations (both kernels and models) to `fla`. +- **$\texttt{[2024-12]}$:** Integrated `flash-bidirectional-attention` to `fla-org` ([repo](https://github.com/fla-org/flash-bidirectional-linear-attention)) +- **$\texttt{[2024-12]}$:** 🎉 Add Gated DeltaNet implementation to `fla` ([paper](https://arxiv.org/abs/2412.06464)). +- **$\texttt{[2024-12]}$:** 🚀 `fla` now officially supports kernels with variable-length inputs. +- **$\texttt{[2024-11]}$:** The inputs are now switched from head-first to seq-first format. +- **$\texttt{[2024-11]}$:** 💥 `fla` now provides a flexible way for training hybrid models. +- **$\texttt{[2024-10]}$:** 🔥 Announcing `flame`, a minimal and scalable framework for training `fla` models. Check out the details [here](training/README.md). +- **$\texttt{[2024-09]}$:** `fla` now includes a fused linear and cross-entropy layer, significantly reducing memory usage during training. +- **$\texttt{[2024-09]}$:** 🎉 Add GSA implementation to `fla` ([paper](https://arxiv.org/abs/2409.07146)). +- **$\texttt{[2024-05]}$:** 🎉 Add DeltaNet implementation to `fla` ([paper](https://arxiv.org/abs/2102.11174)). +- **$\texttt{[2024-05]}$:** 💥 `fla` v0.1: a variety of subquadratic kernels/layers/models integrated (RetNet/GLA/Mamba/HGRN/HGRN2/RWKV6, etc., see [Models](#models)). +- **$\texttt{[2023-12]}$:** 💥 Launched `fla`, offering a collection of implementations for state-of-the-art linear attention models. + +## Models + +Roughly sorted according to the timeline supported in `fla`. The recommended training mode is `chunk` when available. + +| Year | Venue | Model | Paper | Code | | +| :--- | :------ | :------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------: | +| 2023 | | RetNet | [Retentive network: a successor to transformer for large language models](https://arxiv.org/abs/2307.08621) | [official](https://github.com/microsoft/torchscale/tree/main) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/multiscale_retention.py) | +| 2024 | ICML | GLA | [Gated Linear Attention Transformers with Hardware-Efficient Training](https://arxiv.org/abs/2312.06635) | [official](https://github.com/berlino/gated_linear_attention) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/gla.py) | +| 2024 | ICML | Based | [Simple linear attention language models balance the recall-throughput tradeoff](https://arxiv.org/abs/2402.18668) | [official](https://github.com/HazyResearch/based) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/based.py) | +| 2024 | ACL | Rebased | [Linear Transformers with Learnable Kernel Functions are Better In-Context Models](https://arxiv.org/abs/2402.10644) | [official](https://github.com/corl-team/rebased/) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/rebased.py) | +| 2024 | NeurIPS | DeltaNet | [Parallelizing Linear Transformers with Delta Rule over Sequence Length](https://arxiv.org/abs/2406.06484) | [official](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/delta_net.py) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/delta_net.py) | +| 2022 | ACL | ABC | [ABC: Attention with Bounded-memory Control](https://arxiv.org/abs/2110.02488) | | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/abc.py) | +| 2023 | NeurIPS | HGRN | [Hierarchically Gated Recurrent Neural Network for Sequence Modeling](https://openreview.net/forum?id=P1TCHxJwLB) | [official](https://github.com/OpenNLPLab/HGRN) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/hgrn.py) | +| 2024 | COLM | HGRN2 | [HGRN2: Gated Linear RNNs with State Expansion](https://arxiv.org/abs/2404.07904) | [official](https://github.com/OpenNLPLab/HGRN2) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/hgrn2.py) | +| 2024 | COLM | RWKV6 | [Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence](https://arxiv.org/abs/2404.05892) | [official](https://github.com/RWKV/RWKV-LM) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/rwkv6.py) | +| 2024 | | LightNet | [You Only Scan Once: Efficient Multi-dimension Sequential Modeling with LightNet](https://arxiv.org/abs/2405.21022) | [official](https://github.com/OpenNLPLab/LightNet) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/lightnet.py) | +| 2025 | ICLR | Samba | [Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling](https://arxiv.org/abs/2406.07522) | [official](https://github.com/microsoft/Samba) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/models/samba) | +| 2024 | ICML | Mamba2 | [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://arxiv.org/abs/2405.21060) | [official](https://github.com/state-spaces/mamba) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/models/mamba2) | +| 2024 | NeurIPS | GSA | [Gated Slot Attention for Efficient Linear-Time Sequence Modeling](https://arxiv.org/abs/2409.07146) | [official](https://github.com/fla-org/flash-linear-attention/tree/main/fla/models/gsa) | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/models/gsa) | +| 2025 | ICLR | Gated DeltaNet | [Gated Delta Networks: Improving Mamba2 with Delta Rule](https://arxiv.org/abs/2412.06464) | [official](https://github.com/NVlabs/GatedDeltaNet) | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/gated_delta_rule) | +| 2025 | | RWKV7 | [RWKV-7 "Goose" with Expressive Dynamic State Evolution](https://arxiv.org/abs/2503.14456) | [official](https://github.com/BlinkDL/RWKV-LM/tree/main/RWKV-v7) | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/rwkv7) | +| 2025 | | NSA | [Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention](https://arxiv.org/abs/2502.11089) | | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/nsa) | +| 2025 | ICLR | FoX | [Forgetting Transformer: Softmax Attention with a Forget Gate](https://arxiv.org/abs/2503.02130) | [official](https://github.com/zhixuan-lin/forgetting-transformer) | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/forgetting_attn) | +| 2025 | | DeltaProduct | [DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products](https://arxiv.org/abs/2502.10297) | | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/layers/gated_deltaproduct.py) | +| 2025 | ICLR | Rodimus* | [Rodimus*: Breaking the Accuracy-Efficiency Trade-Off with Efficient Attentions](https://arxiv.org/abs/2410.06577) | [official](https://github.com/codefuse-ai/rodimus) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/rodimus.py) | +| 2025 | | MesaNet | [MesaNet: Sequence Modeling by Locally Optimal Test-Time Training](https://arxiv.org/abs/2506.05233) | | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/mesa_net.py) | +| 2025 | | Comba | [Comba: Improving Bilinear RNNs with Closed-loop Control](https://arxiv.org/abs/2506.02475) | [official](https://github.com/AwesomeSeq/Comba-triton) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/comba.py) | +| 2025 | | PaTH | [PaTH Attention: Position Encoding via Accumulating Householder Transformations](https://arxiv.org/abs/2505.16381) | | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/path_attn.py) | +| 2025 | | MoM | [MoM: Linear Sequence Modeling with Mixture-of-Memories](https://arxiv.org/abs/2502.13685) | [official](https://github.com/OpenSparseLLMs/MoM) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/mom.py) | +| 2025 | | Log-Linear Attention | [Log-Linear Attention](https://arxiv.org/abs/2506.04761) | [official](https://github.com/HanGuo97/log-linear-attention) | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/log_linear_attn) | +| 2025 | | DeltaFormer | [Understanding Transformer from the Perspective of Associative Memory](https://arxiv.org/abs/2505.19488v1) | | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/deltaformer.py) | +| 2025 | | KDA | [Kimi Linear: An Expressive, Efficient Attention Architecture](https://arxiv.org/abs/2510.26692) | | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/kda) | + +## Installation + +[![nvidia-4090-ci](https://github.com/fla-org/flash-linear-attention/actions/workflows/nvidia-4090.yml/badge.svg?branch=main&event=push)](https://github.com/fla-org/flash-linear-attention/actions/workflows/nvidia-4090.yml) [![nvidia-a100-ci](https://github.com/fla-org/flash-linear-attention/actions/workflows/nvidia-a100.yml/badge.svg?branch=main)](https://github.com/fla-org/flash-linear-attention/actions/workflows/nvidia-a100.yml) [![nvidia-h100-ci](https://github.com/fla-org/flash-linear-attention/actions/workflows/nvidia-h100.yml/badge.svg?branch=main&event=push)](https://github.com/fla-org/flash-linear-attention/actions/workflows/nvidia-h100.yml) [![intel-b580-ci](https://github.com/fla-org/flash-linear-attention/actions/workflows/intel-b580.yml/badge.svg?event=push)](https://github.com/fla-org/flash-linear-attention/actions/workflows/intel-b580.yml) + +The following requirements should be satisfied +- [PyTorch](https://pytorch.org/) >= 2.5 +- [Triton](https://github.com/openai/triton) >=3.0 (or nightly version, see [FAQs](FAQs.md)) +- [einops](https://einops.rocks/) +- [transformers](https://github.com/huggingface/transformers) >=4.45.0 +- [datasets](https://github.com/huggingface/datasets) >=3.3.0 + +Starting from v0.3.2, the packages published on PyPI are `fla-core` and `flash-linear-attention`. The former contains all our customized kernels and only depends on PyTorch, Triton, and einops. The latter is an extension package of the former, containing `fla/layers` and `fla/models`, and depends on transformers. We also provide Triton implementations for conv1d operations, so causal-conv1d is not required. + +You can install `fla` with pip: +```sh +pip install flash-linear-attention +``` + +As `fla` is actively developed now, for the latest features and updates, an alternative way is to install the package from source. Note that installing from git uses the default mode, so you need to uninstall both `fla-core` and `flash-linear-attention` first: +```sh +# uninstall both packages first to ensure a successful upgrade +pip uninstall fla-core flash-linear-attention -y && pip install -U git+https://github.com/fla-org/flash-linear-attention +``` +or manage `fla` with submodules +```sh +git submodule add https://github.com/fla-org/flash-linear-attention.git 3rdparty/flash-linear-attention +ln -s 3rdparty/flash-linear-attention/fla fla +``` + +If you have installed `triton-nightly` and `torch` pre version, please use the following command: +```sh +pip install einops ninja datasets transformers numpy +# uninstall both packages first to ensure a successful upgrade +pip uninstall fla-core flash-linear-attention -y && pip install -U --no-use-pep517 git+https://github.com/fla-org/flash-linear-attention --no-deps +``` + + +## Usage + +### Token Mixing + +We provide ``token mixing'' linear attention layers in `fla.layers` for you to use. +You can replace the standard multihead attention layer in your model with other linear attention layers. +Example usage is as follows: +```py +>>> import torch +>>> from fla.layers import MultiScaleRetention +>>> batch_size, num_heads, seq_len, hidden_size = 32, 4, 2048, 1024 +>>> device, dtype = 'cuda:0', torch.bfloat16 +>>> retnet = MultiScaleRetention(hidden_size=hidden_size, num_heads=num_heads).to(device=device, dtype=dtype) +>>> retnet +MultiScaleRetention( + (q_proj): Linear(in_features=1024, out_features=1024, bias=False) + (k_proj): Linear(in_features=1024, out_features=1024, bias=False) + (v_proj): Linear(in_features=1024, out_features=2048, bias=False) + (g_proj): Linear(in_features=1024, out_features=2048, bias=False) + (o_proj): Linear(in_features=2048, out_features=1024, bias=False) + (g_norm_swish_gate): FusedRMSNormGated(512, eps=1e-05, activation=swish) + (rotary): RotaryEmbedding(dim=256, base=10000.0, interleaved=False, pos_idx_in_fp32=True) +) +>>> x = torch.randn(batch_size, seq_len, hidden_size).to(device=device, dtype=dtype) +>>> y, *_ = retnet(x) +>>> y.shape +torch.Size([32, 2048, 1024]) +``` + +We provide the implementations of models that are compatible with 🤗 Transformers library. +Here's an example of how to initialize a GLA model from the default configs in `fla`: + +```py +>>> from fla.models import GLAConfig +>>> from transformers import AutoModelForCausalLM +>>> config = GLAConfig() +>>> config +GLAConfig { + "attn": null, + "attn_mode": "chunk", + "bos_token_id": 1, + "clamp_min": null, + "conv_size": 4, + "elementwise_affine": true, + "eos_token_id": 2, + "expand_k": 0.5, + "expand_v": 1, + "feature_map": null, + "fuse_cross_entropy": true, + "fuse_norm": true, + "fuse_swiglu": true, + "hidden_act": "swish", + "hidden_ratio": 4, + "hidden_size": 2048, + "initializer_range": 0.006, + "intermediate_size": null, + "max_position_embeddings": 2048, + "model_type": "gla", + "norm_eps": 1e-06, + "num_heads": 4, + "num_hidden_layers": 24, + "num_kv_heads": null, + "tie_word_embeddings": false, + "transformers_version": "4.50.1", + "use_cache": true, + "use_gk": true, + "use_gv": false, + "use_output_gate": true, + "use_short_conv": false, + "vocab_size": 32000 +} + +>>> AutoModelForCausalLM.from_config(config) +GLAForCausalLM( + (model): GLAModel( + (embeddings): Embedding(32000, 2048) + (layers): ModuleList( + (0-23): 24 x GLABlock( + (attn_norm): RMSNorm(2048, eps=1e-06) + (attn): GatedLinearAttention( + (q_proj): Linear(in_features=2048, out_features=1024, bias=False) + (k_proj): Linear(in_features=2048, out_features=1024, bias=False) + (v_proj): Linear(in_features=2048, out_features=2048, bias=False) + (g_proj): Linear(in_features=2048, out_features=2048, bias=False) + (gk_proj): Sequential( + (0): Linear(in_features=2048, out_features=16, bias=False) + (1): Linear(in_features=16, out_features=1024, bias=True) + ) + (o_proj): Linear(in_features=2048, out_features=2048, bias=False) + (g_norm_swish_gate): FusedRMSNormGated(512, eps=1e-06, activation=swish) + ) + (mlp_norm): RMSNorm(2048, eps=1e-06) + (mlp): GatedMLP( + (gate_proj): Linear(in_features=2048, out_features=5632, bias=False) + (up_proj): Linear(in_features=2048, out_features=5632, bias=False) + (down_proj): Linear(in_features=5632, out_features=2048, bias=False) + (swiglu_linear): SwiGLULinear() + ) + ) + ) + (norm): RMSNorm(2048, eps=1e-06) + ) + (lm_head): Linear(in_features=2048, out_features=32000, bias=False) +) +``` + +### Fused Modules + +We offer a collection of fused modules in `fla.modules` to facilitate faster training: + +* [`Rotary Embedding`](fla/modules/rotary.py): rotary positional embeddings as adopted by the Llama architecture, a.k.a., Transformer++. +* [`Norm Layers`](fla/modules/layernorm.py): + * `RMSNorm`, `LayerNorm` and `GroupNorm` + * `RMSNormLinear`, `LayerNormLinear` and `GroupNormLinear` to reduce memory usage of intermediate tensors for improved memory efficiency. +* [`Norm Layers with Gating`](fla/modules/fused_norm_gate.py): combine norm layers with element-wise sigmoid or swish gating, as used by RetNet/GLA. +* [`Cross Entropy`](fla/modules/fused_cross_entropy.py): faster Triton implementation of cross entropy loss. +* [`Linear Cross Entropy`](fla/modules/fused_linear_cross_entropy.py): fused linear layer and cross entropy loss to avoid the materialization of large logits tensors. Also refer to implementations by [mgmalek](https://github.com/mgmalek/efficient_cross_entropy) and [Liger-Kernel](https://github.com/linkedin/Liger-Kernel/blob/main/src/liger_kernel/ops/fused_linear_cross_entropy.py). +* [`Linear KL Divergence`](fla/modules/fused_kl_div.py): fused linear layer and KL divergence loss in a similar vein as CE loss. + +> [!IMPORTANT] +> You can control using `fuse_linear_cross_entropy` in the model configuration to enable/disable the fused linear cross entropy loss. +> +> This fused implementation is more memory-efficient but may reduce numerical precision. Due to this trade-off, it is disabled by default. +> If you enable this feature and encounter training instability (e.g., loss divergence), we recommend disabling it to see if the issue is resolved. + +### Generation + +Upon successfully pretraining a model, it becomes accessible for generating text using the 🤗 text generation APIs. +In the following, we give a generation example: +```py +>>> import fla +>>> from transformers import AutoModelForCausalLM, AutoTokenizer +>>> name = 'fla-hub/gla-1.3B-100B' +>>> tokenizer = AutoTokenizer.from_pretrained(name) +>>> model = AutoModelForCausalLM.from_pretrained(name).cuda() +>>> input_prompt = "Power goes with permanence. Impermanence is impotence. And rotation is castration." +>>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.cuda() +>>> outputs = model.generate(input_ids, max_length=64) +>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] +``` + +We also provide a simple script [here](benchmarks/benchmark_generation.py) for benchmarking the generation speed. +Simply run it by: +```sh +$ python -m benchmarks.benchmark_generation \ + --path 'fla-hub/gla-1.3B-100B' \ + --repetition_penalty 2. \ + --prompt="Hello everyone, I'm Songlin Yang" + +Prompt: +Hello everyone, I'm Songlin Yang +Generated: +Hello everyone, I'm Songlin Yang. +I am a 20 year old girl from China who is currently studying in the United States of America for my Master degree and also working as an English teacher at school here on campus since last summer (1st semester). My main goal to be able do well with this course so that we can have + +Prompt length: 10, generation length: 64 +Total prompt processing + decoding time: 4593ms +``` + +All of the pretrained models currently available can be found in [`fla-hub`](https://huggingface.co/fla-hub). +```py +>>> from huggingface_hub import list_models +>>> for model in list_models(author='fla-hub'): print(model.id) +``` + +### Hybrid Models + +`fla` provides a flexible method to incorporate standard attention layers into existing linear attention models. +This is easily achieved by specifying the `attn` argument in the model configuration. + +For example, to create a 2-layer Samba model with interleaved Mamba and local attention layers, using a sliding window size of 2048: + +```py +>>> from fla.models import SambaConfig +>>> from transformers import AutoModelForCausalLM +>>> config = SambaConfig(num_hidden_layers=2) +>>> config.attn = { + 'layers': [1], + 'num_heads': 18, + 'num_kv_heads': 18, + 'qkv_bias': False, + 'rope_theta': 10000., + 'window_size': 2048 +} +>>> config +SambaConfig { + "attn": { + "layers": [ + 1 + ], + "num_heads": 18, + "num_kv_heads": 18, + "qkv_bias": false, + "rope_theta": 10000.0, + "window_size": 2048 + }, + "bos_token_id": 1, + "conv_kernel": 4, + "eos_token_id": 2, + "expand": 2, + "fuse_cross_entropy": true, + "fuse_norm": true, + "fuse_swiglu": true, + "hidden_act": "swish", + "hidden_ratio": 4, + "hidden_size": 2304, + "initializer_range": 0.02, + "intermediate_size": 4608, + "max_position_embeddings": 2048, + "model_type": "samba", + "norm_eps": 1e-05, + "num_hidden_layers": 2, + "pad_token_id": 0, + "rescale_prenorm_residual": false, + "residual_in_fp32": false, + "state_size": 16, + "tie_word_embeddings": false, + "time_step_floor": 0.0001, + "time_step_init_scheme": "random", + "time_step_max": 0.1, + "time_step_min": 0.001, + "time_step_rank": 144, + "time_step_scale": 1.0, + "transformers_version": "4.50.1", + "use_bias": false, + "use_cache": true, + "use_conv_bias": true, + "vocab_size": 32000 +} + +>>> AutoModelForCausalLM.from_config(config) +SambaForCausalLM( + (backbone): SambaModel( + (embeddings): Embedding(32000, 2304) + (layers): ModuleList( + (0): SambaBlock( + (mixer_norm): RMSNorm(2304, eps=1e-05) + (mixer): Mamba( + (conv1d): Conv1d(4608, 4608, kernel_size=(4,), stride=(1,), padding=(3,), groups=4608) + (in_proj): Linear(in_features=2304, out_features=9216, bias=False) + (x_proj): Linear(in_features=4608, out_features=176, bias=False) + (dt_proj): Linear(in_features=144, out_features=4608, bias=True) + (out_proj): Linear(in_features=4608, out_features=2304, bias=False) + ) + (mlp_norm): RMSNorm(2304, eps=1e-05) + (mlp): GatedMLP( + (gate_proj): Linear(in_features=2304, out_features=6144, bias=False) + (up_proj): Linear(in_features=2304, out_features=6144, bias=False) + (down_proj): Linear(in_features=6144, out_features=2304, bias=False) + (swiglu_linear): SwiGLULinear() + ) + ) + (1): SambaBlock( + (mixer_norm): RMSNorm(2304, eps=1e-05) + (mixer): Attention( + (q_proj): Linear(in_features=2304, out_features=2304, bias=False) + (k_proj): Linear(in_features=2304, out_features=2304, bias=False) + (v_proj): Linear(in_features=2304, out_features=2304, bias=False) + (o_proj): Linear(in_features=2304, out_features=2304, bias=False) + (rotary): RotaryEmbedding(dim=128, base=10000.0, interleaved=False, pos_idx_in_fp32=True) + ) + (mlp_norm): RMSNorm(2304, eps=1e-05) + (mlp): GatedMLP( + (gate_proj): Linear(in_features=2304, out_features=6144, bias=False) + (up_proj): Linear(in_features=2304, out_features=6144, bias=False) + (down_proj): Linear(in_features=6144, out_features=2304, bias=False) + (swiglu_linear): SwiGLULinear() + ) + ) + ) + (norm_f): RMSNorm(2304, eps=1e-05) + ) + (lm_head): Linear(in_features=2304, out_features=32000, bias=False) +) +``` + +During inference, you **DO NOT** need to revise anything for generation! +The model will produce output as-is, without any need for additional configurations or modifications. + +## Training + +We provide a minimal framework called [🔥 `flame`](https://github.com/fla-org/flame) built on top of `torchtitan`, for efficient training of `fla` models. + +Checkout [the GLA example](https://github.com/fla-org/flash-linear-attention/blob/main/examples/training.md) for more details. + +## Evaluation + +The [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) library allows you to easily perform (zero-shot) model evaluations. +Follow the steps below to use this library: + +1. Install `lm_eval` following [their instructions](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/README.md). + +2. Run evaluation with: +```sh +$ MODEL='fla-hub/gla-1.3B-100B' +$ python -m evals.harness --model hf \ + --model_args pretrained=$MODEL,dtype=bfloat16 \ + --tasks wikitext,lambada_openai,piqa,hellaswag,winogrande,arc_easy,arc_challenge,boolq,sciq,copa,openbookqa \ + --batch_size 64 \ + --num_fewshot 0 \ + --device cuda \ + --show_config +``` + +We've made `fla` compatible with hf-style evaluations, you can call [evals.harness](evals/harness.py) to finish the evaluations. +Running the command above will provide the task results reported in the GLA paper. + +3. Multi-GPU Evaluation with Hugging Face accelerate 🚀 + +To perform data-parallel evaluation (where each GPU loads a separate full copy of the model), we leverage the accelerate launcher as follows: +```sh +$ MODEL='fla-hub/gla-1.3B-100B' +$ accelerate launch -m evals.harness --model hf \ + --model_args pretrained=$MODEL,dtype=bfloat16,trust_remote_code=True \ + --tasks wikitext,lambada_openai,piqa,hellaswag,winogrande,arc_easy,arc_challenge,boolq,sciq,copa,openbookqa \ + --batch_size 64 \ + --num_fewshot 0 \ + --device cuda \ + --show_config \ + --trust_remote_code +``` + +4. 📏 RULER Benchmark suite + +The RULER benchmarks are commonly used for evaluating model performance on long-context tasks. +You can evaluate `fla` models on RULER directly using `lm-evaluation-harness`. RULER is only available in a relatively recent version of `lm-evaluation-harness`, so make sure you have the latest version installed. + +``` +git clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness +cd lm-evaluation-harness +pip install -e . +``` + + +Then, install the necessary dependencies for RULER: +```sh +pip install lm_eval["ruler"] +``` +and run evaluation by (e.g., 32k contexts): +```sh +$ accelerate launch -m evals.harness \ + --output_path $OUTPUT \ + --tasks niah_single_1,niah_single_2,niah_single_3,niah_multikey_1,niah_multikey_2,niah_multikey_3,niah_multiquery,niah_multivalue,ruler_vt,ruler_cwe,ruler_fwe,ruler_qa_hotpot,ruler_qa_squad \ + --model_args pretrained=$MODEL,dtype=bfloat16,max_length=32768,trust_remote_code=True \ + --metadata='{"max_seq_lengths":[4096,8192,16384,32768]}' \ + --batch_size 2 \ + --show_config \ + --trust_remote_code +``` + +If a GPU can't load a full copy of the model, please refer to [this link](https://github.com/EleutherAI/lm-evaluation-harness?tab=readme-ov-file#multi-gpu-evaluation-with-hugging-face-accelerate) for FSDP settings. + +> [!Tip] +> If you are using `lm-evaluation-harness` as an external library and can't find (almost) any tasks available, before calling `lm_eval.evaluate()` or `lm_eval.simple_evaluate()`, simply run the following to load the library's stock tasks! +```py +>>> from lm_eval.tasks import TaskManager; TaskManager().initialize_tasks() +``` + +## Benchmarks + +We compared our Triton-based RetNet implementation with CUDA-based FlashAttention2, using a batch size of 8, 32 heads, and a head dimension of 128, across different sequence lengths. +These tests were conducted on a single H100 80GB GPU, as illustrated in the following graph +```py +# you might have to first install `fla` to enable its import via `pip install -e .` +$ python benchmark_retention.py +Performance: + T chunk_fwd parallel_fwd flash_fwd chunk_fwdbwd parallel_fwdbwd flash_fwdbwd +0 128.0 0.264032 0.243536 0.083488 1.301856 1.166784 0.320704 +1 256.0 0.273472 0.252848 0.094304 1.345872 1.300608 0.807936 +2 512.0 0.303600 0.278896 0.098112 1.503168 1.433184 0.857216 +3 1024.0 0.357248 0.367360 0.156528 1.773552 2.303424 1.160864 +4 2048.0 0.454624 0.605616 0.340928 2.283728 4.483360 1.955936 +5 4096.0 0.638960 1.378016 1.004992 3.374720 12.271215 4.813776 +6 8192.0 1.012352 4.201344 3.625008 5.581808 40.833618 15.023697 +7 16384.0 1.748512 14.489664 13.710080 10.191552 153.093765 54.336864 +``` + +
        + image +
        + + +## Citation +If you find this repository helpful, please cite our work: +```bib +@software{yang2024fla, + title = {FLA: A Triton-Based Library for Hardware-Efficient Implementations of Linear Attention Mechanism}, + author = {Yang, Songlin and Zhang, Yu}, + url = {https://github.com/fla-org/flash-linear-attention}, + month = jan, + year = {2024} +} + +@misc{zhang2025kda, + title = {Kimi Linear: An Expressive, Efficient Attention Architecture}, + author = {Zhang, Yu and Lin, Zongyu and Yao, Xingcheng and Hu, Jiaxi and Meng, Fanqing and Liu, Chengyin and Men, Xin and Yang, Songlin and Li, Zhiyuan and Li, Wentao and Lu, Enzhe and Liu, Weizhou and Chen, Yanru and Xu, Weixin and Yu, Longhui and Wang, Yejie and Fan, Yu and Zhong, Longguang and Yuan, Enming and Zhang, Dehao and Zhang, Yizhi and T. Liu, Y. and Wang, Haiming and Fang, Shengjun and He, Weiran and Liu, Shaowei and Li, Yiwei and Su, Jianlin and Qiu, Jiezhong and Pang, Bo and Yan, Junjie and Jiang, Zhejun and Huang, Weixiao and Yin, Bohong and You, Jiacheng and Wei, Chu and Wang, Zhengtao and Hong, Chao and Chen, Yutian and Chen, Guanduo and Wang, Yucheng and Zheng, Huabin and Wang, Feng and Liu, Yibo and Dong, Mengnan and Zhang, Zheng and Pan, Siyuan and Wu, Wenhao and Wu, Yuhao and Guan, Longyu and Tao, Jiawen and Fu, Guohong and Xu, Xinran and Wang, Yuzhi and Lai, Guokun and Wu, Yuxin and Zhou, Xinyu and Yang, Zhilin and Du, Yulun}, + year = {2025}, + eprint = {2510.26692}, + archivePrefix = {arXiv}, + primaryClass = {cs.CL} +} + +@inproceedings{yang2025path, + title = {PaTH Attention: Position Encoding via Accumulating Householder Transformations}, + author = {Yang, Songlin and Shen, Yikang and Wen, Kaiyue and Tan, Shawn and Mishra, Mayank and Ren, Liliang and Panda, Rameswar and Kim, Yoon}, + booktitle = {Proceedings of NeurIPS}, + year = {2025} +} + +@inproceedings{yang2024gdn, + title = {Gated Delta Networks: Improving Mamba2 with Delta Rule}, + author = {Yang, Songlin and Kautz, Jan and Hatamizadeh, Ali}, + booktitle = {Proceedings of ICLR}, + year = {2025} +} + +@inproceedings{yang2024deltanet, + title = {Parallelizing Linear Transformers with the Delta Rule over Sequence Length}, + author = {Yang, Songlin and Wang, Bailin and Zhang, Yu and Shen, Yikang and Kim, Yoon}, + booktitle = {Proceedings of NeurIPS}, + year = {2024} +} + +@inproceedings{zhang2024gsa, + title = {Gated Slot Attention for Efficient Linear-Time Sequence Modeling}, + author = {Zhang, Yu and Yang, Songlin and Zhu, Ruijie and Zhang, Yue and Cui, Leyang and Wang, Yiqiao and Wang, Bolun and Shi, Freda and Wang, Bailin and Bi, Wei and Zhou, Peng and Fu, Guohong}, + booktitle = {Proceedings of NeurIPS}, + year = {2024} +} + +@inproceedings{qin2024hgrn2, + title = {HGRN2: Gated Linear RNNs with State Expansion}, + author = {Qin, Zhen and Yang, Songlin and Sun, Weixuan and Shen, Xuyang and Li, Dong and Sun, Weigao and Zhong, Yiran}, + booktitle = {Proceedings of COLM}, + year = {2024} +} + +@inproceedings{yang2024gla, + title = {Gated Linear Attention Transformers with Hardware-Efficient Training}, + author = {Yang, Songlin and Wang, Bailin and Shen, Yikang and Panda, Rameswar and Kim, Yoon}, + booktitle = {Proceedings of ICML}, + year = {2024} +} +``` + +## Star History + +[![Stargazers repo roster for @fla-org/flash-linear-attention](https://bytecrank.com/nastyox/reporoster/php/stargazersSVG.php?user=fla-org&repo=flash-linear-attention)](https://github.com/fla-org/flash-linear-attention/stargazers) + +[![Star History Chart](https://api.star-history.com/svg?repos=fla-org/flash-linear-attention&type=Date)](https://star-history.com/#fla-org/flash-linear-attention&Date) + +## Acknowledgements + +We extend our gratitude to [Bitdeer](https://www.bitdeer.com/) and [Moonshot AI](https://www.moonshot.ai/) for their support in maintaining and powering our project infrastructure. diff --git a/code/flash-linear-attention/benchmarks/benchmark_generation.py b/code/flash-linear-attention/benchmarks/benchmark_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..c7e6becc1b6335daecc0622e5d00569d707bd8fc --- /dev/null +++ b/code/flash-linear-attention/benchmarks/benchmark_generation.py @@ -0,0 +1,90 @@ +# Copyright (c) 2023-2024, Songlin Yang, Yu Zhang. + +import argparse +import time + +import torch +from datasets import load_dataset +from transformers import AutoModelForCausalLM, AutoTokenizer + +import fla # noqa + + +def sizeof_fmt(num, suffix='B'): + for unit in ('', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi'): + if abs(num) < 1024.0: + return f'{num:3.1f}{unit}{suffix}' + num /= 1024.0 + return f'{num:.1f}Yi{suffix}' + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Generation benchmarking") + parser.add_argument("--path", type=str, default="fla-hub/transformer-1.3B-100B") + parser.add_argument("--data", type=str, default="fla-hub/pg19") + parser.add_argument("--length", type=int, default=128) + parser.add_argument("--maxlen", type=int, default=256) + parser.add_argument("--no-cache", action='store_true') + parser.add_argument("--temperature", type=float, default=0.5) + parser.add_argument("--topp", type=float, default=0.2) + parser.add_argument("--repetition_penalty", type=float, default=1.1) + parser.add_argument("--output-generation", action='store_true') + parser.add_argument("--compile", action='store_true') + args = parser.parse_args() + + device = "cuda" + dtype = torch.bfloat16 + torch.manual_seed(0) + + print(f"Loading {args.path}") + tokenizer = AutoTokenizer.from_pretrained( + args.path, + trust_remote_code=True, + add_eos_token=False, + ) + tokenizer.pad_token_id = tokenizer.eos_token_id + print(f"{tokenizer}") + + model = AutoModelForCausalLM.from_pretrained( + args.path, + device_map={"": device}, + torch_dtype=dtype, + use_cache=not args.no_cache, + ) + if args.compile: + print("Compiling the model") + model = torch.compile(model) + model.eval() + print(f"{model.config}\n{model}\nNumber of parameters: {model.num_parameters()} ({sizeof_fmt(model.num_parameters())})\n") + + print(f"Loading {args.data}") + dataset = load_dataset(args.data, split='train', trust_remote_code=True) + print(f"{dataset}") + + prompt = dataset[0]['text'] + tokens = tokenizer(prompt, return_tensors="pt") + input_ids = tokens.input_ids.to(device=device)[:, :args.length].contiguous() + max_length = input_ids.shape[1] + args.maxlen + + torch.cuda.synchronize() + start = time.time() + with torch.inference_mode(): + text = model.generate( + input_ids=input_ids, + use_cache=not args.no_cache, + max_length=max_length, + pad_token_id=tokenizer.eos_token_id, + eos_token_id=tokenizer.bos_token_id, + do_sample=True, + temperature=args.temperature, + top_p=args.topp, + repetition_penalty=args.repetition_penalty, + ) + torch.cuda.synchronize() + elapsed = time.time() - start + if args.output_generation: + print(f"Prompt:\n{tokenizer.batch_decode(input_ids, skip_special_tokens=True)[0].strip()}\n") + print(f"Generated:\n{tokenizer.batch_decode(text, skip_special_tokens=True)[0].strip()}\n") + print(f"Prompt length: {len(input_ids[0])}, generation length: {len(text[0]) - len(input_ids[0])}") + print(f"Total prompt processing + decoding time: {elapsed * 1000:.0f}ms") + print(f"Max memory used: {sizeof_fmt(torch.cuda.max_memory_allocated())}") diff --git a/code/flash-linear-attention/benchmarks/benchmark_training_throughput.py b/code/flash-linear-attention/benchmarks/benchmark_training_throughput.py new file mode 100644 index 0000000000000000000000000000000000000000..e99555d2b214732379b68f2b12c89d007263328b --- /dev/null +++ b/code/flash-linear-attention/benchmarks/benchmark_training_throughput.py @@ -0,0 +1,157 @@ + +import argparse +import time + +import torch +from accelerate import Accelerator +from torch.cuda import max_memory_allocated, memory_allocated +from torch.optim import AdamW +from tqdm import trange +from transformers import AutoConfig, AutoModelForCausalLM, PretrainedConfig +from transformers.optimization import get_cosine_schedule_with_warmup + +import fla + +classes = [getattr(fla.models, i) for i in fla.models.__all__] +configs = {i.model_type: i() for i in classes if issubclass(i, PretrainedConfig)} + + +def sizeof_fmt(num, suffix='B'): + for unit in ('', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi'): + if abs(num) < 1024.0: + return f'{num:.2f}{unit}{suffix}' + num /= 1024.0 + return f'{num:.2f}Yi{suffix}' + + +def prepare_inputs( + batch_size: int, + seq_len: int, + context_len: int, + varlen: bool, + vocab_size: int, + device: torch.device, +): + if varlen: + tokens = torch.randint(high=vocab_size, size=(1, batch_size * seq_len), device=device) + cu_seqlens = torch.cat([ + torch.tensor([0]), + torch.randperm(batch_size * seq_len - 16)[:torch.randint(8, 64, size=(1,))] + 16, + torch.tensor([batch_size * seq_len]), + ], 0).sort()[0].to(dtype=torch.int32, device=device) + if context_len is not None: + cu_seqlens = torch.cat( + [torch.arange(i, j, context_len) for i, j in zip(cu_seqlens[:-1].tolist(), cu_seqlens[1:].tolist(), strict=False)] + + [torch.tensor([len(tokens[0])])], + ).to(dtype=torch.int32, device=device) + else: + tokens = torch.randint(high=vocab_size, size=(batch_size, seq_len), device=device) + cu_seqlens = None + return tokens, cu_seqlens + + +def profile( + name: str, + batch_size: int = 8, + seq_len: int = 2048, + context_len: int = 2048, + varlen: bool = False, + warmup_steps: int = 16, + steps: int = 32, + total_steps: int = 1024, + lr: float = 3e-4, + betas: tuple[float] = (0.9, 0.95), + weight_decay: float = 0.1, + dtype: torch.dtype | None = torch.bfloat16, + mixed_precision: str = 'bf16', + compile: bool = False, +): + device = torch.device('cuda') + config = configs[name] if name in configs else AutoConfig.from_pretrained(name) + model = AutoModelForCausalLM.from_config(config).cuda().to(dtype) + if compile: + print("Compiling the model") + model = torch.compile(model) + num_parameters = model.num_parameters() + print(f"Initializing {name} model from the config:\n{config}\n{model}") + print(f"Number of parameters in total: {num_parameters} ({sizeof_fmt(num_parameters)})") + print(f"Allocated memory after initialization: {sizeof_fmt(memory_allocated(device))}") + + accelerator = Accelerator(mixed_precision=mixed_precision) + optimizer = AdamW( + model.parameters(), + lr=lr, + betas=betas, + weight_decay=weight_decay, + fused=True, + ) + scheduler = get_cosine_schedule_with_warmup(optimizer, 0, total_steps) + + bar = trange(warmup_steps) + + model, optimizer, scheduler = accelerator.prepare(model, optimizer, scheduler) + torch.cuda.synchronize(device) + for _ in bar: + # forward pass + tokens, cu_seqlens = prepare_inputs( + batch_size=batch_size, + seq_len=seq_len, + context_len=context_len, + varlen=varlen, + vocab_size=config.vocab_size, + device=device, + ) + outputs = model(tokens, labels=tokens, cu_seqlens=cu_seqlens) + # backward pass + accelerator.backward(outputs.loss) + optimizer.step() + scheduler.step() + optimizer.zero_grad() + bar.set_description_str(f"Max memory allocated: {sizeof_fmt(max_memory_allocated(device))}") + + start, total_tokens = time.time(), 0 + bar = trange(steps) + torch.cuda.synchronize(device) + for _ in bar: + # forward pass + tokens, cu_seqlens = prepare_inputs( + batch_size=batch_size, + seq_len=seq_len, + context_len=context_len, + varlen=varlen, + vocab_size=config.vocab_size, + device=device, + ) + outputs = model(tokens, labels=tokens, cu_seqlens=cu_seqlens) + # backward pass + accelerator.backward(outputs.loss) + optimizer.step() + optimizer.zero_grad() + + total_tokens += batch_size * seq_len + torch.cuda.synchronize(device) + duration = time.time() - start + bar.set_description_str(f"Thoughput: {total_tokens / duration:10.2f} tokens/s") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--name", default='retnet') + parser.add_argument("--batch_size", default=8, type=int) + parser.add_argument("--seq_len", default=2048, type=int) + parser.add_argument("--context_len", default=None, type=int) + parser.add_argument("--varlen", action='store_true') + parser.add_argument("--warmup_steps", default=64, type=int) + parser.add_argument("--steps", default=256, type=int) + parser.add_argument("--compile", action='store_true') + args = parser.parse_args() + profile( + name=args.name, + batch_size=args.batch_size, + seq_len=args.seq_len, + context_len=args.context_len, + varlen=args.varlen, + warmup_steps=args.warmup_steps, + steps=args.steps, + compile=args.compile, + ) diff --git a/code/flash-linear-attention/benchmarks/modules/benchmark_activations.py b/code/flash-linear-attention/benchmarks/modules/benchmark_activations.py new file mode 100644 index 0000000000000000000000000000000000000000..41ddb1d9a2694c94a8f5e35fa57d1436fd5519f7 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/modules/benchmark_activations.py @@ -0,0 +1,102 @@ + +import torch +import triton + +from fla.modules.activations import fast_gelu_impl as gelu +from fla.modules.activations import logsigmoid, sigmoid, sqrelu, swiglu, swish +from fla.utils import device + +DTYPE = torch.bfloat16 + + +def fwd(fn, *args): + return fn(*args) + + +def fwdbwd(fn, *args): + y = fn(*args) + g = torch.randn_like(y) + y.backward(g) + + +@triton.testing.perf_report( + triton.testing.Benchmark( + x_names=['B', 'T', 'D'], + x_vals=[ + (b, t, d) + for b in [4] + for t in [512, 1024, 2048, 4096, 8192] + for d in [1024, 2048, 4096] + ], + line_arg='provider', + line_vals=[ + 'sigmoid_fwd', 'sigmoid_fwdbwd', + 'logsigmoid_fwd', 'logsigmoid_fwdbwd', + 'swish_fwd', 'swish_fwdbwd', + 'gelu_fwd', 'gelu_fwdbwd', + 'sqrelu_fwd', 'sqrelu_fwdbwd', + 'swiglu_fwd', 'swiglu_fwdbwd', + ], + line_names=[ + 'sigmoid_fwd', 'sigmoid_fwdbwd', + 'logsigmoid_fwd', 'logsigmoid_fwdbwd', + 'swish_fwd', 'swish_fwdbwd', + 'gelu_fwd', 'gelu_fwdbwd', + 'sqrelu_fwd', 'sqrelu_fwdbwd', + 'swiglu_fwd', 'swiglu_fwdbwd', + ], + styles=[('green', '-'), ('green', '--'), + ('blue', '-'), ('blue', '--'), + ('red', '-'), ('red', '--'), + ('cyan', '-'), ('cyan', '--'), + ('magenta', '-'), ('magenta', '--'), + ('yellow', '-'), ('yellow', '--')], + ylabel="Time (ms)", + plot_name="activation_performance", + args={}, + ), +) +def benchmark(B, T, D, provider): + requires_grad = True + x = torch.randn(B, T, D, device=device, dtype=DTYPE, requires_grad=requires_grad) + + if 'swiglu' in provider: + y = torch.randn_like(x) + inputs = (x, y) + elif 'bias_gelu' in provider: + bias = torch.randn(D, device=device, dtype=DTYPE, requires_grad=True) + inputs = (x, bias) + else: + inputs = (x,) + + if provider.startswith('sigmoid'): + fn = sigmoid + elif provider.startswith('logsigmoid'): + fn = logsigmoid + elif provider.startswith('swish'): + fn = swish + elif provider.startswith('gelu'): + fn = gelu + elif provider.startswith('sqrelu'): + fn = sqrelu + elif provider.startswith('swiglu'): + fn = swiglu + else: + raise ValueError(provider) + + if provider.endswith('fwd'): + fn_to_call = lambda: fwd(fn, *inputs) # noqa: E731 + elif provider.endswith('fwdbwd'): + fn_to_call = lambda: fwdbwd(fn, *inputs) # noqa: E731 + else: + raise ValueError(provider) + + ms, min_ms, max_ms = triton.testing.do_bench( + fn_to_call, + quantiles=[0.5, 0.2, 0.8], + ) + return ms, min_ms, max_ms + + +if __name__ == '__main__': + benchmark.run(print_data=True, save_path='./activation_benchmark') diff --git a/code/flash-linear-attention/benchmarks/modules/benchmark_conv.py b/code/flash-linear-attention/benchmarks/modules/benchmark_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..50972fba7e35e069e8f47f02773cc2b3a967c4be --- /dev/null +++ b/code/flash-linear-attention/benchmarks/modules/benchmark_conv.py @@ -0,0 +1,98 @@ + +import torch +import triton +from einops import rearrange + +from fla.modules.convolution import causal_conv1d +from fla.ops.utils.index import prepare_sequence_ids + +try: + from causal_conv1d import causal_conv1d_fn +except ImportError: + causal_conv1d_fn = None + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T', 'D'], + # different possible values for `x_name` + x_vals=[(128 * 2 ** i, d) for d in [256, 512, 1024, 2048, 4096] for i in range(1, 10)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['causal_conv1d_fwd', 'causal_conv1d_cuda_fwd', 'causal_conv1d_fwdbwd', 'causal_conv1d_cuda_fwdbwd'], + # label name for the lines + line_names=['causal_conv1d_fwd', 'causal_conv1d_cuda_fwd', 'causal_conv1d_fwdbwd', 'causal_conv1d_cuda_fwdbwd'], + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), + ('cyan', ':'), ('yellow', 'dotted'), ('cyan', '--'), ('cyan', '-'), ('black', ':')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, D, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, N, W = 1, 16, 4 + if T < 2048: + N = 4 + + x = torch.randn(B, T, D, device=device, requires_grad=requires_grad, dtype=dtype) + weight = torch.randn(D, W).to(device) + bias = torch.randn(D).to(device) + + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + + cu_seqlens = torch.cat([ + torch.tensor([0], dtype=torch.long), + torch.arange(16, T)[torch.randperm(T - 16)[:N-1]], + torch.tensor([T], dtype=torch.long), + ], 0).to(device).sort()[0] + if provider.startswith('causal_conv1d_fwd'): + results = triton.testing.do_bench( + lambda: causal_conv1d(x, weight, bias, activation='swish', cu_seqlens=cu_seqlens), + quantiles=quantiles, + ) + elif provider.startswith('causal_conv1d_cuda_fwd'): + results = triton.testing.do_bench( + lambda: rearrange( + causal_conv1d_fn( + x=rearrange(x, 'b t d -> b d t'), + weight=weight, + bias=bias, + activation='swish', + seq_idx=prepare_sequence_ids(cu_seqlens).to(torch.int32).unsqueeze(0), + ), + 'b d t -> b t d', + ), + quantiles=quantiles, + ) + elif provider.startswith('causal_conv1d_fwdbwd'): + results = triton.testing.do_bench( + lambda: causal_conv1d(x, weight, bias, activation='swish', cu_seqlens=cu_seqlens).backward(x), + quantiles=quantiles, + ) + elif provider.startswith('causal_conv1d_cuda_fwdbwd'): + results = triton.testing.do_bench( + lambda: rearrange( + causal_conv1d_fn( + x=rearrange(x, 'b t d -> b d t'), + weight=weight, + bias=bias, + activation='swish', + seq_idx=prepare_sequence_ids(cu_seqlens).to(torch.int32).unsqueeze(0), + ), + 'b d t -> b t d', + ).backward(x), + quantiles=quantiles, + ) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/modules/benchmark_cross_entropy.py b/code/flash-linear-attention/benchmarks/modules/benchmark_cross_entropy.py new file mode 100644 index 0000000000000000000000000000000000000000..184f2d3ad6a1f7526c79629d7c7ce9e936aa0c20 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/modules/benchmark_cross_entropy.py @@ -0,0 +1,66 @@ + +import torch +import torch.nn as nn +import torch.nn.functional as F +import triton + +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['naive', 'fused', 'fused_linear', 'naive_bwd', 'fused_bwd', 'fused_linear_bwd'], + # label name for the lines + line_names=['naive', 'fused', 'fused_linear', 'naive_bwd', 'fused_bwd', 'fused_linear_bwd'], + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), + ('cyan', ':'), ('yellow', 'dotted'), ('cyan', '--'), ('cyan', '-'), ('black', ':')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, H, V = 4, 4096, 120000 + + x = torch.randn(B * T, H, device=device, requires_grad=requires_grad, dtype=dtype) + target = torch.randint(0, V, (B * T,), device=device, dtype=torch.int64) + w = torch.randn(V, H, device=device, requires_grad=requires_grad, dtype=dtype) + b = torch.randn(V, device=device, requires_grad=requires_grad, dtype=dtype) + + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider == 'naive': + criterion = nn.CrossEntropyLoss() + results = triton.testing.do_bench(lambda: criterion(F.linear(x, w, b), target), quantiles=quantiles) + elif provider == 'naive_bwd': + criterion = nn.CrossEntropyLoss() + results = triton.testing.do_bench(lambda: criterion(F.linear(x, w, b), target).backward(), quantiles=quantiles) + elif provider == 'fused': + criterion = FusedCrossEntropyLoss() + results = triton.testing.do_bench(lambda: criterion(F.linear(x, w, b), target), quantiles=quantiles) + elif provider == 'fused_bwd': + criterion = FusedCrossEntropyLoss() + results = triton.testing.do_bench(lambda: criterion(F.linear(x, w, b), target).backward(), quantiles=quantiles) + elif provider == 'fused_linear': + criterion = FusedLinearCrossEntropyLoss() + results = triton.testing.do_bench(lambda: criterion(x, target, w, b), quantiles=quantiles) + elif provider == 'fused_linear_bwd': + criterion = FusedLinearCrossEntropyLoss() + results = triton.testing.do_bench(lambda: criterion(x, target, w, b).backward(), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/modules/benchmark_l2norm.py b/code/flash-linear-attention/benchmarks/modules/benchmark_l2norm.py new file mode 100644 index 0000000000000000000000000000000000000000..88ae4537e44fc34d4615447aecd2d2d2e4db1c9b --- /dev/null +++ b/code/flash-linear-attention/benchmarks/modules/benchmark_l2norm.py @@ -0,0 +1,62 @@ + +from functools import partial + +import torch +import torch.nn.functional as F +import triton + +from fla.modules.l2norm import l2norm + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['B', 'T', 'H', 'D'], + # different possible values for `x_name` + x_vals=[(16, 128 * 2 ** i, h, 2048//h) for h in [1, 2, 4, 8, 16] for i in range(1, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['naive', 'compiled', 'fused', 'naive_bwd', 'compiled_bwd', 'fused_bwd'], + # label name for the lines + line_names=['naive', 'compiled', 'fused', 'naive_bwd', 'compiled_bwd', 'fused_bwd'], + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), + ('cyan', ':'), ('yellow', 'dotted'), ('cyan', '--'), ('cyan', '-'), ('black', ':')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(B, H, D, T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + x = torch.randn(B * T, D, device=device, requires_grad=requires_grad, dtype=dtype) + + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider.startswith('naive'): + norm = partial(F.normalize, dim=-1, p=2) + results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles) + if provider.startswith('compiled'): + norm = torch.compile(partial(F.normalize, dim=-1, p=2)) + results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles) + if provider.startswith('fused'): + norm = l2norm + results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles) + if provider.startswith('naive_bwd'): + norm = partial(F.normalize, dim=-1, p=2) + results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles) + if provider.startswith('compiled_bwd'): + norm = torch.compile(partial(F.normalize, dim=-1, p=2)) + results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles) + if provider.startswith('fused_bwd'): + norm = l2norm + results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/modules/benchmark_layernorm.py b/code/flash-linear-attention/benchmarks/modules/benchmark_layernorm.py new file mode 100644 index 0000000000000000000000000000000000000000..dd58fd3ca7f1dd6323a0a057447d7dce30bf24d6 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/modules/benchmark_layernorm.py @@ -0,0 +1,70 @@ + +import torch +import torch.nn as nn +import triton + +from fla.modules import GroupNorm, LayerNorm + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['naive_ln', 'fused_ln', 'naive_gn', 'fused_gn', + 'naive_ln_bwd', 'fused_ln_bwd', 'naive_gn_bwd', 'fused_gn_bwd'], + # label name for the lines + line_names=['naive_ln', 'fused_ln', 'naive_gn', 'fused_gn', + 'naive_ln_bwd', 'fused_ln_bwd', 'naive_gn_bwd', 'fused_gn_bwd'], + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), + ('cyan', ':'), ('yellow', 'dotted'), ('cyan', '--'), ('cyan', '-'), ('black', ':')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, D = 16, 1024 + + x = torch.randn(B * T, D, device=device, requires_grad=requires_grad, dtype=dtype) + + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider.startswith('naive_ln'): + norm = nn.LayerNorm(D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype) + results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles) + if provider.startswith('fused_ln'): + norm = LayerNorm(D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype) + results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles) + if provider.startswith('naive_gn'): + norm = nn.GroupNorm(4, D).to(device=device, dtype=dtype) + results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles) + if provider.startswith('fused_gn'): + norm = GroupNorm(4, D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype) + results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles) + if provider.startswith('naive_ln_bwd'): + norm = nn.LayerNorm(D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype) + results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles) + if provider.startswith('fused_ln_bwd'): + norm = LayerNorm(D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype) + results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles) + if provider.startswith('naive_gn_bwd'): + norm = nn.GroupNorm(4, D).to(device=device, dtype=dtype) + results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles) + if provider.startswith('fused_gn_bwd'): + norm = GroupNorm(4, D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype) + results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/modules/benchmark_tokenshift.py b/code/flash-linear-attention/benchmarks/modules/benchmark_tokenshift.py new file mode 100644 index 0000000000000000000000000000000000000000..261ada208ada79ce33f96b7cbe3cdcb2b9c97d24 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/modules/benchmark_tokenshift.py @@ -0,0 +1,59 @@ +import torch +import torch.nn as nn +import triton + +from fla.modules.token_shift import token_shift + + +def token_shift_ref(x): + shifted = nn.functional.pad(x, (0, 0, 1, -1)) + delta = shifted - x + return delta + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 9)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['naive_token_shift', 'fused_token_shift', 'naive_token_shift_bwd', 'fused_token_shift_bwd'], + # label name for the lines + line_names=['naive_token_shift', 'fused_token_shift', 'naive_token_shift_bwd', 'fused_token_shift_bwd'], + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), + ('cyan', ':')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, D = 8, 4096 + + x = torch.randn(B, T, D, device=device, requires_grad=requires_grad, dtype=dtype) + + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider.startswith('naive_token_shift'): + results = triton.testing.do_bench(lambda: token_shift_ref(x), quantiles=quantiles) + if provider.startswith('fused_token_shift'): + results = triton.testing.do_bench(lambda: token_shift(x), quantiles=quantiles) + if provider.startswith('naive_token_shift_bwd'): + grad_output = torch.randn_like(x) + results = triton.testing.do_bench(lambda: token_shift_ref(x).backward(grad_output), quantiles=quantiles) + if provider.startswith('fused_token_shift_bwd'): + grad_output = torch.randn_like(x) + results = triton.testing.do_bench(lambda: token_shift(x).backward(grad_output), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark.py b/code/flash-linear-attention/benchmarks/ops/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..4d24cc1e5a15bf421a10b962e04f790f567c4dc7 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark.py @@ -0,0 +1,268 @@ +# Copyright (c) 2023, Tri Dao. +""" Useful functions for writing test code. """ + +import torch +import torch.utils.benchmark as benchmark + + +def benchmark_forward( + fn, *inputs, repeats=10, desc="", verbose=True, amp=False, amp_dtype=torch.float16, **kwinputs, +): + """Use Pytorch Benchmark on the forward pass of an arbitrary function.""" + if verbose: + print(desc, "- Forward pass") + + def amp_wrapper(*inputs, **kwinputs): + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + fn(*inputs, **kwinputs) + + t = benchmark.Timer( + stmt="fn_amp(*inputs, **kwinputs)", + globals={"fn_amp": amp_wrapper, "inputs": inputs, "kwinputs": kwinputs}, + num_threads=torch.get_num_threads(), + ) + m = t.timeit(repeats) + if verbose: + print(m) + return t, m + + +def benchmark_backward( + fn, + *inputs, + grad=None, + repeats=10, + desc="", + verbose=True, + amp=False, + amp_dtype=torch.float16, + **kwinputs, +): + """Use Pytorch Benchmark on the backward pass of an arbitrary function.""" + if verbose: + print(desc, "- Backward pass") + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + y = fn(*inputs, **kwinputs) + if type(y) is tuple: + y = y[0] + if grad is None: + grad = torch.randn_like(y) + else: + if grad.shape != y.shape: + raise RuntimeError("Grad shape does not match output shape") + + def f(*inputs, y, grad): + # Set .grad to None to avoid extra operation of gradient accumulation + for x in inputs: + if isinstance(x, torch.Tensor): + x.grad = None + y.backward(grad, retain_graph=True) + + t = benchmark.Timer( + stmt="f(*inputs, y=y, grad=grad)", + globals={"f": f, "inputs": inputs, "y": y, "grad": grad}, + num_threads=torch.get_num_threads(), + ) + m = t.timeit(repeats) + if verbose: + print(m) + return t, m + + +def benchmark_combined( + fn, + *inputs, + grad=None, + repeats=10, + desc="", + verbose=True, + amp=False, + amp_dtype=torch.float16, + **kwinputs, +): + """Use Pytorch Benchmark on the forward+backward pass of an arbitrary function.""" + if verbose: + print(desc, "- Forward + Backward pass") + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + y = fn(*inputs, **kwinputs) + if type(y) is tuple: + y = y[0] + if grad is None: + grad = torch.randn_like(y) + else: + if grad.shape != y.shape: + raise RuntimeError("Grad shape does not match output shape") + + def f(grad, *inputs, **kwinputs): + for x in inputs: + if isinstance(x, torch.Tensor): + x.grad = None + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + y = fn(*inputs, **kwinputs) + if type(y) is tuple: + y = y[0] + y.backward(grad, retain_graph=True) + + t = benchmark.Timer( + stmt="f(grad, *inputs, **kwinputs)", + globals={"f": f, "fn": fn, "inputs": inputs, "grad": grad, "kwinputs": kwinputs}, + num_threads=torch.get_num_threads(), + ) + m = t.timeit(repeats) + if verbose: + print(m) + return t, m + + +def benchmark_fwd_bwd( + fn, + *inputs, + grad=None, + repeats=10, + desc="", + verbose=True, + amp=False, + amp_dtype=torch.float16, + **kwinputs, +): + """Use Pytorch Benchmark on the forward+backward pass of an arbitrary function.""" + return ( + benchmark_forward( + fn, + *inputs, + repeats=repeats, + desc=desc, + verbose=verbose, + amp=amp, + amp_dtype=amp_dtype, + **kwinputs, + ), + benchmark_backward( + fn, + *inputs, + grad=grad, + repeats=repeats, + desc=desc, + verbose=verbose, + amp=amp, + amp_dtype=amp_dtype, + **kwinputs, + ), + ) + + +def benchmark_all( + fn, + *inputs, + grad=None, + repeats=10, + desc="", + verbose=True, + amp=False, + amp_dtype=torch.float16, + **kwinputs, +): + """Use Pytorch Benchmark on the forward+backward pass of an arbitrary function.""" + return ( + benchmark_forward( + fn, + *inputs, + repeats=repeats, + desc=desc, + verbose=verbose, + amp=amp, + amp_dtype=amp_dtype, + **kwinputs, + ), + benchmark_backward( + fn, + *inputs, + grad=grad, + repeats=repeats, + desc=desc, + verbose=verbose, + amp=amp, + amp_dtype=amp_dtype, + **kwinputs, + ), + benchmark_combined( + fn, + *inputs, + grad=grad, + repeats=repeats, + desc=desc, + verbose=verbose, + amp=amp, + amp_dtype=amp_dtype, + **kwinputs, + ), + ) + + +def pytorch_profiler( + fn, + *inputs, + trace_filename=None, + backward=False, + amp=False, + amp_dtype=torch.float16, + cpu=False, + verbose=True, + **kwinputs, +): + """Wrap benchmark functions in Pytorch profiler to see CUDA information.""" + if backward: + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + out = fn(*inputs, **kwinputs) + if type(out) is tuple: + out = out[0] + g = torch.randn_like(out) + for _ in range(30): # Warm up + if backward: + for x in inputs: + if isinstance(x, torch.Tensor): + x.grad = None + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + out = fn(*inputs, **kwinputs) + if type(out) is tuple: + out = out[0] + # Backward should be done outside autocast + if backward: + out.backward(g, retain_graph=True) + activities = ([torch.profiler.ProfilerActivity.CPU] if cpu else []) + [ + torch.profiler.ProfilerActivity.CUDA, + ] + with torch.profiler.profile( + activities=activities, + record_shapes=True, + # profile_memory=True, + with_stack=True, + ) as prof: + if backward: + for x in inputs: + if isinstance(x, torch.Tensor): + x.grad = None + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + out = fn(*inputs, **kwinputs) + if type(out) is tuple: + out = out[0] + if backward: + out.backward(g, retain_graph=True) + if verbose: + # print(prof.key_averages().table(sort_by="self_cuda_time_total", row_limit=50)) + print(prof.key_averages().table(row_limit=50)) + if trace_filename is not None: + prof.export_chrome_trace(trace_filename) + + +def benchmark_memory(fn, *inputs, desc="", verbose=True, **kwinputs): + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + torch.cuda.synchronize() + fn(*inputs, **kwinputs) + torch.cuda.synchronize() + mem = torch.cuda.max_memory_allocated() / ((2**20) * 1000) + if verbose: + print(f"{desc} max memory: {mem}GB") + torch.cuda.empty_cache() + return mem diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_abc.py b/code/flash-linear-attention/benchmarks/ops/benchmark_abc.py new file mode 100644 index 0000000000000000000000000000000000000000..10faaf9ace8d9308d328613c7df471c8ac455281 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_abc.py @@ -0,0 +1,73 @@ + +import torch +import triton +from torch.nn import functional as F + +from fla.ops.abc import chunk_abc +from fla.ops.gla import chunk_gla +from fla.ops.retention import chunk_retention + +try: + from flash_attn import flash_attn_func + HAS_FLASH = True +except BaseException: + HAS_FLASH = False + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['abc', 'gla', 'abc_bwd', 'gla_bwd', 'retention_bwd', 'flash_bwd'], + # label name for the lines + line_names=['abc', 'gla', 'abc_bwd', 'gla_bwd', 'retention_bwd', 'flash_bwd'], + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), + ('cyan', ':'), ('yellow', 'dotted'), ('black', ':')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, H, D, M = 16, 4, 128, 64 + + q = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + + if provider.startswith('gla'): + g = F.logsigmoid(torch.randn(B, T, H, D, device=device, dtype=dtype)) + g = g.clamp_min(-5).requires_grad_(requires_grad) + if provider.startswith('abc'): + s = torch.randn(B, T, H, M, device=device, requires_grad=requires_grad, dtype=dtype) + + do = torch.ones_like(v, dtype=dtype) + + quantiles = [0.5, 0.2, 0.8] + if provider == 'abc': + results = triton.testing.do_bench(lambda: chunk_abc(q, k, v, s), quantiles=quantiles) + elif provider == 'gla': + results = triton.testing.do_bench(lambda: chunk_gla(q, k, v, g), quantiles=quantiles) + elif provider == 'abc_bwd': + results = triton.testing.do_bench(lambda: chunk_abc(q, k, v, s)[0].backward(do), quantiles=quantiles) + elif provider == 'gla_bwd': + results = triton.testing.do_bench(lambda: chunk_gla(q, k, v, g)[0].backward(do), quantiles=quantiles) + elif provider == 'retention_bwd': + results = triton.testing.do_bench(lambda: chunk_retention(q, k, v)[0].backward(do), quantiles=quantiles) + elif provider == 'flash_bwd': + results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v, causal=True).backward(do), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_based.py b/code/flash-linear-attention/benchmarks/ops/benchmark_based.py new file mode 100644 index 0000000000000000000000000000000000000000..257ec41427061d8e5435c6e5ede5a73ec573bd38 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_based.py @@ -0,0 +1,88 @@ + +import torch +import triton + +from fla.ops.based import fused_chunk_based, parallel_based +from fla.ops.based.naive import naive_chunk_based, naive_parallel_based + +try: + from flash_attn import flash_attn_func + HAS_FLASH = True +except Exception: + HAS_FLASH = False + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(3, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + line_vals=['fused_chunk', 'torch', 'parallel', 'parallel_chunk', 'fused_chunk_bwd', 'torch_bwd', + 'parallel_bwd', 'parallel_chunk_bwd'] + (['flash', 'flash_bwd'] if HAS_FLASH else []), + # label name for the lines + line_names=['fused_chunk_fwd', 'torch_fwd', 'parallel_fwd', 'parallel_chunk_fwd', + 'fused_chunk_fwdbwd', 'torch_fwdbwd', 'parallel_fwdbwd', + 'parallel_chunk_fwdbwd'] + (['flash_fwd', 'flash_fwdbwd'] if HAS_FLASH else []), + + # line styles + styles=[('green', '-'), ('blue', '-'), ('red', '-'), ('green', 'dotted'), ('blue', 'dotted'), + ('red', 'dotted'), ('red', '--'), ('red', ':')] + ([('cyan', '-'), ('cyan', 'dotted')] if HAS_FLASH else []), + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, H, D = 8, 16, 128 + + if provider == 'flash' or provider == 'flash_bwd': + q = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + elif provider in ('torch', 'torch_bwd', 'parallel_chunk_bwd', 'parallel_chunk'): + q = torch.randn(B, H, T, 16, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, H, T, 16, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, H, T, D, device=device, requires_grad=requires_grad, dtype=dtype) + else: + q = torch.randn(B, T, H, 16, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, 16, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + do = torch.ones_like(v, dtype=dtype) + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider == 'torch': + if T > 1024: + return results + results = triton.testing.do_bench(lambda: naive_parallel_based(q, k, v), quantiles=quantiles) + elif provider == 'fused_chunk': + results = triton.testing.do_bench(lambda: fused_chunk_based(q, k, v), quantiles=quantiles) + elif provider == 'parallel': + results = triton.testing.do_bench(lambda: parallel_based(q, k, v), quantiles=quantiles) + elif provider == 'parallel_chunk': + results = triton.testing.do_bench(lambda: naive_chunk_based(q, k, v), quantiles=quantiles) + elif provider == 'torch_bwd': + if T > 1024: + return results + results = triton.testing.do_bench(lambda: naive_parallel_based(q, k, v).backward(do), quantiles=quantiles) + elif provider == 'fused_chunk_bwd': + results = triton.testing.do_bench(lambda: fused_chunk_based(q, k, v).backward(do), quantiles=quantiles) + elif provider == 'parallel_bwd': + results = triton.testing.do_bench(lambda: parallel_based(q, k, v).backward(do), quantiles=quantiles) + elif provider == 'flash': + results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v, causal=True), quantiles=quantiles) + elif provider == 'flash_bwd': + results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v, causal=True).backward(do), quantiles=quantiles) + elif provider == 'parallel_chunk_bwd': + results = triton.testing.do_bench(lambda: naive_chunk_based(q, k, v).backward(do), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True, show_plots=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_delta_rule.py b/code/flash-linear-attention/benchmarks/ops/benchmark_delta_rule.py new file mode 100644 index 0000000000000000000000000000000000000000..d5d89f09bfb2064a8a57a67ab67e763772d7fe38 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_delta_rule.py @@ -0,0 +1,94 @@ +# Install the newest triton version with +# pip install "git+https://github.com/openai/triton.git#egg=triton&subdirectory=python" + +import torch +from benchmark import benchmark_backward, benchmark_combined, benchmark_forward +from torch.nn import functional as F + +from fla.ops.delta_rule import chunk_delta_rule +from fla.utils import device + + +def time_fwd(func, *args, **kwargs): + time_fb = benchmark_forward(func, *args, **kwargs) + return time_fb[1].mean + + +def time_fwd_bwd(func, *args, **kwargs): + time_fb = benchmark_combined(func, *args, **kwargs) + return time_fb[1].mean + + +def time_bwd(func, *args, **kwargs): + time_fb = benchmark_backward(func, *args, **kwargs) + return time_fb[1].mean + + +repeats = 256 +dtype = torch.bfloat16 + + +bs_seqlen_vals = [(8, 2048), (4, 4096), (2, 8192)] +causal_vals = [True] +headdim_vals = [64, 128, 256] +dim = 2048 +dropout_p = 0.0 + + +methods = (["chunk_delta_rule"]) +time_f = {} +time_b = {} +time_f_b = {} +speed_f = {} +speed_b = {} +speed_f_b = {} +for causal in causal_vals: + for headdim in headdim_vals: + for B, seqlen in bs_seqlen_vals: + config = (causal, headdim, B, seqlen) + H = dim // headdim + q = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + k = F.normalize(torch.randn(B, seqlen, H, headdim, device=device, dtype=dtype), p=2, dim=-1).requires_grad_(True) + v = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + beta = torch.rand(B, seqlen, H, device=device, dtype=dtype).sigmoid().requires_grad_(True) + o1, _ = chunk_delta_rule(q, k, v, beta) + o1.sum().backward(retain_graph=True) + f_b = time_fwd_bwd( + chunk_delta_rule, q, k, v, beta, verbose=False, + ) + time_f_b[config, "chunk_delta_rule"] = f_b + + # q = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + # k = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + # v = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) +# f_b = time_fwd_bwd( +# fused_chunk_delta_rule, q, k, v, beta, verbose=False +# ) +# time_f_b[config, "fused_chunk_delta_rule"] = f_b + + print(f"### causal={causal}, headdim={headdim}, B={B}, seqlen={seqlen} ###") + for method in methods: + # time_f_b[config, method] = time_f[config, method] + time_b[config, method] + print(f"{method:>50} fwd + bwd:\t {time_f_b[config, method]*1000:>6.4f} ms ") + + # speed_f[config, method] = efficiency( + # flops(B, seqlen, headdim, H, causal, mode="fwd"), + # time_f[config, method] + # ) + # speed_b[config, method] = efficiency( + # flops(B, seqlen, headdim, H, causal, mode="bwd"), + # time_b[config, method] + # ) + # speed_f_b[config, method] = efficiency( + # flops(B, seqlen, headdim, H, causal, mode="fwd_bwd"), + # time_f_b[config, method] + # ) + # print( + # f"{method} fwd: {speed_f[config, method]:.2f} TFLOPs/s, " + # f"bwd: {speed_b[config, method]:.2f} TFLOPs/s, " + # f"fwd + bwd: {speed_f_b[config, method]:.2f} TFLOPs/s" + # ) + + +# with open('flash2_attn_time.plk', 'wb') as fp: +# pickle.dump((speed_f, speed_b, speed_f_b), fp, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_fla.py b/code/flash-linear-attention/benchmarks/ops/benchmark_fla.py new file mode 100644 index 0000000000000000000000000000000000000000..167ac4e89eda4255d44691514fc76663816962b1 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_fla.py @@ -0,0 +1,78 @@ + +import torch +import triton + +from fla.ops.based import parallel_based +from fla.ops.gla import fused_chunk_gla +from fla.ops.retention import fused_chunk_retention, parallel_retention + +try: + from flash_attn import flash_attn_func + HAS_FLASH = True +except ImportError: + HAS_FLASH = False + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['retention_parallel', 'retention_fused_chunk', + 'gla_fused_chunk', 'based_parallel'] + (['flash'] if HAS_FLASH else []), + # label name for the lines + line_names=['retention_parallel_fwdbwd', 'retention_fused_chunk_fwdbwd', + 'gla_fused_chunk_fwdbwd', 'based_parallel_fwdbwd'] + (['flash_fwdbwd'] if HAS_FLASH else []), + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), ('cyan', ':')] + \ + ([('yellow', 'dotted')] if HAS_FLASH else []), + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, H, D = 16, 8, 128 + + if "based" in provider: + q = torch.randn(B, T, H, 16, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, 16, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + elif "gla" in provider: + q = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + g = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + else: + q = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + + do = torch.rand_like(v, dtype=dtype) + + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider == 'flash': + results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v).backward(do), quantiles=quantiles) + elif provider == 'retention_parallel': + results = triton.testing.do_bench(lambda: parallel_retention(q, k, v)[0].backward(do), quantiles=quantiles) + elif provider == 'retention_fused_chunk': + results = triton.testing.do_bench(lambda: fused_chunk_retention(q, k, v)[0].backward(do), quantiles=quantiles) + elif provider == 'based_parallel': + results = triton.testing.do_bench(lambda: parallel_based(q, k, v).backward(do), quantiles=quantiles) + elif provider == 'gla_fused_chunk': + results = triton.testing.do_bench(lambda: fused_chunk_gla(q, k, v, g)[0].backward(do), quantiles=quantiles) + + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True, show_plots=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_gla.py b/code/flash-linear-attention/benchmarks/ops/benchmark_gla.py new file mode 100644 index 0000000000000000000000000000000000000000..0b1310005d5b048354ace35245b9719665d7dfca --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_gla.py @@ -0,0 +1,87 @@ + +import torch +import triton +from torch.nn import functional as F + +from fla.ops.gla import chunk_gla, fused_chunk_gla, fused_recurrent_gla +from fla.ops.retention import chunk_retention, parallel_retention +from fla.ops.retention.naive import naive_retention + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['fused_chunk_gla', 'recurrent_gla', 'chunk_gla', 'chunk_retention', + 'fused_chunk_gla_bwd', 'recurrent_gla_bwd', 'chunk_gla_bwd', 'chunk_retention_bwd'], + # label name for the lines + line_names=['fused_chunk_gla', 'recurrent_gla', 'chunk_gla', 'chunk_retention', + 'fused_chunk_gla_bwd', 'recurrent_gla_bwd', 'chunk_gla_bwd', 'chunk_retention_bwd'], + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), + ('cyan', ':'), ('yellow', 'dotted'), ('cyan', '--'), ('cyan', '-'), ('black', ':')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 +# dtype = torch.float32 + requires_grad = True + B, H, D = 16, 8, 128 + + if provider in ("fused_chunk_gla", "fused_chunk_gla_bwd"): + q = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + g = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + else: + q = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + g = F.logsigmoid(torch.randn(B, T, H, D, device=device, dtype=dtype)).clamp_min(-5).requires_grad_(requires_grad) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + + do = torch.ones_like(q, dtype=dtype) + + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider == 'torch': + if T > 2048: + return results + results = triton.testing.do_bench(lambda: naive_retention(q, k, v), quantiles=quantiles) + elif provider == 'recurrent_gla': + results = triton.testing.do_bench(lambda: fused_recurrent_gla(q, k, v, g), quantiles=quantiles) + elif provider == 'fused_chunk_gla': + results = triton.testing.do_bench(lambda: fused_chunk_gla(q, k, v, g), quantiles=quantiles) + elif provider == 'chunk_retention': + results = triton.testing.do_bench(lambda: chunk_retention(q, k, v), quantiles=quantiles) + elif provider == 'chunk_gla': + results = triton.testing.do_bench(lambda: chunk_gla(q, k, v, g), quantiles=quantiles) + elif provider == 'parallel': + results = triton.testing.do_bench(lambda: parallel_retention(q, k, v), quantiles=quantiles) + elif provider == 'torch_bwd': + if T > 2048: + return results + elif provider == 'chunk_retention_bwd': + results = triton.testing.do_bench(lambda: chunk_retention(q, k, v)[0].backward(do), quantiles=quantiles) + elif provider == 'recurrent_gla_bwd': + results = triton.testing.do_bench(lambda: fused_recurrent_gla(q, k, v, gk=g)[0].backward(do), quantiles=quantiles) + elif provider == 'fused_chunk_gla_bwd': + results = triton.testing.do_bench(lambda: fused_chunk_gla(q, k, v, g)[0].backward(do), quantiles=quantiles) + elif provider == 'chunk_gla_bwd': + results = triton.testing.do_bench(lambda: chunk_gla(q, k, v, g)[0].backward(do), quantiles=quantiles) + elif provider == 'parallel_bwd': + results = triton.testing.do_bench(lambda: parallel_retention(q, k, v)[0].backward(do), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_gsa.py b/code/flash-linear-attention/benchmarks/ops/benchmark_gsa.py new file mode 100644 index 0000000000000000000000000000000000000000..5986938ac2aadbcf73af046ffc24f335567b5329 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_gsa.py @@ -0,0 +1,78 @@ + +import torch +import triton +from torch.nn import functional as F + +from fla.ops.gla import chunk_gla +from fla.ops.gsa import chunk_gsa, fused_recurrent_gsa +from fla.ops.retention import chunk_retention + +try: + from flash_attn import flash_attn_func + HAS_FLASH = True +except BaseException: + HAS_FLASH = False + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['gsa_recurrent', 'gsa_chunk', 'gla', + 'gsa_recurrent_bwd', 'gsa_chunk_bwd', 'gla_bwd', 'retention_bwd', 'flash_bwd'], + # label name for the lines + line_names=['gsa_recurrent', 'gsa_chunk', 'gla', + 'gsa_recurrent_bwd', 'gsa_chunk_bwd', 'gla_bwd', 'retention_bwd', 'flash_bwd'], + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), + ('cyan', ':'), ('yellow', 'dotted'), ('black', ':'), ('green', ':'), ('green', 'dotted'), ('green', ':')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, H, D, M = 16, 4, 128, 64 + + q = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + if provider.startswith('gsa'): + f = F.logsigmoid(torch.randn(B, T, H, M, device=device, dtype=dtype)) + s = (1 - f.exp()).to(f.dtype) + if provider.startswith('gla'): + g = F.logsigmoid(torch.randn(B, T, H, D, device=device, dtype=dtype)) + g = g.clamp_min(-5).requires_grad_(requires_grad) + + do = torch.ones_like(v, dtype=dtype) + + quantiles = [0.5, 0.2, 0.8] + if provider == 'gsa_recurrent': + return triton.testing.do_bench(lambda: fused_recurrent_gsa(q, k, v, s, f), quantiles=quantiles) + if provider == 'gsa_chunk': + return triton.testing.do_bench(lambda: chunk_gsa(q, k, v, s, f), quantiles=quantiles) + elif provider == 'gla': + return triton.testing.do_bench(lambda: chunk_gla(q, k, v, g), quantiles=quantiles) + elif provider == 'gsa_recurrent_bwd': + return triton.testing.do_bench(lambda: fused_recurrent_gsa(q, k, v, s, f)[0].backward(do), quantiles=quantiles) + elif provider == 'gsa_chunk_bwd': + return triton.testing.do_bench(lambda: chunk_gsa(q, k, v, s, f)[0].backward(do), quantiles=quantiles) + elif provider == 'gla_bwd': + return triton.testing.do_bench(lambda: chunk_gla(q, k, v, g)[0].backward(do), quantiles=quantiles) + elif provider == 'retention_bwd': + return triton.testing.do_bench(lambda: chunk_retention(q, k, v)[0].backward(do), quantiles=quantiles) + elif provider == 'flash_bwd': + return triton.testing.do_bench(lambda: flash_attn_func(q, k, v, causal=True).backward(do), quantiles=quantiles) + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_hgrn.py b/code/flash-linear-attention/benchmarks/ops/benchmark_hgrn.py new file mode 100644 index 0000000000000000000000000000000000000000..a340f22409853e49fa704c1c914556664400165d --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_hgrn.py @@ -0,0 +1,52 @@ + +import torch +import triton + +from fla.ops.hgrn import chunk_hgrn, fused_recurrent_hgrn + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['chunk', 'recurrent', 'chunk_bwd', 'recurrent_bwd'], + # label name for the lines + line_names=['chunk', 'recurrent', 'chunk_bwd', 'recurrent_bwd'], + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), ('cyan', ':'), ('yellow', 'dotted'), ('black', 'dashed')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + B, D = 16, 512 + + x = torch.randn((B, T, D), dtype=dtype, device=device) + g = torch.randn((B, T, D), dtype=dtype, device=device).sigmoid() + x = (1 - g) * x + x, g = (i.detach().clone().to(dtype).requires_grad_() for i in (x, g)) + do = torch.randn_like(x, dtype=dtype) + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider == 'chunk': + results = triton.testing.do_bench(lambda: chunk_hgrn(x, g), quantiles=quantiles) + if provider == 'recurrent': + results = triton.testing.do_bench(lambda: fused_recurrent_hgrn(x, g), quantiles=quantiles) + if provider == 'chunk_bwd': + results = triton.testing.do_bench(lambda: chunk_hgrn(x, g)[0].backward(do), quantiles=quantiles) + if provider == 'recurrent_bwd': + results = triton.testing.do_bench(lambda: fused_recurrent_hgrn(x, g)[0].backward(do), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_kda.py b/code/flash-linear-attention/benchmarks/ops/benchmark_kda.py new file mode 100644 index 0000000000000000000000000000000000000000..a5801c8a1c4b43375932921279a29869f5b1f93f --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_kda.py @@ -0,0 +1,146 @@ + +import os + +import torch +import triton +from flash_attn import flash_attn_func +from torch.nn import functional as F + +from fla.ops.comba import chunk_comba +from fla.ops.gated_delta_rule import chunk_gated_delta_rule +from fla.ops.generalized_delta_rule import chunk_dplr_delta_rule +from fla.ops.kda import chunk_kda + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['gdn', 'comba', 'kda', 'dplr', 'attn'], + # label name for the lines + line_names=['gdn', 'comba', 'kda', 'dplr', 'attn'], + # line styles + styles=[('blue', '-'), ('red', '-.'), ('green', '-'), ('orange', '-.'), + ('purple', '-'), ('brown', '-.'), ('pink', '-'), ('gray', '-.')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + B, H, D = 1, 16, 128 + + # Set TMA environment variable based on provider + original_tma_env = os.environ.get('FLA_USE_TMA', '0') + + if provider.endswith('_no_tma'): + os.environ['FLA_USE_TMA'] = '0' + provider_base = provider.replace('_no_tma', '') + else: + os.environ['FLA_USE_TMA'] = '1' + provider_base = provider + + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + + do = torch.randn(B, T, H, D, dtype=dtype, device=device) + if provider_base == 'gdn': + q = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + k = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + v = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + g = F.logsigmoid(torch.randn(B, T, H, dtype=dtype, device=device)).requires_grad_(True) + beta = torch.randn(B, T, H, dtype=dtype, device=device).sigmoid().requires_grad_(True) + results = triton.testing.do_bench( + lambda: chunk_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=beta, + use_qk_l2norm_in_kernel=True, + )[0].backward(do), + quantiles=quantiles, + ) + elif provider_base == 'attn': + q = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + k = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + v = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + results = triton.testing.do_bench( + lambda: flash_attn_func( + q=q, + k=k, + v=v, + ).backward(do), + quantiles=quantiles, + ) + elif provider_base == 'comba': + q = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + k = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + p = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + v = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + g = F.logsigmoid(torch.randn(B, T, H, dtype=torch.float, device=device)).requires_grad_(True) + beta = torch.randn(B, T, H, dtype=dtype, device=device).sigmoid().requires_grad_(True) + results = triton.testing.do_bench( + lambda: chunk_comba( + q=q, + k=k, + p=p, + v=v, + g=g, + beta=beta, + use_qk_l2norm_in_kernel=True, + )[0].backward(do), + quantiles=quantiles, + ) + elif provider_base == 'kda': + q = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + k = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + v = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + g = F.logsigmoid(torch.randn(B, T, H, D, dtype=dtype, device=device)).requires_grad_(True) + beta = torch.randn(B, T, H, dtype=dtype, device=device).sigmoid().requires_grad_(True) + results = triton.testing.do_bench( + lambda: chunk_kda( + q=q, + k=k, + v=v, + g=g, + beta=beta, + use_qk_l2norm_in_kernel=True, + )[0].backward(do), + quantiles=quantiles, + ) + elif provider_base == 'dplr': + q = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + k = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + a = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + b = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + v = torch.randn(B, T, H, D, dtype=dtype, device=device).requires_grad_(True) + g = F.logsigmoid(torch.randn(B, T, H, D, dtype=dtype, device=device)).requires_grad_(True) + beta = torch.randn(B, T, H, dtype=dtype, device=device).sigmoid().requires_grad_(True) + results = triton.testing.do_bench( + lambda: chunk_dplr_delta_rule( + q=q, + k=k, + v=v, + a=a, + b=b, + gk=g, + )[0].backward(do), + quantiles=quantiles, + ) + + # Restore original TMA environment variable + os.environ['FLA_USE_TMA'] = original_tma_env + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_nsa.py b/code/flash-linear-attention/benchmarks/ops/benchmark_nsa.py new file mode 100644 index 0000000000000000000000000000000000000000..37dcd80b29a1839c72af0de1614d0dbe3a878491 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_nsa.py @@ -0,0 +1,76 @@ + +import torch +import triton +from flash_attn import flash_attn_func + +from fla.ops.nsa import parallel_nsa + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['nsa', 'nsa_bwd', 'flash', 'flash_bwd'], + # label name for the lines + line_names=['nsa', 'nsa_bwd', 'flash', 'flash_bwd'], + # line styles + styles=[('green', '-'), ('blue', '-'), ('red', '-'), ('green', 'dotted'), + ('blue', 'dotted'), ('red', 'dotted'), ('cyan', '-'), ('cyan', 'dotted')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, H, HQ, D, S = 4, 4, 64, 128, 16 + block_size = 64 + + q = torch.randn(B, T, HQ, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + do = torch.ones_like(q, dtype=dtype) + + indices = torch.full((B, T, H, S), T, dtype=torch.long, device=device) + for b in range(B): + for t in range(T): + for h in range(H): + i_i = torch.randperm(max(1, triton.cdiv(t, block_size)))[:S] + indices[b, t, h, :len(i_i)] = i_i + indices = indices.sort(-1)[0] + + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider == 'nsa': + results = triton.testing.do_bench( + lambda: parallel_nsa(q, k, v, block_indices=indices, block_size=block_size), + quantiles=quantiles, + ) + elif provider == 'nsa_bwd': + results = triton.testing.do_bench( + lambda: parallel_nsa(q, k, v, block_indices=indices, block_size=block_size).backward(do), + quantiles=quantiles, + ) + elif provider == 'flash': + results = triton.testing.do_bench( + lambda: flash_attn_func(q, k, v, causal=True), + quantiles=quantiles, + ) + elif provider == 'flash_bwd': + results = triton.testing.do_bench( + lambda: flash_attn_func(q, k, v, causal=True).backward(do), + quantiles=quantiles, + ) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True, save_path='.') diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_retention.py b/code/flash-linear-attention/benchmarks/ops/benchmark_retention.py new file mode 100644 index 0000000000000000000000000000000000000000..7a5693a95c36b53d5b9d22f57c99cfb9ae5f7bc6 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_retention.py @@ -0,0 +1,61 @@ + +import os + +import torch +import triton +from flash_attn import flash_attn_func + +from fla.ops.retention import chunk_retention, parallel_retention + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['chunk', 'parallel', 'flash', 'chunk_bwd', 'parallel_bwd', 'flash_bwd'], + # label name for the lines + line_names=['chunk_fwd', 'parallel_fwd', 'flash_fwd', 'chunk_fwdbwd', 'parallel_fwdbwd', 'flash_fwdbwd'], + # line styles + styles=[('green', '-'), ('blue', '-'), ('red', '-'), ('green', 'dotted'), ('blue', 'dotted'), ('red', 'dotted')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, H, D = 4, 8, 256 + os.environ['CUDA_LAUNCH_BLOCKING'] = '1' + + q = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + do = torch.ones_like(q, dtype=dtype) + + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider == 'chunk': + results = triton.testing.do_bench(lambda: chunk_retention(q, k, v), quantiles=quantiles) + elif provider == 'parallel': + results = triton.testing.do_bench(lambda: parallel_retention(q, k, v), quantiles=quantiles) + elif provider == 'flash': + results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v, causal=True), quantiles=quantiles) + elif provider == 'chunk_bwd': + results = triton.testing.do_bench(lambda: chunk_retention(q, k, v)[0].backward(do), quantiles=quantiles) + elif provider == 'parallel_bwd': + results = triton.testing.do_bench(lambda: parallel_retention(q, k, v)[0].backward(do), quantiles=quantiles) + elif provider == 'flash_bwd': + results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v, causal=True).backward(do), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True, save_path='.') diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_rwkv.py b/code/flash-linear-attention/benchmarks/ops/benchmark_rwkv.py new file mode 100644 index 0000000000000000000000000000000000000000..bbd57a75b744ec9519232837f8940d8190dbfc6a --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_rwkv.py @@ -0,0 +1,110 @@ + +import os + +import torch +import triton +from torch.nn import functional as F + +from fla.ops.gla import chunk_gla +from fla.ops.retention import chunk_retention +from fla.ops.rwkv6 import chunk_rwkv6 +from fla.ops.rwkv7 import chunk_rwkv7 + +try: + from flash_attn import flash_attn_func + HAS_FLASH = True +except BaseException: + HAS_FLASH = False + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + # line styles + line_vals=['rwkv6', 'rwkv7', 'gla', 'flash', 'rwkv6_bwd', 'rwkv7_bwd', 'gla_bwd', 'retention_bwd', 'flash_bwd'], + # label name for the lines + line_names=['rwkv6', 'rwkv7', 'gla', 'flash', 'rwkv6_bwd', 'rwkv7_bwd', 'gla_bwd', 'retention_bwd', 'flash_bwd'], + # # line styles + styles=[ + ('green', '-'), # rwkv6 + ('blue', '--'), # rwkv7 + ('red', '-.'), # gla + ('cyan', ':'), # rwkv6_bwd + ('magenta', '-'), # rwkv7_bwd + ('yellow', 'dotted'), # gla_bwd + ('black', ':'), # retention_bwd + ('gray', ':'), # flash + ('gray', '--'), # flash_bwd + ], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + # Read B, H, D from environment variables, default to 16, 8, 128 if not set + B = int(os.getenv('BENCH_B', '8')) # Batch size + H = int(os.getenv('BENCH_H', '64')) # Number of heads + D = int(os.getenv('BENCH_D', '64')) # Dimension per head + with torch.no_grad(): + q = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + if provider.startswith('flash'): + q = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype) + if provider.startswith('gla'): + g = F.logsigmoid(torch.randn(B, T, H, D, device=device, dtype=dtype)) + g = g.clamp_min(-5).requires_grad_(requires_grad) + if provider.startswith('rwkv6'): + w = F.logsigmoid(torch.randn(B, T, H, D, device=device, dtype=dtype)).requires_grad_(True) + u = torch.randn(H, D, device=device, dtype=dtype).requires_grad_(True) + if provider.startswith('rwkv7'): + q = torch.empty(B, T, H, D, device=device).uniform_(-1, 1).to(dtype=dtype).requires_grad_(True) + k = torch.empty(B, T, H, D, device=device).uniform_(-1, 1).to(dtype=dtype).requires_grad_(True) + v = torch.empty(B, T, H, D, device=device).uniform_(-1, 1).to(dtype=dtype).requires_grad_(True) + w = F.logsigmoid(torch.randn(B, T, H, D, device=device, dtype=dtype)).requires_grad_(True) + kk = torch.empty(B, T, H, D, device=device).uniform_(-1, 1) + kk = torch.nn.functional.normalize(kk, dim=-1).to(dtype=dtype) + + a = -kk.clone().requires_grad_(True) # -kk + a_scale = torch.empty(B, T, H, D, device=device).uniform_(0, 0.1).to(dtype=dtype) + b = (kk * a_scale).requires_grad_(True) # kk*a + + do = torch.ones_like(v, dtype=dtype) + + quantiles = [0.5, 0.2, 0.8] + if provider == 'rwkv6': + results = triton.testing.do_bench(lambda: chunk_rwkv6(q, k, v, w, u), quantiles=quantiles) + elif provider == 'rwkv7': + results = triton.testing.do_bench(lambda: chunk_rwkv7(q, w, k, v, a, b), quantiles=quantiles) + elif provider == 'gla': + results = triton.testing.do_bench(lambda: chunk_gla(q, k, v, g), quantiles=quantiles) + elif provider == 'rwkv6_bwd': + results = triton.testing.do_bench(lambda: chunk_rwkv6(q, k, v, w, u)[0].backward(do), quantiles=quantiles) + elif provider == 'rwkv7_bwd': + results = triton.testing.do_bench(lambda: chunk_rwkv7(q, w, k, v, a, b)[0].backward(do), quantiles=quantiles) + elif provider == 'gla_bwd': + results = triton.testing.do_bench(lambda: chunk_gla(q, k, v, g)[0].backward(do), quantiles=quantiles) + elif provider == 'retention_bwd': + results = triton.testing.do_bench(lambda: chunk_retention(q, k, v)[0].backward(do), quantiles=quantiles) + elif provider == 'flash': + results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v, causal=True), quantiles=quantiles) + elif provider == 'flash_bwd': + results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v, causal=True).backward(do), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_rwkv7_fused_addcmul.py b/code/flash-linear-attention/benchmarks/ops/benchmark_rwkv7_fused_addcmul.py new file mode 100644 index 0000000000000000000000000000000000000000..9837554bb7117bd8c15c24925eef22cf4ee3ad66 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_rwkv7_fused_addcmul.py @@ -0,0 +1,90 @@ + + +import torch +import triton + +from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, torch_addcmul_rwkv7 + + +@torch.compile +def torch_compile_addcmul(hidden_states, delta, x_r, x_w, x_k, x_v, x_a, x_g): + xr = torch.addcmul(hidden_states, delta, x_r) + xw = torch.addcmul(hidden_states, delta, x_w) + xk = torch.addcmul(hidden_states, delta, x_k) + xv = torch.addcmul(hidden_states, delta, x_v) + xa = torch.addcmul(hidden_states, delta, x_a) + xg = torch.addcmul(hidden_states, delta, x_g) + return xr, xw, xk, xv, xa, xg + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + line_vals=['addcmul_torch', 'addcmul_triton', 'compile', + 'addcmul_torch_bwd', 'addcmul_triton_bwd', 'compile_bwd'], + # label name for the lines + line_names=['torch', 'triton', 'compile', + 'torch_bwd', 'triton_bwd', 'compile_bwd'], + # line styles + styles=[ + ('green', '-'), + ('blue', '--'), + ('red', '-.'), + ('cyan', ':'), + ('magenta', '-'), + ('yellow', 'dotted'), + ], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + hidden_size = 4096 + batch_size = 8 + seq_len = T + hidden_states = torch.randn(batch_size, seq_len, hidden_size, device=device, + requires_grad=requires_grad, dtype=dtype).to(device) + delta = torch.randn(batch_size, seq_len, hidden_size).uniform_(-8, 8).to(device).to(dtype).requires_grad_() + x_r = torch.randn(1, 1, hidden_size).uniform_(-8, 8).to(device).to(dtype).requires_grad_() + x_w = torch.randn(1, 1, hidden_size).uniform_(-8, 8).to(device).to(dtype).requires_grad_() + x_k = torch.randn(1, 1, hidden_size).uniform_(-8, 8).to(device).to(dtype).requires_grad_() + x_v = torch.randn(1, 1, hidden_size).uniform_(-8, 8).to(device).to(dtype).requires_grad_() + x_a = torch.randn(1, 1, hidden_size).uniform_(-8, 8).to(device).to(dtype).requires_grad_() + x_g = torch.randn(1, 1, hidden_size).uniform_(-8, 8).to(device).to(dtype).requires_grad_() + + quantiles = [0.5, 0.2, 0.8] + if provider == 'addcmul_torch': + results = triton.testing.do_bench(lambda: torch_addcmul_rwkv7( + hidden_states, delta, x_r, x_w, x_k, x_v, x_a, x_g), quantiles=quantiles) + elif provider == 'compile': + results = triton.testing.do_bench(lambda: torch_compile_addcmul( + hidden_states, delta, x_r, x_w, x_k, x_v, x_a, x_g), quantiles=quantiles) + elif provider == 'addcmul_triton': + results = triton.testing.do_bench(lambda: fused_addcmul_rwkv7( + hidden_states, delta, x_r, x_w, x_k, x_v, x_a, x_g), quantiles=quantiles) + elif provider == 'addcmul_torch_bwd': + results = triton.testing.do_bench(lambda: (lambda outputs: sum([o.sum() for o in outputs]).backward())( + torch_addcmul_rwkv7(hidden_states, delta, x_r, x_w, x_k, x_v, x_a, x_g))) + elif provider == 'addcmul_triton_bwd': + results = triton.testing.do_bench(lambda: (lambda outputs: sum([o.sum() for o in outputs]).backward())( + fused_addcmul_rwkv7(hidden_states, delta, x_r, x_w, x_k, x_v, x_a, x_g))) + elif provider == 'compile_bwd': + results = triton.testing.do_bench(lambda: (lambda outputs: sum([o.sum() for o in outputs]).backward())( + torch_compile_addcmul(hidden_states, delta, x_r, x_w, x_k, x_v, x_a, x_g))) + else: + raise ValueError(f"Unknown provider: {provider}") + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_rwkv7_k_update.py b/code/flash-linear-attention/benchmarks/ops/benchmark_rwkv7_k_update.py new file mode 100644 index 0000000000000000000000000000000000000000..e9dae3d4d2d144b70e41a7553ea5605cc1177dec --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_rwkv7_k_update.py @@ -0,0 +1,58 @@ +import torch +import triton + +from fla.ops.rwkv7.fused_k_update import fused_k_rwkv7 + + +@torch.jit.script +def k_update_ref(k: torch.Tensor, a: torch.Tensor, ka: torch.Tensor) -> torch.Tensor: + return k.addcmul(k * (a - 1), ka) + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[128 * 2 ** i for i in range(0, 9)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=['naive_k_update', 'fused_k_update', 'naive_k_update_bwd', 'fused_k_update_bwd'], + # label name for the lines + line_names=['naive_k_update', 'fused_k_update', 'naive_k_update_bwd', 'fused_k_update_bwd'], + # line styles + styles=[('green', '-'), ('blue', '--'), ('red', '-.'), + ('cyan', ':')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + from fla.utils import device + dtype = torch.bfloat16 + requires_grad = True + B, D = 8, 4096 + + x = torch.randn(B, T, D, device=device, requires_grad=requires_grad, dtype=dtype) + a = torch.randn(B, T, D, device=device, requires_grad=requires_grad, dtype=dtype) + ka = torch.randn(1, 1, D, device=device, requires_grad=requires_grad, dtype=dtype) + quantiles = [0.5, 0.2, 0.8] + results = 0, 0, 0 + if provider.startswith('naive_k_update'): + results = triton.testing.do_bench(lambda: k_update_ref(x, a, ka), quantiles=quantiles) + if provider.startswith('fused_k_update'): + results = triton.testing.do_bench(lambda: fused_k_rwkv7(x, a, ka), quantiles=quantiles) + if provider.startswith('naive_k_update_bwd'): + grad_output = torch.randn_like(x) + results = triton.testing.do_bench(lambda: k_update_ref(x, a, ka).backward(grad_output), quantiles=quantiles) + if provider.startswith('fused_k_update_bwd'): + grad_output = torch.randn_like(x) + results = triton.testing.do_bench(lambda: fused_k_rwkv7(x, a, ka).backward(grad_output), quantiles=quantiles) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_simple_gla_vs_mamba2.py b/code/flash-linear-attention/benchmarks/ops/benchmark_simple_gla_vs_mamba2.py new file mode 100644 index 0000000000000000000000000000000000000000..9a26952dbd3ab7d9f1c9df3ecec6295d345e5ea9 --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_simple_gla_vs_mamba2.py @@ -0,0 +1,86 @@ +""" +Dependencies: +$ pip install mamba-ssm==2.2.2 triton==2.3.1 + +For correctness check, see: +https://github.com/sustcsonglin/flash-linear-attention/pull/49 +""" + +import torch +import triton +from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined + +from fla.ops.simple_gla import chunk_simple_gla + + +@triton.testing.perf_report( + triton.testing.Benchmark( + # argument names to use as an x-axis for the plot + x_names=['T'], + # different possible values for `x_name` + x_vals=[64] + [128 * 2 ** i for i in range(0, 8)], + # argument name whose value corresponds to a different line in the plot + line_arg='provider', + # possible values for `line_arg`` + line_vals=["chunk_simple_gla", "mamba2_ssd"], + # label name for the lines + line_names=["chunk_simple_gla", "mamba2_ssd"], + # line styles + styles=[('blue', '-'), ('red', '-')], + ylabel="Execution Time (ms)", # label name for the y-axis + # name for the plot. Used also as a file name for saving the plot. + plot_name="Performance", + args={}, + ), +) +def benchmark(T, provider): + # TODO: also add bwd pass benchmark + from fla.utils import device + dtype = torch.bfloat16 + B, H, D = 16, 8, 128 + # TODO: test more shapes + # TODO: different values for D_V and D_QK + # TODO: different values for H_Q and H_KV + final_state = False # does not impact performance + + # initialize Mamba2-format inputs + X_mamba = 0.1 * torch.randn(B, T, H, D, dtype=dtype, device=device) + dt_mamba = torch.ones(B, T, H, dtype=dtype, device=device) + A_mamba = -0.1 * torch.rand(H, dtype=dtype, device=device) + B_mamba = 0.1 * torch.randn(B, T, H, D, dtype=dtype, device=device) + C_mamba = 0.1 * torch.randn(B, T, H, D, dtype=dtype, device=device) + + quantiles = [0.5, 0.2, 0.8] + if provider == 'chunk_simple_gla': + # mapping inputs Mamba2 -> FLA + # C, B, X: [B, T, H, D] -> [B, H, T, D] + # g: [B, T, H] -> [B, H, T] + q = C_mamba.transpose(1, 2).contiguous() + k = B_mamba.transpose(1, 2).contiguous() + v = X_mamba.transpose(1, 2).contiguous() + g = (A_mamba * dt_mamba).transpose(1, 2).contiguous() + # NOTE: whether to include the memory-copy cost of `contiguous()`? + # this depends on the memory layout used by surrounding non-SSM layers + + results = triton.testing.do_bench( + lambda: chunk_simple_gla( + q, k, v, g, scale=1.0, output_final_state=final_state, + ), quantiles=quantiles, + ) + + elif provider == 'mamba2_ssd': + # NOTE: `chunk_size` is configurable in mamba2 kernel + # here sets to the same hard-coded `BT = 64` as in simple_gla kernel + # TODO: benchmark different chunk sizes + results = triton.testing.do_bench( + lambda: mamba_chunk_scan_combined( + X_mamba, dt_mamba, A_mamba, B_mamba, C_mamba, + chunk_size=64, D=None, return_final_states=final_state, + ), + quantiles=quantiles, + ) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True, save_path='.') diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_solv_tril.py b/code/flash-linear-attention/benchmarks/ops/benchmark_solv_tril.py new file mode 100644 index 0000000000000000000000000000000000000000..a3390f9638ddb5458c6642dcbe37986f3060d4ed --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_solv_tril.py @@ -0,0 +1,57 @@ + +import torch +import triton + + +@triton.testing.perf_report( + triton.testing.Benchmark( + x_names=['B', 'T', 'H', 'chunk_size'], + x_vals=[ + (b, t, h, c) + for b in [8] + for t in [2048, 4096, 8192] + for h in [16, 64] + for c in [16, 32, 64] + ], + line_arg='provider', + line_vals=[ + 'solve_tril_tma', + ], + line_names=[ + 'solve_tril_tma', + ], + styles=[('green', '-'), ('green', '--')], + ylabel="Time (ms)", + plot_name="solve_tril_performance", + args={}, + ), +) +def benchmark(B, T, H, chunk_size, provider): + from fla.ops.utils.solve_tril import solve_tril + from fla.utils import device + + requires_grad = True + dtype = torch.float32 + + k = torch.randn((B, H, T, 64), dtype=dtype, device=device, requires_grad=requires_grad) + k = torch.nn.functional.normalize(k, dim=-1) + + padding_size = (chunk_size - T % chunk_size) % chunk_size + T_padded = T + padding_size + k_padded = torch.nn.functional.pad(k, (0, 0, 0, padding_size, 0, 0, 0, 0)) + k_padded = k_padded.reshape(B, H, T_padded // chunk_size, chunk_size, 64) + + A = (k_padded @ k_padded.transpose(-1, -2)).tril(-1) + A = A.permute(0, 2, 1, 3, 4).contiguous() + A = A.view(B, T_padded, H, chunk_size) + A = A[:, :T, :, :] + + results = triton.testing.do_bench( + lambda: solve_tril(A), + quantiles=[0.5, 0.2, 0.8], + ) + return results + + +if __name__ == '__main__': + benchmark.run(print_data=True, save_path=".") diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_titans.py b/code/flash-linear-attention/benchmarks/ops/benchmark_titans.py new file mode 100644 index 0000000000000000000000000000000000000000..ad95f99fc66a627413fb781c0920086a49d3970d --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_titans.py @@ -0,0 +1,138 @@ +# Install the newest triton version with +# pip install "git+https://github.com/openai/triton.git#egg=triton&subdirectory=python" + +import torch +from benchmark import benchmark_backward, benchmark_combined, benchmark_forward +from torch.nn import functional as F + +from fla.ops.titans.naive import chunk_titans_linear_ref + +# from flash_attn import flash_attn_func + + +def time_fwd(func, *args, **kwargs): + time_fb = benchmark_forward(func, *args, **kwargs) + return time_fb[1].mean + + +def time_fwd_bwd(func, *args, **kwargs): + time_fb = benchmark_combined(func, *args, **kwargs) + return time_fb[1].mean + + +def time_bwd(func, *args, **kwargs): + time_fb = benchmark_backward(func, *args, **kwargs) + return time_fb[1].mean + + +repeats = 256 +device = "cuda" +dtype = torch.bfloat16 + +bs_seqlen_vals = [(2, 1024), (2, 2048)] +causal_vals = [True] +headdim_vals = [4, 8] +dim = 16 +dropout_p = 0.0 + +methods = ["naive_titans", "chunk_titans"] +time_f = {} +time_b = {} +time_f_b = {} +speed_f = {} +speed_b = {} +speed_f_b = {} +for causal in causal_vals: + for headdim in headdim_vals: + for B, seqlen in bs_seqlen_vals: + config = (causal, headdim, B, seqlen) + H = dim // headdim + + q = torch.randn( + B, H, seqlen, headdim, device=device, requires_grad=True, dtype=dtype, + ) + k = F.normalize( + torch.randn(B, H, seqlen, headdim, device=device, dtype=dtype), + p=2, + dim=-1, + ).requires_grad_(True) + v = torch.randn( + B, H, seqlen, headdim, device=device, requires_grad=True, dtype=dtype, + ) + w = torch.randn(seqlen, headdim, device=device, requires_grad=True, dtype=dtype) + b = torch.randn(seqlen, headdim, device=device, requires_grad=True, dtype=dtype) + theta = torch.rand( + B, H, seqlen, 1, dtype=dtype, device=device, requires_grad=True, + ) + alpha = torch.rand( + B, H, seqlen, 1, dtype=dtype, device=device, requires_grad=True, + ) + eta = torch.rand( + B, H, seqlen, 1, dtype=dtype, device=device, requires_grad=True, + ) + o2, _ = chunk_titans_linear_ref( + q, k, v, w, b, theta, alpha, eta, chunk_size=16, use_chunk=False, + ) + o2.sum().backward(retain_graph=True) + f_b = time_fwd_bwd( + chunk_titans_linear_ref, + q, + k, + v, + w, + b, + theta, + alpha, + eta, + use_chunk=False, + verbose=False, + ) + time_f_b[config, "naive_titans"] = f_b + + o3, _ = chunk_titans_linear_ref( + q, k, v, w, b, theta, alpha, eta, chunk_size=16, use_chunk=True, + ) + o3.sum().backward(retain_graph=True) + f_b = time_fwd_bwd( + chunk_titans_linear_ref, + q, + k, + v, + w, + b, + theta, + alpha, + eta, + chunk_size=16, + use_chunk=True, + verbose=False, + ) + time_f_b[config, "chunk_titans"] = f_b + + print(f"### causal={causal}, headdim={headdim}, B={B}, seqlen={seqlen} ###") + for method in methods: + # time_f_b[config, method] = time_f[config, method] + time_b[config, method] + print( + f"{method:>50} fwd + bwd:\t {time_f_b[config, method] * 1000:>6.4f} ms ", + ) + + # speed_f[config, method] = efficiency( + # flops(B, seqlen, headdim, H, causal, mode="fwd"), + # time_f[config, method] + # ) + # speed_b[config, method] = efficiency( + # flops(B, seqlen, headdim, H, causal, mode="bwd"), + # time_b[config, method] + # ) + # speed_f_b[config, method] = efficiency( + # flops(B, seqlen, headdim, H, causal, mode="fwd_bwd"), + # time_f_b[config, method] + # ) + # print( + # f"{method} fwd: {speed_f[config, method]:.2f} TFLOPs/s, " + # f"bwd: {speed_b[config, method]:.2f} TFLOPs/s, " + # f"fwd + bwd: {speed_f_b[config, method]:.2f} TFLOPs/s" + # ) + +# with open('flash2_attn_time.plk', 'wb') as fp: +# pickle.dump((speed_f, speed_b, speed_f_b), fp, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/code/flash-linear-attention/benchmarks/ops/benchmark_ttt.py b/code/flash-linear-attention/benchmarks/ops/benchmark_ttt.py new file mode 100644 index 0000000000000000000000000000000000000000..a42164712986e7e8da67666dfc798bcc1d03d86d --- /dev/null +++ b/code/flash-linear-attention/benchmarks/ops/benchmark_ttt.py @@ -0,0 +1,130 @@ +# Install the newest triton version with +# pip install "git+https://github.com/openai/triton.git#egg=triton&subdirectory=python" + +import torch +from benchmark import benchmark_backward, benchmark_combined, benchmark_forward +from torch.nn import functional as F + +from fla.ops.delta_rule import chunk_delta_rule +from fla.ops.gla import chunk_gla +from fla.ops.ttt import chunk_ttt_linear, fused_chunk_ttt_linear +from fla.utils import device + +# from flash_attn import flash_attn_func + + +def time_fwd(func, *args, **kwargs): + time_fb = benchmark_forward(func, *args, **kwargs) + return time_fb[1].mean + + +def time_fwd_bwd(func, *args, **kwargs): + time_fb = benchmark_combined(func, *args, **kwargs) + return time_fb[1].mean + + +def time_bwd(func, *args, **kwargs): + time_fb = benchmark_backward(func, *args, **kwargs) + return time_fb[1].mean + + +repeats = 256 + + +dtype = torch.bfloat16 + + +bs_seqlen_vals = [(8, 2048), (4, 4096), (2, 8192)] +causal_vals = [True] +# headdim_vals = [64, 128] +headdim_vals = [64] +dim = 2048 +dropout_p = 0.0 + + +methods = (["chunk_gla", "chunk_delta_rule", "chunk_ttt_linear", "fused_chunk_ttt_linear"]) +time_f = {} +time_b = {} +time_f_b = {} +speed_f = {} +speed_b = {} +speed_f_b = {} +for causal in causal_vals: + for headdim in headdim_vals: + for B, seqlen in bs_seqlen_vals: + config = (causal, headdim, B, seqlen) + H = dim // headdim + q = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + k = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + v = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + g = torch.randn(B, seqlen, H, headdim, device=device, dtype=dtype).sigmoid().requires_grad_(True) / 16 + o1, _ = chunk_gla(q, k, v, g) + o1.sum().backward(retain_graph=True) + f_b = time_fwd_bwd( + chunk_gla, q, k, v, g, verbose=False, + ) + time_f_b[config, "chunk_gla"] = f_b + + q = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + k = F.normalize(torch.randn(B, seqlen, H, headdim, device=device, dtype=dtype), p=2, dim=-1).requires_grad_(True) + v = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + beta = torch.rand(B, seqlen, H, device=device, dtype=dtype).sigmoid().requires_grad_(True) + o2, _ = chunk_delta_rule(q, k, v, beta) + o2.sum().backward(retain_graph=True) + f_b = time_fwd_bwd( + chunk_delta_rule, q, k, v, beta, verbose=False, + ) + time_f_b[config, "chunk_delta_rule"] = f_b + + q = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + k = F.normalize(torch.randn(B, seqlen, H, headdim, device=device, dtype=dtype), p=2, dim=-1).requires_grad_(True) + v = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + w = torch.randn(H, headdim, device=device, requires_grad=True, dtype=dtype) + b = torch.randn(H, headdim, device=device, requires_grad=True, dtype=dtype) + eta = torch.rand(B, H, seqlen, 1, device=device, requires_grad=True, dtype=dtype) * 5e-3 + o3, _, _ = chunk_ttt_linear(q, k, v, w, b, eta, chunk_size=16) + o3.sum().backward(retain_graph=True) + f_b = time_fwd_bwd( + chunk_ttt_linear, q, k, v, w, b, eta, chunk_size=16, verbose=False, + ) + time_f_b[config, "chunk_ttt_linear"] = f_b + + q = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + k = F.normalize(torch.randn(B, seqlen, H, headdim, device=device, dtype=dtype), p=2, dim=-1).requires_grad_(True) + v = torch.randn(B, seqlen, H, headdim, device=device, requires_grad=True, dtype=dtype) + w = torch.randn(H, headdim, device=device, requires_grad=True, dtype=dtype) + b = torch.randn(H, headdim, device=device, requires_grad=True, dtype=dtype) + eta = torch.rand(B, seqlen, H, 1, device=device, requires_grad=True, dtype=dtype) * 5e-3 + o4, _, _ = fused_chunk_ttt_linear(q, k, v, w, b, eta, chunk_size=16) + o4.sum().backward(retain_graph=True) + f_b = time_fwd_bwd( + fused_chunk_ttt_linear, q, k, v, w, b, eta, chunk_size=16, verbose=False, + ) + time_f_b[config, "fused_chunk_ttt_linear"] = f_b + + print(f"### causal={causal}, headdim={headdim}, B={B}, seqlen={seqlen} ###") + for method in methods: + # time_f_b[config, method] = time_f[config, method] + time_b[config, method] + print(f"{method:>50} fwd + bwd:\t {time_f_b[config, method]*1000:>6.4f} ms ") + + # speed_f[config, method] = efficiency( + # flops(B, seqlen, headdim, H, causal, mode="fwd"), + # time_f[config, method] + # ) + # speed_b[config, method] = efficiency( + # flops(B, seqlen, headdim, H, causal, mode="bwd"), + # time_b[config, method] + # ) + # speed_f_b[config, method] = efficiency( + # flops(B, seqlen, headdim, H, causal, mode="fwd_bwd"), + # time_f_b[config, method] + # ) + # print( + # f"{method} fwd: {speed_f[config, method]:.2f} TFLOPs/s, " + # f"bwd: {speed_b[config, method]:.2f} TFLOPs/s, " + # f"fwd + bwd: {speed_f_b[config, method]:.2f} TFLOPs/s" + # ) + + +# with open('flash2_attn_time.plk', 'wb') as fp: +# pickle.dump((speed_f, speed_b, speed_f_b), fp, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/code/flash-linear-attention/evals/harness.py b/code/flash-linear-attention/evals/harness.py new file mode 100644 index 0000000000000000000000000000000000000000..0b599d47454e3aa0c9a902586fdaf071f3d9d113 --- /dev/null +++ b/code/flash-linear-attention/evals/harness.py @@ -0,0 +1,20 @@ + +from __future__ import annotations + +import fla # noqa +from lm_eval.__main__ import cli_evaluate +from lm_eval.api.registry import register_model +from lm_eval.models.huggingface import HFLM + + +@register_model('fla') +class FlashLinearAttentionLMWrapper(HFLM): + def __init__(self, **kwargs) -> FlashLinearAttentionLMWrapper: + + # TODO: provide options for doing inference with different kernels + + super().__init__(**kwargs) + + +if __name__ == "__main__": + cli_evaluate() diff --git a/code/flash-linear-attention/evals/ppl.py b/code/flash-linear-attention/evals/ppl.py new file mode 100644 index 0000000000000000000000000000000000000000..ae7d14b940bdb76b109359ed815bbb8f1f5a2eff --- /dev/null +++ b/code/flash-linear-attention/evals/ppl.py @@ -0,0 +1,232 @@ + +import argparse +import math +from collections.abc import Iterator +from functools import partial +from typing import Any + +import torch +from datasets import Dataset, load_dataset +from tqdm import tqdm +from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel, PreTrainedTokenizer + +from fla.modules.fused_cross_entropy import FusedCrossEntropyLoss + + +class PerplexityEvaluator: + def __init__( + self, + model: PreTrainedModel, + tokenizer: PreTrainedTokenizer, + device: str = "cuda", + block_size: int = 32768, + bucket_size: int = 2048, + batch_size: int = 1, + ): + self.model = model + self.tokenizer = tokenizer + self.device = device + self.block_size = block_size + self.bucket_size = bucket_size + self.batch_size = batch_size + self.loss_fct = FusedCrossEntropyLoss(reduction='sum') + + @staticmethod + def preprocess( + examples: dict[str, list[Any]], + tokenizer: PreTrainedTokenizer, + column_name: str = 'text', + ) -> dict[str, list[list[int]]]: + """Preprocess text data""" + tokenized = tokenizer(examples[column_name]) + return { + 'input_ids': tokenized['input_ids'], + 'length': [len(ids) for ids in tokenized['input_ids']], + } + + def batchify(self, dataset: Dataset, tokens_per_batch: int) -> Iterator[list[torch.Tensor]]: + """Split dataset into batches of exactly block_size length""" + current_tokens = [] # Buffer to store all tokens + + for sentence in dataset: + # Convert input_ids to list and add to buffer + tokens = sentence['input_ids'].tolist() if torch.is_tensor(sentence['input_ids']) else list(sentence['input_ids']) + if not tokens: + continue + current_tokens.extend(tokens) + + # When we have enough tokens, yield batches + while len(current_tokens) >= self.block_size * self.batch_size: + batch = [] + for _ in range(self.batch_size): + # Extract exactly block_size tokens + batch.append(torch.tensor(current_tokens[:self.block_size], dtype=torch.long)) + current_tokens = current_tokens[self.block_size:] + yield batch + + # Handle remaining tokens if they form complete blocks + if len(current_tokens) >= self.block_size: + remaining_batches = len(current_tokens) // self.block_size + remaining_batches = min(remaining_batches, self.batch_size) + if remaining_batches > 0: + batch = [] + for _ in range(remaining_batches): + batch.append(torch.tensor(current_tokens[:self.block_size], dtype=torch.long)) + current_tokens = current_tokens[self.block_size:] + yield batch + + def process_batch(self, batch: list[torch.Tensor]) -> dict[str, torch.Tensor]: + """Process a single batch of data""" + # Stack the tensors - no need for padding since all sequences are block_size + input_ids = torch.stack(batch).to(self.device) + + # Calculate number of blocks for each sequence + blocks = [ + (self.block_size-1)//self.bucket_size + for _ in range(input_ids.shape[0]) + ] + + # Prepare labels + labels = input_ids.clone() + + # Forward pass + outputs = self.model(input_ids, labels=labels) + + # Calculate next token prediction labels + next_token_labels = torch.cat(( + input_ids[..., 1:], + torch.full_like(input_ids[:, :1], self.tokenizer.eos_token_id), + ), -1) + + # Calculate negative log likelihood + nlls = (-outputs['logits'].log_softmax(-1)).gather(-1, next_token_labels.unsqueeze(-1)).squeeze(-1) + + return { + 'input_ids': input_ids, + 'loss': outputs['loss'], + 'nlls': nlls, + 'labels': next_token_labels, + 'blocks': blocks, + } + + def evaluate(self, dataset: Dataset) -> dict[str, Any]: + """Evaluate perplexity on the entire dataset""" + total_loss = 0 + total_tokens = 0 + total_sentences = 0 + + # Initialize block statistics + num_blocks = (self.block_size - 1) // self.bucket_size + 1 + block_loss = [torch.tensor(0., dtype=torch.float, device=self.device) for _ in range(num_blocks)] + block_tokens = [1e-10 for _ in range(num_blocks)] + bucket_sizes = [0 for _ in range(num_blocks)] + + # Create progress bar + bar = tqdm(self.batchify(dataset, self.block_size)) + + for batch in bar: + batch_outputs = self.process_batch(batch) + input_ids = batch_outputs['input_ids'] + + nlls = batch_outputs['nlls'] + labels = batch_outputs['labels'] + blocks = batch_outputs['blocks'] + + # Update statistics + total_tokens += input_ids.ne(self.loss_fct.ignore_index).sum() + total_sentences += input_ids.shape[0] + print(input_ids.shape[1]) + + for i in blocks: + bucket_sizes[i] += 1 + + # Calculate block-level loss + for i, j in enumerate(range(0, min(input_ids.shape[-1], self.block_size), self.bucket_size)): + block_loss[i] += nlls[:, j:j+self.bucket_size].sum() + block_tokens[i] += labels[:, j:j+self.bucket_size].ne(self.loss_fct.ignore_index).sum() + + # Update total loss + total_loss += batch_outputs['loss'].item() * labels.ne(self.loss_fct.ignore_index).sum() + + # Update progress bar + ppls = [f"{math.exp(loss / toks):6.2f}" for loss, toks in zip(block_loss, block_tokens, strict=False)] + bar.set_description_str(f"[{total_tokens:10} tokens, {total_sentences:8} sentences] " + ' '.join(ppls)) + + # Calculate final results + final_ppl = math.exp(total_loss / total_tokens) + block_ppls = [math.exp(loss / toks) for loss, toks in zip(block_loss, block_tokens, strict=False)] + + return { + 'perplexity': final_ppl, + 'block_perplexities': block_ppls, + 'total_tokens': total_tokens, + 'total_sentences': total_sentences, + } + + +def main(): + parser = argparse.ArgumentParser(description="Evaluate perplexity") + parser.add_argument('-p', '--path', type=str, default='fla-hub/gla-1.3B-100B') + parser.add_argument('-d', '--data', type=str, default='fla-hub/pg19') + parser.add_argument('-s', '--split', type=str, default='train') + parser.add_argument('-n', '--column_name', type=str, default='text') + parser.add_argument('--block_size', type=int, default=28672) + parser.add_argument('--bucket_size', type=int, default=2048) + parser.add_argument('--batch_size', type=int, default=1) + parser.add_argument('--device', type=str, default=None) + args = parser.parse_args() + + # Set device and random seed + if args.device is None: + from fla.utils import device + else: + device = args.device + torch.manual_seed(0) + + # Load model and tokenizer + print(f"Loading model {args.path}") + tokenizer = AutoTokenizer.from_pretrained(args.path) + model = AutoModelForCausalLM.from_pretrained( + args.path, + device_map={"": device}, + ).bfloat16().eval() + print(f"{model}") + + # Load dataset + print(f"Loading data {args.data}") + dataset = load_dataset(args.data, split=args.split) + dataset = dataset.map( + partial(PerplexityEvaluator.preprocess, tokenizer=tokenizer, column_name=args.column_name), + batched=True, + num_proc=32, + ) + print(dataset) + print("batch_size", args.batch_size, + "block_size", args.block_size, + "total_tokens_per_batch", args.batch_size * args.block_size) + + # Create evaluator and run evaluation + evaluator = PerplexityEvaluator( + model=model, + tokenizer=tokenizer, + device=device, + block_size=args.block_size, + bucket_size=args.bucket_size, + batch_size=args.batch_size, + ) + + with torch.no_grad(): + results = evaluator.evaluate(dataset) + + # Print results + print("\nEvaluation Results:") + print(f"Final Perplexity: {results['perplexity']:.2f}") + print(f"Total Tokens: {results['total_tokens']}") + print(f"Total Sentences: {results['total_sentences']}") + print("\nBlock-wise Perplexities:") + for i, ppl in enumerate(results['block_perplexities']): + print(f"Block {i}: {ppl:.2f}") + + +if __name__ == "__main__": + main() diff --git a/code/flash-linear-attention/examples/training.md b/code/flash-linear-attention/examples/training.md new file mode 100644 index 0000000000000000000000000000000000000000..4c54fcec883a8a0dcb6317b0cb1eaf041ca35f3c --- /dev/null +++ b/code/flash-linear-attention/examples/training.md @@ -0,0 +1,152 @@ +
        + +# 🔥 Flame: Flash Linear Attention Made Easy + +
        + +Welcome to 🔥 `flame`, a minimal and efficient framework built on `torchtitan` for training Flash Linear Attention (FLA) models with blazing efficiency. + +This guide will walk you through training GLA models while demonstrating `flame`'s flexibility to extend to other FLA architectures. + +## Setup + +To get started, clone the `flame` repository and install the required dependencies: + +```bash +git clone https://github.com/fla-org/flame.git +cd flame +pip install . +``` + +`flame` includes `fla` and `torchtitan` as submodules. After installation, initialize and update the submodules using: +```sh +git submodule update --init --recursive +``` + +## Preparing the dataset + +Unlike the [legacy codebase](legacy/training), which required extensive pre-processing, +`flame` streamlines dataset handling with smart on-the-fly processing. + +For most datasets: +```py +from datasets import load_dataset + +# Load fineweb-edu with parallel processing +dataset = load_dataset("HuggingFaceFW/fineweb-edu", name="default", num_proc=64) +``` + +For SlimPajama-627B (used in [GLA paper](https://proceedings.mlr.press/v235/yang24ab.html)): +```bash +git lfs install +git clone https://huggingface.co/datasets/cerebras/SlimPajama-627B --depth 1 +``` + +## Training from scratch + +To train your 340M model from scratch, execute the following command: + +```sh +bash train.sh \ + --job.config_file flame/models/fla.toml \ + --job.dump_folder exp/gla-340M-10B/batch32.seqlen2048.warmup1024.update1.steps20480.lr3e-4 \ + --model.config configs/gla_340M.json \ + --model.tokenizer_path fla-hub/gla-1.3B-100B \ + --optimizer.name AdamW \ + --optimizer.eps 1e-15 \ + --optimizer.lr 3e-4 \ + --lr_scheduler.warmup_steps 1024 \ + --lr_scheduler.lr_min 0.1 \ + --lr_scheduler.decay_type cosine \ + --training.batch_size 32 \ + --training.seq_len 2048 \ + --training.gradient_accumulation_steps 1 \ + --training.steps 20480 \ + --training.max_norm 1.0 \ + --training.skip_nan_inf \ + --training.dataset HuggingFaceFW/fineweb-edu \ + --training.dataset_name default \ + --training.dataset_split train \ + --training.streaming \ + --training.num_workers 32 \ + --training.prefetch_factor 2 \ + --training.seed 42 \ + --training.compile \ + --training.tensor_parallel_degree 1 \ + --training.disable_loss_parallel \ + --checkpoint.interval 2048 \ + --checkpoint.load_step -1 \ + --metrics.log_freq 1 +``` + +We provide several [config files](https://github.com/fla-org/flame/tree/main/configs) in the `flame` repository for different models. +By default, the learning rate is set to `3e-4` with a cosine scheduler. +Other schedulers, such as WSD (wsd), are also supported. For a detailed explanation of all parameters, run: +```sh +bash train.sh -h +``` + +`flame` supports resuming interrupted training from the last checkpoint. +If a checkpoint exists, the training process will automatically resume from it. Alternatively, you can resume from a specific step by specifying `--checkpoint.load_step `. + +The training progress is logged using `wandb` for easy monitoring. + +## Continual Pretraining + +`flame` supports continual training from a pretrained checkpoint. +Below, we provide an example of how to finetune Mistral-7B to GLA. +You can follow similar steps to reproduce the results in the [GSA paper](https://arxiv.org/abs/2409.07146): + +1. Initialize a brand-new GLA-7B model from the config and copy the mathced pretrained weights from Mistral-7B: +```bash +cd ../utils +python convert_from_llama.py \ + --model mistralai/Mistral-7B-v0.1 \ + --config \ + --output +cd - +``` + +2. Convert the 🤗 format model back into DCP format. +```bash +python -m flame.utils.convert_hf_to_dcp --model --checkpoint +``` +Here, is the directory where your distributed checkpoints will be stored. The checkpoint is intentionally saved at within the checkpoint folder to ensure it is loadable by flame during the initial training step, similar to how a seed checkpoint is handled. + +3. Directly launch training from the converted checkpoint: +```sh +bash train.sh \ + --job.config_file flame/models/fla.toml \ + --job.dump_folder \ + --model.config \ + --model.tokenizer_path fla-hub/gla-1.3B-100B \ + --optimizer.name AdamW \ + --optimizer.eps 1e-15 \ + --optimizer.lr 3e-5 \ + --lr_scheduler.warmup_steps 512 \ + --lr_scheduler.lr_min 0.1 \ + --lr_scheduler.decay_type cosine \ + --training.batch_size 4 \ + --training.seq_len 2048 \ + --training.gradient_accumulation_steps 1 \ + --training.steps 10240 \ + --training.max_norm 1.0 \ + --training.skip_nan_inf \ + --training.dataset HuggingFaceFW/fineweb-edu \ + --training.dataset_name default \ + --training.dataset_split train \ + --training.streaming \ + --training.num_workers 32 \ + --training.prefetch_factor 2 \ + --training.seed 42 \ + --checkpoint.interval 1024 \ + --checkpoint.load_step 0 \ + --metrics.log_freq 1 +``` + +Finetuning on a single node may not be the most efficient approach. +If you have access to multi-node GPUs, consider leveraging them for optimal performance. +This process is straightforward and well-documented in the PyTorch [docs](https://pytorch.org/docs/stable/elastic/run.html). + +Simply set the environment variables `MASTER_ADDR=` and `MASTER_PORT=` before running the training script across all nodes. If you're using a job scheduler like Slurm, it will handle these variables for you. +`torchtitan` provides a [Slurm script](https://github.com/pytorch/torchtitan/blob/main/multinode_trainer.slurm) for multi-node training. diff --git a/code/flash-linear-attention/fla/__init__.py b/code/flash-linear-attention/fla/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..839221b4dfd70237ff113b6e2ed533c1daa03acc --- /dev/null +++ b/code/flash-linear-attention/fla/__init__.py @@ -0,0 +1,110 @@ + +from fla.layers import ( + ABCAttention, + Attention, + BasedLinearAttention, + BitAttention, + Comba, + DeltaFormerAttention, + DeltaNet, + GatedDeltaNet, + GatedDeltaProduct, + GatedLinearAttention, + GatedSlotAttention, + HGRN2Attention, + HGRNAttention, + LightNetAttention, + LinearAttention, + LogLinearMamba2, + MesaNet, + MomAttention, + MultiheadLatentAttention, + MultiScaleRetention, + NativeSparseAttention, + PaTHAttention, + ReBasedLinearAttention, + RodimusAttention, + RWKV6Attention, + RWKV7Attention, +) +from fla.models import ( + ABCForCausalLM, + ABCModel, + BitNetForCausalLM, + BitNetModel, + CombaForCausalLM, + CombaModel, + DeltaFormerForCausalLM, + DeltaFormerModel, + DeltaNetForCausalLM, + DeltaNetModel, + GatedDeltaNetForCausalLM, + GatedDeltaNetModel, + GatedDeltaProductForCausalLM, + GatedDeltaProductModel, + GLAForCausalLM, + GLAModel, + GSAForCausalLM, + GSAModel, + HGRN2ForCausalLM, + HGRN2Model, + HGRNForCausalLM, + HGRNModel, + LightNetForCausalLM, + LightNetModel, + LinearAttentionForCausalLM, + LinearAttentionModel, + LogLinearMamba2ForCausalLM, + LogLinearMamba2Model, + MesaNetForCausalLM, + MesaNetModel, + MLAForCausalLM, + MLAModel, + MomForCausalLM, + MomModel, + NSAForCausalLM, + NSAModel, + PaTHAttentionForCausalLM, + PaTHAttentionModel, + RetNetForCausalLM, + RetNetModel, + RodimusForCausalLM, + RodimusModel, + RWKV6ForCausalLM, + RWKV6Model, + RWKV7ForCausalLM, + RWKV7Model, + TransformerForCausalLM, + TransformerModel, +) + +__all__ = [ + 'ABCAttention', 'ABCForCausalLM', 'ABCModel', + 'Attention', 'TransformerForCausalLM', 'TransformerModel', + 'BasedLinearAttention', + 'BitAttention', 'BitNetForCausalLM', 'BitNetModel', + 'Comba', 'CombaForCausalLM', 'CombaModel', + 'DeltaNet', 'DeltaNetForCausalLM', 'DeltaNetModel', + 'DeltaFormerAttention', 'DeltaFormerForCausalLM', 'DeltaFormerModel', + 'GatedDeltaNet', 'GatedDeltaNetForCausalLM', 'GatedDeltaNetModel', + 'GatedDeltaProduct', 'GatedDeltaProductForCausalLM', 'GatedDeltaProductModel', + 'GatedLinearAttention', 'GLAForCausalLM', 'GLAModel', + 'GatedSlotAttention', 'GSAForCausalLM', 'GSAModel', + 'HGRNAttention', 'HGRNForCausalLM', 'HGRNModel', + 'HGRN2Attention', 'HGRN2ForCausalLM', 'HGRN2Model', + 'LightNetAttention', 'LightNetForCausalLM', 'LightNetModel', + 'LinearAttention', 'LinearAttentionForCausalLM', 'LinearAttentionModel', + 'LogLinearMamba2', 'LogLinearMamba2ForCausalLM', 'LogLinearMamba2Model', + 'MesaNet', 'MesaNetForCausalLM', 'MesaNetModel', + 'MomAttention', 'MomForCausalLM', 'MomModel', + 'MultiheadLatentAttention', 'MLAForCausalLM', 'MLAModel', + 'MultiScaleRetention', 'RetNetForCausalLM', 'RetNetModel', + 'NativeSparseAttention', 'NSAForCausalLM', 'NSAModel', + 'PaTHAttention', 'PaTHAttentionForCausalLM', 'PaTHAttentionModel', + 'ReBasedLinearAttention', + 'RodimusAttention', 'RodimusForCausalLM', 'RodimusModel', + 'RWKV6Attention', 'RWKV6ForCausalLM', 'RWKV6Model', + 'RWKV7Attention', 'RWKV7ForCausalLM', 'RWKV7Model', +] + +__version__ = '0.4.0' diff --git a/code/flash-linear-attention/fla/layers/__init__.py b/code/flash-linear-attention/fla/layers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3e9710ff8706ce206845e18214f375a332bbe4a8 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/__init__.py @@ -0,0 +1,69 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from .abc import ABCAttention +from .attn import Attention +from .based import BasedLinearAttention +from .bitattn import BitAttention +from .comba import Comba +from .delta_net import DeltaNet +from .deltaformer import DeltaFormerAttention +from .forgetting_attn import ForgettingAttention +from .gated_deltanet import GatedDeltaNet +from .gated_deltaproduct import GatedDeltaProduct +from .gla import GatedLinearAttention +from .gsa import GatedSlotAttention +from .hgrn import HGRNAttention +from .hgrn2 import HGRN2Attention +from .kda import KimiDeltaAttention +from .lightnet import LightNetAttention +from .linear_attn import LinearAttention +from .log_linear_mamba2 import LogLinearMamba2 +from .mamba import Mamba +from .mamba2 import Mamba2 +from .mesa_net import MesaNet +from .mla import MultiheadLatentAttention +from .mom import MomAttention +from .multiscale_retention import MultiScaleRetention +from .nsa import NativeSparseAttention +from .path_attn import PaTHAttention +from .rebased import ReBasedLinearAttention +from .rodimus import RodimusAttention, SlidingWindowSharedKeyAttention +from .rwkv6 import RWKV6Attention +from .rwkv7 import RWKV7Attention +from .sse import SSEGLA, SSEGDN + +__all__ = [ + 'ABCAttention', + 'Attention', + 'BasedLinearAttention', + 'BitAttention', + 'Comba', + 'DeltaNet', + 'ForgettingAttention', + 'GatedDeltaNet', + 'GatedDeltaProduct', + 'GatedLinearAttention', + 'GatedSlotAttention', + 'HGRNAttention', + 'HGRN2Attention', + 'KimiDeltaAttention', + 'LightNetAttention', + 'LinearAttention', + 'LogLinearMamba2', + 'Mamba', + 'Mamba2', + 'MesaNet', + 'MomAttention', + 'MultiheadLatentAttention', + 'MultiScaleRetention', + 'NativeSparseAttention', + 'PaTHAttention', + 'ReBasedLinearAttention', + 'RodimusAttention', + 'RWKV6Attention', + 'RWKV7Attention', + 'SlidingWindowSharedKeyAttention', + 'SSEGLA', + 'SSEGDN', + 'DeltaFormerAttention', +] diff --git a/code/flash-linear-attention/fla/layers/abc.py b/code/flash-linear-attention/fla/layers/abc.py new file mode 100644 index 0000000000000000000000000000000000000000..f1515b215d892a02de8f6c1527d0348693373ade --- /dev/null +++ b/code/flash-linear-attention/fla/layers/abc.py @@ -0,0 +1,231 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange + +from fla.modules import FusedRMSNormGated, RMSNorm, RotaryEmbedding, ShortConvolution +from fla.modules.activations import swiglu, swish +from fla.ops.abc.chunk import chunk_abc + +if TYPE_CHECKING: + from fla.models.utils import Cache + + +class ABCAttention(nn.Module): + + def __init__( + self, + hidden_size: int = 1024, + expand_k: float = 0.5, + expand_v: float = 1.0, + num_heads: int = 4, + use_short_conv: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + num_slots: int | None = None, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-5, + gate_low_rank_dim: int = 16, + gate_logit_normalizer: int = 16, + use_rope: bool = True, + use_input_gate: bool = False, + use_output_gate: bool = True, + use_norm: bool = True, + clamp_min: float | None = -32, + clamp_max: float | None = 32, + layer_idx: int | None = None, + **kwargs, + ) -> ABCAttention: + super().__init__() + + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.num_heads = num_heads + self.key_dim = int(self.hidden_size * self.expand_k) + self.value_dim = int(self.hidden_size * self.expand_v) + self.head_k_dim = self.key_dim // self.num_heads + self.head_v_dim = self.value_dim // self.num_heads + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.gate_low_rank_dim = gate_low_rank_dim + self.gate_logit_normalizer = gate_logit_normalizer + + self.use_rope = use_rope + self.use_input_gate = use_input_gate + self.use_output_gate = use_output_gate + self.use_norm = use_norm + + if num_slots is None: + num_slots = self.head_k_dim + self.num_slots = num_slots + + self.norm_eps = norm_eps + + self.clamp_min = clamp_min + self.clamp_max = clamp_max + self.layer_idx = layer_idx + + if layer_idx is None: + warnings.warn( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class.", + ) + + self.q_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.value_dim, bias=False) + + if use_output_gate: + self.g_proj = nn.Linear(self.hidden_size, self.value_dim, bias=False) + self.s_proj = nn.Linear(self.hidden_size, self.num_heads * self.num_slots, bias=False) + self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + + if self.use_norm: + if self.use_output_gate: + self.g_norm = FusedRMSNormGated( + hidden_size=self.head_v_dim, + elementwise_affine=elementwise_affine, + eps=norm_eps, + ) + else: + self.g_norm = RMSNorm( + hidden_size=self.head_v_dim, + elementwise_affine=elementwise_affine, + eps=norm_eps, + ) + + if self.use_rope: + self.rotary = RotaryEmbedding(self.head_k_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if cu_seqlens is not None: + raise NotImplementedError("Training with cu_seqlens is not supported yet for ABCAttention") + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + conv_mask = attention_mask[:, -hidden_states.shape[1]:] if attention_mask is not None else None + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + mask=conv_mask, + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + mask=conv_mask, + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + mask=conv_mask, + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + + if self.use_input_gate: + q, k, v = map(lambda x: swish(x), (q, k, v)) + # dealing with left-padding + if attention_mask is not None: + v = v.mul_(attention_mask[:, -v.shape[-2]:, None]) + + q, k = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q, k)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + if self.use_rope: + seqlen_offset = 0 + if past_key_values is not None: + seqlen_offset = past_key_values.get_seq_length(self.layer_idx) + q, k = self.rotary(q, k, seqlen_offset=seqlen_offset) + + s = rearrange(self.s_proj(hidden_states), '... (h m) -> ... h m', m=self.num_slots) + s = s.clamp_(self.clamp_min, self.clamp_max) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + o, recurrent_state = chunk_abc( + q=q, + k=k, + v=v, + s=s, + initial_state=recurrent_state, + output_final_state=use_cache, + ) + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q.shape[1], + ) + + if self.use_norm and not self.use_output_gate: + o = self.g_norm(o) + elif self.use_output_gate: + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.g_norm(o, g) if self.use_norm else swiglu(g, o) + o = rearrange(o, '... h d -> ... (h d)') + o = self.o_proj(o) + + return o, None, past_key_values + + def state_size(self, seq_len: int = 2048): + return 2 * self.num_slots * self.hidden_size diff --git a/code/flash-linear-attention/fla/layers/attn.py b/code/flash-linear-attention/fla/layers/attn.py new file mode 100644 index 0000000000000000000000000000000000000000..e034e835e3745fd15c75ffc69b911d68a494f272 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/attn.py @@ -0,0 +1,176 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange +from transformers.utils import logging + +from fla.layers.utils import pad_input, unpad_input +from fla.modules import RMSNorm, RotaryEmbedding +from fla.ops.utils.index import prepare_lens_from_mask + +if TYPE_CHECKING: + from fla.models.utils import Cache + +try: + from flash_attn import flash_attn_func, flash_attn_varlen_func +except ImportError: + warnings.warn( + "Flash Attention is not installed. Please install it via `pip install flash-attn --no-build-isolation`", + category=ImportWarning, + ) + flash_attn_func = None + +logger = logging.get_logger(__name__) + + +class Attention(nn.Module): + + def __init__( + self, + hidden_size: int = 2048, + num_heads: int = 32, + num_kv_heads: int | None = None, + head_dim: int = 128, + qkv_bias: bool = False, + qk_norm: bool = False, + window_size: int | None = None, + rope_theta: float | None = 10000., + max_position_embeddings: int | None = None, + layer_idx: int = None, + norm_eps: float = 1e-5, + ): + super().__init__() + + self.hidden_size = hidden_size + self.num_heads = num_heads + if num_kv_heads is None: + self.num_kv_heads = self.num_heads + else: + self.num_kv_heads = num_kv_heads + self.num_kv_groups = num_heads // self.num_kv_heads + self.head_dim = head_dim + self.q_dim = self.num_heads * self.head_dim + self.kv_dim = self.num_kv_heads * self.head_dim + self.qkv_bias = qkv_bias + self.qk_norm = qk_norm + + self.window_size = window_size + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + self.layer_idx = layer_idx + + if flash_attn_func is None: + raise ImportError("Please install Flash Attention via `pip install flash-attn --no-build-isolation` first") + + self.q_proj = nn.Linear(self.hidden_size, self.q_dim, bias=self.qkv_bias) + self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) + self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) + self.o_proj = nn.Linear(self.q_dim, self.hidden_size, bias=False) + + if qk_norm: + self.q_norm = RMSNorm(self.head_dim, eps=norm_eps) + self.k_norm = RMSNorm(self.head_dim, eps=norm_eps) + + self.rotary = RotaryEmbedding(dim=self.head_dim, base=self.rope_theta) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.size() + + q = rearrange(self.q_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + k = rearrange(self.k_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(self.v_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + + if self.qk_norm: + q, k = self.q_norm(q), self.k_norm(k) + + # equivalent to cu_seqlens in `flash_attn` + cu_seqlens = kwargs.get('cu_seqlens') + + seqlen_offset, max_seqlen = 0, q_len + if past_key_values is not None: + seqlen_offset = past_key_values.get_seq_length(self.layer_idx) + max_seqlen = q.shape[1] + seqlen_offset + + if attention_mask is not None: + # to deliminate the offsets of padding tokens + seqlen_offset = seqlen_offset + prepare_lens_from_mask(attention_mask) - attention_mask.shape[-1] + max_seqlen = q.shape[1] + max(seqlen_offset) + + if self.max_position_embeddings is not None: + max_seqlen = max(max_seqlen, self.max_position_embeddings) + q, k = self.rotary(q, k, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens) + + if past_key_values is not None: + cache_has_content = past_key_values.get_seq_length(self.layer_idx) > 0 + k_cached, v_cached = past_key_values.update( + attn_state=(k.flatten(-2, -1), v.flatten(-2, -1)), + layer_idx=self.layer_idx, + offset=q_len, + cache_kwargs=dict(window_size=self.window_size), + )['attn_state'] + if cache_has_content: + k, v = k_cached, v_cached + k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim) + + # Contains at least one padding token in the sequence + if attention_mask is not None: + if q.shape[1] == 1 and self.window_size is not None: + attention_mask = attention_mask[:, -self.window_size:] + q, (k, v), indices_q, cu_seqlens, max_seq_lens = unpad_input(q, (k, v), attention_mask, q_len) + cu_seqlens_q, cu_seqlens_k = cu_seqlens + max_seqlen_q, max_seqlen_k = max_seq_lens + o = flash_attn_varlen_func( + q, k, v, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ) + o = pad_input(o, indices_q, batch_size, q_len) + elif cu_seqlens is not None: + o = flash_attn_varlen_func( + q.squeeze(0), k.squeeze(0), v.squeeze(0), + cu_seqlens_q=cu_seqlens, + cu_seqlens_k=cu_seqlens, + max_seqlen_q=max_seqlen, + max_seqlen_k=max_seqlen, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ).unsqueeze(0) + else: + o = flash_attn_func( + q, k, v, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ) + o = o.reshape(batch_size, q_len, -1) + o = self.o_proj(o) + + if not output_attentions: + attentions = None + + return o, attentions, past_key_values diff --git a/code/flash-linear-attention/fla/layers/based.py b/code/flash-linear-attention/fla/layers/based.py new file mode 100644 index 0000000000000000000000000000000000000000..fe1adfebdc0c2ca28c860ff7fff942ae8238b082 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/based.py @@ -0,0 +1,93 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +""" +Linear attention in Based. +https://github.com/HazyResearch/zoology/blob/main/zoology/mixers/based.py +""" + +import torch +import torch.nn as nn +from einops import rearrange + +from fla.modules.feature_map import TaylorFeatureMap +from fla.ops.based import parallel_based +from fla.ops.linear_attn import chunk_linear_attn, fused_chunk_linear_attn + + +class BasedLinearAttention(nn.Module): + + def __init__( + self, + hidden_size: int, + feature_dim: int = 16, + num_key_value_heads: int = 12, + num_heads: int = 12, + feature_name: str = "taylor_exp", + eps: float = 1e-12, + causal: bool = True, + mode: str = "parallel", + ): + super().__init__() + + self.hidden_size = hidden_size + self.mode = mode + self.feature_name = feature_name + self.feature_dim = feature_dim + self.num_key_value_heads = num_key_value_heads + self.num_heads = num_heads + self.head_dim = self.hidden_size // self.num_key_value_heads + assert self.hidden_size % self.head_dim == 0 + self.causal = causal + + self.q_proj = nn.Linear(self.hidden_size, self.feature_dim * self.num_heads, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.feature_dim * self.num_heads, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self.dropout = nn.Identity() + self.feature_map = TaylorFeatureMap(feature_dim) + self.eps = eps + + def forward(self, hidden_states: torch.Tensor, **kwargs): + mode = self.mode + q, k, v = self.q_proj(hidden_states), self.k_proj(hidden_states), self.v_proj(hidden_states) + q, k, v = map(lambda x: rearrange(x, "... (h d) -> ... h d", d=self.head_dim), [q, k, v]) + if mode == "fused_chunk": + q, k = self.feature_map(q), self.feature_map(k) + o, _ = fused_chunk_linear_attn(q, k, v, normalize=True, scale=1) + elif mode == 'chunk': + q, k = self.feature_map(q), self.feature_map(k) + o, _ = chunk_linear_attn(q, k, v, normalize=True, scale=1) + elif mode == 'parallel': + assert q.shape[-1] <= 128 + o = parallel_based(q, k, v, scale=1, use_norm=True) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + o = self.dropout(o) + return o + + def forward_reference(self, hidden_states: torch.Tensor, **kwargs): + """ + x (torch.Tensor): tensor of shape (b, d, t) + y (torch.Tensor): tensor of shape (b, d, t) + """ + # hidden_states = hidden_states.transpose(1, 2) + b, t, _ = hidden_states.size() + q, k, v = self.q_proj(hidden_states), self.k_proj(hidden_states), self.v_proj(hidden_states) + + q = q.view(b, t, self.num_heads, self.feature_dim).transpose(1, 2) + k = k.view(b, t, self.num_key_value_heads, self.feature_dim).transpose(1, 2) + v = v.view(b, t, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + # Linear attention + q, k = self.feature_map(q), self.feature_map(k) + q, k, v = q.unsqueeze(-2), k.unsqueeze(-2), v.unsqueeze(-1) + + # Compute attention + if self.causal: + y = ((q * (k * v).cumsum(2)).sum(-1) / ((q * k.cumsum(2)).sum(-1) + self.eps)) + else: + y = ((q * (k * v).sum(2, True)).sum(-1) / ((q * k.sum(2, True)).sum(-1) + self.eps)) + y = rearrange(y, 'b h t d -> b t (h d)') + y = self.o_proj(y.to(hidden_states.dtype)) + y = self.dropout(y) + return y.to(hidden_states.dtype) diff --git a/code/flash-linear-attention/fla/layers/bitattn.py b/code/flash-linear-attention/fla/layers/bitattn.py new file mode 100644 index 0000000000000000000000000000000000000000..40d0aeff0989b9f65216b7bd3350de4e6991c06a --- /dev/null +++ b/code/flash-linear-attention/fla/layers/bitattn.py @@ -0,0 +1,162 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange +from transformers.utils import logging + +from fla.layers.utils import pad_input, unpad_input +from fla.modules import RotaryEmbedding +from fla.modules.fused_bitlinear import FusedBitLinear +from fla.ops.utils.index import prepare_lens_from_mask + +if TYPE_CHECKING: + from fla.models.utils import Cache + +try: + from flash_attn import flash_attn_func, flash_attn_varlen_func +except ImportError: + warnings.warn( + "Flash Attention is not installed. Please install it via `pip install flash-attn --no-build-isolation`", + category=ImportWarning, + ) + flash_attn_func = None + +logger = logging.get_logger(__name__) + + +class BitAttention(nn.Module): + + def __init__( + self, + hidden_size: int = 2048, + num_heads: int = 32, + num_kv_heads: int | None = None, + window_size: int | None = None, + rope_theta: float | None = 10000., + max_position_embeddings: int | None = None, + norm_eps: float = 1e-5, + layer_idx: int = None, + ): + super().__init__() + + self.num_heads = num_heads + if num_kv_heads is None: + self.num_kv_heads = self.num_heads + else: + self.num_kv_heads = num_kv_heads + self.num_kv_groups = num_heads // self.num_kv_heads + self.hidden_size = hidden_size + self.head_dim = self.hidden_size // self.num_heads + self.kv_dim = self.num_kv_heads * self.head_dim + self.kv_dim = self.num_kv_heads * self.head_dim + self.window_size = window_size + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + self.layer_idx = layer_idx + + self.q_proj = FusedBitLinear(self.hidden_size, self.hidden_size, bias=False) + self.k_proj = FusedBitLinear(self.hidden_size, self.kv_dim, bias=False) + self.v_proj = FusedBitLinear(self.hidden_size, self.kv_dim, bias=False) + self.o_proj = FusedBitLinear(self.hidden_size, self.hidden_size, bias=False) + + self.rotary = RotaryEmbedding(dim=self.head_dim, base=self.rope_theta) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.size() + + q = rearrange(self.q_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + k = rearrange(self.k_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(self.v_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + + # equivalent to cu_seqlens in `flash_attn` + cu_seqlens = kwargs.get('cu_seqlens') + + seqlen_offset, max_seqlen = 0, q_len + if past_key_values is not None: + seqlen_offset = past_key_values.get_seq_length(self.layer_idx) + max_seqlen = q.shape[1] + seqlen_offset + + if attention_mask is not None: + # to deliminate the offsets of padding tokens + seqlen_offset = seqlen_offset + prepare_lens_from_mask(attention_mask) - attention_mask.shape[-1] + max_seqlen = q.shape[1] + max(seqlen_offset) + + if self.max_position_embeddings is not None: + max_seqlen = max(max_seqlen, self.max_position_embeddings) + q, k = self.rotary(q, k, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens) + + if past_key_values is not None: + cache_has_content = past_key_values.get_seq_length(self.layer_idx) > 0 + k_cached, v_cached = past_key_values.update( + attn_state=(k.flatten(-2, -1), v.flatten(-2, -1)), + layer_idx=self.layer_idx, + offset=q_len, + cache_kwargs=dict(window_size=self.window_size), + )['attn_state'] + if cache_has_content: + k, v = k_cached, v_cached + k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim) + + if flash_attn_func is None: + raise ImportError("Please install Flash Attention via `pip install flash-attn --no-build-isolation` first") + + # Contains at least one padding token in the sequence + if attention_mask is not None: + q, (k, v), indices_q, cu_seqlens, max_seq_lens = unpad_input(q, (k, v), attention_mask, q_len) + cu_seqlens_q, cu_seqlens_k = cu_seqlens + max_seqlen_q, max_seqlen_k = max_seq_lens + o = flash_attn_varlen_func( + q, k, v, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ) + o = pad_input(o, indices_q, batch_size, q_len) + elif cu_seqlens is not None: + o = flash_attn_varlen_func( + q.squeeze(0), k.squeeze(0), v.squeeze(0), + cu_seqlens_q=cu_seqlens, + cu_seqlens_k=cu_seqlens, + max_seqlen_q=max_seqlen, + max_seqlen_k=max_seqlen, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ).unsqueeze(0) + else: + o = flash_attn_func( + q, k, v, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ) + o = o.reshape(batch_size, q_len, -1) + o = self.o_proj(o) + + if not output_attentions: + attentions = None + + return o, attentions, past_key_values diff --git a/code/flash-linear-attention/fla/layers/comba.py b/code/flash-linear-attention/fla/layers/comba.py new file mode 100644 index 0000000000000000000000000000000000000000..e4a60caef087dfb241f14e1892f562f5e9392d21 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/comba.py @@ -0,0 +1,331 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange, repeat +from torch.nn import functional as F + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.ops.comba import chunk_comba, fused_recurrent_comba + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +class Comba(nn.Module): + """ + The layer implementaion for [Comba: Improving Bilinear RNNs with Closed-loop Control](https://arxiv.org/abs/2506.02475). + + Similar to Mamba2 and Gated-DeltaNet, each layer contains around 6*hidden_size*hidden_size parameters. + + Parameter alloation when use_output_gate=True: + - 0.75 * hidden_size * hidden_size for the q_proj and k_proj each + - 1.5 * hidden_size * hidden_size for the v_proj, g_proj and o_proj each + - Others are ignorably small. + - In total = 0.75 * 2 + 1.5 * 3 = 6 * hidden_size * hidden_size + NOTE: num_heads * head_dim = 0.75 * hidden_size, please make sure to set the correct num_heads and head_dim. + + Parameter allocation when use_output_gate=False: + - 1 * hidden_size * hidden_size for the q_proj and k_proj each + - 2 * hidden_size * hidden_size for the v_proj and o_proj each + - Others are ignorably small. + - In total = 1 * 2 + 2 * 2 = 6 * hidden_size * hidden_size + + Args: + hidden_size (int, Optional): + The hidden size of the input. Default: 2048. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 2.0. + head_dim (int, Optional): + The dimension of each head. Default: 256. + num_heads (int, Optional): + The number of heads. Default: 4. + num_v_heads (int, Optional): + The number of heads for the value projection, equal to `num_heads` if `None`. + GVA is applied if `num_v_heads` > `num_heads`. Default: `None`. + mode (str, Optional): + Which Gated DeltaNet kernel to use. + Currently available: `chunk` and `fused_recurrent`. + Default: `chunk`. + use_beta (bool, Optional): + Whether to use beta. Default: `True`. + use_output_gate (bool, Optional): + Whether to use output gate. Default: `True`. + use_output_correction (bool, Optional): + Whether to use . Default: `True`. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `True`. + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + layer_idx (int, Optional): + The index of the layer. Default: None. + norm_eps (float, Optional): + The epsilon value for the normalization layer. Default: 1e-5. + """ + + def __init__( + self, + hidden_size: int = 2048, + expand_v: float = 2, + head_dim: int = 256, + num_heads: int = 6, + num_v_heads: int = None, + mode: str = 'chunk', + use_short_conv: bool = True, + use_output_gate: bool = True, + use_output_correction: bool = True, + use_inner_decay: bool = True, + correction_factor: float = 1., + conv_size: int = 4, + conv_bias: bool = False, + layer_idx: int = None, + norm_eps: float = 1e-5, + **kwargs, + ) -> Comba: + super().__init__() + + self.mode = mode + + self.hidden_size = hidden_size + self.expand_v = expand_v + + self.use_short_conv = use_short_conv + self.use_output_gate = use_output_gate + self.use_output_correction = use_output_correction + self.use_inner_decay = use_inner_decay + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads + + self.head_k_dim = head_dim + self.head_v_dim = int(self.head_dim * self.expand_v) + self.key_dim = int(self.num_heads * self.head_k_dim) + self.value_dim = int(self.num_v_heads * self.head_v_dim) + self.layer_idx = layer_idx + + # Consistency check: Ensure expand_v produces integer values + if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. " + f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.", + ) + if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0: + raise ValueError( + f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.", + ) + + if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. " + f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.", + ) + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.a_proj = nn.Linear(hidden_size, self.num_v_heads, bias=False) + self.b_proj = nn.Linear(hidden_size, self.num_v_heads, bias=False) + + if use_inner_decay: + self.decay = nn.Parameter(torch.ones(self.num_heads)) + + if use_output_correction: + warnings.warn( + "The correction_factor is set to 1 by default similar to Mamba2. " + "However, we find that sometimes correction_factor = 0.02 works better for small-scale models. " + "In practice, we recommend trying both settings. ", + ) + self.D = nn.Parameter(torch.ones(self.num_heads) * correction_factor) + self.D._no_weight_decay = True + + A = torch.empty(self.num_v_heads, dtype=torch.float32).uniform_(0, 16) + self.A_log = nn.Parameter(torch.log(A)) + self.A_log._no_weight_decay = True + # hard coded for now + dt_min = 0.001 + dt_max = 0.1 + dt_init_floor = 1e-4 + dt = torch.exp( + torch.rand(self.num_v_heads) * (math.log(dt_max) - math.log(dt_min)) + + math.log(dt_min), + ) + dt = torch.clamp(dt, min=dt_init_floor) + # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + self.dt_bias = nn.Parameter(inv_dt) + # Just to be explicit. Without this we already don't put wd on dt_bias because of the check + # name.endswith("bias") in param_grouping.py + self.dt_bias._no_weight_decay = True + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + else: + warnings.warn( + "ShortConvolution is crucial to the performance. " + "Do not turn it off, i.e., setting `use_short_conv=False` unless you know what you are doing.", + ) + if use_output_gate: + self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.o_norm = FusedRMSNormGated(self.head_v_dim, activation='sigmoid', eps=norm_eps) + else: + self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + # change to inference mode. + mode = 'fused_recurrent' if q_len <= 64 else self.mode + if self.training: + assert mode == 'chunk', "Only chunk mode is supported in training." + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = F.silu(self.q_proj(hidden_states)) + k = F.silu(self.k_proj(hidden_states)) + v = F.silu(self.v_proj(hidden_states)) + + q, k = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q, k)) + + if self.use_inner_decay: + p = k * self.decay[None, None, :, None].sigmoid() + else: + p = k + + if self.use_output_correction: + q = q - self.D[None, None, :, None] * p + + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + + if self.num_v_heads > self.num_heads: + q, k = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q, k)) + + beta = self.b_proj(hidden_states).sigmoid() + g = -self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'chunk': + o, recurrent_state = chunk_comba( + q=q, + k=k, + v=v, + p=p, + g=g, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=True, + ) + elif mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_comba( + q=q, + k=k, + v=v, + p=p, + g=g, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=True, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + if self.use_output_gate: + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.o_norm(o, g) + else: + o = self.o_norm(o) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/delta_net.py b/code/flash-linear-attention/fla/layers/delta_net.py new file mode 100644 index 0000000000000000000000000000000000000000..993dd74d34c1bb827350538ac5e33cdc53f5a2a5 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/delta_net.py @@ -0,0 +1,289 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange +from torch.nn import functional as F + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.ops.delta_rule import chunk_delta_rule, fused_recurrent_delta_rule + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +def elu_p1(x): + return (F.elu(x, 1., False) + 1.).to(x) + + +def sum_norm(x): + return (x / x.sum(-1, keepdim=True)).to(x) + + +class DeltaNet(nn.Module): + r""" + The layer implementaion for [Parallelizing Linear Transformers with the Delta Rule over Sequence Length](https://arxiv.org/abs/2406.06484). # noqa: + DeltaNet was originally proposed in [Linear Transformers Are Secretly Fast Weight Programmers](https://arxiv.org/abs/2102.11174). # noqa + + Args: + mode (str, Optional): + Which DeltaNet kernel to use. + Currently available: `chunk`, `fused_recurrent`, and `fused_chunk`. + Default: `chunk`. + hidden_size (int, Optional): + The hidden size of the input. Default: 1024. + expand_k (float, Optional): + The expansion ratio for the key dim. Default: 1.0. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 1.0. + num_heads (int, Optional): + The number of heads. Default: 4. + use_beta (bool, Optional): + Whether to use beta. Default: `True`. + use_gate (bool, Optional): + Whether to use output gate. Default: `False`. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `True`. + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + allow_neg_eigval (bool, Optional): + Allow negative eigenvalues. Default: `False`. If set to `True`, the beta will be multiplied by 2. + See reference: [Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues](https://arxiv.org/abs/2411.12537) + layer_idx (int, Optional): + The index of the layer. Default: None. + norm_eps (float, Optional): + The epsilon value for the layernorm/rmsnorm layer. Default: 1e-5. + qk_activation (str, Optional): + The activation function for the query and key. Default: `silu`. + qk_norm (str, Optional): + The normalization method for the query and key. Default: `l2`. + """ + + def __init__( + self, + mode: str = 'chunk', + d_model: int = None, + hidden_size: int = 1024, + expand_k: float = 1.0, + expand_v: float = 1.0, + num_heads: int = 4, + use_beta: bool = True, + use_gate: bool = False, + use_short_conv: bool = True, + conv_size: int = 4, + conv_bias: bool = False, + allow_neg_eigval: bool = False, + layer_idx: int = None, + qk_activation: str = 'silu', + qk_norm: str = 'l2', + norm_eps: float = 1e-5, + **kwargs, + ) -> DeltaNet: + super().__init__() + + self.mode = mode + self.qk_activation = qk_activation + self.qk_norm = qk_norm + + assert self.qk_activation in ['silu', 'relu', 'elu', 'identity'] + assert self.qk_norm in ['l2', 'sum'] + + if d_model is not None: + hidden_size = d_model + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.num_heads = num_heads + self.use_gate = use_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + self.allow_neg_eigval = allow_neg_eigval + + self.key_dim = int(hidden_size * expand_k) + self.value_dim = int(hidden_size * expand_v) + self.head_k_dim = self.key_dim // num_heads + self.head_v_dim = self.value_dim // num_heads + self.layer_idx = layer_idx + + if mode == 'fused_chunk': + raise NotImplementedError("fused_chunk_delta_rule is now deprecated. Please use `chunk_delta_rule` instead.") + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}" + assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}" + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + + self.use_beta = use_beta + if self.use_beta: + self.b_proj = nn.Linear(hidden_size, self.num_heads, bias=False) + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu' if qk_activation == 'silu' else None, + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu' if qk_activation == 'silu' else None, + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + else: + warnings.warn( + "ShortConvolution is crucial to the performance. " + "Do not turn it off, i.e., setting `use_short_conv=False` unless you know what you are doing.", + ) + if use_gate: + self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps) + else: + self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) + + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + # change to inference mode. + mode = 'fused_recurrent' if q_len <= 64 else self.mode + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + if self.qk_activation == 'silu': + q, k = F.silu(q), F.silu(k) + v = F.silu(self.v_proj(hidden_states)) + + q, k = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q, k)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + if self.qk_activation != 'silu': + if self.qk_activation == 'relu': + q, k = q.relu(), k.relu() + elif self.qk_activation == 'elu': + q, k = elu_p1(q), elu_p1(k) + elif self.qk_activation != 'identity': + raise NotImplementedError + + if self.qk_norm == 'sum': + q = sum_norm(q).to(q) + k = sum_norm(k).to(k) + + if self.use_beta: + beta = self.b_proj(hidden_states).sigmoid() + else: + beta = torch.ones_like(q[..., 0]) + + if self.allow_neg_eigval: + beta = beta * 2. + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_delta_rule( + q=q, + k=k, + v=v, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=(self.qk_norm == 'l2'), + ) + elif mode == 'chunk': + o, recurrent_state = chunk_delta_rule( + q=q, + k=k, + v=v, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=(self.qk_norm == 'l2'), + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + if self.use_gate: + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.o_norm(o, g) + else: + o = self.o_norm(o) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/deltaformer.py b/code/flash-linear-attention/fla/layers/deltaformer.py new file mode 100644 index 0000000000000000000000000000000000000000..d0eacdf7dbdb0554946636d6460e8dbf38a721bd --- /dev/null +++ b/code/flash-linear-attention/fla/layers/deltaformer.py @@ -0,0 +1,152 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange +from transformers.utils import logging + +from fla.modules import RMSNorm, RotaryEmbedding +from fla.ops.deltaformer import deltaformer_attn +from fla.ops.utils.index import prepare_lens_from_mask + +if TYPE_CHECKING: + from fla.models.utils import Cache + +logger = logging.get_logger(__name__) + + +class DeltaFormerAttention(nn.Module): + + r""" + The layer implementation for DeltaFormer, + [Understanding Transformer from the Perspective of Associative Memory] + (https://arxiv.org/pdf/2505.19488). + + Notes + - DeltaFormer attention is implemented with Triton kernels in `fla.ops.deltaformer` and is tuned + for typical head dimensions (e.g., 64/128). It currently supports fixed-length inputs. + - For variable-length inputs (padding masks), the deltaformer computation falls back to using the + fixed-length path, while the second stage (softmax attention over U) uses FlashAttention's + varlen path when an attention mask is provided. + - K/V grouping (GQA) is supported natively by FlashAttention via `num_kv_heads`. + - Uses K-K similarity in deltaformer computation instead of Q-K similarity for better performance. + + Args: + hidden_size (int, Optional): + The hidden size of the input. Default: 2048. + num_heads (int, Optional): + The number of attention heads. Default: 32. + num_kv_heads (int, Optional): + The number of key/value heads for grouped-query attention. If None, equals `num_heads`. + Default: None. + qkv_bias (bool, Optional): + Whether to use bias for Q/K/V projections. Default: False. + qk_norm (bool, Optional): + Whether to apply per-head RMSNorm to Q and K before attention. Default: False. + rope_theta (float, Optional): + The base frequency for rotary position embedding. Default: 10000. + max_position_embeddings (int, Optional): + The maximum position embeddings. Default: None. + layer_idx (int, Optional): + The index of the layer (used for cache compatibility). Default: None. + """ + + def __init__( + self, + hidden_size: int = 2048, + num_heads: int = 32, + num_kv_heads: int | None = None, + qkv_bias: bool = False, + qk_norm: bool = False, + rope_theta: float = 10000., + max_position_embeddings: int | None = None, + layer_idx: int | None = None, + ): + super().__init__() + + self.hidden_size = hidden_size + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads + self.num_kv_groups = num_heads // self.num_kv_heads + self.head_dim = self.hidden_size // self.num_heads + self.kv_dim = self.num_kv_heads * self.head_dim + self.qkv_bias = qkv_bias + self.qk_norm = qk_norm + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + self.layer_idx = layer_idx + + self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=self.qkv_bias) + self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) + self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) + self.b_proj = nn.Linear(self.hidden_size, self.num_heads, bias=True) + self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + + if qk_norm: + self.q_norm = RMSNorm(self.head_dim) + self.k_norm = RMSNorm(self.head_dim) + + self.rotary = RotaryEmbedding(dim=self.head_dim, base=self.rope_theta) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + attentions = None + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.size() + + q = rearrange(self.q_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + k = rearrange(self.k_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(self.v_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + beta = self.b_proj(hidden_states) + + if self.qk_norm: + q, k = self.q_norm(q), self.k_norm(k) + + cu_seqlens_kw = kwargs.get('cu_seqlens') + seqlen_offset, max_seqlen = 0, q_len + if past_key_values is not None: + seqlen_offset = past_key_values.get_seq_length(self.layer_idx) + max_seqlen = q_len + seqlen_offset + + if attention_mask is not None: + seqlen_offset = seqlen_offset + prepare_lens_from_mask(attention_mask) - attention_mask.shape[-1] + max_seqlen = q_len + max(seqlen_offset) + + if self.max_position_embeddings is not None: + max_seqlen = max(max_seqlen, self.max_position_embeddings) + + q, k = self.rotary(q, k, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens_kw) + + o = deltaformer_attn( + q=q, + k=k, + v=v, + beta=beta, + attention_mask=attention_mask, + cu_seqlens=cu_seqlens_kw, + ) + + o = o.reshape(batch_size, q_len, -1) + o = self.o_proj(o) + + if not output_attentions: + attentions = None + + return o, attentions, past_key_values diff --git a/code/flash-linear-attention/fla/layers/forgetting_attn.py b/code/flash-linear-attention/fla/layers/forgetting_attn.py new file mode 100644 index 0000000000000000000000000000000000000000..5dcf9cd62c997cc7b16ffcc4a4ff1fbf7c029392 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/forgetting_attn.py @@ -0,0 +1,133 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint +from einops import rearrange +from transformers.utils import logging + +from fla.layers.utils import pad_input, unpad_input +from fla.modules import GroupNorm +from fla.ops.attn.decoding import attn_decoding_one_step +from fla.ops.forgetting_attn.parallel import parallel_forgetting_attn + +if TYPE_CHECKING: + from fla.models.utils import Cache + +logger = logging.get_logger(__name__) + + +class ForgettingAttention(nn.Module): + + def __init__( + self, + hidden_size: int = 2048, + num_heads: int = 32, + num_kv_heads: int | None = None, + qkv_bias: bool = False, + qk_norm: bool = False, + window_size: int | None = None, + use_output_gate: bool = False, + layer_idx: int = None, + ): + super().__init__() + + self.hidden_size = hidden_size + self.num_heads = num_heads + if num_kv_heads is None: + self.num_kv_heads = self.num_heads + else: + self.num_kv_heads = num_kv_heads + self.num_kv_groups = num_heads // self.num_kv_heads + self.head_dim = self.hidden_size // self.num_heads + self.kv_dim = self.num_kv_heads * self.head_dim + self.qkv_bias = qkv_bias + self.qk_norm = qk_norm + + self.window_size = window_size + self.use_output_gate = use_output_gate + self.layer_idx = layer_idx + + self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=self.qkv_bias) + self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) + self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) + self.f_proj = nn.Linear(self.hidden_size, self.num_heads, bias=True) + + if use_output_gate: + self.g_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + + if qk_norm: + self.q_norm = GroupNorm( + num_groups=self.num_heads, + hidden_size=self.hidden_size, + is_rms_norm=True, + ) + self.k_norm = GroupNorm( + num_groups=self.num_kv_heads, + hidden_size=self.kv_dim, + is_rms_norm=True, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.size() + + q, k, v = self.q_proj(hidden_states), self.k_proj(hidden_states), self.v_proj(hidden_states) + f = F.logsigmoid(self.f_proj(hidden_states).float()) + if self.qk_norm: + q, k = self.q_norm(q), self.k_norm(k) + + cu_seqlens = kwargs.get('cu_seqlens') + if past_key_values is not None: + assert cu_seqlens is None, "cu_seqlens should not be provided when past_key_values is not None" + state = past_key_values.update( + attn_state=(k, v, f), + layer_idx=self.layer_idx, + offset=q_len, + cache_kwargs=dict(window_size=self.window_size), + ) + k, v, f = state['attn_state'] + + q = rearrange(q, '... (h d) -> ... h d', d=self.head_dim) + k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim) + + if attention_mask is not None: + q, (k, v, f), indices_q, cu_seqlens, max_seq_lens = unpad_input(q, (k, v, f), attention_mask, q_len, keepdim=True) + _, cu_seqlens_k = cu_seqlens + cu_seqlens = cu_seqlens_k + max_seqlen_q, max_seqlen_k = max_seq_lens + if max_seqlen_q != max_seqlen_k: + assert max_seqlen_q == 1, "only support q_len == 1 for decoding" + o = attn_decoding_one_step(q, k, v, f, cu_seqlens=cu_seqlens) + else: + o = parallel_forgetting_attn(q, k, v, f, cu_seqlens=cu_seqlens) + else: + o = parallel_forgetting_attn(q, k, v, f, cu_seqlens=cu_seqlens) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices_q, batch_size, q_len) + o = rearrange(o, '... h d -> ... (h d)') + if self.use_output_gate: + o = self.g_proj(hidden_states).sigmoid() * o + o = self.o_proj(o) + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/gated_deltanet.py b/code/flash-linear-attention/fla/layers/gated_deltanet.py new file mode 100644 index 0000000000000000000000000000000000000000..3ec7e6f2f88d9610f6debcc55ab58eca2a155506 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/gated_deltanet.py @@ -0,0 +1,318 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange, repeat +from torch.nn import functional as F + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +@torch.compile +def elu_p1(x): + return (F.elu(x, 1., False) + 1.).to(x) + + +@torch.compile +def sum_norm(x): + return (x / x.sum(-1, keepdim=True)).to(x) + + +class GatedDeltaNet(nn.Module): + """ + The layer implementaion for [Gated Delta Networks: Improving Mamba2 with Delta Rule](https://arxiv.org/abs/2412.06464). # noqa + + Similar to Mamba2, each layer contains around 6*hidden_size*hidden_size parameters. + + Parameter alloation when use_gate=True: + - 0.75 * hidden_size * hidden_size for the q_proj and k_proj each + - 1.5 * hidden_size * hidden_size for the v_proj, g_proj and o_proj each + - Others are ignorably small. + - In total = 0.75 * 2 + 1.5 * 3 = 6 * hidden_size * hidden_size + NOTE: num_heads * head_dim = 0.75 * hidden_size, please make sure to set the correct num_heads and head_dim. + + Parameter allocation when use_gate=False: + - 1 * hidden_size * hidden_size for the q_proj and k_proj each + - 2 * hidden_size * hidden_size for the v_proj and o_proj each + - Others are ignorably small. + - In total = 1 * 2 + 2 * 2 = 6 * hidden_size * hidden_size + + Args: + hidden_size (int, Optional): + The hidden size of the input. Default: 2048. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 2.0. + head_dim (int, Optional): + The dimension of each head. Default: 256. + num_heads (int, Optional): + The number of heads. Default: 4. + num_v_heads (int, Optional): + The number of heads for the value projection, equal to `num_heads` if `None`. + GVA is applied if `num_v_heads` > `num_heads`. Default: `None`. + mode (str, Optional): + Which Gated DeltaNet kernel to use. + Currently available: `chunk` and `fused_recurrent`. + Default: `chunk`. + use_beta (bool, Optional): + Whether to use beta. Default: `True`. + use_gate (bool, Optional): + Whether to use output gate. Default: `True`. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `True`. + allow_neg_eigval (bool, Optional): + Allow negative eigenvalues. Default: `False`. If set to `True`, the beta will be multiplied by 2. + See reference: [Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues](https://arxiv.org/abs/2411.12537) + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + layer_idx (int, Optional): + The index of the layer. Default: None. + norm_eps (float, Optional): + The epsilon value for the normalization layer. Default: 1e-5. + """ + + def __init__( + self, + hidden_size: int = 2048, + expand_v: float = 2, + head_dim: int = 256, + num_heads: int = 6, + num_v_heads: int = None, + mode: str = 'chunk', + use_gate: bool = True, + use_short_conv: bool = True, + allow_neg_eigval: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + layer_idx: int = None, + norm_eps: float = 1e-5, + **kwargs, + ) -> GatedDeltaNet: + super().__init__() + + self.mode = mode + self.allow_neg_eigval = allow_neg_eigval + self.hidden_size = hidden_size + self.expand_v = expand_v + + self.use_gate = use_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads + + self.head_k_dim = head_dim + self.head_v_dim = int(self.head_dim * self.expand_v) + self.key_dim = int(self.num_heads * self.head_k_dim) + self.value_dim = int(self.num_v_heads * self.head_v_dim) + self.layer_idx = layer_idx + + # Consistency check: Ensure expand_v produces integer values + if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. " + f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.", + ) + if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0: + raise ValueError( + f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.", + ) + + if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. " + f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.", + ) + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.a_proj = nn.Linear(hidden_size, self.num_v_heads, bias=False) + self.b_proj = nn.Linear(hidden_size, self.num_v_heads, bias=False) + + A = torch.empty(self.num_v_heads, dtype=torch.float32).uniform_(0, 16) + self.A_log = nn.Parameter(torch.log(A)) + self.A_log._no_weight_decay = True + # hard coded for now + dt_min = 0.001 + dt_max = 0.1 + dt_init_floor = 1e-4 + dt = torch.exp( + torch.rand(self.num_v_heads) * (math.log(dt_max) - math.log(dt_min)) + + math.log(dt_min), + ) + dt = torch.clamp(dt, min=dt_init_floor) + # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + self.dt_bias = nn.Parameter(inv_dt) + # Just to be explicit. Without this we already don't put wd on dt_bias because of the check + # name.endswith("bias") in param_grouping.py + self.dt_bias._no_weight_decay = True + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + else: + warnings.warn( + "ShortConvolution is crucial to the performance. " + "Do not turn it off, i.e., setting `use_short_conv=False` unless you know what you are doing.", + ) + if use_gate: + self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps) + else: + self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + # change to inference mode. + mode = 'fused_recurrent' if q_len <= 64 else self.mode + if self.training: + assert mode == 'chunk', "Only chunk mode is supported in training." + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = F.silu(self.q_proj(hidden_states)) + k = F.silu(self.k_proj(hidden_states)) + v = F.silu(self.v_proj(hidden_states)) + + q, k = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q, k)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + + if self.num_v_heads > self.num_heads: + q, k = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q, k)) + + beta = self.b_proj(hidden_states).sigmoid() + if self.allow_neg_eigval: + beta = beta * 2. + + g = -self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'chunk': + o, recurrent_state = chunk_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=True, + ) + elif mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=True, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + if self.use_gate: + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.o_norm(o, g) + else: + o = self.o_norm(o) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/gated_deltaproduct.py b/code/flash-linear-attention/fla/layers/gated_deltaproduct.py new file mode 100644 index 0000000000000000000000000000000000000000..c0143db9c3fff1543f82516de7aee15fb1c1f5c0 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/gated_deltaproduct.py @@ -0,0 +1,290 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange, repeat +from torch.nn import functional as F + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.ops.gated_delta_product import chunk_gated_delta_product +from fla.ops.gated_delta_rule import fused_recurrent_gated_delta_rule + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +class GatedDeltaProduct(nn.Module): + """ + Generalized version of GatedDoubleDeltaNet that supports arbitrary number of householder transformations. + """ + + def __init__( + self, + hidden_size: int = 2048, + expand_v: float = 2, + head_dim: int = 256, + num_heads: int = 6, + num_v_heads: int = None, + mode: str = 'chunk', + use_output_gate: bool = True, + use_short_conv: bool = True, + conv_size: int = 4, + conv_bias: bool = False, + layer_idx: int = None, + norm_eps: float = 1e-5, + use_forget_gate: bool = True, + allow_neg_eigval: bool = True, + num_householder: int = 2, + **kwargs, + ) -> GatedDeltaProduct: + super().__init__() + + self.mode = mode + + self.hidden_size = hidden_size + self.expand_v = expand_v + + self.use_forget_gate = use_forget_gate + self.allow_neg_eigval = allow_neg_eigval + self.num_householder = num_householder + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads + + self.head_k_dim = head_dim + self.head_v_dim = int(self.head_dim * self.expand_v) + self.key_dim = int(self.num_heads * self.head_k_dim) + self.value_dim = int(self.num_v_heads * self.head_v_dim) + self.layer_idx = layer_idx + + # Consistency check: Ensure expand_v produces integer values + if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. " + f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.", + ) + if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0: + raise ValueError( + f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.", + ) + + if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. " + f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.", + ) + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim * num_householder, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim * num_householder, bias=False) + self.b_proj = nn.Linear(hidden_size, self.num_v_heads * num_householder, bias=False) + + if self.use_forget_gate: + self.a_proj = nn.Linear(hidden_size, self.num_v_heads, bias=False) + A = torch.empty(self.num_v_heads, dtype=torch.float32).uniform_(0, 16) + self.A_log = nn.Parameter(torch.log(A)) + self.A_log._no_weight_decay = True + # hard coded for now + dt_min = 0.001 + dt_max = 0.1 + dt_init_floor = 1e-4 + dt = torch.exp( + torch.rand(self.num_v_heads) * (math.log(dt_max) - math.log(dt_min)) + + math.log(dt_min), + ) + dt = torch.clamp(dt, min=dt_init_floor) + # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + self.dt_bias = nn.Parameter(inv_dt) + # Just to be explicit. Without this we already don't put wd on dt_bias because of the check + # name.endswith("bias") in param_grouping.py + self.dt_bias._no_weight_decay = True + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim * num_householder, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim * num_householder, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + else: + warnings.warn( + "ShortConvolution is crucial to the performance. " + "Do not turn it off, i.e., setting `use_short_conv=False` unless you know what you are doing.", + ) + if use_output_gate: + self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps) + else: + self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + def _initialize_weights(self, module: nn.Module): + if getattr(module, "_is_hf_initialized", False): + return + if isinstance(module, nn.Linear): + nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5) + if module.bias is not None: + nn.init.zeros_(module.bias) + module._is_hf_initialized = True + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + # change to inference mode. + mode = 'fused_recurrent' if q_len <= 64 else self.mode + + if self.training: + assert mode == 'chunk', "Only chunk mode is supported in training." + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = F.silu(self.q_proj(hidden_states)) + k = F.silu(self.k_proj(hidden_states)) + v = F.silu(self.v_proj(hidden_states)) + + q = rearrange(q, '... (h d) -> ... h d', d=self.head_k_dim) + k = rearrange(k, '... t (n h d) -> ... (t n) h d', n=self.num_householder, d=self.head_k_dim) + v = rearrange(v, '... t (n h d) -> ... (t n) h d', n=self.num_householder, d=self.head_v_dim) + + if self.num_v_heads > self.num_heads: + q, k = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q, k)) + + beta = self.b_proj(hidden_states).sigmoid() + if self.allow_neg_eigval: + beta = beta * 2. + + beta = rearrange(beta, '... t (n h) -> ... (t n) h', n=self.num_householder) + if self.use_forget_gate: + g = -self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias) + else: + g = None + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'chunk': + o, recurrent_state = chunk_gated_delta_product( + q=q, + k=k, + v=v, + g=g, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + num_householder=self.num_householder, + use_qk_l2norm_in_kernel=True, + ) + + elif mode == 'fused_recurrent': + if self.use_forget_gate: + g_new = g.new_zeros(g.shape[0], g.shape[1], self.num_householder, g.shape[2]) + g_new[:, :, 0] = g + g = rearrange(g_new, '... t n h -> ... (t n) h') + + q_new = q.new_zeros(q.shape[0], q.shape[1], self.num_householder, q.shape[2], q.shape[3]) + q_new[:, :, -1] = q + q = rearrange(q_new, '... t n h d-> ... (t n) h d') + o, recurrent_state = fused_recurrent_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens * self.num_householder if cu_seqlens is not None else None, + use_qk_l2norm_in_kernel=True, + ) + o = rearrange(o, '... (t n) h d -> ... t n h d', n=self.num_householder)[..., -1, :, :].contiguous() + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + if self.use_output_gate: + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.o_norm(o, g) + else: + o = self.o_norm(o) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/gla.py b/code/flash-linear-attention/fla/layers/gla.py new file mode 100644 index 0000000000000000000000000000000000000000..2e86ef67b519930b9a09cf72a8b42cccc4d69423 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/gla.py @@ -0,0 +1,299 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.modules.activations import ACT2FN +from fla.ops.gla import chunk_gla, fused_chunk_gla, fused_recurrent_gla + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +class GatedLinearAttention(nn.Module): + r""" + The layer implementaion for [Gated Linear Attention Transformers with Hardware-Efficient Training](https://arxiv.org/abs/2312.06635). # noqa + + Args: + mode (str, Optional): + Which GLA kernel to use. + Currently available: `chunk`, `fused_recurrent`, and `fused_chunk`. + Default: `chunk`. + hidden_size (int, Optional): + The hidden size of the input. Default: 1024. + expand_k (float, Optional): + The expansion ratio for the key dim. Default: 0.5. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 1.0. + num_heads (int, Optional): + The number of heads. Default: 4. + num_kv_heads (int, Optional): + The number of key/value heads, used for MQA. Default: None. + feature_map (str, Optional): + Feature map function applied to queries/keys. Default: None. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `False`. + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + use_output_gate (bool, Optional): + Whether to use output gate. Default: `True`. + gate_fn (str, Optional): + The activation function for the output gate. Default: `swish`. + elementwise_affine (bool, Optional): + If `True`, applies elementwise affine to LayerNorm with learnable parameters. Default: `True`. + norm_eps (float, Optional): + The epsilon value for the layernorm/rmsnorm layer. Default: 1e-5. + gate_logit_normalizer (int, Optional): + The normalizer for the gate logits, appied after `logsigmoid`. Default: 16. + gate_low_rank_dim (int, Optional): + The low rank dim for the gate projection. Default: 16. + clamp_min (float, Optional): + The minimum value for the gate logits. Default: None. + fuse_norm (bool, Optional): + Whether to fuse the norm and the output gate for better memory footprint. Default: `True`. + layer_idx (int, Optional): + The index of the layer. Default: None. + """ + + def __init__( + self, + mode: str = 'chunk', + hidden_size: int = 1024, + expand_k: float = 0.5, + expand_v: float = 1.0, + num_heads: int = 4, + num_kv_heads: int | None = None, + feature_map: str | None = None, + use_short_conv: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + use_output_gate: bool = True, + gate_fn: str = 'swish', + elementwise_affine: bool | None = True, + norm_eps: float = 1e-5, + gate_logit_normalizer: int = 16, + gate_low_rank_dim: int = 16, + clamp_min: float | None = None, + fuse_norm: bool = True, + layer_idx: int = None, + ) -> GatedLinearAttention: + super().__init__() + + self.mode = mode + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads + self.num_kv_groups = self.num_heads // self.num_kv_heads + self.feature_map_fn = ACT2FN[feature_map] if feature_map is not None else None + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + self.use_output_gate = use_output_gate + + self.key_dim = int(hidden_size * expand_k) + self.value_dim = int(hidden_size * expand_v) + self.key_dim_per_group = self.key_dim // self.num_kv_groups + self.value_dim_per_group = self.value_dim // self.num_kv_groups + self.clamp_min = clamp_min + self.layer_idx = layer_idx + + assert mode in ['chunk', 'fused_recurrent', 'fused_chunk'], f"Not supported mode `{mode}`." + assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}" + assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}" + + self.head_k_dim = self.key_dim // num_heads + self.head_v_dim = self.value_dim // num_heads + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim_per_group, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim_per_group, bias=False) + if self.use_output_gate: + self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim_per_group, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim_per_group, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + + self.gk_proj = nn.Sequential(nn.Linear(hidden_size, gate_low_rank_dim, bias=False), + nn.Linear(gate_low_rank_dim, self.key_dim_per_group, bias=True)) + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + if gate_fn == 'swish' and fuse_norm and use_output_gate: + self.g_norm_swish_gate = FusedRMSNormGated( + hidden_size=self.head_v_dim, + elementwise_affine=elementwise_affine, + eps=norm_eps, + ) + self.fuse_norm_and_gate = True + else: + self.fuse_norm_and_gate = False + self.g_norm = RMSNorm( + hidden_size=self.head_v_dim, + elementwise_affine=elementwise_affine, + eps=norm_eps, + ) + self.gate_fn = ACT2FN[gate_fn] + + self.gate_logit_normalizer = gate_logit_normalizer + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + gk = self.gk_proj(hidden_states) + + q = rearrange(q, '... (h d) -> ... h d', d=self.head_k_dim) + if self.num_kv_groups > 1: + k, gk = (repeat(x, '... (h d) -> ... (h g) d', g=self.num_kv_groups, d=self.head_k_dim) for x in (k, gk)) + v = repeat(v, '... (h d) -> ... (h g) d', g=self.num_kv_groups, d=self.head_v_dim) + else: + k, gk = (rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim) for x in (k, gk)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + + gk = F.logsigmoid(gk) / self.gate_logit_normalizer + if self.clamp_min is not None: + gk = torch.clamp_min(gk, self.clamp_min) + + if self.feature_map_fn is not None: + q, k = map(self.feature_map_fn, (q, k)) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_gla( + q=q, + k=k, + v=v, + gk=gk, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + elif mode == 'fused_chunk': + o, recurrent_state = fused_chunk_gla( + q=q, + k=k, + v=v, + g=gk, + initial_state=recurrent_state, + output_final_state=use_cache, + ) + elif mode == 'chunk': + o, recurrent_state = chunk_gla( + q=q, + k=k, + v=v, + g=gk, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + if self.use_output_gate: + g = self.g_proj(hidden_states) + if self.fuse_norm_and_gate: + g = rearrange(g, '... (h d) -> ... h d', d=self.head_v_dim) + o = self.g_norm_swish_gate(o, g) + o = rearrange(o, '... h d -> ... (h d)') + else: + o = rearrange(self.g_norm(o), '... h d -> ... (h d)') + o = o * self.gate_fn(g) + else: + o = rearrange(self.g_norm(o), '... h d -> ... (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/gsa.py b/code/flash-linear-attention/fla/layers/gsa.py new file mode 100644 index 0000000000000000000000000000000000000000..2fc7be244919b922b92818d8fc1ae92ae8971c4d --- /dev/null +++ b/code/flash-linear-attention/fla/layers/gsa.py @@ -0,0 +1,239 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import RMSNorm, ShortConvolution +from fla.modules.feature_map import ReLUFeatureMap, SwishFeatureMap, T2RFeatureMap +from fla.modules.layernorm import rms_norm_linear +from fla.ops.gsa import chunk_gsa, fused_recurrent_gsa + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +class GatedSlotAttention(nn.Module): + + def __init__( + self, + mode: str = 'chunk', + hidden_size: int = 1024, + expand_k: float = 1., + expand_v: float = 1., + num_heads: int = 4, + num_kv_heads: int | None = None, + use_short_conv: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + num_slots: int | None = None, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-5, + gate_logit_normalizer: int = 8, + feature_map: str = 'swish', + use_output_gate: bool = False, + use_norm: bool = True, + layer_idx: int | None = None, + scale: float | None = 1., + **kwargs, + ) -> GatedSlotAttention: + super().__init__() + + self.mode = mode + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.num_heads = num_heads + self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads + self.num_kv_groups = self.num_heads // self.num_kv_heads + self.key_dim = int(hidden_size * expand_k) + self.value_dim = int(hidden_size * expand_v) + self.key_dim_per_group = self.key_dim // self.num_kv_groups + self.value_dim_per_group = self.value_dim // self.num_kv_groups + self.head_k_dim = self.key_dim // self.num_heads + self.head_v_dim = self.value_dim // self.num_heads + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.gate_logit_normalizer = gate_logit_normalizer + + self.use_output_gate = use_output_gate + self.use_norm = use_norm + self.scale = scale + + if num_slots is None: + num_slots = self.head_k_dim + self.num_slots = num_slots + + self.layer_idx = layer_idx + + if layer_idx is None: + warnings.warn( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class.", + ) + + self.register_module('feature_map', None) + if feature_map == 'swish': + self.feature_map = SwishFeatureMap() + elif feature_map == 'relu': + self.feature_map = ReLUFeatureMap() + elif feature_map == 't2r': + self.feature_map = T2RFeatureMap(self.head_k_dim, self.head_k_dim) + else: + raise NotImplementedError(f"Feature map `{feature_map}` is not supported now.") + + self.q_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.key_dim_per_group, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.value_dim_per_group, bias=False) + self.f_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.num_slots, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim_per_group, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim_per_group, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + + self.g_norm = RMSNorm(self.hidden_size, elementwise_affine, eps=norm_eps) + self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + f = self.f_proj(hidden_states) + + q = rearrange(q, '... (h d) -> ... h d', d=self.head_k_dim) + k = rearrange(k, '... (h d) -> ... h d', d=self.head_k_dim) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + f = rearrange(f, '... (h m) -> ... h m', m=self.num_slots) + + if self.feature_map is not None: + q, k = map(lambda x: self.feature_map(x), (q, k)) + v = F.silu(v) + + f = F.logsigmoid(f) / self.gate_logit_normalizer + s = (1 - f.exp()).to(f.dtype) + + if self.num_kv_groups > 1: + k, v, f, s = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_kv_groups), (k, v, f, s)) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_gsa( + q=q, + k=k, + v=v, + s=s, + g=f, + initial_state=recurrent_state, + output_final_state=use_cache, + scale=self.scale, + cu_seqlens=cu_seqlens, + ) + elif mode == 'chunk': + o, recurrent_state = chunk_gsa( + q=q, + k=k, + v=v, + s=s, + g=f, + initial_state=recurrent_state, + output_final_state=use_cache, + scale=self.scale, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + o = rearrange(o, '... h d -> ... (h d)') + o = rms_norm_linear(F.silu(o), self.g_norm.weight, self.g_norm.bias, self.o_proj.weight, self.o_proj.bias) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/hgrn.py b/code/flash-linear-attention/fla/layers/hgrn.py new file mode 100644 index 0000000000000000000000000000000000000000..88dbff41b864e3a1c83bd319e6475e563b8fc10d --- /dev/null +++ b/code/flash-linear-attention/fla/layers/hgrn.py @@ -0,0 +1,175 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +# "Hierarchically Gated Recurrent Neural Network for Sequence Modeling" [https://arxiv.org/abs/2311.04823] + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fla.modules import FusedRMSNormGated, ShortConvolution +from fla.modules.activations import swiglu +from fla.ops.hgrn import chunk_hgrn, fused_recurrent_hgrn + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +class HGRNAttention(nn.Module): + + def __init__( + self, + mode: str = 'chunk', + hidden_size: int = 1024, + expand_ratio: int | None = 1, + use_short_conv: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-5, + layer_idx: int = None, + ) -> HGRNAttention: + super().__init__() + + self.mode = mode + self.hidden_size = hidden_size + self.expand_ratio = expand_ratio + self.input_dim = int(hidden_size * expand_ratio) + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.layer_idx = layer_idx + + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + + self.i_proj = nn.Linear(hidden_size, self.input_dim, bias=False) + self.f_proj = nn.Linear(hidden_size, self.input_dim, bias=False) + self.g_proj = nn.Linear(hidden_size, self.input_dim, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.f_conv1d = ShortConvolution( + hidden_size=self.input_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + self.i_conv1d = ShortConvolution( + hidden_size=self.input_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + + self.g_norm = FusedRMSNormGated( + hidden_size=self.input_dim, + elementwise_affine=elementwise_affine, + eps=norm_eps, + ) + self.o_proj = nn.Linear(self.input_dim, hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + lower_bound: torch.Tensor | None = None, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + # launching the triton kernel for just one token will actually be slower + mode = 'fused_recurrent' if not self.training and hidden_states.shape[1] <= 64 else self.mode + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if self.use_short_conv: + conv_state_i, conv_state_f = None, None + if last_state is not None: + conv_state_i, conv_state_f = last_state['conv_state'] + conv_mask = attention_mask[:, -hidden_states.shape[1]:] if attention_mask is not None else None + i, conv_state_i = self.i_conv1d( + x=self.i_proj(hidden_states), + mask=conv_mask, + cache=conv_state_i, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + f, conv_state_f = self.f_conv1d( + x=self.f_proj(hidden_states), + mask=conv_mask, + cache=conv_state_f, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + i = self.i_proj(hidden_states) + f = self.f_proj(hidden_states) + + f = F.logsigmoid(f) + # the lower bound for the first layer is zero + if lower_bound is not None and self.layer_idx > 0: + f = torch.logaddexp(lower_bound.log(), torch.log1p(-lower_bound) + f).to(f) + i = swiglu(i, 1 - f.exp()) + + # dealing with left-padding + if attention_mask is not None: + i = i.mul_(attention_mask[:, -i.shape[-2]:, None]) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'chunk': + if cu_seqlens is not None: + raise NotImplementedError("Chunk mode does not support variable-length sequences.") + o, recurrent_state = chunk_hgrn( + x=i, + g=f, + initial_state=recurrent_state, + output_final_state=use_cache, + ) + elif mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_hgrn( + x=i, + g=f, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_i, conv_state_f) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=i.shape[2], + ) + + o = self.g_norm(o, self.g_proj(hidden_states)) + o = self.o_proj(o) + + return o, None, past_key_values + + def state_size(self, **kwargs) -> int: + state_size = self.hidden_size + for module in self.children(): + if isinstance(module, ShortConvolution): + state_size += module.state_size + return state_size diff --git a/code/flash-linear-attention/fla/layers/hgrn2.py b/code/flash-linear-attention/fla/layers/hgrn2.py new file mode 100644 index 0000000000000000000000000000000000000000..5443e2dcb326099c343bd6b85454e7f91d6e41e5 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/hgrn2.py @@ -0,0 +1,211 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +# "HGRN2: Gated Linear RNNs with State Expansion"[https://arxiv.org/abs/2404.07904] + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import RMSNorm, ShortConvolution +from fla.modules.activations import swish +from fla.modules.layernorm import rms_norm_linear +from fla.ops.gla import chunk_gla, fused_chunk_gla, fused_recurrent_gla + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +class HGRN2Attention(nn.Module): + + def __init__( + self, + mode: str = 'chunk', + hidden_size: int = 1024, + num_heads: int | None = None, + expand_ratio: int | None = 128, + use_short_conv: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-5, + layer_idx: int = None, + ) -> HGRN2Attention: + super().__init__() + + self.mode = mode + self.hidden_size = hidden_size + + if expand_ratio is not None: + num_heads = hidden_size // expand_ratio + elif expand_ratio is None and num_heads is not None: + expand_ratio = hidden_size // num_heads + elif expand_ratio is None and num_heads is None: + raise RuntimeError("One of `expand_ratio` or `num_heads` should be provided.") + self.num_heads = num_heads + self.expand_ratio = expand_ratio + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.forget_dim = int(self.num_heads * self.expand_ratio) + self.input_dim = hidden_size + self.layer_idx = layer_idx + + assert mode in ['chunk', 'fused_recurrent', 'fused_chunk'], f"Not supported mode `{mode}`." + assert self.forget_dim % num_heads == 0, f"forget dim must be divisible by num_heads of {num_heads}" + assert self.input_dim % num_heads == 0, f"input dim must be divisible by num_heads of {num_heads}" + + self.head_f_dim = self.expand_ratio + self.head_i_dim = self.hidden_size // num_heads + + self.q_proj = nn.Linear(hidden_size, self.forget_dim, bias=False) + self.f_proj = nn.Linear(hidden_size, self.forget_dim, bias=False) + self.i_proj = nn.Linear(hidden_size, self.input_dim, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.forget_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + self.f_conv1d = ShortConvolution( + hidden_size=self.forget_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + self.i_conv1d = ShortConvolution( + hidden_size=self.input_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + + self.g_norm = RMSNorm(hidden_size=self.hidden_size, elementwise_affine=elementwise_affine, eps=norm_eps) + self.o_proj = nn.Linear(self.input_dim, hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + lower_bound: torch.Tensor | None = None, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + if self.use_short_conv: + conv_state_q, conv_state_f, conv_state_i = None, None, None + if last_state is not None: + conv_state_q, conv_state_f, conv_state_i = last_state['conv_state'] + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + f, conv_state_f = self.f_conv1d( + x=self.f_proj(hidden_states), + cache=conv_state_f, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + i, conv_state_i = self.i_conv1d( + x=self.i_proj(hidden_states), + cache=conv_state_i, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = self.q_proj(hidden_states) + f = self.f_proj(hidden_states) + i = self.i_proj(hidden_states) + + q = swish(q) + + g = F.logsigmoid(f) + # the lower bound for the first layer is zero + if lower_bound is not None and self.layer_idx > 0: + g = torch.logaddexp(lower_bound.log(), torch.log1p(-lower_bound) + g) + k = 1 - g.exp() + + q, k, g = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_f_dim), (q, k.to(i), g)) + i = rearrange(i, '... (h d) -> ... h d', d=self.head_i_dim) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_gla( + q=q, + k=k, + v=i, + gk=g, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + elif mode == 'fused_chunk': + o, recurrent_state = fused_chunk_gla( + q=q, + k=k, + v=i, + g=g, + initial_state=recurrent_state, + output_final_state=use_cache, + ) + elif mode == 'chunk': + o, recurrent_state = chunk_gla( + q=q, + k=k, + v=i, + g=g, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_f, conv_state_i) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + o = rearrange(o, '... h d -> ... (h d)') + o = rms_norm_linear(o, self.g_norm.weight, self.g_norm.bias, self.o_proj.weight, self.o_proj.bias) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/kda.py b/code/flash-linear-attention/fla/layers/kda.py new file mode 100644 index 0000000000000000000000000000000000000000..6d06f6bb3fe45fa63f01afcc80dbc65fabe16ff1 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/kda.py @@ -0,0 +1,272 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import math +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange, repeat +from torch.nn import functional as F + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import FusedRMSNormGated, ShortConvolution +from fla.ops.kda import chunk_kda, fused_recurrent_kda +from fla.ops.kda.gate import fused_kda_gate + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +class KimiDeltaAttention(nn.Module): + """ + Kimi Delta Attention (KDA) layer implementation. + + Args: + hidden_size (int, Optional): + The hidden size of the input. Default: 2048. + expand_v (float, Optional): + The expansion ratio for the value dimension. Default: 1.0. + head_dim (int, Optional): + The dimension of each head. Default: 128. + num_heads (int, Optional): + The number of heads. Default: 16. + num_v_heads (int, Optional): + The number of heads for the value projection, equal to `num_heads` if `None`. + GVA (Grouped Value Attention) is applied if `num_v_heads` > `num_heads`. Default: `None`. + mode (str, Optional): + Which Kimi Delta Attention kernel to use. + Currently available: `chunk` and `fused_recurrent`. + Default: `chunk`. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `True`. + allow_neg_eigval (bool, Optional): + Allow negative eigenvalues. Default: `False`. If set to `True`, the beta will be multiplied by 2. + See reference: + [Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues](https://arxiv.org/abs/2411.12537) + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + layer_idx (int, Optional): + The index of the layer. Default: None. + norm_eps (float, Optional): + The epsilon value for the normalization layer. Default: 1e-5. + """ + + def __init__( + self, + hidden_size: int = 2048, + expand_v: float = 1, + head_dim: int = 128, + num_heads: int = 16, + num_v_heads: int = None, + mode: str = 'chunk', + use_short_conv: bool = True, + allow_neg_eigval: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + layer_idx: int = None, + norm_eps: float = 1e-5, + **kwargs, + ) -> KimiDeltaAttention: + super().__init__() + + self.mode = mode + self.allow_neg_eigval = allow_neg_eigval + self.hidden_size = hidden_size + self.expand_v = expand_v + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads + + self.head_k_dim = head_dim + self.head_v_dim = int(self.head_dim * self.expand_v) + self.key_dim = int(self.num_heads * self.head_k_dim) + self.value_dim = int(self.num_v_heads * self.head_v_dim) + self.layer_idx = layer_idx + + # Consistency check: Ensure expand_v produces integer values + if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. " + f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.", + ) + if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0: + raise ValueError( + f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.", + ) + + if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. " + f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.", + ) + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + + if use_short_conv: + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + + self.f_proj = nn.Sequential( + nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.key_dim, bias=False), + ) + self.b_proj = nn.Linear(hidden_size, self.num_heads, bias=False) + + self.A_log = nn.Parameter(torch.log(torch.empty(self.num_heads, dtype=torch.float32).uniform_(1, 16))) + self.A_log._no_weight_decay = True + self.dt_bias = nn.Parameter(torch.zeros(self.key_dim, dtype=torch.float32)) + self.dt_bias._no_weight_decay = True + + self.g_proj = nn.Sequential( + nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.value_dim, bias=True), + ) + self.o_norm = FusedRMSNormGated(self.head_v_dim, activation='sigmoid', eps=norm_eps) + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + # change to inference mode. + mode = 'fused_recurrent' if q_len <= 64 else self.mode + if self.training: + assert mode == 'chunk', "Only chunk mode is supported in training." + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = F.silu(self.q_proj(hidden_states)) + k = F.silu(self.k_proj(hidden_states)) + v = F.silu(self.v_proj(hidden_states)) + + g = self.f_proj(hidden_states) + g = fused_kda_gate(g, self.A_log, self.head_k_dim, g_bias=self.dt_bias) + beta = self.b_proj(hidden_states).sigmoid() + + q, k = (rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim) for x in (q, k)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + + # for multi-value attention, we repeat the inputs for simplicity. + if self.num_v_heads > self.num_heads: + q, k, g = (repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads) for x in (q, k, g)) + beta = repeat(beta, '... h -> ... (h g)', g=self.num_v_heads // self.num_heads) + + if self.allow_neg_eigval: + beta = beta * 2. + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'chunk': + o, recurrent_state = chunk_kda( + q=q, + k=k, + v=v, + g=g, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + use_qk_l2norm_in_kernel=True, + cu_seqlens=cu_seqlens, + ) + elif mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_kda( + q=q, + k=k, + v=v, + g=g, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + use_qk_l2norm_in_kernel=True, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + o = self.o_norm(o, rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim)) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/lightnet.py b/code/flash-linear-attention/fla/layers/lightnet.py new file mode 100644 index 0000000000000000000000000000000000000000..86c6ab501e7e8ad8788137def85f9c6f3337708d --- /dev/null +++ b/code/flash-linear-attention/fla/layers/lightnet.py @@ -0,0 +1,222 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +# ["You Only Scan Once: Efficient Multi-dimension Sequential Modeling with LightNet"](https://arxiv.org/abs/2405.21022) + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from fla.modules import FusedRMSNormGated, ShortConvolution +from fla.modules.fused_norm_gate import rms_norm_swish_gate_linear +from fla.ops.gla import chunk_gla, fused_recurrent_gla + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +class LightNetAttention(nn.Module): + + def __init__( + self, + mode: str = 'chunk', + hidden_size: int = 1024, + num_heads: int | None = None, + expand_ratio: int | None = 128, + use_short_conv: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + gate_low_rank_dim: int = 128, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-5, + layer_idx: int = None, + ) -> LightNetAttention: + super().__init__() + + self.mode = mode + self.hidden_size = hidden_size + + if expand_ratio is None and num_heads is not None: + expand_ratio = hidden_size // num_heads + elif expand_ratio is not None and num_heads is None: + num_heads = hidden_size // expand_ratio + elif expand_ratio is None and num_heads is None: + raise RuntimeError("One of `expand_ratio` or `num_heads` should be provided.") + self.num_heads = num_heads + self.expand_ratio = expand_ratio + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.key_dim = int(self.num_heads * self.expand_ratio) + self.value_dim = hidden_size + self.gate_low_rank_dim = gate_low_rank_dim + self.layer_idx = layer_idx + + assert mode in ['chunk', 'fused_chunk'], f"Not supported mode `{mode}`." + assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}" + assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}" + + self.head_f_dim = self.expand_ratio + self.head_i_dim = self.hidden_size // num_heads + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + + self.g_proj = nn.Sequential( + nn.Linear(hidden_size, gate_low_rank_dim, bias=False), + nn.Linear(gate_low_rank_dim, hidden_size, bias=False), + ) + self.g_norm = FusedRMSNormGated( + hidden_size=hidden_size, + elementwise_affine=elementwise_affine, + eps=norm_eps, + ) + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + # launching the triton kernel for just one token will actually be slower + mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + conv_mask = attention_mask[:, -hidden_states.shape[1]:] if attention_mask is not None else None + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + mask=conv_mask, + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + mask=conv_mask, + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + mask=conv_mask, + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + + # dealing with left-padding + if attention_mask is not None: + v = v.mul_(attention_mask[:, -v.shape[-2]:, None]) + + q = F.silu(q) + q, k = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_f_dim), (q, k)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_i_dim) + # TODO: this 2 steps took huge amount of time, which should be optimized + z = k.float().logcumsumexp(1) + + if cu_seqlens is not None: + raise NotImplementedError("LightNet does not support variable-length sequences for now.") + k, g = torch.exp(k - z).to(k.dtype), (torch.cat((z[:, :1], z[:, :-1]), 1) - z).to(k.dtype) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_gla( + q=q, + k=k, + v=v, + gk=g, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + elif mode == 'chunk': + o, recurrent_state = chunk_gla( + q=q, + k=k, + v=v, + g=g, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q.shape[1], + ) + + o = rms_norm_swish_gate_linear( + rearrange(o, 'b t h d -> b t (h d)'), + self.g_proj(hidden_states), + self.g_norm.weight, + self.g_norm.bias, + self.o_proj.weight, + self.o_proj.bias, + ) + return o, None, past_key_values + + def state_size(self, **kwargs) -> int: + state_size = self.key_dim * self.head_i_dim + for module in self.children(): + if isinstance(module, ShortConvolution): + state_size += module.state_size + return state_size diff --git a/code/flash-linear-attention/fla/layers/linear_attn.py b/code/flash-linear-attention/fla/layers/linear_attn.py new file mode 100644 index 0000000000000000000000000000000000000000..7afbe7ac11e697c245c761859cbfafcddcc1fe6c --- /dev/null +++ b/code/flash-linear-attention/fla/layers/linear_attn.py @@ -0,0 +1,163 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat + +from fla.modules import RMSNorm +from fla.modules.feature_map import DPFPFeatureMap, HadamardFeatureMap, HedgehogFeatureMap, T2RFeatureMap +from fla.ops.linear_attn import chunk_linear_attn, fused_chunk_linear_attn, fused_recurrent_linear_attn + + +class LinearAttention(nn.Module): + + def __init__( + self, + mode: str = 'chunk', + hidden_size: str = 1024, + expand_k: float = 1.0, + expand_v: float = 1.0, + num_heads: int = 8, + num_kv_heads: int | None = None, + feature_map: str = 'elementwise_product', + tie_feature_map_qk: bool = False, + output_norm: str = 'rmsnorm', + norm_q: bool = False, + norm_k: bool = False, + do_feature_map_norm: bool = False, + elementwise_affine: bool = True, + norm_eps: float = 1e-5, + **kwargs, + ): + super().__init__() + + self.hidden_size = hidden_size + self.mode = mode + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads + self.num_kv_groups = self.num_heads // self.num_kv_heads + self.key_dim = int(hidden_size * expand_k) + self.value_dim = int(hidden_size * expand_v) + self.key_dim_per_group = self.key_dim // self.num_kv_groups + self.value_dim_per_group = self.value_dim // self.num_kv_groups + + assert mode in ['chunk', 'fused_chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}" + assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}" + + self.head_k_dim = self.key_dim // num_heads + self.head_v_dim = self.value_dim // num_heads + self.do_feature_map_norm = do_feature_map_norm + + if feature_map == 'hedgehog': + if tie_feature_map_qk: + self.feature_map_q = self.feature_map_k = HedgehogFeatureMap(head_dim=self.head_k_dim) + else: + self.feature_map_q = HedgehogFeatureMap(head_dim=self.head_k_dim) + self.feature_map_k = HedgehogFeatureMap(head_dim=self.head_k_dim) + + elif feature_map == 't2r': + if tie_feature_map_qk: + self.feature_map_q = self.feature_map_k = T2RFeatureMap(head_dim=self.head_k_dim) + else: + self.feature_map_q = T2RFeatureMap(head_dim=self.head_k_dim) + self.feature_map_k = T2RFeatureMap(head_dim=self.head_k_dim) + + elif feature_map == 'elementwise_product': + if tie_feature_map_qk: + self.feature_map_q = self.feature_map_k = HadamardFeatureMap(head_dim=self.head_k_dim) + else: + self.feature_map_q = HadamardFeatureMap(head_dim=self.head_k_dim) + self.feature_map_k = HadamardFeatureMap(head_dim=self.head_k_dim) + + elif feature_map == 'dpfp': + self.feature_map_q = DPFPFeatureMap(head_dim=self.head_k_dim) + self.feature_map_k = DPFPFeatureMap(head_dim=self.head_k_dim) + + elif feature_map == 'elu': + def elu(x): + return F.elu(x) + 1 + self.feature_map_q = elu + self.feature_map_k = elu + + elif feature_map == 'relu': + self.feature_map_q = nn.ReLU() + self.feature_map_k = nn.ReLU() + + elif feature_map == 'identity': + self.feature_map_q = nn.Identity() + self.feature_map_k = nn.Identity() + else: + raise NotImplementedError(f"Not supported feature map `{feature_map}`.") + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim_per_group, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim_per_group, bias=False) + + if output_norm == 'rmsnorm': + self.norm = RMSNorm(hidden_size=self.head_v_dim, elementwise_affine=elementwise_affine, eps=norm_eps) + elif output_norm == 'identity': + self.norm = nn.Identity() + else: + raise NotImplementedError(f"Not supported output norm `{output_norm}`.") + + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + self.norm_q = norm_q + self.norm_k = norm_k + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs, + ) -> torch.Tensor: + mode = self.mode + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + + q = rearrange(q, '... (h d) -> ... h d', d=self.head_k_dim) + if self.num_kv_groups > 1: + k = repeat(k, '... (h d) -> ... (h g) d', d=self.head_k_dim, g=self.num_kv_groups) + v = repeat(v, '... (h d) -> ... (h g) d', d=self.head_v_dim, g=self.num_kv_groups) + else: + k = rearrange(k, '... (h d) -> ... h d', d=self.head_k_dim) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + + q = self.feature_map_q(q) + k = self.feature_map_k(k) + + if self.norm_q: + q = q / (q.sum(-1, True) + 1e-4) + if self.norm_k: + k = k / (k.sum(-1, True) + 1e-4) + + if mode == 'chunk': + o, final_state = chunk_linear_attn( + q=q, + k=k, + v=v, + normalize=self.do_feature_map_norm, + ) + elif mode == 'fused_chunk': + o, final_state = fused_chunk_linear_attn( + q=q, + k=k, + v=v, + normalize=self.do_feature_map_norm, + ) + elif mode == 'fused_recurrent': + o, final_state = fused_recurrent_linear_attn( + q=q, + k=k, + v=v, + normalize=self.do_feature_map_norm, + ) + else: + raise NotImplementedError + o = self.norm(o) + o = rearrange(o, '... h d -> ... (h d)') + o = self.o_proj(o) + return o diff --git a/code/flash-linear-attention/fla/layers/log_linear_mamba2.py b/code/flash-linear-attention/fla/layers/log_linear_mamba2.py new file mode 100644 index 0000000000000000000000000000000000000000..014f2ccaf33cd153e9a38c5ffd39860bcd084024 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/log_linear_mamba2.py @@ -0,0 +1,650 @@ +import math +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from einops import rearrange +from transformers.activations import ACT2FN +from transformers.utils import logging + +from fla.layers.mamba2 import apply_mask_to_padding_states, causal_conv1d_fn, causal_conv1d_update, is_fast_path_available +from fla.modules.layernorm_gated import RMSNormGated, rmsnorm_fn +from fla.ops.log_linear_attn.chunk import LogLinearAttentionState, chunk_log_linear_attn + +if TYPE_CHECKING: + from fla.models.log_linear_mamba2.modeling_log_linear_mamba2 import LogLinearMamba2Cache + +logger = logging.get_logger(__name__) + + +def ceil_log(x: int, b: int) -> int: + return math.ceil(math.log(x, b)) + + +def get_num_levels(length: int, base: int) -> int: + return ceil_log(length, base) + 1 + + +MAX_SEQUENCE_LENGTH = 2048 * 8 +LAMBDA_LEVEL_BASE = 2 +MAX_NUM_LEVELS = get_num_levels(length=MAX_SEQUENCE_LENGTH, base=LAMBDA_LEVEL_BASE) + + +def hmamba_chunk_scan_combined( + x: torch.Tensor, + dt: torch.Tensor, + A: torch.Tensor, + B: torch.Tensor, + C: torch.Tensor, + dl: torch.Tensor, + L: torch.Tensor, + chunk_size: int, + D: torch.Tensor | None = None, + z: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + initial_states: LogLinearAttentionState | None = None, + seq_idx: torch.Tensor | None = None, + cu_seqlens: torch.Tensor | None = None, + dt_softplus: bool = False, + dt_limit: tuple[float, float] = (0.0, float("inf")), + return_final_states: bool = False, +): + if z is not None: + raise NotImplementedError + if seq_idx is not None: + raise NotImplementedError + if cu_seqlens is not None: + raise NotImplementedError + if dt_softplus is not True: + raise NotImplementedError + if tuple(dt_limit) != (0.0, float("inf")): + raise NotImplementedError + if chunk_size != 64: + raise NotImplementedError + if not B.shape == C.shape: + raise ValueError("B and C must have the same shape") + + if D is not None: + if D.dim() != 1: + raise ValueError + D = rearrange(D, "h -> 1 1 h 1") + D_residual = x * D + + if dt_bias is not None: + dt = dt + rearrange(dt_bias, "h -> 1 1 h") + if dt_softplus: + dt = torch.nn.functional.softplus(dt) + if dt_limit != (0.0, float("inf")): + dt = torch.clamp(dt, min=dt_limit[0], max=dt_limit[1]) + x = (x * rearrange(dt, "b l h -> b l h 1")).to(x.dtype) + A = rearrange(A, "h -> 1 1 h") * dt + + L = torch.nn.functional.softplus(rearrange(L, "h ell -> 1 1 h ell") * dl).to(L.dtype) + + y, state = chunk_log_linear_attn( + q=C, + k=B, + v=x, + g=A, + level_scales=L, + initial_state=initial_states, + output_final_state=return_final_states, + cu_seqlens=cu_seqlens, + ) + + if D is not None: + y = y + D_residual + + return y, state + + +def hmamba_split_conv1d_scan_combined( + zxbcdtdl: torch.Tensor, + conv1d_weight: torch.Tensor, + conv1d_bias: torch.Tensor, + dt_bias: torch.Tensor, + A: torch.Tensor, + L: torch.Tensor, + D: torch.Tensor, + chunk_size: int, + initial_states: torch.Tensor | None = None, + seq_idx: torch.Tensor | None = None, + dt_limit: tuple[float, float] = (0.0, float("inf")), + return_final_states: bool = False, + activation: str = "silu", + rmsnorm_weight: torch.Tensor | None = None, + rmsnorm_eps: float = 1e-6, + outproj_weight: torch.Tensor | None = None, + outproj_bias: torch.Tensor | None = None, + headdim: int | None = None, + ngroups: int = 1, + norm_before_gate: bool = True, +) -> torch.Tensor: + """ + Argument: + zxbcdtdl: (batch, seqlen, 2 * dim + 2 * ngroups * dstate + nheads) where dim == nheads * headdim + conv1d_weight: (dim + 2 * ngroups * dstate, width) + conv1d_bias: (dim + 2 * ngroups * dstate,) + dt_bias: (nheads,) + A: (nheads) + L: (nheads, nlevels) + D: (nheads, headdim) or (nheads,) + initial_states: (batch, nheads, headdim, dstate) + seq_idx: (batch, seqlen), int32 + rmsnorm_weight: (dim,) + outproj_weight: (out_dim, dim) + outproj_bias: (out_dim,) + headdim: if D is 1D, headdim must be passed in + norm_before_gate: if True, we do RMSNorm(x) * F.silu(z). If False, we do RMSNorm(x * F.silu(z)) + Return: + out: (batch, seqlen, dim) + """ + if initial_states is not None: + raise NotImplementedError + if seq_idx is not None: + raise NotImplementedError + if dt_limit != (0.0, float("inf")): + raise NotImplementedError + if return_final_states is not False: + raise NotImplementedError + if norm_before_gate is not False: + raise NotImplementedError + if rmsnorm_weight is None: + raise NotImplementedError + if activation not in ["silu", "swish"]: + raise NotImplementedError + + batch, seqlen, _ = zxbcdtdl.shape + dlambda = L.shape[-1] + (nheads,) = D.shape + dim = nheads * headdim + dstate = (zxbcdtdl.shape[-1] - 2 * dim - nheads - nheads * dlambda) // ngroups // 2 + + if D.dim() != 1: + raise ValueError + if headdim is None: + raise ValueError + if nheads % ngroups != 0: + raise ValueError + if zxbcdtdl.shape != ( + batch, + seqlen, + 2 * dim + 2 * ngroups * dstate + nheads + nheads * dlambda, + ): + raise ValueError + if dt_bias.shape != (nheads,): + raise ValueError + if A.shape != (nheads,): + raise ValueError + if L.shape != (nheads, dlambda): + raise ValueError + if D.shape != (nheads,): + raise ValueError + if rmsnorm_weight is None: + raise ValueError + + zxBCdtl_splits = [dim, dim + 2 * ngroups * dstate, nheads, nheads * dlambda] + xBC_splits = [dim, ngroups * dstate, ngroups * dstate] + z, xBC, dt, dl = torch.split(zxbcdtdl, zxBCdtl_splits, dim=-1) + xBC = rearrange( + causal_conv1d_fn( + rearrange(xBC, "b s d -> b d s"), + conv1d_weight, + bias=conv1d_bias, + activation=activation, + seq_idx=seq_idx, + ), + "b d s -> b s d", + ) + x, B, C = torch.split(xBC, xBC_splits, dim=-1) + x = rearrange(x, "b l (h p) -> b l h p", h=nheads, p=headdim) + B = rearrange(B, "b l (g n) -> b l g n", g=ngroups, n=dstate) + C = rearrange(C, "b l (g n) -> b l g n", g=ngroups, n=dstate) + dl = rearrange(dl, "b l (h ell) -> b l h ell", h=nheads, ell=dlambda) + y, _ = hmamba_chunk_scan_combined( + x=x, + dt=dt, + A=A, + B=B, + C=C, + dl=dl, + L=L, + chunk_size=chunk_size, + D=D, + z=z if rmsnorm_weight is None else None, + dt_bias=dt_bias, + dt_softplus=True, + seq_idx=seq_idx, + cu_seqlens=None, + dt_limit=dt_limit, + return_final_states=return_final_states, + ) + + y = rearrange(y, "b l h p -> b l (h p)") + if rmsnorm_weight is not None: + y = rmsnorm_fn( + x=y, + weight=rmsnorm_weight, + bias=None, + z=z, + eps=rmsnorm_eps, + group_size=None, + norm_before_gate=False, + ) + out = torch.nn.functional.linear(y, outproj_weight, outproj_bias) + return out + + +class LogLinearMamba2(nn.Module): + """ + Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`. + A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective) + ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4, + and is why Mamba is called **selective** state spaces) + """ + + def __init__( + self, + num_heads: int, + head_dim: int = 64, + hidden_size: int = 2048, + state_size: int = 128, + expand: int = 2, + n_groups: int = 1, + conv_kernel: int = 4, + use_conv_bias: bool = False, + hidden_act: str = "silu", + rms_norm: bool = True, + chunk_size: int = 64, + time_step_rank: float = 256, + time_step_limit: tuple[float, float] = (0.0, float("inf")), + time_step_min: float = 0.001, + time_step_max: float = 0.1, + use_bias: bool = True, + norm_eps: float = 1e-5, + layer_idx: int = None, + ): + super().__init__() + self.num_heads = num_heads + self.hidden_size = hidden_size + self.ssm_state_size = state_size + self.conv_kernel_size = conv_kernel + self.intermediate_size = int(expand * self.hidden_size) + self.time_step_rank = int(time_step_rank) + self.layer_idx = layer_idx + self.use_conv_bias = use_conv_bias + self.activation = hidden_act + self.act = ACT2FN[hidden_act] + + self.layer_norm_epsilon = norm_eps + self.rms_norm = rms_norm + + self.n_groups = n_groups + self.head_dim = head_dim + self.chunk_size = chunk_size + + self.time_step_limit = time_step_limit + self.time_step_min = time_step_min + self.time_step_max = time_step_max + + self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size + self.conv1d = nn.Conv1d( + in_channels=self.conv_dim, + out_channels=self.conv_dim, + bias=use_conv_bias, + kernel_size=conv_kernel, + groups=self.conv_dim, + padding=conv_kernel - 1, + ) + + self.num_lambda_dims = MAX_NUM_LEVELS + self.lambda_level_module = None + + # projection of the input hidden states + projection_size = ( + self.intermediate_size + + self.conv_dim + + self.num_heads * (self.num_lambda_dims + 1) + ) + self.in_proj = nn.Linear( + self.hidden_size, + projection_size, + bias=use_bias, + ) + # selective projection used to make dt, B and C input dependant + + # time step projection (discretization) + # instantiate once and copy inv_dt in init_weights of PretrainedModel + self.dt_bias = nn.Parameter(torch.ones(self.num_heads)) + + # S4D real initialization. These are not discretized! + # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded + A = torch.arange(1, self.num_heads + 1) + self.A_log = nn.Parameter(torch.log(A)) + self.A_log._no_weight_decay = True + + self.lambda_mode = "positive" + L = torch.ones(self.num_heads, self.num_lambda_dims) + self.L = nn.Parameter(L) + self.L._no_weight_decay = True + + self.norm = RMSNormGated( + self.intermediate_size, eps=self.layer_norm_epsilon, norm_before_gate=False, + ) + self.D = nn.Parameter(torch.ones(self.num_heads)) + self.D._no_weight_decay = True + + self.out_proj = nn.Linear( + self.intermediate_size, self.hidden_size, bias=use_bias, + ) + self.use_bias = use_bias + + if not is_fast_path_available: + logger.warning_once( + "The fast path is not available because one of " + "`(selective_state_update, causal_conv1d_fn, causal_conv1d_update)` is None. " + "Falling back to the naive implementation. " + "To install follow https://github.com/state-spaces/mamba/#installation and" + "https://github.com/Dao-AILab/causal-conv1d", + ) + + def cuda_kernels_forward( + self, + hidden_states: torch.Tensor, + cache_params: Optional["LogLinearMamba2Cache"] = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + ): + if attention_mask is not None: + # only supporting this in decoding + if cache_params is None: + raise NotImplementedError + if self.activation not in ["silu", "swish"]: + raise ValueError + + # 1. Gated MLP's linear projection + hidden_states = apply_mask_to_padding_states( + hidden_states=hidden_states, + attention_mask=attention_mask, + ) + projected_states = self.in_proj(hidden_states) + + # Set up dimensions for reshapes later + batch_size, seq_len, _ = hidden_states.shape + groups_time_state_size = self.n_groups * self.ssm_state_size + d_mlp = ( + projected_states.shape[-1] + - 2 * self.intermediate_size + - 2 * self.n_groups * self.ssm_state_size + - self.num_heads * (self.num_lambda_dims + 1) + ) // 2 + if d_mlp != 0: + raise ValueError + + # Single step calculations via cache + if ( + cache_params is not None + and cache_position is not None + and cache_position[0] > 0 + ): + if hidden_states.shape[1] != 1: + raise ValueError + + gate, xBC, dt, dl = torch.split( + projected_states.squeeze(1), + [ + self.intermediate_size, + self.conv_dim, + self.num_heads, + self.num_heads * self.num_lambda_dims, + ], + dim=-1, + ) + + # 2. Convolution sequence transformation + xBC = causal_conv1d_update( + xBC, + cache_params.conv_states[self.layer_idx], + rearrange(self.conv1d.weight, "d 1 w -> d w"), + self.conv1d.bias, + self.activation, + ) + + x, B, C = torch.split( + xBC, + [ + self.intermediate_size, + groups_time_state_size, + groups_time_state_size, + ], + dim=-1, + ) + + # 3. SSM transformation + A = -torch.exp(self.A_log.float()) # (nheads,) + B = rearrange( + B, + "b (g n) -> b g n", + b=batch_size, + g=self.n_groups, + n=self.ssm_state_size, + ) + C = rearrange( + C, + "b (g n) -> b g n", + b=batch_size, + g=self.n_groups, + n=self.ssm_state_size, + ) + x_reshaped = rearrange( + x, + "b (h p) -> b h p", + b=batch_size, + h=self.num_heads, + p=self.head_dim, + ) + dl_reshaped = rearrange( + dl, + "b (h ell) -> b h ell", + b=batch_size, + h=self.num_heads, + ell=self.num_lambda_dims, + ) + y, hssm_state = hmamba_chunk_scan_combined( + x_reshaped, + dt=dt, + A=A, + B=B, + C=C, + dl=dl_reshaped, + L=self.L, + D=self.D, + z=None, + dt_bias=self.dt_bias, + dt_softplus=True, + initial_states=cache_params.hssm_states[self.layer_idx], + return_final_states=True, + ) + cache_params.update_hssm_state( + layer_idx=self.layer_idx, + new_hssm_state=hssm_state, + ) + y = rearrange( + y, + "b h p -> b (h p)", + b=batch_size, + h=self.num_heads, + p=self.head_dim, + ) + y = self.norm(y, gate) + + # 4. Final linear projection + out = self.out_proj(y)[:, None, ...] + + # Fused calculations or step by step if no initialized cache is found + else: + A = -torch.exp( + self.A_log.float(), + ) # (num_heads) or (intermediate_size, state_size) + dt_limit_kwargs = ( + {} + if self.time_step_limit == (0.0, float("inf")) + else {"dt_limit": self.time_step_limit} + ) + + # 2-4. Fused kernel for conv1d, SSM, and the final projection + if self.training and cache_params is None: + out = torch.utils.checkpoint.checkpoint( + hmamba_split_conv1d_scan_combined, + use_reentrant=False, + # function arguments + zxbcdtdl=projected_states, + conv1d_weight=rearrange(self.conv1d.weight, "d 1 w -> d w"), + conv1d_bias=self.conv1d.bias, + dt_bias=self.dt_bias, + A=A, + L=self.L, + D=self.D, + chunk_size=self.chunk_size, + seq_idx=None, # was seq_idx + activation=self.activation, + rmsnorm_weight=self.norm.weight, + rmsnorm_eps=self.norm.eps, + outproj_weight=self.out_proj.weight, + outproj_bias=self.out_proj.bias, + headdim=self.head_dim, + ngroups=self.n_groups, + norm_before_gate=False, + return_final_states=False, + **dt_limit_kwargs, + ) + + else: + gate, xBC, dt, dl = torch.split( + projected_states, + [ + self.intermediate_size, + self.conv_dim, + self.num_heads, + self.num_heads * self.num_lambda_dims, + ], + dim=-1, + ) + + # 2. Convolution sequence transformation + # Init cache + if cache_params is not None: + xBC_t = rearrange(xBC, "b l d -> b d l") + conv_states = torch.nn.functional.pad( + xBC_t, + (cache_params.conv_kernel_size - xBC_t.shape[-1], 0), + ) + cache_params.update_conv_state( + layer_idx=self.layer_idx, + new_conv_state=conv_states, + cache_init=True, + ) + + xBC = causal_conv1d_fn( + x=xBC.transpose(1, 2), + weight=rearrange(self.conv1d.weight, "d 1 w -> d w"), + bias=self.conv1d.bias, + activation=self.activation, + ).transpose(1, 2) + + xBC = apply_mask_to_padding_states( + hidden_states=xBC, + attention_mask=attention_mask, + ) + + x, B, C = torch.split( + xBC, + [ + self.intermediate_size, + groups_time_state_size, + groups_time_state_size, + ], + dim=-1, + ) + + # 3. SSM transformation + y, hssm_state = hmamba_chunk_scan_combined( + rearrange( + x, + "b l (h p) -> b l h p", + b=batch_size, + l=seq_len, + p=self.head_dim, + ), + dt=dt, + A=A, + B=rearrange( + B, + "b l (g n) -> b l g n", + b=batch_size, + l=seq_len, + g=self.n_groups, + ), + C=rearrange( + C, + "b l (g n) -> b l g n", + b=batch_size, + l=seq_len, + g=self.n_groups, + ), + dl=rearrange( + dl, + "b l (h ell) -> b l h ell", + b=batch_size, + h=self.num_heads, + ell=self.num_lambda_dims, + ), + L=self.L, + chunk_size=self.chunk_size, + D=self.D, + z=None, + seq_idx=None, + return_final_states=True, + dt_bias=self.dt_bias, + dt_softplus=True, + **dt_limit_kwargs, + ) + + # Init cache + if hssm_state is not None and cache_params is not None: + cache_params.update_hssm_state( + layer_idx=self.layer_idx, + new_hssm_state=hssm_state, + ) + + y = rearrange( + y, + "b l h p -> b l (h p)", + b=batch_size, + l=seq_len, + h=self.num_heads, + p=self.head_dim, + ) + # Multiply "gate" branch and apply extra normalization layer + y = self.norm(y, gate) + + # 4. Final linear projection + out = self.out_proj(y) + + return out + + def forward( + self, + hidden_states, + cache_params: Optional["LogLinearMamba2Cache"] = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + ): + if "cuda" in self.in_proj.weight.device.type: + return self.cuda_kernels_forward( + hidden_states=hidden_states, + cache_params=cache_params, + cache_position=cache_position, + attention_mask=attention_mask, + ) + raise NotImplementedError diff --git a/code/flash-linear-attention/fla/layers/mamba.py b/code/flash-linear-attention/fla/layers/mamba.py new file mode 100644 index 0000000000000000000000000000000000000000..7dc18782ffbd31628c4465c627c8feff936a0099 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/mamba.py @@ -0,0 +1,334 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from transformers.utils import logging + +from fla.modules.activations import ACT2FN + +with warnings.catch_warnings(): + warnings.simplefilter('ignore') + try: + from mamba_ssm.ops.selective_scan_interface import mamba_inner_fn, selective_scan_fn + from mamba_ssm.ops.triton.selective_state_update import selective_state_update + except ImportError: + selective_state_update, selective_scan_fn, mamba_inner_fn = None, None, None + + try: + from causal_conv1d import causal_conv1d_fn, causal_conv1d_update + except ImportError: + causal_conv1d_update, causal_conv1d_fn = None, None + is_fast_path_available = all(( + selective_state_update, + selective_scan_fn, + mamba_inner_fn, + )) +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.mamba.modeling_mamba import MambaCache + +logger = logging.get_logger(__name__) + + +class Mamba(nn.Module): + """ + Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`. + A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective) + ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4, + and is why Mamba is called **selective** state spaces) + """ + + def __init__( + self, + hidden_size: int = 2048, + state_size: int = 16, + conv_kernel: int = 4, + use_conv_bias: bool = True, + intermediate_size: int = 2048, + time_step_rank: int = 256, + use_bias: bool = True, + hidden_act: str = "silu", + layer_idx: int = None, + backend: str = "cuda", + ): + super().__init__() + + self.hidden_size = hidden_size + self.ssm_state_size = state_size + self.conv_kernel_size = conv_kernel + self.use_conv_bias = use_conv_bias + self.intermediate_size = intermediate_size + self.time_step_rank = time_step_rank + self.use_bias = use_bias + + self.conv1d = nn.Conv1d( + in_channels=self.intermediate_size, + out_channels=self.intermediate_size, + bias=use_conv_bias, + kernel_size=conv_kernel, + groups=self.intermediate_size, + padding=conv_kernel - 1, + ) + + self.activation = hidden_act + self.act = ACT2FN[hidden_act] + + self.layer_idx = layer_idx + + # projection of the input hidden states + self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=use_bias) + # selective projection used to make dt, B and C input dependant + self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False) + # time step projection (discretization) + self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True) + + # S4D real initialization. These are not discretized! + # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded + A = torch.arange(1, self.ssm_state_size + 1, dtype=torch.float32)[None, :] + A = A.expand(self.intermediate_size, -1).contiguous() + + self.A_log = nn.Parameter(torch.log(A)) + self.D = nn.Parameter(torch.ones(self.intermediate_size)) + self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=use_bias) + + if not is_fast_path_available: + logger.warning_once( + "The fast path is not available because on of " + "`(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)`" + " is None. Falling back to the naive implementation. " + "To install follow https://github.com/state-spaces/mamba/#installation and" + " https://github.com/Dao-AILab/causal-conv1d", + ) + import os + backend = os.environ.get('FLA_CONV_BACKEND', backend) + assert backend in ['cuda', 'triton'], f"Unsupported backend: {backend}" + if backend == 'cuda' and causal_conv1d_fn is None: + logger.warning_once( + "The CUDA backend is not available because `causal_conv1d` is None. " + "Falling back to the Triton backend. " + "To install follow https://github.com/Dao-AILab/causal-conv1d", + ) + backend = 'triton' + if backend == 'triton': + from fla.modules.convolution import causal_conv1d as causal_conv1d_triton + from fla.modules.convolution import causal_conv1d_update as causal_conv1d_update_triton + self.causal_conv1d_fn = causal_conv1d_triton + self.causal_conv1d_update = causal_conv1d_update_triton + else: + self.causal_conv1d_fn = causal_conv1d_fn + self.causal_conv1d_update = causal_conv1d_update + self.backend = backend + + def cuda_kernels_forward( + self, + hidden_states: torch.Tensor, + cache_params: MambaCache | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + **kwargs: Unpack[dict], + ): + # 1. Gated MLP's linear projection + projected_states = self.in_proj(hidden_states).transpose(1, 2) + + if self.training and cache_params is None: # Doesn't support outputting the states -> used for training + contextualized_states = mamba_inner_fn( + projected_states, + self.conv1d.weight, + self.conv1d.bias if self.use_conv_bias else None, + self.x_proj.weight, + self.dt_proj.weight, + self.out_proj.weight, + self.out_proj.bias.float() if self.use_bias else None, + -torch.exp(self.A_log.float()), + None, # input-dependent B + None, # input-dependent C + self.D.float(), + delta_bias=self.dt_proj.bias.float(), + delta_softplus=True, + ) + + else: + hidden_states, gate = projected_states.chunk(2, dim=1) + + if attention_mask is not None: + hidden_states = hidden_states * attention_mask.unsqueeze(1) + + # 2. Convolution sequence transformation + conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2)) + if cache_params is not None and cache_position[0] > 0: + hidden_states = self.causal_conv1d_update( + hidden_states.squeeze(-1), + cache_params.conv_states[self.layer_idx], + conv_weights, + self.conv1d.bias, + self.activation, + ) + hidden_states = hidden_states.unsqueeze(-1) + else: + if cache_params is not None: + conv_states = nn.functional.pad( + hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0), + ) + cache_params.update_conv_state(self.layer_idx, conv_states, cache_position) + hidden_states = self.causal_conv1d_fn( + hidden_states, conv_weights, self.conv1d.bias, activation=self.activation, + ) + + if attention_mask is not None: + hidden_states = hidden_states * attention_mask.unsqueeze(1) + + # 3. State Space Model sequence transformation + # 3.a. input varying initialization of time_step, B and C + ssm_parameters = self.x_proj(hidden_states.transpose(1, 2)) + time_step, B, C = torch.split( + ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1, + ) + discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2) + + A = -torch.exp(self.A_log.float()) + # 3.c perform the recurrence y ← SSM(A, B, C)(x) + time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None + if cache_params is not None and cache_position[0] > 0: + scan_outputs = selective_state_update( + cache_params.ssm_states[self.layer_idx], + hidden_states[..., 0], + discrete_time_step[..., 0], + A, + B[:, 0], + C[:, 0], + self.D, + gate[..., 0], + time_proj_bias, + dt_softplus=True, + ).unsqueeze(-1) + else: + scan_outputs, ssm_state = selective_scan_fn( + hidden_states, + discrete_time_step, + A, + B.transpose(1, 2), + C.transpose(1, 2), + self.D.float(), + gate, + time_proj_bias, + delta_softplus=True, + return_last_state=True, + ) + if ssm_state is not None and cache_params is not None: + cache_params.update_ssm_state(self.layer_idx, ssm_state) + + # 4. Final linear projection + contextualized_states = self.out_proj(scan_outputs.transpose(1, 2)) + return contextualized_states + + def slow_forward( + self, + input_states, + cache_params: MambaCache | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + **kwargs: Unpack[dict], + ): + batch_size, seq_len, _ = input_states.shape + dtype = input_states.dtype + # 1. Gated MLP's linear projection + # [batch, 2 * intermediate_size, seq_len] + projected_states = self.in_proj(input_states).transpose(1, 2) + hidden_states, gate = projected_states.chunk(2, dim=1) + + if attention_mask is not None: + hidden_states = hidden_states * attention_mask.unsqueeze(1) + + # 2. Convolution sequence transformation + if cache_params is not None: + ssm_state = cache_params.ssm_states[self.layer_idx].clone() + ssm_state = ssm_state.to(hidden_states.device) + # use `cache_position.shape[0]` to check whether we are in prefill + # stage, it's equivalent to check `cache_position[0] == 0`, which + # breaks dynamo fullgraph constraints + if cache_position.shape[0] == self.conv_kernel_size: + conv_state = nn.functional.pad( + hidden_states, + (self.conv_kernel_size - hidden_states.shape[-1], 0), + ) + + cache_params.update_conv_state(self.layer_idx, conv_state, cache_position) + # [batch, intermediate_size, seq_len] + hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) + else: + conv_state = cache_params.update_conv_state(self.layer_idx, hidden_states, cache_position) + hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1) + if self.use_conv_bias: + hidden_states += self.conv1d.bias + # [batch, intermediate_size, 1] : decoding + hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1) + else: + ssm_state = torch.zeros( + (batch_size, self.intermediate_size, self.ssm_state_size), + device=hidden_states.device, dtype=dtype, + ) + # [batch, intermediate_size, seq_len] + hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) + + if attention_mask is not None: + hidden_states = hidden_states * attention_mask.unsqueeze(1) + + # 3. State Space Model sequence transformation + # 3.a. Selection: [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2] + ssm_parameters = self.x_proj(hidden_states.transpose(1, 2)) + time_step, B, C = torch.split( + ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1, + ) + # [batch, seq_len, intermediate_size] + discrete_time_step = self.dt_proj(time_step) + # [batch, intermediate_size, seq_len] + discrete_time_step = nn.functional.softplus(discrete_time_step).transpose(1, 2) + + # 3.b. Discretization: B and C to [batch, seq_len, intermediate_size, ssm_state_size] (SRAM) + # [intermediate_size, ssm_state_size] + A = -torch.exp(self.A_log.float()) + # [batch, intermediate_size, seq_len, ssm_state_size] + discrete_A = torch.exp(A[None, :, None, :] * discrete_time_step[:, :, :, None]) + # [batch, intermediate_size, seq_len, ssm_state_size] + discrete_B = discrete_time_step[:, :, :, None] * B[:, None, :, :].float() + deltaB_u = discrete_B * hidden_states[:, :, :, None].float() + + # 3.c perform the recurrence y ← SSM(A, B, C)(x) + scan_outputs = [] + for i in range(seq_len): + # [batch, intermediade_size, ssm_state] + ssm_state = discrete_A[:, :, i, :] * ssm_state + deltaB_u[:, :, i, :] + # [batch, intermediade_size, 1] + scan_output = torch.matmul(ssm_state.to(dtype), C[:, i, :].unsqueeze(-1)) + scan_outputs.append(scan_output[:, :, 0]) + # [batch, seq_len, intermediade_size] + scan_output = torch.stack(scan_outputs, dim=-1) + scan_output = scan_output + (hidden_states * self.D[None, :, None]) + scan_output = (scan_output * self.act(gate)) + + if cache_params is not None: + cache_params.ssm_states[self.layer_idx].copy_(ssm_state) + + # 4. Final linear projection + # [batch, seq_len, hidden_size] + contextualized_states = self.out_proj(scan_output.transpose(1, 2)) + return contextualized_states + # fmt: on + + def forward( + self, + hidden_states, + cache_params: MambaCache | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + **kwargs: Unpack[dict], + ): + if is_fast_path_available and "cuda" in self.x_proj.weight.device.type: + return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask, **kwargs) + return self.slow_forward(hidden_states, cache_params, cache_position, attention_mask, **kwargs) diff --git a/code/flash-linear-attention/fla/layers/mamba2.py b/code/flash-linear-attention/fla/layers/mamba2.py new file mode 100644 index 0000000000000000000000000000000000000000..fd43ac63e2cd07813c8c214a0d3cfdc8f71343a7 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/mamba2.py @@ -0,0 +1,612 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from transformers.utils import logging + +from fla.modules.activations import ACT2FN +from fla.modules.layernorm_gated import RMSNormGated + +with warnings.catch_warnings(): + warnings.simplefilter('ignore') + try: + from mamba_ssm.ops.triton.selective_state_update import selective_state_update + from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined + except ImportError: + selective_state_update, mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined = None, None, None + try: + from causal_conv1d import causal_conv1d_fn, causal_conv1d_update + except ImportError: + causal_conv1d_update, causal_conv1d_fn = None, None + is_fast_path_available = selective_state_update is not None + +if TYPE_CHECKING: + from fla.models.mamba2.modeling_mamba2 import Mamba2Cache + +logger = logging.get_logger(__name__) + + +def apply_mask_to_padding_states(hidden_states, attention_mask): + """ + Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66 + """ + if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1: + dtype = hidden_states.dtype + hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype) + + return hidden_states + + +def pad_tensor_by_size(input_tensor: torch.Tensor, pad_size: int): + """ + Padding x tensor with `pad_size` on the seq_len dim (dim=1) + + Assumes that we only have tensors of either size 4 or 3 + """ + pad_shape = (0, 0, 0, 0, 0, pad_size, 0, 0) if len(input_tensor.shape) == 4 else (0, 0, 0, pad_size, 0, 0) + + return torch.nn.functional.pad(input_tensor, pad_shape, mode="constant", value=0) + + +def reshape_into_chunks(input_tensor, pad_size, chunk_size): + """ + Padding input_tensor with `pad_size` on the seq_len dim (dim=1) and + simultaneously splitting it into chunk sequences. + + Assumes that we only have tensors of either size 4 or 3 + """ + # [bsz, seq_len, ...] -> [bsz, seq_len multiple of chunk_size, ...] + input_tensor = pad_tensor_by_size(input_tensor, pad_size) + + if len(input_tensor.shape) == 3: + # [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads] + return input_tensor.reshape(input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2]) + else: + # [bsz, seq_len multiple of chunk_size, num_heads, head_dim or state_size] -> + # [bsz, -1, chunk_size, num_heads, head_dim or state_size] + return input_tensor.reshape( + input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2], input_tensor.shape[3], + ) + + +def segment_sum(input_tensor): + """ + More stable segment sum calculation. Uses cumulative sums and masking instead of direct subtractions. + """ + chunk_size = input_tensor.size(-1) + # 1. expand input tensor to have an additional dimension and repeat along that dimension + # [..., chunk_size] -> [..., chunk_size, chunk_size] + input_tensor = input_tensor[..., None].expand(*input_tensor.size(), chunk_size) + # 2. create a lower triangular mask with the diagonal set to 0 to 0 out elements above diag + mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=-1) + input_tensor = input_tensor.masked_fill(~mask, 0) + # 3. compute actual cumsum + tensor_segsum = torch.cumsum(input_tensor, dim=-2) + + # 4. apply mask to keep only the lower triangular part of the cumulative sum result (incl diagonal this time) + mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=0) + tensor_segsum = tensor_segsum.masked_fill(~mask, -torch.inf) + return tensor_segsum + + +class Mamba2(nn.Module): + """ + Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`. + A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective) + ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4, + and is why Mamba is called **selective** state spaces) + """ + + def __init__( + self, + num_heads: int, + head_dim: int = 64, + hidden_size: int = 2048, + state_size: int = 128, + expand: int = 2, + n_groups: int = 1, + conv_kernel: int = 4, + use_conv_bias: bool = False, + hidden_act: str = "silu", + rms_norm: bool = True, + chunk_size: int = 256, + time_step_rank: float = 256, + time_step_limit: tuple[float, float] = (0.0, float("inf")), + time_step_min: float = 0.001, + time_step_max: float = 0.1, + use_bias: bool = True, + norm_eps: float = 1e-5, + layer_idx: int = None, + backend: str = "cuda", + ) -> Mamba2: + super().__init__() + + self.num_heads = num_heads + self.head_dim = head_dim + self.hidden_size = hidden_size + self.ssm_state_size = state_size + self.expand = expand + self.intermediate_size = int(expand * hidden_size) + self.n_groups = n_groups + + self.conv_kernel_size = conv_kernel + self.use_conv_bias = use_conv_bias + self.activation = hidden_act + self.act = ACT2FN[hidden_act] + + self.rms_norm = rms_norm + self.norm_eps = norm_eps + + self.chunk_size = chunk_size + + self.time_step_rank = int(time_step_rank) + self.time_step_limit = time_step_limit + self.time_step_min = time_step_min + self.time_step_max = time_step_max + + self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size + self.conv1d = nn.Conv1d( + in_channels=self.conv_dim, + out_channels=self.conv_dim, + bias=use_conv_bias, + kernel_size=conv_kernel, + groups=self.conv_dim, + padding=conv_kernel - 1, + ) + + # projection of the input hidden states + projection_size = self.intermediate_size + self.conv_dim + self.num_heads + self.in_proj = nn.Linear( + self.hidden_size, + projection_size, + bias=use_bias, + ) + # selective projection used to make dt, B and C input dependant + + # time step projection (discretization) + # instantiate once and copy inv_dt in init_weights of PretrainedModel + self.dt_bias = nn.Parameter(torch.ones(self.num_heads)) + + # S4D real initialization. These are not discretized! + # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded + A = torch.arange(1, self.num_heads + 1) + self.A_log = nn.Parameter(torch.log(A)) + self.A_log._no_weight_decay = True + self.norm = RMSNormGated( + self.intermediate_size, eps=self.norm_eps, norm_before_gate=False, + ) + self.D = nn.Parameter(torch.ones(self.num_heads)) + self.D._no_weight_decay = True + + self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=use_bias) + self.use_bias = use_bias + + self.layer_idx = layer_idx + + if not is_fast_path_available: + logger.warning_once( + "The fast path is not available because one of " + "`(selective_state_update)` is None. " + "Falling back to the naive implementation. " + "To install follow https://github.com/state-spaces/mamba/#installation", + ) + import os + backend = os.environ.get('FLA_CONV_BACKEND', backend) + assert backend in ['cuda', 'triton'], f"Unsupported backend: {backend}" + if backend == 'cuda' and causal_conv1d_fn is None: + logger.warning_once( + "The CUDA backend is not available because `causal_conv1d` is None. " + "Falling back to the Triton backend. " + "To install follow https://github.com/Dao-AILab/causal-conv1d", + ) + backend = 'triton' + if backend == 'triton': + from fla.modules.convolution import causal_conv1d as causal_conv1d_triton + from fla.modules.convolution import causal_conv1d_update as causal_conv1d_update_triton + self.causal_conv1d_fn = causal_conv1d_triton + self.causal_conv1d_update = causal_conv1d_update_triton + logger.warning( + "Mamba2 does not recommend using Triton's conv1d backend, " + "as it is untested and may contain bugs.", + ) + else: + self.causal_conv1d_fn = causal_conv1d_fn + self.causal_conv1d_update = causal_conv1d_update + self.backend = backend + + def cuda_kernels_forward( + self, + hidden_states: torch.Tensor, + cache_params: Mamba2Cache | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + ): + # 1. Gated MLP's linear projection + hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask) + projected_states = self.in_proj(hidden_states) + + # Set up dimensions for reshapes later + batch_size, seq_len, _ = hidden_states.shape + groups_time_state_size = self.n_groups * self.ssm_state_size + d_mlp = ( + projected_states.shape[-1] + - 2 * self.intermediate_size + - 2 * self.n_groups * self.ssm_state_size + - self.num_heads + ) // 2 + + # Single step calculations via cache + if cache_params is not None and cache_position is not None and cache_position[0] > 0: + _, _, gate, hidden_states_B_C, dt = projected_states.squeeze(1).split( + [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1, + ) + + # 2. Convolution sequence transformation + hidden_states_B_C = self.causal_conv1d_update( + hidden_states_B_C.contiguous(), + cache_params.conv_states[self.layer_idx], + self.conv1d.weight.squeeze(1), + self.conv1d.bias, + self.activation, + ) + + hidden_states, B, C = torch.split( + hidden_states_B_C, + [ + self.intermediate_size, + groups_time_state_size, + groups_time_state_size, + ], + dim=-1, + ) + + # 3. SSM transformation + A = -torch.exp(self.A_log.float()) # (nheads,) + A = A[:, None, ...][:, :, None].expand(-1, self.head_dim, self.ssm_state_size).to(dtype=torch.float32) + dt = dt[:, :, None].expand(-1, -1, self.head_dim) + dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim) + D = self.D[:, None, ...].expand(-1, self.head_dim) + B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups) + C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups) + hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim) + + hidden_states = selective_state_update( + cache_params.ssm_states[self.layer_idx], + hidden_states_reshaped, + dt, + A, + B, + C, + D, + z=None, + dt_bias=dt_bias, + dt_softplus=True, + ) + hidden_states = hidden_states.view(batch_size, self.num_heads * self.head_dim) + hidden_states = self.norm(hidden_states, gate) + + # 4. Final linear projection + out = self.out_proj(hidden_states)[:, None, ...] + + # Fused calculations or step by step if no initialized cache is found + else: + A = -torch.exp(self.A_log.float()) # (num_heads) or (intermediate_size, state_size) + dt_limit_kwargs = {} if self.time_step_limit == (0.0, float("inf")) else {"dt_limit": self.time_step_limit} + + # 2-4. Fused kernel for conv1d, SSM, and the final projection + if self.training and cache_params is None: + out = mamba_split_conv1d_scan_combined( + projected_states, + self.conv1d.weight.squeeze(1), + self.conv1d.bias, + self.dt_bias, + A, + D=self.D, + chunk_size=self.chunk_size, + seq_idx=None, # was seq_idx + activation=self.activation, + rmsnorm_weight=self.norm.weight, + rmsnorm_eps=self.norm.eps, + outproj_weight=self.out_proj.weight, + outproj_bias=self.out_proj.bias, + headdim=self.head_dim, + ngroups=self.n_groups, + norm_before_gate=False, + return_final_states=False, + **dt_limit_kwargs, + ) + + else: + _, _, gate, hidden_states_B_C, dt = projected_states.split( + [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1, + ) + + # 2. Convolution sequence transformation + # Init cache + if cache_params is not None: + hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2) + conv_states = nn.functional.pad( + hidden_states_B_C_transposed, + (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0), + ) + cache_params.update_conv_state( + layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True, + ) + + if self.activation not in ["silu", "swish"]: + hidden_states_B_C = self.act( + self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2), + ) + else: + _conv1d_output = self.causal_conv1d_fn( + x=hidden_states_B_C.transpose(1, 2).contiguous(), + weight=self.conv1d.weight.squeeze(1), + bias=self.conv1d.bias, + activation=self.activation, + ) + if self.backend == 'cuda': + hidden_states_B_C = _conv1d_output + hidden_states_B_C = hidden_states_B_C.transpose(1, 2) + elif self.backend == 'triton': + hidden_states_B_C, _ = _conv1d_output + hidden_states_B_C = hidden_states_B_C.transpose(1, 2).contiguous() + else: + raise ValueError(f"Unsupported backend: {self.backend}") + + hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask) + hidden_states, B, C = torch.split( + hidden_states_B_C, + [self.intermediate_size, groups_time_state_size, groups_time_state_size], + dim=-1, + ) + + # 3. SSM transformation + scan_output, ssm_state = mamba_chunk_scan_combined( + hidden_states.view(batch_size, seq_len, -1, self.head_dim), + dt, + A, + B.view(batch_size, seq_len, self.n_groups, -1), + C.view(batch_size, seq_len, self.n_groups, -1), + chunk_size=self.chunk_size, + D=self.D, + z=None, + seq_idx=None, + return_final_states=True, + dt_bias=self.dt_bias, + dt_softplus=True, + **dt_limit_kwargs, + ) + + # Init cache + if ssm_state is not None and cache_params is not None: + cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state) + + scan_output = scan_output.view(batch_size, seq_len, -1) + # Multiply "gate" branch and apply extra normalization layer + scan_output = self.norm(scan_output, gate) + + # 4. Final linear projection + out = self.out_proj(scan_output) + return out + + # fmt: off + def torch_forward( + self, + input_states, + cache_params: Mamba2Cache | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + ): + batch_size, seq_len, _ = input_states.shape + dtype = input_states.dtype + + # 1. Gated MLP's linear projection + input_states = apply_mask_to_padding_states(input_states, attention_mask) + projected_states = self.in_proj(input_states) + d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - + 2 * self.n_groups * self.ssm_state_size - self.num_heads) // 2 + _, _, gate, hidden_states_B_C, dt = projected_states.split( + [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1, + ) + + # 2. Convolution sequence transformation + if cache_params is not None and cache_position is not None and cache_position[0] > 0: + cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=hidden_states_B_C, cache_init=False) + + # We need to guarantee that anything regarding the cache is on the same device + conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device) + + hidden_states_B_C = torch.sum( + conv_states * self.conv1d.weight.squeeze(1), dim=-1, + ) + if self.use_conv_bias: + hidden_states_B_C = hidden_states_B_C + self.conv1d.bias + hidden_states_B_C = self.act(hidden_states_B_C) + else: + # Init cache + if cache_params is not None: + hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2) + conv_states = nn.functional.pad( + hidden_states_B_C_transposed, (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0), + ) + cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True) + + hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2)) + + hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask) + hidden_states, B, C = torch.split( + hidden_states_B_C, + [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], + dim=-1, + ) + + # 3. SSM transformation + A = -torch.exp(self.A_log.float()) # [num_heads] + if cache_params is not None and cache_position is not None and cache_position[0] > 0: + # We need to guarantee that anything regarding the cache is on the same device + cache_device = cache_params.ssm_states.device + + # Note: there is no need to pad parameter matrices here, as there is just one new token + # for batched generation + dt = dt[:, 0, :][:, None, ...] + dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim) + # [num_heads] -> [num_heads, head_dim] + dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim) + + dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype)) + dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1]) + A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32) + # [bsz, num_heads, head_dim, state_size] + dA = (torch.exp(dt[..., None] * A)).to(device=cache_device) + + # Discretize B + # [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] -> + # -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size] + B = B.reshape(batch_size, self.n_groups, -1)[..., None, :] + B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous() + B = B.reshape(batch_size, -1, B.shape[-1]) + # [bsz, num_heads, head_dim, state_size] + dB = dt[..., None] * B[..., None, :] + + # Discretize x into dB + # [bsz, intermediate_size] -> [bsz, num_heads, head_dim] + hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim) + dBx = (dB * hidden_states[..., None]).to(device=cache_device) + + # State calculation + cache_params.update_ssm_state( + layer_idx=self.layer_idx, + new_ssm_state=cache_params.ssm_states[self.layer_idx] * dA + dBx, + ) + + # Subsequent output + # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size] + C = C.reshape(batch_size, self.n_groups, -1)[..., None, :] + C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous() + C = C.reshape(batch_size, -1, C.shape[-1]) + # [bsz, num_heads, head_dim] + + ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype) # Shape: [b, h, d, n] + # Reshape ssm_states to merge the first two dimensions + # Shape: [b*h, d, n] + ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size) + C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1) # Shape: [b*h, n, 1] + y = torch.bmm(ssm_states_reshaped, C_reshaped) + y = y.view(batch_size, self.num_heads, self.head_dim) + + # D skip connection + # [num_heads] -> [num_heads, head_dim] + D = self.D[..., None].expand(self.D.shape[0], self.head_dim) + y = (y + hidden_states * D).to(y.dtype) + + # [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size] + y = y.reshape(batch_size, -1)[:, None, ...] + else: + # begin ssd naive implementation without einsums + dt = nn.functional.softplus(dt + self.dt_bias) + dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1]) + hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float() + B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).float() + C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float() + B = B.repeat(1, 1, self.num_heads // self.n_groups, 1) + C = C.repeat(1, 1, self.num_heads // self.n_groups, 1) + pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size + + D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size) + + # Discretize x and A + hidden_states = hidden_states * dt[..., None] + A = A.to(hidden_states.dtype) * dt + + # Rearrange into blocks/chunks + hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)] + + # [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size] + A = A.permute(0, 3, 1, 2) + A_cumsum = torch.cumsum(A, dim=-1) + + # 1. Compute the output for each intra-chunk (diagonal blocks) + # This is the analog of a causal mask + L = torch.exp(segment_sum(A)) + + # Contraction of C and B to get G (attention-weights like) + # shape: (b, c, l, s, h, n) + G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, :, :] + G = G_intermediate.sum(dim=-1) # shape: (b, c, l, s, h) + + # Compute M, equivalent to applying attention mask to weights + M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None] + M = M_intermediate.sum(dim=-1) + + # Compute Y_diag (apply to values) + Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(dim=3) + + # 2. Compute the state for each intra-chunk + # (right term of low-rank factorization of off-diagonal blocks; B terms) + decay_states = torch.exp(A_cumsum[:, :, :, -1:] - A_cumsum) + B_decay = B * decay_states.permute(0, -2, -1, 1)[..., None] + states = (B_decay[..., None, :] * hidden_states[..., None]).sum(dim=2) + + # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries + # (middle term of factorization of off-diag blocks; A terms) + if cache_params is not None and cache_position is not None and cache_position[0] > 0: + previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device) + else: + previous_states = torch.zeros_like(states[:, :1]) + states = torch.cat([previous_states, states], dim=1) + decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0)))) + decay_chunk = decay_chunk.transpose(1, 3) + new_states = (decay_chunk[..., None, None] * states[:, :, None, ...]).sum(dim=1) + states, ssm_state = new_states[:, :-1], new_states[:, -1] + + # 4. Compute state -> output conversion per chunk + # (left term of low-rank factorization of off-diagonal blocks; C terms) + state_decay_out = torch.exp(A_cumsum) + C_times_states = (C[..., None, :] * states[:, :, None, ...]) + state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1) + Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None]) + + # Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks) + y = Y_diag + Y_off + # [bsz, -1, self.chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim] + y = y.reshape(batch_size, -1, self.num_heads, self.head_dim) + + y = y + D_residual + # Cutting off padded chunks + if pad_size > 0: + y = y[:, :seq_len, :, :] + y = y.reshape(batch_size, seq_len, -1) + + # Init cache + if ssm_state is not None and cache_params is not None: + cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state) + + scan_output = self.norm(y, gate) + + # end ssd naive + + # 4. Final linear projection + contextualized_states = self.out_proj(scan_output.to(dtype)) # [batch, seq_len, hidden_size] + return contextualized_states + # fmt: on + + def forward( + self, + hidden_states, + cache_params: Mamba2Cache | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + ): + if is_fast_path_available and "cuda" in self.in_proj.weight.device.type: + return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask) + dtype = hidden_states.dtype + if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1: + # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66 + hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype) + + return self.torch_forward(hidden_states, cache_params, cache_position, attention_mask) diff --git a/code/flash-linear-attention/fla/layers/mesa_net.py b/code/flash-linear-attention/fla/layers/mesa_net.py new file mode 100644 index 0000000000000000000000000000000000000000..203e658566f0df5cd220cf0d4097c484082b1c3d --- /dev/null +++ b/code/flash-linear-attention/fla/layers/mesa_net.py @@ -0,0 +1,223 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange +from torch.nn import functional as F + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.modules.l2norm import l2_norm +from fla.ops.mesa_net import chunk_mesa_net, mesa_net_decoding_one_step + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +class MesaNet(nn.Module): + """ + The layer implementaion for [MesaNet: Sequence Modeling by Locally Optimal Test-Time Training]. # noqa + + Args: + hidden_size (int, Optional): + The hidden size of the input. Default: 2048. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 1. + num_heads (int, Optional): + The number of heads. Default: 16. + mode (str, Optional): + Which MesaNet kernel to use. + Currently available: `chunk`. + Default: `chunk`. + use_output_gate (bool, Optional): + Whether to use output gate. Default: `False`. + conv_size (int): + The kernel size of the short convolution. Default: 4. + layer_idx (int, Optional): + The index of the layer. Default: None. + norm_eps (float, Optional): + The epsilon value for the normalization layer. Default: 1e-5. + lambda_lower_bound (float): + The lower bound for the lambda parameter. Default: 0.25. + max_cg_step_training (int): + The maximum number of CG steps for training. Default: 30. + max_cg_step_decoding (int): + The maximum number of CG steps for decoding. Default: 30. + """ + + def __init__( + self, + hidden_size: int = 2048, + num_heads: int = 16, + head_dim: int = 128, + mode: str = 'chunk', + use_output_gate: bool = False, + use_short_conv: bool = True, + conv_size: int = 4, + conv_bias: bool = False, + layer_idx: int = None, + norm_eps: float = 1e-5, + lambda_lower_bound: float = 0.25, + max_cg_step_training: int = 30, + max_cg_step_decoding: int = 30, + **kwargs, + ) -> MesaNet: + super().__init__() + + self.mode = mode + self.hidden_size = hidden_size + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + self.num_heads = num_heads + self.head_dim = head_dim + self.key_dim = self.num_heads * self.head_dim + self.value_dim = self.key_dim + self.head_k_dim = self.head_dim + self.head_v_dim = self.head_dim + self.layer_idx = layer_idx + self.lambda_lower_bound = lambda_lower_bound + self.max_cg_step_training = max_cg_step_training + self.max_cg_step_decoding = max_cg_step_decoding + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.a_proj = nn.Linear(hidden_size, self.num_heads, bias=True) + self.b_proj = nn.Linear(hidden_size, self.num_heads, bias=True) + + lambda_initial_value = 1.0 + init_lamb_value = torch.log(torch.exp(torch.tensor(lambda_initial_value - lambda_lower_bound)) - 1.0) + init_lamb_params = torch.empty(self.key_dim, dtype=torch.float32).fill_(init_lamb_value) + + self.lambda_params = nn.Parameter(init_lamb_params) + self.lambda_params._no_weight_decay = True + + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=self.conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=self.conv_bias, + activation='silu', + ) + if use_output_gate: + self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps) + else: + self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + conv_state_q, conv_state_k = None, None + if last_state is not None: + conv_state_q, conv_state_k = last_state['conv_state'] + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v = self.v_proj(hidden_states) + + q, k = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q, k)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + beta = self.b_proj(hidden_states).float().sigmoid() + g = F.logsigmoid(self.a_proj(hidden_states).float()) + lamb = F.softplus(self.lambda_params.float()) + self.lambda_lower_bound + lamb = lamb.reshape(self.num_heads, -1) + + last_h_kk, last_h_kv = last_state['recurrent_state'] if last_state is not None else (None, None) + + # prefilling or training + # Note that QK will be normalized inside the kernel to avoid saving the activations, thereby reducing the memory usage. + if last_state is None: + o, h_kk, h_kv = chunk_mesa_net( + q=q, + k=k, + v=v, + g=g, + beta=beta, + lamb=lamb, + output_final_state=use_cache, + max_CG_iteration=self.max_cg_step_training, + use_qk_l2norm_in_kernel=True, + cu_seqlens=cu_seqlens, + ) + # decoding + else: + q = l2_norm(q) + k = l2_norm(k) + o, h_kk, h_kv = mesa_net_decoding_one_step( + q=q.squeeze(0), + k=k.squeeze(0), + v=v.squeeze(0), + g=g.squeeze(0), + beta=beta.squeeze(0), + lamb=lamb, + prev_h_kk=last_h_kk, + prev_h_kv=last_h_kv, + max_CG_iteration=self.max_cg_step_decoding, + ) + o = o.unsqueeze(0).to(q) + + if past_key_values is not None: + past_key_values.update( + recurrent_state=(h_kk, h_kv), + conv_state=(conv_state_q, conv_state_k), + layer_idx=self.layer_idx, + offset=q_len, + ) + if self.use_output_gate: + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.o_norm(o, g) + else: + o = self.o_norm(o) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/mla.py b/code/flash-linear-attention/fla/layers/mla.py new file mode 100644 index 0000000000000000000000000000000000000000..95a7ab2c1c6c585e15ff47891d8c47751c3a6ced --- /dev/null +++ b/code/flash-linear-attention/fla/layers/mla.py @@ -0,0 +1,225 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +""" Implementing the Deepseek Multi Latent Attention (MLA) module. Reference: + +https://github.com/huggingface/transformers/blob/main/src/transformers/models/deepseek_v3/modeling_deepseek_v3.py#L328 +""" + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat +from transformers.utils import logging + +from fla.layers.utils import pad_input, unpad_input +from fla.modules import RMSNorm, RotaryEmbedding +from fla.ops.utils.index import prepare_lens_from_mask + +if TYPE_CHECKING: + from fla.models.utils import Cache + +try: + from flash_attn import flash_attn_func, flash_attn_varlen_func +except ImportError: + warnings.warn( + "Flash Attention is not installed. Please install it via `pip install flash-attn --no-build-isolation`", + category=ImportWarning, + ) + flash_attn_func = None + +logger = logging.get_logger(__name__) + + +def yarn_get_mscale(scale=1, mscale=1): + if scale <= 1: + return 1.0 + return 0.1 * mscale * math.log(scale) + 1.0 + + +class MultiheadLatentAttention(nn.Module): + r""" + Multi-headed attention from [Deepseek V2](https://arxiv.org/abs/2405.04434) + """ + + def __init__( + self, + hidden_size: int = 2048, + num_heads: int = 16, + q_lora_rank: int | None = 1536, # q lora rank is optional, None indicates no q lora + qk_rope_head_dim: int = 64, + kv_lora_rank: int = 512, # following the original Deepseek paper + v_head_dim: int = 128, + qk_nope_head_dim: int = 128, + qk_head_dim: int | None = 192, # qk_nope_head_dim + qk_rope_head_dim + window_size: int | None = None, + rope_theta: float = 10000., + max_position_embeddings: int | None = None, + rope_scaling: dict | None = None, + layer_idx: int = None, + ) -> MultiheadLatentAttention: + super().__init__() + + # sanity check + if qk_head_dim is not None: + assert qk_head_dim == qk_nope_head_dim + qk_rope_head_dim, \ + f"qk_head_dim {qk_head_dim} != qk_nope_head_dim {qk_nope_head_dim} + qk_rope_head_dim {qk_rope_head_dim}" + else: + qk_head_dim = qk_nope_head_dim + qk_rope_head_dim + + # attention params info + self.hidden_size = hidden_size + self.num_heads = num_heads + self.q_lora_rank = q_lora_rank + self.qk_rope_head_dim = qk_rope_head_dim + self.kv_lora_rank = kv_lora_rank + self.v_head_dim = v_head_dim + self.qk_nope_head_dim = qk_nope_head_dim + self.qk_head_dim = qk_head_dim + + self.window_size = window_size + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + self.layer_idx = layer_idx + + if flash_attn_func is None: + raise ImportError("Please install Flash Attention via `pip install flash-attn --no-build-isolation` first") + + if q_lora_rank is not None: + self.q_proj = nn.Sequential( + nn.Linear(hidden_size, q_lora_rank, bias=False), + RMSNorm(q_lora_rank), + nn.Linear(q_lora_rank, self.num_heads * self.qk_head_dim, bias=False), + ) + else: + self.q_proj = nn.Linear(hidden_size, self.num_heads * self.qk_head_dim, bias=False) + + self.k_rope = nn.Linear(hidden_size, self.qk_rope_head_dim, bias=False) + self.kv_proj = nn.Sequential( + nn.Linear(hidden_size, self.kv_lora_rank, bias=False), + RMSNorm(self.kv_lora_rank), + nn.Linear(self.kv_lora_rank, self.num_heads * (self.qk_nope_head_dim + self.v_head_dim), bias=False), + ) + + self.o_proj = nn.Linear(self.num_heads * self.v_head_dim, hidden_size, bias=False) + + self.scaling = self.qk_head_dim ** (-0.5) + if rope_scaling is not None: + mscale_all_dim = rope_scaling.get("mscale_all_dim", 0) + scaling_factor = rope_scaling["factor"] + if mscale_all_dim: + mscale = yarn_get_mscale(scaling_factor, mscale_all_dim) + self.scaling = self.scaling * mscale * mscale + + self.rotary = RotaryEmbedding(dim=self.qk_rope_head_dim, base=self.rope_theta) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + # if attention_mask is not None, this is doing inference + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + # prepare q, k, v + batch_size, q_len, _ = hidden_states.shape + + q_states = self.q_proj(hidden_states) + q_states = rearrange(q_states, '... (h d) -> ... h d', d=self.qk_head_dim) + q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) + k_pass, k_rot = self.kv_proj(hidden_states), self.k_rope(hidden_states) + + k_rot = rearrange(k_rot, 'b t d -> b t 1 d') + k_pass = rearrange(k_pass, '... (h d) -> ... h d', d=self.qk_nope_head_dim + self.v_head_dim) + k_pass, v = torch.split(k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1) + + # apply rotary position embedding + seqlen_offset, max_seqlen = 0, q_len + if past_key_values is not None: + seqlen_offset = past_key_values.get_seq_length(self.layer_idx) + max_seqlen = q_len + seqlen_offset + + if attention_mask is not None: + seqlen_offset = seqlen_offset + prepare_lens_from_mask(attention_mask) - attention_mask.shape[-1] + max_seqlen = q_len + max(seqlen_offset) + + if self.max_position_embeddings is not None: + max_seqlen = max(max_seqlen, self.max_position_embeddings) + cu_seqlens = kwargs.get("cu_seqlens") + q_rot, k_rot = self.rotary( + q_rot, k_rot, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens, + ) + + k_rot = repeat(k_rot, 'b t 1 d -> b t h d', h=self.num_heads) + q = torch.cat((q_pass, q_rot), dim=-1) + k = torch.cat((k_pass, k_rot), dim=-1) + + # TODO: instead of caching the full k, v, we can actually only cache the compressed_kv and k_rot + # and recover the full k, v from compressed_kv and k_rot + if past_key_values is not None: + cache_has_content = past_key_values.get_seq_length(self.layer_idx) > 0 + k_cached, v_cached = past_key_values.update( + attn_state=(k, v), + layer_idx=self.layer_idx, + offset=q_len, + )['attn_state'] + if cache_has_content: + k, v = k_cached, v_cached + + # Head dim match to use flash-attn + if self.qk_head_dim != self.v_head_dim: + v = F.pad(v, [0, self.qk_head_dim - self.v_head_dim]) + + # Contains at least one padding token in the sequence + if attention_mask is not None: + if q.shape[1] == 1 and self.window_size is not None: + attention_mask = attention_mask[:, -self.window_size:] + q, (k, v), indices_q, cu_seqlens, max_seq_lens = unpad_input(q, (k, v), attention_mask, q_len) + cu_seqlens_q, cu_seqlens_k = cu_seqlens + max_seqlen_q, max_seqlen_k = max_seq_lens + o = flash_attn_varlen_func( + q, k, v, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ) + o = pad_input(o, indices_q, batch_size, q_len) + elif cu_seqlens is not None: + o = flash_attn_varlen_func( + q.squeeze(0), k.squeeze(0), v.squeeze(0), + cu_seqlens_q=cu_seqlens, + cu_seqlens_k=cu_seqlens, + max_seqlen_q=max_seqlen, + max_seqlen_k=max_seqlen, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ).unsqueeze(0) + else: + o = flash_attn_func( + q, k, v, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ) + + if self.qk_head_dim != self.v_head_dim: + o = o[:, :, :, :self.v_head_dim] + o = o.reshape(batch_size, q_len, -1) + o = self.o_proj(o) + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/mom.py b/code/flash-linear-attention/fla/layers/mom.py new file mode 100644 index 0000000000000000000000000000000000000000..c72691cccc5569c698f23c159bc8f701f487957c --- /dev/null +++ b/code/flash-linear-attention/fla/layers/mom.py @@ -0,0 +1,833 @@ + +from __future__ import annotations + +import math +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange +from torch.nn import functional as F + +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input, unpad_input + + +def _upad_input( + query_layer: torch.Tensor, + key_layer: torch.Tensor, + value_layer: torch.Tensor, + gate_layer: torch.Tensor, + beta_layer: torch.Tensor, + attention_mask: torch.Tensor, +): + """ + Unpads query, key, and values tensors, using a single dimension for all tokens even though they belong to + different batches. + + This function is used instead of `flash_attn.bert_padding.unpad_input` in order to avoid the recomputation + of the same intermediary + tensors for query, key, value tensors. + + Arguments: + query_layer (`torch.Tensor`): + Query state with padding. Shape: (batch_size, query_length, num_heads, head_dim). + key_layer (`torch.Tensor`): + Key state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim). + value_layer (`torch.Tensor`): + Value state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim). + attention_mask (`torch.Tensor`): + Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid. + query_length (`int`): + Target length. + + Return: + query_layer (`torch.Tensor`): + Query state without padding. Shape: (total_target_length, num_heads, head_dim). + key_layer (`torch.Tensor`): + Key state with padding. Shape: (total_source_length, num_key_value_heads, head_dim). + value_layer (`torch.Tensor`): + Value state with padding. Shape: (total_source_length, num_key_value_heads, head_dim). + indices_q (`torch.Tensor`): + The indices of non-masked tokens from the flattened input target sequence. + (cu_seqlens_q, cu_seqlens_k) (`Tuple[int]`): + The cumulative sequence lengths for the target (query) and source (key, value), used to index + into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,). + (max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`Tuple[int]`): + Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence i.e. query, + `max_seqlen_in_batch_k` for the source sequence i.e. key/value). + """ + query_length = query_layer.shape[1] + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = get_unpad_data(attention_mask) + batch_size, kv_seq_len, dim = key_layer.shape + v_dim = value_layer.shape[-1] + + key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, dim), indices_k) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, v_dim), indices_k, + ) + gate_layer = index_first_axis(gate_layer.reshape(batch_size * kv_seq_len, -1), indices_k) + beta_layer = index_first_axis(beta_layer.reshape(batch_size * kv_seq_len, -1), indices_k) + if query_length == kv_seq_len: + query_layer = index_first_axis(query_layer.reshape(batch_size * kv_seq_len, dim), indices_k) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device, + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + gate_layer, + beta_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +def transform( + x: torch.Tensor, + routing_mask: torch.Tensor, + num_memories: int, + selected_memories: torch.Tensor, + attention_mask: torch.Tensor, +): + """ + Reorganize token embeddings into memory-aligned chunks. + + Steps: + - Expand for top-k routing if needed. + - Mask out padded tokens via `attention_mask`. + - Sort tokens by (batch, memory). + - Gather and pad tokens per memory slot. + + Args: + x: (batch, seq, hidden) input embeddings. + routing_mask: (batch, seq, num_memories) binary routing mask. + num_memories: number of memory slots. + selected_memories: memory indices per token, + (batch, seq) if k=1 else (batch, seq, topk). + attention_mask: (batch, seq) valid-token mask. + + Returns: + transformed_x: (num_memories, batch, max_len, hidden) reorganized tokens. + truncation_indices: (batch*num_memories, max_len) gather indices. + sorted_indices: (batch*seq*topk,) global sort order. + max_len: int, max tokens per memory. + mask: (batch*num_memories, max_len) validity mask. + mask_2: (num_memories, batch, max_len) validity mask reshaped. + """ + if selected_memories.dim() == 3: + # (batch, seq, topk) + topk = selected_memories.shape[2] + # x (batch, seq, hidden) + x = x.repeat_interleave(topk, dim=1) + # x (batch, seq * topk, hidden) + # (batch, seq, topk) + selected_memories = selected_memories.reshape(selected_memories.shape[0], -1) + # (batch, seq * topk) + + if attention_mask is not None: + attention_mask = attention_mask[:, -routing_mask.shape[1]:] + # mask out the masked tokens + routing_mask[attention_mask.bitwise_not().unsqueeze(-1).expand(-1, -1, num_memories)] = 0 + + b, s, d = x.shape + x_flat = x.reshape(b * s, d) # [b*s, d] + + with torch.no_grad(): + batch_indices = torch.arange(b, device=x.device).unsqueeze(-1) + batch_indices = batch_indices.repeat(1, s).reshape(-1) + if attention_mask is not None: + # sort the masked tokens to the end + batch_indices[attention_mask.repeat_interleave(topk, dim=1).bitwise_not().flatten()] = b + # (b * s) + memories_flat = selected_memories.reshape(-1) # [b*s] + + combined = batch_indices * (memories_flat.max() + 1) + memories_flat + sorted_indices = combined.argsort() + + x_sorted = x_flat[sorted_indices] # [b*s, d] + # (b*s, hidden) -> (b, s, hidd) + with torch.no_grad(): + # routing_mask (b, s, num_memories) + batch_memory_tokens = routing_mask.sum(dim=1) + # (b, num_memories) + flatten_offset = batch_memory_tokens.flatten().cumsum(dim=0) + max_len = batch_memory_tokens.max() + indices = ( + torch.arange(max_len, device=flatten_offset.device).unsqueeze(0).expand(b * num_memories, -1) + + torch.cat([torch.tensor([0], device=flatten_offset.device), flatten_offset[:-1]], dim=0).unsqueeze(1) + ) + mask = indices < flatten_offset.unsqueeze(-1) + truncation_indices = torch.where(mask, indices, torch.zeros_like(indices)) + + gathered_x = torch.gather(x_sorted, 0, truncation_indices.reshape(-1).unsqueeze(-1).expand(-1, d)) + transformed_x = gathered_x.reshape(b * num_memories, -1, d).reshape((b, num_memories, max_len, d)).transpose(0, 1) + # transformed_x = transformed_x * mask.unsqueeze(-1).expand_as(transformed_x) + # pad_x = torch.zeros((b * num_memories, capacity_len-max_len, d), dtype=transformed_x.dtype, device=transformed_x.device) + # pad_mask = torch.zeros((b * num_memories, capacity_len-max_len), dtype=transformed_x.dtype, device=transformed_x.device) + # left pad + # transformed_x = torch.cat((pad_x, transformed_x), dim=1).reshape((b, num_memories, capacity_len, d)).transpose(0, 1) + mask_2 = mask.reshape((b, num_memories, max_len)).transpose(0, 1) + # truncation_indices += capacity_len-max_len + # if attention_mask is not None: + # mask_2 + + return transformed_x, truncation_indices, sorted_indices, max_len, mask, mask_2 + + +def reconstruct( + transformed_x, + indices: torch.Tensor, + sorted_indices: torch.Tensor, + batch_size: int, + seq_len: int, + topk: int, + routing_weights: torch.Tensor, + mask: torch.Tensor, +): + ''' + Reconstruct and mix transformed outputs back into the original input sequence shape. + + Key operations: + 1. Reshapes and transposes `transformed_x` to prepare for scattering. + 2. Applies the `mask` to zero out invalid positions. + 3. Uses `torch.scatter_add_` to scatter and sum the transformed outputs back to their original positions + based on `indices`. + 4. Rearranges the scattered outputs using `sorted_indices` to ensure correct ordering. + 5. Applies the `routing_weights` to weight the outputs. + 6. Sums over the `topk` dimension to produce the final reconstructed output. + + Args: + transformed_x (torch.Tensor): + The transformed output tensor from memory units or experts. + Shape: (num_memories, batch_size, capacity_len, hidden_size) + indices (torch.Tensor): + Indices used for scattering the transformed outputs back to their corresponding positions. + Shape: (batch*num_memories, max_len) + sorted_indices (torch.Tensor): + Sorting indices used to rearrange the scattered outputs back into the original sequence order. + Shape: (batch_size*seq_len*topk) + batch_size (int): + The size of the batch. + seq_len (int): + The length of the input sequence. + topk (int): + The number of top elements selected (`topk`) per token during the selection process. + routing_weights (torch.Tensor): + Routing weights assigned to the top-k selected outputs when reconstructing the final output. + Shape: (batch_size, seq_len, topk) + mask (torch.Tensor): + Boolean mask indicating valid positions in the sequence. + Shape: (batch*num_memories, max_len) + + Returns: + restored_x (torch.Tensor): + The reconstructed output tensor in the original input sequence shape. + Shape: (batch_size, seq_len, hidden_size) + ''' + transformed_x = transformed_x.transpose(0, 1).reshape( + (-1, transformed_x.shape[2], transformed_x.shape[3])) + b, s, k, d = batch_size, seq_len, topk, transformed_x.shape[2] + gathered_x = transformed_x.reshape( + (transformed_x.shape[0] * transformed_x.shape[1], transformed_x.shape[2])) + mask_expanded = mask.reshape(-1).unsqueeze(-1).expand_as(gathered_x) + gathered_x = gathered_x * mask_expanded + + assert (indices >= 0).all(), "Indices should be non-negative" + + resortd_x = torch.zeros((b * s * k, d), device=gathered_x.device, dtype=gathered_x.dtype).scatter_add_( + 0, + indices.reshape(-1).unsqueeze(-1).expand(-1, d), + gathered_x, + ) + assert (indices < resortd_x.size(0)).all(), "Indices should be less than resortd_x size" + + inverse_indices = sorted_indices.argsort() + rearranged_x_flat = resortd_x[inverse_indices] + restored_x = rearranged_x_flat.reshape((b, s * k, d)) + restored_x = restored_x.reshape(b, s, k, d) * routing_weights.reshape(b, s, k).unsqueeze(-1) + restored_x = restored_x.sum(dim=2) + return restored_x + + +class MomAttention(nn.Module): + """ + The layer implementaion for [MoM: Linear Sequence Modeling with Mixture-of-Memories](https://arxiv.org/abs/2502.13685). + """ + + def __init__( + self, + hidden_size: int = 2048, + head_dim: int = 256, + num_heads: int = 4, + expand_v: float = 2, + mode: str = 'chunk', + use_output_gate: bool = True, + use_short_conv: bool = True, + conv_size: int = 4, + conv_bias: bool = False, + layer_idx: int = None, + norm_eps: float = 1e-5, + num_memories: int = 8, + topk: int = 2, + capacity: float = 1.0, + shared_mem: bool = False, + single_kv_proj: bool = False, + **kwargs, + ) -> MomAttention: + super().__init__() + self.num_memories = num_memories + self.topk = topk + self.capacity = capacity + self.shared_mem = shared_mem + self.single_kv_proj = single_kv_proj + + self.mode = mode + + self.hidden_size = hidden_size + self.expand_v = expand_v + + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.head_dim = head_dim + self.num_heads = num_heads + + self.key_dim = int(self.num_heads * self.head_dim) + self.value_dim = int(self.key_dim * self.expand_v) + self.head_qk_dim = head_dim + self.head_v_dim = int(head_dim * self.expand_v) + self.layer_idx = layer_idx + self.silu = nn.SiLU() + + assert mode in ['chunk', 'fused_recurrent'], f"Not suppoerted mode `{mode}`." + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.gate = nn.Linear(self.hidden_size, self.num_memories, bias=False) + if self.single_kv_proj: + self.shared_k = nn.Linear(hidden_size, self.key_dim, bias=False) + self.shared_v = nn.Linear(hidden_size, self.value_dim, bias=False) + self.shared_b = nn.Linear(hidden_size, self.num_heads, bias=False) + self.shared_a = nn.Linear(hidden_size, self.num_heads, bias=False) + else: + self.k_proj = nn.ModuleList([ + nn.Linear(self.hidden_size, self.key_dim, bias=False) + for _ in range(self.num_memories) + ]) + self.v_proj = nn.ModuleList([ + nn.Linear(self.hidden_size, self.value_dim, bias=False) + for _ in range(self.num_memories) + ]) + self.b_proj = nn.ModuleList([ + nn.Linear(self.hidden_size, self.num_heads, bias=False) + for _ in range(self.num_memories) + ]) + self.a_proj = nn.ModuleList([ + nn.Linear(self.hidden_size, self.num_heads, bias=False) + for _ in range(self.num_memories) + ]) + if self.shared_mem: + self.shared_k = nn.Linear(hidden_size, self.key_dim, bias=False) + self.shared_v = nn.Linear(hidden_size, self.value_dim, bias=False) + self.shared_b = nn.Linear(hidden_size, self.num_heads, bias=False) + self.shared_a = nn.Linear(hidden_size, self.num_heads, bias=False) + + A = torch.empty(self.num_heads, dtype=torch.float32).uniform_(0, 16) + self.A_log = nn.Parameter(torch.log(A)) + self.A_log._no_weight_decay = True + # hard coded for now + dt_min = 0.001 + dt_max = 0.1 + dt_init_floor = 1e-4 + dt = torch.exp( + torch.rand(self.num_heads) * (math.log(dt_max) - math.log(dt_min)) + + math.log(dt_min), + ) + dt = torch.clamp(dt, min=dt_init_floor) + # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + self.dt_bias = nn.Parameter(inv_dt) + # Just to be explicit. Without this we already don't put wd on dt_bias because of the check + # name.endswith("bias") in param_grouping.py + self.dt_bias._no_weight_decay = True + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + else: + raise UserWarning( + "ShortConvolution is crucial to the performance. " + "Do not turn it off, i.e., setting `use_short_conv=False` unless you know what you are doing.", + ) + if use_output_gate: + self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps) + else: + self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + self.apply(self._initialize_weights) + + def _initialize_weights(self, module: nn.Module): + if getattr(module, "_is_hf_initialized", False): + return + if isinstance(module, nn.Linear): + nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5) + if module.bias is not None: + nn.init.zeros_(module.bias) + module._is_hf_initialized = True + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + attention_mask = (attention_mask == 1) + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + origin_cu_seqlens = kwargs.get('cu_seqlens') + if origin_cu_seqlens is not None: + hidden_states, attention_mask = self.cu2pad(hidden_states, origin_cu_seqlens) + + mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode + if self.training: + assert mode == 'chunk', "Only chunk mode is supported in training." + + last_state = None + # _, q_len = hidden_states.shape[0], hidden_states.shape[1] + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + # 🔍 topk gating + router_logits = self.gate(hidden_states) # (bsz, q_len, num_memories) + scores = F.softmax(router_logits, dim=2, dtype=torch.float) + routing_weights, selected_memories = torch.topk(scores, self.topk, dim=-1) # (bsz, seq, topk) + routing_weights /= routing_weights.sum(dim=-1, keepdim=True) + routing_weights = routing_weights.to(hidden_states.dtype) # we cast back to the input dtype + routing_weights_full = torch.zeros( + routing_weights.shape[0], + routing_weights.shape[1], + self.num_memories, + dtype=routing_weights.dtype, + device=routing_weights.device, + ).scatter(-1, selected_memories, routing_weights) + routing_mask = routing_weights_full.bool().int() + + # if self.use_output_gate: + # o_g = self.g_proj(hidden_states) + + batch_size, seq_len = hidden_states.shape[0], hidden_states.shape[1] + + shared_hidden_states = hidden_states + hidden_states, indices, sorted_indices, max_len, mask, mask_2 = transform( + hidden_states, routing_mask, self.num_memories, selected_memories, attention_mask) + + q = self.q_proj(hidden_states) + if self.single_kv_proj: + k = self.shared_k(hidden_states) + v = self.shared_v(hidden_states) + beta = self.shared_b(hidden_states).sigmoid() + g = -self.A_log.float().exp() * F.softplus(self.shared_a(hidden_states).float() + self.dt_bias) + else: + k = torch.stack([k_expert(hidden_states[i]) for i, k_expert in enumerate(self.k_proj)], dim=0) + v = torch.stack([v_expert(hidden_states[i]) for i, v_expert in enumerate(self.v_proj)], dim=0) + beta = torch.stack([b_expert(hidden_states[i]).sigmoid() for i, b_expert in enumerate(self.b_proj)], dim=0) + g = torch.stack([-self.A_log.float().exp() * F.softplus(a_expert(hidden_states[i]).float() + self.dt_bias) + for i, a_expert in enumerate(self.a_proj)], dim=0) + + q, k, v, g, beta, mask_2 = (rearrange(x, 'e b l ... -> (e b) l ...') for x in (q, k, v, g, beta, mask_2)) + cu_q, cu_k, cu_v, cu_g, cu_beta, indices_q, cu_seqlen_all, max_seq_lens = _upad_input(q, k, v, g, beta, mask_2) + cu_seqlens, reverse_indices = cu_seqlen_all[0].to(torch.long).unique(return_inverse=True) + cu_q, cu_k, cu_v, cu_g, cu_beta = (x.unsqueeze(0).contiguous() for x in (cu_q, cu_k, cu_v, cu_g, cu_beta)) + + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = [None, None], [None, None], [None, None] + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + + conv_cu_seqlens = cu_seqlens + padded = False + if self.training: + conv_cu_seqlens = None + elif seq_len != 1 and (cu_seqlens[1:] - cu_seqlens[:-1]).min().item() < self.conv_size: + padded = True + conv_cu_seqlens, cu_q, cu_k, cu_v, pad_lengths = self.pad_for_conv(cu_seqlens, cu_q, cu_k, cu_v) + + conv_q = self.prepare_recurrent_state( + conv_state_q[0], + conv_cu_seqlens, + cu_seqlen_all[0], + reverse_indices, + batch_size, + ) + cu_q, conv_q_new = self.q_conv1d( + x=cu_q, + cache=conv_q, + output_final_state=use_cache, + cu_seqlens=conv_cu_seqlens, + ) + conv_state_q[0] = self.handle_recurrent_state( + conv_state_q[0], + conv_q_new, + conv_cu_seqlens, + cu_seqlen_all[0], + reverse_indices, + ) + conv_k = self.prepare_recurrent_state( + conv_state_k[0], + conv_cu_seqlens, + cu_seqlen_all[0], + reverse_indices, + batch_size, + ) + cu_k, conv_k_new = self.k_conv1d( + x=cu_k, + cache=conv_k, + output_final_state=use_cache, + cu_seqlens=conv_cu_seqlens, + ) + conv_state_k[0] = self.handle_recurrent_state( + conv_state_k[0], + conv_k_new, + conv_cu_seqlens, + cu_seqlen_all[0], + reverse_indices, + ) + conv_v = self.prepare_recurrent_state( + conv_state_v[0], + conv_cu_seqlens, + cu_seqlen_all[0], + reverse_indices, + batch_size, + ) + cu_v, conv_v_new = self.v_conv1d( + x=cu_v, + cache=conv_v, + output_final_state=use_cache, + cu_seqlens=conv_cu_seqlens, + ) + conv_state_v[0] = self.handle_recurrent_state( + conv_state_v[0], + conv_v_new, conv_cu_seqlens, + cu_seqlen_all[0], + reverse_indices, + ) + + if padded: + cu_q, cu_k, cu_v = self.unpad_after_conv(conv_cu_seqlens, cu_seqlens, cu_q, cu_k, cu_v, pad_lengths) + + else: + q, k, v = self.silu(q), self.silu(k), self.silu(v) + + cu_q, cu_k, cu_v = map(lambda x: rearrange(x, 'b t (h d) -> b t h d', h=self.num_heads), (cu_q, cu_k, cu_v)) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else [ + None for _ in range(1 + self.shared_mem)] + if mode == 'chunk': + o, recurrent_state_ = chunk_gated_delta_rule( + q=cu_q, + k=cu_k, + v=cu_v, + g=cu_g, + beta=cu_beta, + initial_state=recurrent_state[0], + output_final_state=use_cache, + use_qk_l2norm_in_kernel=True, + cu_seqlens=cu_seqlens, + ) + recurrent_state[0] = self.handle_recurrent_state( + recurrent_state[0], + recurrent_state_, + cu_seqlens, + cu_seqlen_all[0], + reverse_indices, + ) + + elif mode == 'fused_recurrent': + memories = self.prepare_recurrent_state( + recurrent_state[0], + cu_seqlens, cu_seqlen_all[0], + reverse_indices, batch_size, + ) + o, recurrent_state_ = fused_recurrent_gated_delta_rule( + q=cu_q, + k=cu_k, + v=cu_v, + g=cu_g, + beta=cu_beta, + initial_state=memories, + output_final_state=use_cache, + use_qk_l2norm_in_kernel=True, + cu_seqlens=cu_seqlens, + ) + recurrent_state[0] = self.handle_recurrent_state( + recurrent_state[0], + recurrent_state_, + cu_seqlens, + cu_seqlen_all[0], + reverse_indices, + ) + + o = o.squeeze(0).contiguous() + o = pad_input(o, indices_q, batch_size*self.num_memories, max_len) + o = rearrange(o, '(e b) l h d -> e b l (h d)', b=batch_size) + o = reconstruct(o, indices=indices, sorted_indices=sorted_indices, batch_size=batch_size, + seq_len=seq_len, topk=self.topk, routing_weights=routing_weights, mask=mask) + o = rearrange(o, 'b l (h d) -> b l h d', h=self.num_heads) + + if self.shared_mem: + shared_o = self.shared_o(shared_hidden_states, attention_mask, recurrent_state, + use_cache, conv_state_q, conv_state_k, conv_state_v) + o += shared_o + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q.shape[2], + ) + + if self.use_output_gate: + g = rearrange(self.g_proj(shared_hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.o_norm(o, g) + else: + o = self.o_norm(o) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + + if origin_cu_seqlens is not None: + indices, _, _ = get_unpad_data(attention_mask[:, -seq_len:]) + o = index_first_axis(rearrange(o, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + return o, None, past_key_values, router_logits.view(-1, self.num_memories) + + def shared_o( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + recurrent_state=None, + use_cache: bool | None = False, + conv_state_q=[None, None], + conv_state_k=[None, None], + conv_state_v=[None, None], + **kwargs, + ) -> torch.Tensor: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode + if self.training: + assert mode == 'chunk', "Only chunk mode is supported in training." + + cu_seqlens = None + if attention_mask is not None: + batch_size, q_len = hidden_states.shape[0], hidden_states.shape[1] + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + if self.use_short_conv: + q, conv_state_q[1] = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q[1], + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k[1] = self.k_conv1d( + x=self.shared_k(hidden_states), + cache=conv_state_k[1], + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v[1] = self.v_conv1d( + x=self.shared_v(hidden_states), + cache=conv_state_v[1], + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = self.silu(self.q_proj(hidden_states)) + k = self.silu(self.shared_k(hidden_states)) + v = self.silu(self.shared_v(hidden_states)) + + q, k, v = map(lambda x: rearrange(x, 'b t (h d) -> b t h d', h=self.num_heads), (q, k, v)) + beta = self.shared_b(hidden_states).sigmoid() + g = -self.A_log.float().exp() * F.softplus(self.shared_a(hidden_states).float() + self.dt_bias) + + if mode == 'chunk': + o, recurrent_state[-1] = chunk_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=beta, + initial_state=recurrent_state[-1], + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=True, + ) + elif mode == 'fused_recurrent': + o, recurrent_state[-1] = fused_recurrent_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=beta, + initial_state=recurrent_state[-1], + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=True, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + return o + + def cu2pad(self, x, cu_seqlens): + batch_size = cu_seqlens.shape[0] - 1 + max_len = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item() + indices = torch.tensor([], dtype=torch.long, device=x.device) + attention_mask = torch.ones((batch_size, max_len), dtype=torch.bool, device=x.device) + for i in range(batch_size): + seq_len = cu_seqlens[i+1] - cu_seqlens[i] + pad_len = max_len - seq_len + batch_indices = torch.arange(pad_len, max_len, device=x.device) + batch_indices = batch_indices + i * max_len + indices = torch.cat([indices, batch_indices]) + attention_mask[i, :pad_len] = False + x = pad_input(x.squeeze(0), indices, batch_size, max_len) + return x, attention_mask + + def pad_for_conv(self, cu_seqlens, cu_q, cu_k, cu_v): + lengths = cu_seqlens[1:] - cu_seqlens[:-1] + pad_lengths = torch.clamp(self.conv_size - lengths, min=0) + new_lengths = lengths + pad_lengths + new_cu_seqlens = torch.cat([ + torch.tensor([0], device=cu_seqlens.device, dtype=cu_seqlens.dtype), + torch.cumsum(new_lengths, dim=0), + ]) + final_total_len = new_cu_seqlens[-1].item() + new_q = torch.zeros((1, final_total_len, cu_q.shape[-1]), dtype=cu_q.dtype, device=cu_q.device) + new_k = torch.zeros((1, final_total_len, cu_k.shape[-1]), dtype=cu_k.dtype, device=cu_k.device) + new_v = torch.zeros((1, final_total_len, cu_v.shape[-1]), dtype=cu_v.dtype, device=cu_v.device) + num_sequences = len(lengths) + for i in range(num_sequences): + src_start = cu_seqlens[i] + src_end = cu_seqlens[i+1] + dest_start = new_cu_seqlens[i] + pad_lengths[i] + dest_end = new_cu_seqlens[i+1] + new_q[:, dest_start:dest_end, ...] = cu_q[:, src_start:src_end, ...] + new_k[:, dest_start:dest_end, ...] = cu_k[:, src_start:src_end, ...] + new_v[:, dest_start:dest_end, ...] = cu_v[:, src_start:src_end, ...] + + return new_cu_seqlens, new_q, new_k, new_v, pad_lengths + + def unpad_after_conv(self, conv_cu_seqlens, cu_seqlens, cu_q, cu_k, cu_v, pad_lengths): + original_total_len = cu_seqlens[-1].item() + orig_q = torch.empty((1, original_total_len, cu_q.shape[-1]), dtype=cu_q.dtype, device=cu_q.device) + orig_k = torch.empty((1, original_total_len, cu_k.shape[-1]), dtype=cu_k.dtype, device=cu_k.device) + orig_v = torch.empty((1, original_total_len, cu_v.shape[-1]), dtype=cu_v.dtype, device=cu_v.device) + + num_sequences = len(pad_lengths) + for i in range(num_sequences): + dest_start = cu_seqlens[i] + dest_end = cu_seqlens[i+1] + src_start = conv_cu_seqlens[i] + pad_lengths[i] + src_end = conv_cu_seqlens[i+1] + + orig_q[:, dest_start:dest_end, ...] = cu_q[:, src_start:src_end, ...] + orig_k[:, dest_start:dest_end, ...] = cu_k[:, src_start:src_end, ...] + orig_v[:, dest_start:dest_end, ...] = cu_v[:, src_start:src_end, ...] + return orig_q, orig_k, orig_v + + def prepare_recurrent_state(self, recurrent_state, cu_seqlens, cu_seqlen_all, reverse_indices, batch_size): + if recurrent_state is None: + return None + + if cu_seqlens is None: + return recurrent_state + + total_len = len(cu_seqlen_all) + if len(cu_seqlens) != total_len: + # select memories that are activated + memories = torch.zeros_like(recurrent_state[:self.topk*batch_size]) + mem_id = 0 + for i in range(total_len-1): + if cu_seqlen_all[i] != cu_seqlen_all[i+1]: + memories[mem_id] = recurrent_state[i] + mem_id += 1 + assert mem_id == self.topk * batch_size, f"The number of memories {mem_id} is not correct." + else: + memories = recurrent_state + + return memories + + def handle_recurrent_state(self, recurrent_state, recurrent_state_new, cu_seqlens, cu_seqlen_all, reverse_indices): + if recurrent_state_new is None: + return None + if cu_seqlens is None: + return recurrent_state_new + if recurrent_state is None: + recurrent_state = torch.zeros_like(recurrent_state_new[reverse_indices[1:]-1]) + total_len = len(cu_seqlen_all) + if len(cu_seqlens) != total_len: + for i in range(total_len-1): + if cu_seqlen_all[i] != cu_seqlen_all[i+1]: + recurrent_state[i] = recurrent_state_new[reverse_indices[i+1]-1] + else: + recurrent_state = recurrent_state_new + return recurrent_state diff --git a/code/flash-linear-attention/fla/layers/multiscale_retention.py b/code/flash-linear-attention/fla/layers/multiscale_retention.py new file mode 100644 index 0000000000000000000000000000000000000000..7215bf4b9ddeb8e95e6c3108741b0ffbf223b7c3 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/multiscale_retention.py @@ -0,0 +1,304 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange, repeat +from transformers.activations import ACT2FN + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.modules.rotary import RotaryEmbedding +from fla.ops.retention import chunk_retention, fused_chunk_retention, fused_recurrent_retention, parallel_retention +from fla.ops.utils.index import prepare_lens_from_mask + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +class MultiScaleRetention(nn.Module): + r""" + The layer implementaion for [Retentive Network: A Successor to Transformer for Large Language Models](https://arxiv.org/pdf/2307.08621.pdf). # noqa + + Args: + mode (str, Optional): + Which Retention kernel to use. + Currently available: `chunk`, `fused_recurrent`, `parallel`, and `fused_chunk`. + Default: `chunk`. + hidden_size (int, Optional): + The hidden size of the input. Default: 1024. + expand_k (float, Optional): + The expansion ratio for the key dim. Default: 1.0. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 2.0. + num_heads (int, Optional): + The number of heads. Default: 8. + num_kv_heads (int, Optional): + The number of key/value heads, used for MQA. Default: None. + feature_map (str, Optional): + Feature map function applied to queries/keys. Default: None. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `False`. + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + use_output_gate (bool, Optional): + Whether to use output gate. Default: `True`. + gate_fn (str, Optional): + The activation function for the output gate. Default: `swish`. + elementwise_affine (bool, Optional): + If `True`, applies elementwise affine to LayerNorm with learnable parameters. Default: `True`. + norm_eps (float, Optional): + The epsilon value for the layernorm/rmsnorm layer. Default: 1e-5. + fuse_norm (bool, Optional): + Whether to fuse the norm and the output gate for better memory footprint. Default: `True`. + layer_idx (int, Optional): + The index of the layer. Default: None. + """ + + def __init__( + self, + mode: str = 'chunk', + hidden_size: int = 1024, + expand_k: float = 1.0, + expand_v: float = 2.0, + num_heads: int = 8, + num_kv_heads: int | None = None, + feature_map: str | None = None, + use_short_conv: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + use_output_gate: bool = True, + gate_fn: str = 'swish', + elementwise_affine: bool | None = True, + norm_eps: float = 1e-5, + fuse_norm: bool = True, + layer_idx: int = None, + **kwargs, + ) -> MultiScaleRetention: + super().__init__() + + self.mode = mode + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads + self.num_kv_groups = self.num_heads // self.num_kv_heads + self.feature_map_fn = ACT2FN[feature_map] if feature_map is not None else None + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + self.use_output_gate = use_output_gate + + self.key_dim = int(hidden_size * expand_k) + self.value_dim = int(hidden_size * expand_v) + self.key_dim_per_group = self.key_dim // self.num_kv_groups + self.value_dim_per_group = self.value_dim // self.num_kv_groups + self.layer_idx = layer_idx + + assert mode in ['chunk', 'fused_chunk', 'parallel', 'fused_recurrent'], f"Not supported mode `{mode}`." + assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}" + assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}" + + self.head_k_dim = self.key_dim // num_heads + self.head_v_dim = self.value_dim // num_heads + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim_per_group, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim_per_group, bias=False) + if self.use_output_gate: + self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim_per_group, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim_per_group, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + if gate_fn == 'swish' and fuse_norm and use_output_gate: + self.g_norm_swish_gate = FusedRMSNormGated( + hidden_size=self.head_v_dim, + elementwise_affine=elementwise_affine, + eps=norm_eps, + ) + self.fuse_norm_and_gate = True + else: + self.fuse_norm_and_gate = False + self.g_norm = RMSNorm( + hidden_size=self.head_v_dim, + elementwise_affine=elementwise_affine, + eps=norm_eps, + ) + self.gate_fn = ACT2FN[gate_fn] + + # TODO: fix this issue + # https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/ops/triton/rotary.py#L180 + # Ideally, we would want to support arbitrary d_head_qk + assert self.head_k_dim <= 256, "head_k_dim must be less than or equal to 256" + self.rotary = RotaryEmbedding(dim=self.head_k_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + mode = 'fused_recurrent' if q_len <= 64 else self.mode + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + + q = rearrange(q, '... (h d) -> ... h d', d=self.head_k_dim) + k = rearrange(k, '... (h d) -> ... h d', d=self.head_k_dim) + if self.feature_map_fn is not None: + q, k = map(self.feature_map_fn, (q, k)) + + seqlen_offset, max_seqlen = 0, q.shape[1] + if past_key_values is not None: + seqlen_offset = past_key_values.get_seq_length(self.layer_idx) + max_seqlen = q.shape[1] + seqlen_offset + + if attention_mask is not None and seqlen_offset > 0: + # to deliminate the offsets of padding tokens + seqlen_offset = prepare_lens_from_mask(attention_mask) - q_len + max_seqlen = q.shape[1] + seqlen_offset.max().item() + + q, k = self.rotary(q, k, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens) + + if self.num_kv_groups > 1: + k = repeat(k, '... h d -> ... (h g) d', g=self.num_kv_groups) + v = repeat(v, '... (h d) -> ... (h g) d', d=self.head_v_dim, g=self.num_kv_groups) + else: + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'chunk': + o, recurrent_state = chunk_retention( + q=q, + k=k, + v=v, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + elif mode == 'fused_chunk': + o, recurrent_state = fused_chunk_retention( + q=q, + k=k, + v=v, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + elif mode == 'parallel': + o, recurrent_state = parallel_retention( + q=q, + k=k, + v=v, + cu_seqlens=cu_seqlens, + ) + elif mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_retention( + q=q, + k=k, + v=v, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + if self.use_output_gate: + g = self.g_proj(hidden_states) + if self.fuse_norm_and_gate: + g = rearrange(g, '... (h d) -> ... h d', d=self.head_v_dim) + o = self.g_norm_swish_gate(o, g) + o = rearrange(o, '... h d -> ... (h d)') + else: + o = rearrange(self.g_norm(o), '... h d -> ... (h d)') + o = o * self.gate_fn(g) + else: + o = rearrange(self.g_norm(o), '... h d -> ... (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/nsa.py b/code/flash-linear-attention/fla/layers/nsa.py new file mode 100644 index 0000000000000000000000000000000000000000..d21bea41083950dd1ceb294bd15d4e46b4901dc0 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/nsa.py @@ -0,0 +1,137 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange +from transformers.utils import logging + +from fla.modules import RotaryEmbedding +from fla.ops.nsa.parallel import parallel_nsa +from fla.ops.utils.index import prepare_lens_from_mask + +if TYPE_CHECKING: + from fla.models.utils import Cache + +logger = logging.get_logger(__name__) + + +class NativeSparseAttention(nn.Module): + + def __init__( + self, + hidden_size: int = 2048, + num_heads: int = 64, + num_kv_heads: int | None = 4, + head_dim: int = 64, + qkv_bias: bool = False, + block_size: int | None = 64, + block_counts: torch.LongTensor | int | None = 16, + window_size: int | None = 512, + rope_theta: float | None = 10000., + max_position_embeddings: int | None = None, + layer_idx: int = None, + ): + super().__init__() + + self.hidden_size = hidden_size + self.num_heads = num_heads + if num_kv_heads is None: + self.num_kv_heads = self.num_heads + else: + self.num_kv_heads = num_kv_heads + self.num_kv_groups = num_heads // self.num_kv_heads + self.head_dim = head_dim + self.kv_dim = self.num_kv_heads * self.head_dim + self.qkv_bias = qkv_bias + + self.block_size = block_size + self.block_counts = block_counts + self.window_size = window_size + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + self.layer_idx = layer_idx + + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=self.qkv_bias) + self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) + self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) + self.g_proj = nn.Linear(self.hidden_size, self.num_heads * 3, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + + self.rotary = RotaryEmbedding(dim=self.head_dim, base=self.rope_theta) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, seq_len, _ = hidden_states.size() + + q = rearrange(self.q_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + k = rearrange(self.k_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(self.v_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=3) + g_cmp, g_slc, g_swa = g.sigmoid().unbind(-1) + + cu_seqlens = kwargs.get('cu_seqlens') + + seqlen_offset, max_seqlen = 0, seq_len + if past_key_values is not None: + seqlen_offset = past_key_values.get_seq_length(self.layer_idx) + max_seqlen = q.shape[1] + seqlen_offset + + if attention_mask is not None: + # to deliminate the offsets of padding tokens + seqlen_offset = seqlen_offset + prepare_lens_from_mask(attention_mask) - attention_mask.shape[-1] + max_seqlen = q.shape[1] + max(seqlen_offset) + + if self.max_position_embeddings is not None: + max_seqlen = max(max_seqlen, self.max_position_embeddings) + q, k = self.rotary(q, k, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens) + + if past_key_values is not None: + cache_has_content = past_key_values.get_seq_length(self.layer_idx) > 0 + k_cached, v_cached = past_key_values.update( + attn_state=(k.flatten(-2, -1), v.flatten(-2, -1)), + layer_idx=self.layer_idx, + offset=seq_len, + cache_kwargs=dict(window_size=self.window_size), + )['attn_state'] + if cache_has_content: + k, v = k_cached, v_cached + k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim) + + o = parallel_nsa( + q=q, + k=k, + v=v, + g_cmp=g_cmp, + g_slc=g_slc, + g_swa=g_swa, + block_size=self.block_size, + block_counts=self.block_counts, + window_size=self.window_size, + cu_seqlens=cu_seqlens, + ) + o = o.reshape(batch_size, seq_len, -1) + o = self.o_proj(o) + + if not output_attentions: + attentions = None + + return o, attentions, past_key_values diff --git a/code/flash-linear-attention/fla/layers/path_attn.py b/code/flash-linear-attention/fla/layers/path_attn.py new file mode 100644 index 0000000000000000000000000000000000000000..19466356d64868d6f65c0ec03d360404fe18127f --- /dev/null +++ b/code/flash-linear-attention/fla/layers/path_attn.py @@ -0,0 +1,216 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from transformers.utils import logging + +from fla.layers.utils import pad_input, unpad_input +from fla.modules import RMSNorm, ShortConvolution +from fla.modules.l2norm import l2_norm +from fla.ops.attn.decoding import attn_decoding_one_step +from fla.ops.path_attn.parallel import parallel_path_attn + +if TYPE_CHECKING: + from fla.models.utils import Cache + +logger = logging.get_logger(__name__) + + +class PaTHAttention(nn.Module): + def __init__( + self, + hidden_size: int = 2048, + num_heads: int = 32, + num_kv_heads: int | None = None, + use_forget_gate: bool = False, + use_qk_norm: bool = False, + layer_idx: int = None, + use_low_rank_w: bool = True, + use_w_shortconv: bool = True, + conv_size: int = 3, + conv_bias: bool = False, + ): + super().__init__() + + self.hidden_size = hidden_size + self.num_heads = num_heads + if num_kv_heads is None: + self.num_kv_heads = self.num_heads + else: + self.num_kv_heads = num_kv_heads + self.head_dim = self.hidden_size // self.num_heads + self.kv_dim = self.num_kv_heads * self.head_dim + + self.layer_idx = layer_idx + + self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False) + + # We use low-rank parameterization for the w_proj to reduce parameters in MHA settings. + if use_low_rank_w: + self.w_proj = nn.Sequential( + nn.Linear(self.hidden_size, 32, bias=False), + nn.Linear(32, self.kv_dim, bias=False), + ) + # In MQA/GQA settings, key/value heads are shared, so we use a standard linear projection + # which doesn't introduce too many parameters + else: + self.w_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False) + + # per head norm + if use_qk_norm: + self.maybe_q_norm = RMSNorm(self.head_dim) + self.maybe_k_norm = RMSNorm(self.head_dim) + else: + self.maybe_q_norm = nn.Identity() + self.maybe_k_norm = nn.Identity() + + if use_w_shortconv: + self.w_conv1d = ShortConvolution(hidden_size=self.kv_dim, kernel_size=conv_size, bias=conv_bias, activation='silu') + self.use_w_shortconv = use_w_shortconv + self.bt_proj = nn.Linear(self.hidden_size, self.num_kv_heads, bias=True) + self.use_forget_gate = use_forget_gate + if use_forget_gate: + self.g_proj = nn.Linear(self.hidden_size, self.num_heads, bias=True) + self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + if use_cache: + assert past_key_values is not None, "past_key_values must be provided when use_cache is True" + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + batch_size, q_len, _ = hidden_states.size() + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + w = self.w_proj(hidden_states) + beta = self.bt_proj(hidden_states).float().sigmoid() * 2 # allowing negative eigenvalues + g = F.logsigmoid(self.g_proj(hidden_states).float()) if self.use_forget_gate else None + cu_seqlens = kwargs.get('cu_seqlens') + assert not (cu_seqlens is not None and attention_mask is not None), ( + "cu_seqlens should not be provided when attention_mask is not None" + ) + # Training + if attention_mask is None: + assert use_cache is False, "use_cache should be False in training" + if self.use_w_shortconv: + w, _ = self.w_conv1d(w, cache=None, output_final_state=False, cu_seqlens=cu_seqlens) + q = rearrange(q, '... (h d) -> ... h d', d=self.head_dim) + k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim) + w = rearrange(w, '... (h d) -> ... h d', d=self.head_dim) + q, k = self.maybe_q_norm(q), self.maybe_k_norm(k) + w = l2_norm(w, output_dtype=torch.float32) + o, _ = parallel_path_attn(q=q, k=k, v=v, w=w, beta=beta, g=g, cu_seqlens=cu_seqlens) + + # Prefilling or decoding + else: + assert self.training is False, "attention mask is not supported in training. Please use variable length input." + try: + last_state = past_key_values[self.layer_idx] + except KeyError: + last_state = None + # Decoding + if last_state is not None: + if g is not None: + past_k, past_v, past_g = last_state['attn_state'] + else: + past_k, past_v = last_state['attn_state'] + past_g = None + w_conv_state = last_state['conv_state'] + past_k = rearrange(past_k, '... (h d) -> ... h d', d=self.head_dim) + if self.use_w_shortconv: + w, w_conv_state = self.w_conv1d(w, cache=w_conv_state, output_final_state=use_cache, cu_seqlens=cu_seqlens) + w = rearrange(w, '... (h d) -> ... h d', d=self.head_dim) + w = l2_norm(w, output_dtype=torch.float32) + + @torch.compile + def rank_one_update(k, w, beta): + original_dtype = k.dtype + k = k.float() + w = w.float() + beta = beta.float() + k = k - beta[..., None].float() * (k * w).sum(-1, keepdim=True) * w + return k.to(original_dtype) + + past_k = rank_one_update(past_k, w, beta) + past_k = rearrange(past_k, '... h d -> ... (h d)') + k = torch.cat([past_k, k], dim=1) + v = torch.cat([past_v, v], dim=1) + g = torch.cat([past_g, g], dim=1) if g is not None else None + past_key_values[self.layer_idx]['attn_state'] = (k, v, g) if g is not None else (k, v) + past_key_values.update( + conv_state=w_conv_state, + layer_idx=self.layer_idx, + offset=q_len, + ) + if g is not None: + q, (k, v, g), indices_q, cu_seqlens, max_seq_lens = unpad_input( + q, (k, v, g), attention_mask, q_len, keepdim=True) + max_seqlen_q, max_seqlen_k = max_seq_lens + else: + q, (k, v), indices_q, cu_seqlens, max_seq_lens = unpad_input( + q, (k, v), attention_mask, q_len, keepdim=True) + max_seqlen_q, max_seqlen_k = max_seq_lens + _, cu_seqlens = cu_seqlens + q = rearrange(q, '... (h d) -> ... h d', d=self.head_dim) + k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim) + assert max_seqlen_q == 1, "only support q_len == 1 for decoding" + o = attn_decoding_one_step(q, k, v, g, cu_seqlens=cu_seqlens, do_gate_scale=True) # reduced to fox's decoding + # Prefilling + else: + v_cache = v.clone() + g_cache = g.clone() if g is not None else None + if g is None: + q, (k, v, w, beta), indices_q, cu_seqlens, max_seq_lens = unpad_input( + q, (k, v, w, beta), attention_mask, q_len, keepdim=True) + else: + q, (k, v, w, beta, g), indices_q, cu_seqlens, max_seq_lens = unpad_input( + q, (k, v, w, beta, g), attention_mask, q_len, keepdim=True) + max_seqlen_q, max_seqlen_k = max_seq_lens + assert max_seqlen_q == max_seqlen_k, "max_seqlen_q should be equal to max_seqlen_k in prefilling" + _, cu_seqlens = cu_seqlens + if self.use_w_shortconv: + w, w_conv_state = self.w_conv1d(w, cache=None, output_final_state=use_cache, cu_seqlens=cu_seqlens) + else: + w_conv_state = None + q = rearrange(q, '... (h d) -> ... h d', d=self.head_dim) + k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim) + w = rearrange(w, '... (h d) -> ... h d', d=self.head_dim) + w = l2_norm(w, output_dtype=torch.float32) + o, k_cache = parallel_path_attn(q=q, k=k, v=v, w=w, beta=beta, g=g, + cu_seqlens=cu_seqlens, use_cache=use_cache) + if use_cache: + k_cache = pad_input(k_cache.squeeze(0), indices_q, batch_size, q_len) + k_cache = rearrange(k_cache, '... h d -> ... (h d)') + past_key_values.update( + attn_state=(k_cache, v_cache, g_cache) if g_cache is not None else (k_cache, v_cache), + conv_state=w_conv_state, + layer_idx=self.layer_idx, + offset=q_len, + ) + o = pad_input(o.squeeze(0), indices_q, batch_size, q_len) + o = rearrange(o, '... h d -> ... (h d)') + o = self.o_proj(o) + return o, None, past_key_values diff --git a/code/flash-linear-attention/fla/layers/rebased.py b/code/flash-linear-attention/fla/layers/rebased.py new file mode 100644 index 0000000000000000000000000000000000000000..716d9b947d708533e17a486c45d8165dcc7f6a2c --- /dev/null +++ b/code/flash-linear-attention/fla/layers/rebased.py @@ -0,0 +1,127 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +""" +https://github.com/corl-team/rebased/blob/main/flash_linear_attention/fla/layers/rebased_fast.py +""" + +from __future__ import annotations + +import torch +import torch.nn as nn +from einops import rearrange + +from fla.modules.feature_map import RebasedFeatureMap +from fla.ops.linear_attn import chunk_linear_attn, fused_chunk_linear_attn +from fla.ops.rebased import parallel_rebased + + +class ReBasedLinearAttention(nn.Module): + + def __init__( + self, + hidden_size: int, + l_max: int = 2048, + feature_dim: int = 16, + num_key_value_heads: int = 16, + num_heads: int = 16, + use_gamma: bool | None = True, + use_beta: bool | None = True, + normalize: bool | None = True, + causal: bool = True, + eps: float = 1e-5, + mode: str = "parallel", + layer_idx: int | None = None, + **kwargs, + ) -> ReBasedLinearAttention: + super().__init__() + self.hidden_size = hidden_size + self.l_max = l_max + self.mode = mode + assert self.mode in ["fused_chunk", "parallel", 'chunk'] + + self.feature_dim = feature_dim + self.num_key_value_heads = num_key_value_heads + self.num_heads = num_heads + self.head_dim = self.hidden_size // self.num_key_value_heads + self.use_gamma = use_gamma + self.use_beta = use_beta + self.normalize = normalize + self.causal = causal + self.eps = eps + self.mode = mode + self.layer_idx = layer_idx + + self.feature_map = RebasedFeatureMap(self.feature_dim, use_gamma, use_beta, normalize) + self.q_proj = nn.Linear(self.hidden_size, self.feature_dim * self.num_heads, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.feature_dim * self.num_heads, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self.dropout = nn.Identity() + + def forward(self, hidden_states: torch.Tensor, **kwargs): + mode = self.mode + q, k, v = self.q_proj(hidden_states), self.k_proj(hidden_states), self.v_proj(hidden_states) + q, k, v = map(lambda x: rearrange(x, "... (h d) -> ... h d", d=self.head_dim), [q, k, v]) + q, k = self.feature_map(q, flatten=(mode != 'parallel')), self.feature_map(k, flatten=(mode != 'parallel')) + if mode == "fused_chunk": + o = fused_chunk_linear_attn( + q=q, + k=k, + v=v, + normalize=True, + scale=1, + ) + elif mode == 'chunk': + o = chunk_linear_attn( + q=q, + k=k, + v=v, + normalize=True, + scale=1, + ) + elif mode == 'parallel': + assert q.shape[-1] <= 128 + o = parallel_rebased( + q=q, + k=k, + v=v, + eps=self.eps, + use_scale=True, + use_normalize=True, + ) + o = self.o_proj(o) + o = self.dropout(o) + return o + + # https://github.com/HazyResearch/zoology/blob/main/zoology/mixers/based.py#L119 + def forward_reference( + self, + hidden_states: torch.Tensor, + filters: torch.Tensor = None, + *args, + **kwargs, + ): + """ + x (torch.Tensor): tensor of shape (b, d, t) + y (torch.Tensor): tensor of shape (b, d, t) + """ + b, t, _ = hidden_states.size() + q, k, v = self.q_proj(hidden_states), self.k_proj(hidden_states), self.v_proj(hidden_states) + + q = q.view(b, t, -1, self.feature_dim).transpose(1, 2) + k = k.view(b, t, -1, self.feature_dim).transpose(1, 2) + v = v.view(b, t, -1, self.head_dim).transpose(1, 2) + + # Linear attention + q, k = self.feature_map(q), self.feature_map(k) + q, k, v = q.unsqueeze(-2), k.unsqueeze(-2), v.unsqueeze(-1) + + # Compute attention + if self.causal: + y = ((q * (k * v).cumsum(2)).sum(-1) / ((q * k.cumsum(2)).sum(-1) + self.eps)) + else: + y = ((q * (k * v).sum(2, True)).sum(-1) / ((q * k.sum(2, True)).sum(-1) + self.eps)) + y = rearrange(y, 'b h t d -> b t (h d)') + y = self.o_proj(y.to(hidden_states.dtype)) + y = self.dropout(y) + return y.to(hidden_states.dtype) diff --git a/code/flash-linear-attention/fla/layers/rodimus.py b/code/flash-linear-attention/fla/layers/rodimus.py new file mode 100644 index 0000000000000000000000000000000000000000..884157f637dc1dc2efc55a72e558e105a5c4c1a8 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/rodimus.py @@ -0,0 +1,389 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint +from einops import rearrange, repeat +from transformers.utils import logging + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input, unpad_input +from fla.modules import RMSNorm, RotaryEmbedding, ShortConvolution +from fla.modules.layernorm_gated import RMSNormGated +from fla.ops.gla import chunk_gla, fused_chunk_gla, fused_recurrent_gla + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + +try: + from flash_attn import flash_attn_func, flash_attn_varlen_func +except ImportError: + warnings.warn( + "Flash Attention is not installed. Please install it via `pip install flash-attn --no-build-isolation`", + category=ImportWarning, + ) + flash_attn_func = None + +logger = logging.get_logger(__name__) + + +def align_multiple(value, multiple_size=8): + if value % multiple_size != 0: + value += multiple_size - (value % multiple_size) + return value + + +def autocast_to_fp16(x): + if x.dtype not in {torch.float16, torch.bfloat16}: + return x.to(dtype=torch.bfloat16) + else: + return x + + +class RodimusAttention(nn.Module): + def __init__( + self, + block_type: str = 'rodimus', + mode: str = 'chunk', + hidden_size: int = 1024, + input_gate_low_rank: float | str | None = 'auto', + expand_ratio: int = 64, + use_short_conv: bool = True, + conv_size: int = 4, + conv_bias: bool = True, + norm_eps: float = 1e-5, + k_norm_eps: float | None = None, + residual_in_fp32: bool = True, + layer_idx: int = None, + ): + super().__init__() + + self.block_type = block_type + self.mode = mode + self.hidden_size = hidden_size + self.d_inner = align_multiple(int(self.hidden_size * 2), 8) + + self.expand_ratio = expand_ratio + self.input_gate_low_rank = max(self.hidden_size // 64, 16) if input_gate_low_rank == "auto" else input_gate_low_rank + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.norm_eps = norm_eps + self.k_norm_eps = k_norm_eps if k_norm_eps is not None else 1e-12 + self.mem_size = expand_ratio + + self.residual_in_fp32 = residual_in_fp32 + self.layer_idx = layer_idx + + assert mode in ['chunk', 'fused_recurrent', 'fused_chunk'], f"Not supported mode `{mode}`." + + self.gate_proj = nn.Linear(self.hidden_size, self.d_inner, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.d_inner, bias=False) + self.activation_norm = RMSNormGated(hidden_size=self.d_inner, eps=norm_eps, norm_before_gate=False) + self.down_proj = nn.Linear(self.d_inner, self.hidden_size, bias=False) + + if use_short_conv: + self.short_conv = ShortConvolution( + hidden_size=self.d_inner, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + + self.residual_weight = nn.Parameter(torch.ones( + (self.d_inner, ), dtype=torch.float32 if self.residual_in_fp32 else None), requires_grad=True) + + self.k_proj = nn.Linear(self.d_inner, self.mem_size, bias=False) + self.q_proj = nn.Linear(self.d_inner, self.mem_size, bias=False) + + self.g_gate_proj = nn.Linear(self.d_inner, self.mem_size, bias=True) + self.tau_gate_proj = nn.Linear(self.d_inner, self.mem_size, bias=True) + self.i_gate_proj = nn.Sequential( + nn.Linear(self.d_inner, self.input_gate_low_rank, bias=False), + nn.Linear(self.input_gate_low_rank, self.d_inner, bias=True), + nn.Sigmoid(), + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + # mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode + mode = 'fused_recurrent' if hidden_states.shape[1] == 1 else self.mode + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + hidden_states, final_gate = self.up_proj(hidden_states), self.gate_proj(hidden_states) + + if self.use_short_conv: + conv_state = None + if last_state is not None: + conv_state = last_state['conv_state'] + shift_hidden_states, conv_state = self.short_conv( + x=hidden_states, + cache=conv_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + shift_hidden_states = hidden_states + + q = self.q_proj(shift_hidden_states) + k = self.k_proj(shift_hidden_states) + v = self.i_gate_proj(hidden_states) * hidden_states + + g_gate = F.linear(shift_hidden_states, self.g_gate_proj.weight) + self.g_gate_proj.bias.float() + tau_gate = F.linear(shift_hidden_states, self.tau_gate_proj.weight) + self.tau_gate_proj.bias.float() + + g_gate = F.softplus(g_gate) + it_gate = g_gate + rt_gate_log = -g_gate + + tau_gate = F.sigmoid(tau_gate) + it_gate = it_gate ** tau_gate + rt_gate_log = rt_gate_log * tau_gate + + k = F.normalize(k.float(), dim=-1, eps=self.k_norm_eps) * it_gate + q, k, v, rt_gate_log = map(lambda x: x.unsqueeze(1).transpose(1, 2), (q, k, v, rt_gate_log)) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_gla( + q=q, + k=k, + v=v, + gk=rt_gate_log, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + head_first=False, + ) + elif mode == 'fused_chunk': + o, recurrent_state = fused_chunk_gla( + q=q, + k=k, + v=v, + g=rt_gate_log, + initial_state=recurrent_state, + output_final_state=use_cache, + head_first=False, + ) + elif mode == 'chunk': + q, k, rt_gate_log = map(lambda x: x.to(v.dtype), (q, k, rt_gate_log)) + o, recurrent_state = chunk_gla( + q=q, + k=k, + v=v, + g=rt_gate_log, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + head_first=False, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + rodimus_caches = None + if past_key_values is not None: + if self.block_type == 'rodimus': + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=conv_state if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + else: + rodimus_caches = (recurrent_state, conv_state if self.use_short_conv else None) + + o = (o.transpose(1, 2).squeeze(1) + (shift_hidden_states.float() + if self.residual_in_fp32 else shift_hidden_states) * self.residual_weight).to(o.dtype) + + o = self.activation_norm(o, final_gate) + o = self.down_proj(o) + + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + if self.block_type == 'rodimus': + return o, None, past_key_values + else: + return o, None, (past_key_values, rodimus_caches) + + +class SlidingWindowSharedKeyAttention(nn.Module): + def __init__( + self, + hidden_size: int = 2048, + num_heads: int = 32, + qkv_bias: bool = False, + qk_norm: bool = False, + window_size: int = 2048, + rope_theta: float | None = 10000., + max_position_embeddings: int | None = None, + layer_idx: int = None, + ): + super().__init__() + + self.hidden_size = hidden_size + self.num_heads = num_heads + self.head_dim = self.hidden_size // self.num_heads + self.qkv_bias = qkv_bias + self.qk_norm = qk_norm + + self.window_size = window_size + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + self.layer_idx = layer_idx + + self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=self.qkv_bias) + self.k_proj = nn.Linear(self.hidden_size, self.head_dim, bias=self.qkv_bias) + self.v_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=self.qkv_bias) + self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + + if qk_norm: + self.q_norm = RMSNorm(self.head_dim) + self.k_norm = RMSNorm(self.head_dim) + + self.rotary = RotaryEmbedding(dim=self.head_dim, base=self.rope_theta) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + rodimus_caches = kwargs.get('rodimus_caches') + + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.size() + + q = rearrange(self.q_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + k = rearrange(self.k_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(self.v_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) + + if self.qk_norm: + q, k = self.q_norm(q), self.k_norm(k) + + # equivalent to cu_seqlens in `flash_attn` + cu_seqlens = kwargs.get('cu_seqlens') + + seqlen_offset, max_seqlen = 0, q.shape[1] + if past_key_values is not None: + seqlen_offset = past_key_values.get_seq_length(self.layer_idx) + max_seqlen = q.shape[1] + seqlen_offset + + if attention_mask is not None: + # to deliminate the offsets of padding tokens + seqlen_offset = seqlen_offset + attention_mask.sum(-1) - attention_mask.shape[-1] + max_seqlen = q.shape[1] + max(seqlen_offset) + + if self.max_position_embeddings is not None: + max_seqlen = max(max_seqlen, self.max_position_embeddings) + q, k = self.rotary(q, k, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens) + + if past_key_values is not None: + if rodimus_caches is not None: + recurrent_state, conv_state = rodimus_caches + else: + recurrent_state, conv_state = None, None + + cache_has_content = past_key_values.get_seq_length(self.layer_idx) > 0 + k_cached, v_cached = past_key_values.update( + recurrent_state=recurrent_state, + conv_state=conv_state, + attn_state=[k.flatten(-2, -1), v.flatten(-2, -1)], + layer_idx=self.layer_idx, + offset=q_len, + cache_kwargs=dict(window_size=self.window_size), + )['attn_state'] + if cache_has_content: + k, v = k_cached, v_cached + k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim) + + if flash_attn_func is None: + raise ImportError("Please install Flash Attention via `pip install flash-attn --no-build-isolation` first") + + q, k, v = map(autocast_to_fp16, (q, k, v)) + k = repeat(k, "... h d -> ... (n h) d", n=self.num_heads) + # Contains at least one padding token in the sequence + if attention_mask is not None: + q, (k, v), indices_q, cu_seqlens, max_seq_lens = unpad_input( + q=q, + states=(k, v), + attention_mask=attention_mask[:, -max(self.window_size, q_len):], + q_len=q_len, + ) + cu_seqlens_q, cu_seqlens_k = cu_seqlens + max_seqlen_q, max_seqlen_k = max_seq_lens + o = flash_attn_varlen_func( + q, k, v, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ) + o = pad_input(o, indices_q, batch_size, q_len) + elif cu_seqlens is not None: + o = flash_attn_varlen_func( + q.squeeze(0), k.squeeze(0), v.squeeze(0), + cu_seqlens_q=cu_seqlens, + cu_seqlens_k=cu_seqlens, + max_seqlen_q=max_seqlen, + max_seqlen_k=max_seqlen, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ).unsqueeze(0) + else: + o = flash_attn_func( + q, k, v, + causal=True, + window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0), + ) + o = o.reshape(batch_size, q_len, -1) + o = self.o_proj(o.to(dtype=self.o_proj.weight.dtype)) + + if not output_attentions: + attentions = None + + return o, attentions, past_key_values diff --git a/code/flash-linear-attention/fla/layers/rwkv6.py b/code/flash-linear-attention/fla/layers/rwkv6.py new file mode 100644 index 0000000000000000000000000000000000000000..c00901b4edacde4a2c3de5acf27b880cc2bf3d6d --- /dev/null +++ b/code/flash-linear-attention/fla/layers/rwkv6.py @@ -0,0 +1,361 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +# "Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence"[https://arxiv.org/abs/2404.05892] + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange + +from fla.modules import GroupNorm +from fla.modules.activations import ACT2FN +from fla.modules.token_shift import token_shift +from fla.ops.rwkv6 import chunk_rwkv6, fused_recurrent_rwkv6 + +if TYPE_CHECKING: + from fla.models.utils import Cache + + +class RWKV6Attention(nn.Module): + + def __init__( + self, + mode: str = 'chunk', + hidden_size: int = 1024, + expand_k: float = 0.5, + expand_v: float = 1.0, + num_heads: int = 4, + gate_fn: str = 'swish', + proj_low_rank_dim: int = 32, + gate_low_rank_dim: int = 64, + fuse_norm: bool = True, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-5, + layer_idx: int = None, + **kwargs, + ) -> RWKV6Attention: + super().__init__() + + self.mode = mode + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.num_heads = num_heads + self.proj_low_rank_dim = proj_low_rank_dim + self.gate_low_rank_dim = gate_low_rank_dim + + self.key_dim = int(hidden_size * expand_k) + self.value_dim = int(hidden_size * expand_v) + self.layer_idx = layer_idx + + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}" + assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}" + + self.head_k_dim = self.key_dim // num_heads + self.head_v_dim = self.value_dim // num_heads + + self.time_shift = nn.ZeroPad2d((0, 0, 1, -1)) + self.x_proj = nn.Sequential( + LerpLinear(hidden_size, proj_low_rank_dim * 5), + nn.Tanh(), + nn.Linear(proj_low_rank_dim * 5, hidden_size, bias=False), + ) + self.x_bias = nn.Parameter(torch.zeros(5, hidden_size)) + + self.r_proj = DDLerpLinear(hidden_size, self.key_dim) + self.w_proj = DDLerpLinear(hidden_size, self.key_dim, low_rank_dim=gate_low_rank_dim) + self.k_proj = DDLerpLinear(hidden_size, self.key_dim) + self.v_proj = DDLerpLinear(hidden_size, self.value_dim) + self.g_proj = DDLerpLinear(hidden_size, self.value_dim) + self.bonus = nn.Parameter(torch.zeros(num_heads, self.head_k_dim)) + + # TODO: fuse GroupNorm and output gate + self.g_norm = GroupNorm(self.num_heads, self.value_dim, elementwise_affine=elementwise_affine, bias=True, eps=norm_eps) + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + self.gate_fn = ACT2FN[gate_fn] + + try: + from transformers.modeling_utils import _init_weights + except ImportError: + _init_weights = True + if _init_weights: + self.apply(self._initialize_weights) + + warnings.warn( + "According to Bo, you are using a potentially buggy FLA implementation of RWKV. " + "If you plan to report any numbers based on this implementation, we strongly recommend " + "cross-checking with the official repo: https://github.com/BlinkDL/RWKV-LM. " + "Bo may disagree with results reported from this version.", + ) + + def _initialize_weights(self, module: nn.Module): + if getattr(module, "_is_hf_initialized", False): + return + if isinstance(module, nn.Linear): + nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5) + if module.bias is not None: + nn.init.zeros_(module.bias) + if isinstance(module, nn.Parameter): + nn.init.xavier_uniform_(module, gain=2 ** -2.5) + module._is_hf_initialized = True + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + cu_seqlens: torch.LongTensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, seq_len, hidden_size = hidden_states.shape + # launching the triton kernel for just one token will actually be slower + mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + if attention_mask is not None: + hidden_states = hidden_states.mul_(attention_mask[:, -hidden_states.shape[-2]:, None]) + + if hidden_states.shape[1] == 1 and last_state is not None: + shifted = last_state['conv_state'].unsqueeze(1) + delta = shifted - hidden_states + elif last_state is None: + delta = token_shift(hidden_states, cu_seqlens) + else: + shifted = self.time_shift(hidden_states) + shifted[:, 0] = last_state['conv_state'] + delta = shifted - hidden_states + + x = self.x_proj[0](hidden_states, delta, cu_seqlens).view(batch_size, seq_len, -1, self.proj_low_rank_dim) + x = torch.einsum('b t n r, h n r-> b t n h', self.x_proj[1](x), self.x_proj[2].weight.view(hidden_size, 5, -1)) + + r, w, k, v, g = x.add_(self.x_bias).unbind(-2) + r = self.r_proj(hidden_states, r, delta, cu_seqlens) + w = self.w_proj(hidden_states, w, delta, cu_seqlens) + k = self.k_proj(hidden_states, k, delta, cu_seqlens) + v = self.v_proj(hidden_states, v, delta, cu_seqlens) + g = self.g_proj(hidden_states, g, delta, cu_seqlens) + + # dealing with left-padding + if attention_mask is not None: + v = v.mul_(attention_mask[:, -v.shape[-2]:, None]) + r, w, k = map(lambda x: rearrange(x, 'b t (h d) -> b t h d', d=self.head_k_dim), (r, w, k)) + v = rearrange(v, 'b t (h d) -> b t h d', d=self.head_v_dim) + w = -torch.exp(w) + u = self.bonus + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + + if mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_rwkv6( + r=r, + k=k, + v=v, + w=w, + u=u, + scale=1., + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + elif mode == 'chunk': + o, recurrent_state = chunk_rwkv6( + r=r, + k=k, + v=v, + w=w, + u=u, + scale=1., + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=hidden_states[:, -1], + layer_idx=self.layer_idx, + offset=r.shape[2], + ) + + o = self.g_norm(rearrange(o, '... h d -> ... (h d)')) * self.gate_fn(g) + o = self.o_proj(o) + + return o, None, past_key_values + + +class LoRA(nn.Module): + + def __init__( + self, + input_dim: int, + output_dim: int, + low_rank_dim: int, + bias: bool | None = True, + activation: str | None = 'tanh', + ): + super().__init__() + + self.input_dim = input_dim + self.output_dim = output_dim + self.low_rank_dim = low_rank_dim + self.bias = bias + + if activation is None: + self.activation = nn.Identity() + elif activation == 'sigmoid': + self.activation = nn.Sigmoid() + elif activation == 'tanh': + self.activation = nn.Tanh() + elif activation == 'relu': + self.activation = nn.ReLU() + else: + raise ValueError(f"Not supported activation `{activation}`.") + + self.lora = nn.Sequential( + nn.Linear(input_dim, low_rank_dim, bias=False), + self.activation, + nn.Linear(low_rank_dim, output_dim, bias=bias), + ) + try: + from transformers.modeling_utils import _init_weights + except ImportError: + _init_weights = True + if _init_weights: + self.apply(self._initialize_weights) + + def __repr__(self) -> str: + s = f"{self.__class__.__name__}(" + s += f"input_dim={self.input_dim}, low_rank_dim={self.low_rank_dim}, output_dim={self.output_dim}" + if not self.bias: + s += f", bias={self.bias}" + s += ")" + return s + + def _initialize_weights(self, module: nn.Module): + if getattr(module, "_is_hf_initialized", False): + return + + # Initialize weights to zero as in original code + nn.init.zeros_(self.lora[0].weight) + original_dtype = self.lora[2].weight.dtype + shape = self.lora[2].weight.shape + # Convert to float32 for numerical stability in orthogonal init + weight_fp32 = self.lora[2].weight.float() + + # Calculate gain based on dimensions + gain = math.sqrt(shape[1] / shape[0]) if shape[1] > shape[0] else 1 + + # Apply orthogonal initialization with scaling factor 0.1 + nn.init.orthogonal_(weight_fp32, gain=gain * 0.1) + + # Convert back to original dtype + self.lora[2].weight.data.copy_(weight_fp32.to(original_dtype)) + # Set Lora[2] bias to zero + if self.lora[2].bias is not None: + nn.init.zeros_(self.lora[2].bias) + + module._is_hf_initialized = True + + def set_bias_value(self, value): + """Set bias to a specific value (for v0, w0 etc.)""" + if self.bias and self.lora[2].bias is not None: + if isinstance(value, torch.Tensor): + # Handle tensor values + self.lora[2].bias.data.copy_(value.to(self.lora[2].bias.dtype)) + else: + # Handle scalar values + nn.init.constant_(self.lora[2].bias, value) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.lora(x) + + +class LerpLinear(nn.Module): + + def __init__( + self, + input_dim: int, + output_dim: int, + low_rank_dim: int | None = None, + ): + super().__init__() + + self.input_dim = input_dim + self.output_dim = output_dim + self.low_rank_dim = low_rank_dim + + self.time_shift = nn.ZeroPad2d((0, 0, 1, -1)) + if low_rank_dim is None: + self.linear = nn.Linear(input_dim, output_dim, bias=False) + else: + self.linear = LoRA(input_dim, output_dim, low_rank_dim) + self.mu = nn.Parameter(torch.zeros(input_dim)) + + def __repr__(self) -> str: + s = f"{self.__class__.__name__}({self.input_dim}, {self.output_dim}" + if self.low_rank_dim is not None: + s += f", low_rank_dim={self.low_rank_dim}" + s += ")" + return s + + def forward(self, x: torch.Tensor, delta: torch.Tensor | None = None, + cu_seqlens: torch.LongTensor | None = None) -> torch.Tensor: + if delta is None: + delta = token_shift(x, cu_seqlens) + return self.linear(x + delta * self.mu) + + +class DDLerpLinear(nn.Module): + + def __init__( + self, + input_dim: int, + output_dim: int, + low_rank_dim: int | None = None, + ): + super().__init__() + + self.input_dim = input_dim + self.output_dim = output_dim + self.low_rank_dim = low_rank_dim + + self.time_shift = nn.ZeroPad2d((0, 0, 1, -1)) + if low_rank_dim is None: + self.linear = nn.Linear(input_dim, output_dim, bias=False) + else: + self.linear = LoRA(input_dim, output_dim, low_rank_dim) + + def __repr__(self) -> str: + s = f"{self.__class__.__name__}({self.input_dim}, {self.output_dim}" + if self.low_rank_dim is not None: + s += f", low_rank_dim={self.low_rank_dim}" + s += ")" + return s + + def forward(self, x: torch.Tensor, mu: torch.Tensor, + delta: torch.Tensor | None = None, + cu_seqlens: torch.LongTensor | None = None) -> torch.Tensor: + if delta is None: + delta = token_shift(x, cu_seqlens) + return self.linear(x + delta * mu) diff --git a/code/flash-linear-attention/fla/layers/rwkv7.py b/code/flash-linear-attention/fla/layers/rwkv7.py new file mode 100644 index 0000000000000000000000000000000000000000..8d32ecb3dd26a37056bd3d1410394af3b8c10fb6 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/rwkv7.py @@ -0,0 +1,346 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange +from torch.nn import functional as F + +from fla.layers.rwkv6 import LoRA +from fla.modules import GroupNorm +from fla.modules.l2norm import l2_norm +from fla.modules.token_shift import token_shift +from fla.ops.rwkv7 import chunk_rwkv7, fused_mul_recurrent_rwkv7 +from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7 +from fla.ops.rwkv7.fused_k_update import fused_k_rwkv7 +from fla.ops.rwkv7.gate_output_correction import gate_output_correction + +if TYPE_CHECKING: + from fla.models.utils import Cache + + +class RWKV7Attention(nn.Module): + + def __init__( + self, + mode: str = 'chunk', + hidden_size: int = 1024, + head_dim: int | None = 64, + num_heads: int | None = None, + decay_low_rank_dim: int | None = None, + gate_low_rank_dim: int | None = None, + a_low_rank_dim: int | None = None, + v_low_rank_dim: int | None = None, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-5, + layer_idx: int = None, + fuse_norm: bool = False, + value_dim: int = None, + num_hidden_layers: int = None, + **kwargs, + ) -> RWKV7Attention: + super().__init__() + + self.mode = mode + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + self.hidden_size = hidden_size + + self.key_dim = hidden_size + self.value_dim = value_dim if value_dim is not None else hidden_size + if head_dim is None and num_heads is None: + raise ValueError("Either `head_dim` or `num_heads` must be specified.") + elif head_dim is not None: + self.head_dim = head_dim + self.num_heads = int(hidden_size // head_dim) + elif num_heads is not None: + self.head_dim = int(hidden_size // num_heads) + self.num_heads = num_heads + self.head_v_dim = int(self.value_dim // self.num_heads) + + # Increase lora dimension for headdim>64 + factor = self.head_dim / 64 + if decay_low_rank_dim is None: + decay_low_rank_dim = max(32, int(round((2.5 * (hidden_size**0.5)) * factor / 32) * 32)) + self.decay_low_rank_dim = decay_low_rank_dim + else: + self.decay_low_rank_dim = decay_low_rank_dim + + if gate_low_rank_dim is None: + gate_low_rank_dim = max(32, int(round((5 * (hidden_size**0.5)) / 32) * 32)) + self.gate_low_rank_dim = gate_low_rank_dim + else: + self.gate_low_rank_dim = gate_low_rank_dim + + if a_low_rank_dim is None: + a_low_rank_dim = max(32, int(round((2.5 * (hidden_size**0.5)) * factor / 32) * 32)) + self.a_low_rank_dim = a_low_rank_dim + else: + self.a_low_rank_dim = a_low_rank_dim + + if v_low_rank_dim is None: + v_low_rank_dim = max(32, int(round((1.7 * (hidden_size**0.5)) * factor / 32) * 32)) + self.v_low_rank_dim = v_low_rank_dim + else: + self.v_low_rank_dim = v_low_rank_dim + + self.layer_idx = layer_idx + self.num_hidden_layers = num_hidden_layers + self.fuse_norm = fuse_norm + + self.time_shift = nn.ZeroPad2d((0, 0, 1, -1)) + self.x_r = nn.Parameter(torch.zeros(1, 1, hidden_size)) + self.x_w = nn.Parameter(torch.zeros(1, 1, hidden_size)) + self.x_k = nn.Parameter(torch.zeros(1, 1, hidden_size)) + self.x_v = nn.Parameter(torch.zeros(1, 1, hidden_size)) + self.x_a = nn.Parameter(torch.zeros(1, 1, hidden_size)) + self.x_g = nn.Parameter(torch.zeros(1, 1, hidden_size)) + + self.k_k = nn.Parameter(torch.zeros(self.key_dim)) + self.k_a = nn.Parameter(torch.zeros(self.key_dim)) + self.r_k = nn.Parameter(torch.zeros(self.num_heads, self.head_dim)) + + self.r_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + self.w_lora = LoRA(hidden_size, self.key_dim, low_rank_dim=decay_low_rank_dim, activation='tanh') + if self.layer_idx != 0: + self.v_lora = LoRA(hidden_size, self.value_dim, low_rank_dim=v_low_rank_dim, activation=None) + self.a_lora = LoRA(hidden_size, self.key_dim, low_rank_dim=a_low_rank_dim, activation=None) + self.g_lora = LoRA(hidden_size, self.value_dim, low_rank_dim=gate_low_rank_dim, activation='sigmoid', bias=False) + + if self.fuse_norm: + self.g_norm = GroupNorm( + num_groups=self.num_heads, + hidden_size=self.value_dim, + elementwise_affine=elementwise_affine, + eps=self.head_dim*norm_eps, + bias=True, + ) + else: + self.g_norm = nn.GroupNorm( + num_groups=self.num_heads, + num_channels=self.value_dim, + eps=self.head_dim*norm_eps, + affine=elementwise_affine, + ) + + try: + from transformers.modeling_utils import _init_weights + except ImportError: + _init_weights = True + if _init_weights: + self.apply(self._initialize_weights) + for name, module in self.named_modules(): + module._in_rwkv_module = True + + warnings.warn( + "According to Bo, you are using a potentially buggy FLA implementation of RWKV. " + "If you plan to report any numbers based on this implementation, we strongly recommend " + "cross-checking with the official repo: https://github.com/BlinkDL/RWKV-LM. " + "Bo may disagree with results reported from this version.", + ) + + @torch.no_grad() + @torch.compiler.disable + def _initialize_weights(self, module: nn.Module): + if getattr(module, "_is_hf_initialized", False): + return + + # Initialize only when we're processing the RWKV7Attention module itself + if isinstance(module, RWKV7Attention) and self.layer_idx is not None: + ratio_0_to_1 = self.layer_idx / (self.num_hidden_layers - 1) # 0 to 1 + ratio_1_to_almost0 = 1.0 - (self.layer_idx / self.num_hidden_layers) # 1 to ~0 + + # Create position-based initialization tensor + ddd = torch.ones(1, 1, self.hidden_size, device=self.x_r.device) + www = torch.zeros(self.hidden_size, device=self.x_r.device) + zigzag = torch.zeros(self.hidden_size, device=self.x_r.device) + linear = torch.zeros(self.hidden_size, device=self.x_r.device) + for n in range(self.hidden_size): + linear[n] = n / (self.hidden_size-1) - 0.5 + zigzag[n] = ((n % self.head_dim) - ((self.head_dim-1) / 2)) / ((self.head_dim-1) / 2) + zigzag[n] = zigzag[n] * abs(zigzag[n]) + www[n] = -6 + 6 * (n / (self.hidden_size - 1)) ** (1 + 1 * ratio_0_to_1 ** 0.3) + ddd[0, 0, n] = n / self.hidden_size + + # Initialize x_* parameters directly + self.x_r.data = (1.0 - torch.pow(ddd, 0.2 * ratio_1_to_almost0)).to(self.x_r.dtype) + self.x_w.data = (1.0 - torch.pow(ddd, 0.9 * ratio_1_to_almost0)).to(self.x_w.dtype) + self.x_k.data = (1.0 - torch.pow(ddd, 0.7 * ratio_1_to_almost0)).to(self.x_k.dtype) + self.x_v.data = (1.0 - torch.pow(ddd, 0.7 * ratio_1_to_almost0)).to(self.x_v.dtype) + self.x_a.data = (1.0 - torch.pow(ddd, 0.9 * ratio_1_to_almost0)).to(self.x_a.dtype) + self.x_g.data = (1.0 - torch.pow(ddd, 0.2 * ratio_1_to_almost0)).to(self.x_g.dtype) + + # Initialize k_k, k_a, r_k + nn.init.constant_(self.k_a, 1.02) + nn.init.constant_(self.r_k, -0.04) + self.k_k.data.copy_((torch.zeros(self.hidden_size, device=self.k_k.device) + + 0.71 - linear*0.1).to(self.k_k.dtype)) + # Set specific bias values for LoRA modules + # 0.5 comes from F.softplus + self.w_lora.set_bias_value(www + 0.5 + zigzag*2.5) + self.a_lora.set_bias_value(-0.19 + zigzag*0.3 + linear*0.4) + + # v0 initialization - ones (for non-first layers) + if self.layer_idx != 0: + self.v_lora._initialize_weights(self.v_lora) + self.v_lora.set_bias_value(0.73 - linear*0.4) + + # Initialize GroupNorm + self.g_norm.weight.data[:] = ((self.layer_idx + 1) / self.num_hidden_layers) ** 0.7 + + # Initialize Linear projections + self._orthogonal_init(self.r_proj.weight) + self._orthogonal_init(self.k_proj.weight, gain=0.1) + self._orthogonal_init(self.v_proj.weight) + self.o_proj.weight.data.zero_() + + # Clean up temporary tensors to free memory + del ddd, www, zigzag, linear + + module._is_hf_initialized = True + + @staticmethod + def _orthogonal_init(weight, gain=1.0): + oringinal_dtype = weight.dtype + weight = weight.float() + nn.init.orthogonal_(weight, gain=gain) + weight = weight.to(oringinal_dtype) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + v_first: torch.Tensor = None, + cu_seqlens: torch.LongTensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + batch_size, seq_len, _ = hidden_states.shape + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + am = attention_mask.narrow(1, attention_mask.size(1) - seq_len, seq_len).unsqueeze(-1) + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + if attention_mask is not None: + hidden_states = hidden_states.mul(am) + + # delta [batch_size, seq_len, hidden_size] + # conv_cache [N, D] + if last_state is None: + conv_cache = None + recurrent_state = None + else: + conv_cache = last_state['conv_state'] + recurrent_state = last_state['recurrent_state'] + + delta, conv_state = token_shift( + hidden_states, cu_seqlens, output_cache=True, cache=conv_cache, + ) + xr, xw, xk, xv, xa, xg = fused_addcmul_rwkv7(hidden_states, delta, self.x_r, self.x_w, + self.x_k, self.x_v, self.x_a, self.x_g) + + r = self.r_proj(xr) + # Using bf16 for LoRA computation is numerically safe here because: + # 1. After sigmoid activation: + # - Max absolute error (vs float32): 0.003 + # - Mean absolute error: 0.0004 + # 2. Subsequent scaling by -0.6065 will further reduce relative error + # (error scales linearly with constant multiplication) + # 3. Final compounded error remains within acceptable bounds for bf16 precision + # Empirical observation confirms bf16 introduces no practical degradation + w = -0.6065306597126334 * self.w_lora(xw).sigmoid() + + k = self.k_proj(xk) + v = self.v_proj(xv) + + if self.layer_idx == 0: + v_first = v + else: + v = torch.lerp(v, v_first, self.v_lora(xv).sigmoid()) + a = self.a_lora(xa).sigmoid() + g = self.g_lora(xg) + + if self.fuse_norm: + kk = l2_norm(rearrange(k * self.k_k, 'b t (h d) -> b t h d', d=self.head_dim)) + else: + kk = F.normalize(rearrange(k * self.k_k, 'b t (h d) -> b t h d', d=self.head_dim), dim=-1, p=2.0) + + # Prefer addcmul over expanded form for numerical stability in bf16: + # 1. Fused Multiply-Add (FMA) in addcmul reduces intermediate rounding: + # - Single op vs original 3 ops (mul, sub, mul) + # - 1 less intermediate value storage (bf16 write->read overhead) + # 2. Mathematically equivalent to k*(1 + (a-1)*self.k_a) + # but with better precision preservation + # 3. Particularly crucial for bf16 where intermediate values easily lose precision + # 4. Pytorch method: k = k.addcmul(k * (a - 1), self.k_a) + k = fused_k_rwkv7(k, a, self.k_a) + + # dealing with left-padding + if attention_mask is not None: + v = v * am + + r, w, k, a = map(lambda x: rearrange(x, 'b t (h d) -> b t h d', d=self.head_dim), (r, w, k, a)) + v = rearrange(v, 'b t (h d) -> b t h d', d=self.head_v_dim) + + if self.training or seq_len >= 64: + # if training, use chunk mode no matter how short the sequence is + # launching the triton kernel for just one token will actually be slower + o, recurrent_state = chunk_rwkv7( + r=r, + w=w, + k=k, + v=v, + a=-kk, + b=kk * a, + scale=1., + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + o, recurrent_state = fused_mul_recurrent_rwkv7( + r=r, + w=w, + k=k, + v=v, + kk=kk, + a=a, + scale=1., + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=conv_state, + layer_idx=self.layer_idx, + offset=r.shape[1], + ) + + if self.fuse_norm: + o = self.g_norm(rearrange(o, '... h d -> ... (h d)')) + else: + o = self.g_norm(rearrange(o, 'b t h d -> (b t) (h d)')).view(batch_size, seq_len, -1) + + o = gate_output_correction(o, r, k, self.r_k, v, g) + o = self.o_proj(o) + + return o, None, past_key_values, v_first diff --git a/code/flash-linear-attention/fla/layers/simple_gla.py b/code/flash-linear-attention/fla/layers/simple_gla.py new file mode 100644 index 0000000000000000000000000000000000000000..2a74e36c56766cc3c43ee7e9d83e26877cc2aad5 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/simple_gla.py @@ -0,0 +1,273 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat + +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.modules.activations import ACT2FN +from fla.ops.simple_gla import chunk_simple_gla, fused_recurrent_simple_gla + +if TYPE_CHECKING: + from fla.models.utils import Cache + + +class SimpleGatedLinearAttention(nn.Module): + r""" + The layer implementaion for [Gated Linear Attention Transformers with Hardware-Efficient Training](https://arxiv.org/abs/2312.06635). # noqa + This layer calls the simplified GLA kernel in which the gating is head-wise instead of elementwise. + + Args: + mode (str, Optional): + Which GLA kernel to use. + Currently available: `chunk`. + Default: `chunk`. + hidden_size (int, Optional): + The hidden size of the input. Default: 1024. + expand_k (float, Optional): + The expansion ratio for the key dim. Default: 1.0. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 1.0. + num_heads (int, Optional): + The number of heads. Default: 4. + num_kv_heads (int, Optional): + The number of key/value heads, used for MQA. Default: None. + feature_map (str, Optional): + Feature map function applied to queries/keys. Default: None. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `False`. + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + gate_fn (str, Optional): + The activation function for the output gate. Default: `swish`. + elementwise_affine (bool, Optional): + If `True`, applies elementwise affine to LayerNorm with learnable parameters. Default: `True`. + norm_eps (float, Optional): + The epsilon value for the layernorm/rmsnorm layer. Default: 1e-5. + gate_logit_normalizer (int, Optional): + The normalizer for the gate logits, appied after `logsigmoid`. Default: 16. + fuse_norm (bool, Optional): + Whether to fuse the norm and the output gate for better memory footprint. Default: `True`. + layer_idx (int, Optional): + The index of the layer. Default: None. + """ + + def __init__( + self, + mode: str = 'chunk', + hidden_size: int = 1024, + expand_k: float = 1., + expand_v: float = 1., + num_heads: int = 4, + num_kv_heads: int | None = None, + feature_map: str | None = None, + use_short_conv: bool = True, + conv_size: int = 4, + conv_bias: bool = False, + gate_fn: str = 'swish', + elementwise_affine: bool | None = True, + norm_eps: float = 1e-5, + gate_logit_normalizer: int = 16, + fuse_norm: bool = True, + layer_idx: int = None, + ) -> SimpleGatedLinearAttention: + super().__init__() + + self.mode = mode + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads + self.num_kv_groups = self.num_heads // self.num_kv_heads + self.feature_map_fn = ACT2FN[feature_map] if feature_map is not None else None + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.key_dim = int(hidden_size * expand_k) + self.value_dim = int(hidden_size * expand_v) + self.key_dim_per_group = self.key_dim // self.num_kv_groups + self.value_dim_per_group = self.value_dim // self.num_kv_groups + self.layer_idx = layer_idx + + assert mode in ['chunk', "fused_recurrent"], f"Not supported mode `{mode}`." + assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}" + assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}" + + self.head_k_dim = self.key_dim // num_heads + self.head_v_dim = self.value_dim // num_heads + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim_per_group, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim_per_group, bias=False) + self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.k_conv1d = ShortConvolution( + hidden_size=self.key_dim_per_group, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + self.v_conv1d = ShortConvolution( + hidden_size=self.value_dim_per_group, + kernel_size=conv_size, + bias=conv_bias, + activation='silu', + ) + + self.gk_proj = nn.Linear(hidden_size, self.num_heads) + + if gate_fn == 'swish' and fuse_norm: + self.g_norm_swish_gate = FusedRMSNormGated( + hidden_size=self.head_v_dim, + elementwise_affine=elementwise_affine, + eps=norm_eps, + ) + self.fuse_norm_and_gate = True + else: + self.fuse_norm_and_gate = False + self.g_norm = RMSNorm( + hidden_size=self.head_v_dim, + elementwise_affine=elementwise_affine, + eps=norm_eps, + ) + self.gate_fn = ACT2FN[gate_fn] + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + self.gate_logit_normalizer = gate_logit_normalizer + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + # launching the triton kernel for just one token will actually be slower + mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if self.use_short_conv: + conv_state_q, conv_state_k, conv_state_v = None, None, None + if last_state is not None: + conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] + conv_mask = attention_mask[:, -hidden_states.shape[1]:] if attention_mask is not None else None + q, conv_state_q = self.q_conv1d( + x=self.q_proj(hidden_states), + mask=conv_mask, + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k, conv_state_k = self.k_conv1d( + x=self.k_proj(hidden_states), + mask=conv_mask, + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + v, conv_state_v = self.v_conv1d( + x=self.v_proj(hidden_states), + mask=conv_mask, + cache=conv_state_v, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + gk = self.gk_proj(hidden_states) + + if self.feature_map_fn is not None: + q, k = map(self.feature_map_fn, (q, k)) + # dealing with left-padding + if attention_mask is not None: + v = v.mul_(attention_mask[:, -v.shape[-2]:, None]) + q = rearrange(q, '... (h d) -> ... h d', h=self.num_heads) + if self.num_kv_groups > 1: + k, v = (repeat(x, '... (h d) -> ... (h g) d', h=self.num_kv_heads, g=self.num_kv_groups) for x in (k, v)) + else: + k, v = (rearrange(x, '... (h d) -> ... h d', h=self.num_kv_heads) for x in (k, v)) + gk = F.logsigmoid(gk) / self.gate_logit_normalizer + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + if mode == 'chunk': + o, recurrent_state = chunk_simple_gla( + q=q, + k=k, + v=v, + g=gk, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + elif mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_simple_gla( + q=q, + k=k, + v=v, + g=gk, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q.shape[1], + ) + + g = self.g_proj(hidden_states) + if self.fuse_norm_and_gate: + g = rearrange(g, 'b t (h d) -> b t h d', h=self.num_heads) + o = self.g_norm_swish_gate(o, g) + o = rearrange(o, 'b t h d -> b t (h d)') + else: + o = rearrange(self.g_norm(o), 'b t h d -> b t (h d)') + o = o * self.gate_fn(g) + o = self.o_proj(o) + + return o, None, past_key_values + + def state_size(self, **kwargs) -> int: + state_size = self.key_dim * self.head_v_dim + for module in self.children(): + if isinstance(module, ShortConvolution): + state_size += module.state_size + return state_size diff --git a/code/flash-linear-attention/fla/layers/sse.py b/code/flash-linear-attention/fla/layers/sse.py new file mode 100644 index 0000000000000000000000000000000000000000..59047899e295fd9fd6918d88351f13ad1b09a63d --- /dev/null +++ b/code/flash-linear-attention/fla/layers/sse.py @@ -0,0 +1,1290 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange, repeat +from rotary_embedding_torch.rotary_embedding_torch import rotate_half +from torch.nn import functional as F + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.ops.gla import chunk_gla, fused_recurrent_gla +from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule +from fla.ops.sse import prepare_sample_relpos_global_index_flat, softmax_and_mask + + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +def sort_along_l(q, k, v, gk, beta, e, cu_seqlens, K, emulq, emulk): + _, L, H, D = q.shape + N = e.size(-1) + S = len(cu_seqlens) - 1 + + e = F.softmax(e, dim=-1, dtype=torch.float) + topk_value, topk_expert = torch.topk(e, k=K, dim=2) # [1, L, K] + topk_value = topk_value.to(q.dtype) + mask_w = torch.zeros_like(e, dtype=torch.bool).scatter_(dim=-1, index=topk_expert, src=torch.ones_like(topk_expert, dtype=torch.bool)) + experts_flat = topk_expert.reshape(L * K) # [L*K] + values_flat = topk_value.reshape(L * K) # [L*K] + + sample_idx_flat, relpos_flat, global_idx_flat, lengths = prepare_sample_relpos_global_index_flat(cu_seqlens, K) # ([L*K] * 3, S) + assert sample_idx_flat.dtype == torch.long and relpos_flat.dtype == torch.long and global_idx_flat.dtype == torch.long + + bits_pos = int(lengths.max().item()).bit_length() + bits_exp = int((N - 1)).bit_length() + shift_exp = bits_pos + shift_samp = bits_pos + bits_exp + + ## sort by (sample_idx <- expert_idx <- relpos_in_sample) + key = (sample_idx_flat << shift_samp) | (experts_flat << shift_exp) | relpos_flat + order = torch.argsort(key, stable=False) + experts_sorted = experts_flat.take(order) + sample_sorted = sample_idx_flat.take(order) + global_sorted = global_idx_flat.take(order) # gather index + values_sorted = values_flat.take(order) # sorted eta + # pos_sorted = relpos_flat.take(order) + + ## x: [1, L, H, D] -> y: [1, L*K, H, D] + index4gather = global_sorted[None, :, None, None].expand(1, L * K, H, D) + if beta is None: + q, k, v, gk = [torch.gather(x, dim=1, index=index4gather) for x in (q, k, v, gk)] # GLA + else: + q, k, v = [torch.gather(x, dim=1, index=index4gather) for x in (q, k, v)] # GDN + gk, beta = [torch.gather(x, dim=1, index=index4gather[..., 0]) for x in (gk, beta)] + if emulq: + q = q * values_sorted[None, :, None, None] + if emulk: + k = k * values_sorted[None, :, None, None] + + ## calculate offsets (new cu_seqlens) + pair_id = sample_sorted * N + experts_sorted # [L*K] + counts = torch.bincount(pair_id, minlength=S * N) # [S*N] + state_sizes = counts.view(S, N) + offsets = torch.zeros(1 + S * N, dtype=torch.long, device=q.device) + offsets[1:] = counts.cumsum(dim=0) + offsets = torch.unique(offsets) + + return q, k, v, gk, beta, e, mask_w, offsets, state_sizes, global_sorted + + +class PoseRoPE(nn.Module): + """Camera-pose-conditioned rotary embedding for linear attention. + + Maps each token's camera pose to a per-dim-pair rotation angle and rotates + q/k by it. Because rotations compose to their difference, the bilinear form + q_i . k_j then depends on the RELATIVE pose (angle_j - angle_i), giving the + linear attention an explicit relative-camera signal it cannot recover from + additive absolute-pose injection alone. Zero-init -> identity at start. + """ + + def __init__(self, pose_dim: int, head_dim: int, hidden: int = 64): + super().__init__() + assert head_dim % 2 == 0 + self.net = nn.Sequential( + nn.Linear(pose_dim, hidden, bias=True), + nn.SiLU(), + nn.Linear(hidden, head_dim // 2, bias=True), + ) + nn.init.zeros_(self.net[-1].weight) + nn.init.zeros_(self.net[-1].bias) + + def forward(self, x: torch.Tensor, pose: torch.Tensor) -> torch.Tensor: + # x: [b, l, h, d]; pose: [b, l, pose_dim] + ang = self.net(pose) + self._angle_norm = ang.detach().abs().mean() # diagnostic: rotation magnitude + ang = repeat(ang, "b l n -> b l (n r)", r=2) + ang = ang.unsqueeze(2).float() + out = x.float() * ang.cos() + rotate_half(x).float() * ang.sin() + return out.to(x.dtype) + + +class SSEGLA(nn.Module): + """ + The layer implementaion for [SSE: Scaling Linear Attention with Sparse State Expansion](https://arxiv.org/pdf/2507.16577). + + Args: + hidden_size (int, Optional): + The hidden size of the input. Default: 2048. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 2.0. + head_dim (int, Optional): + The dimension of each head. Default: 256. + num_heads (int, Optional): + The number of heads. Default: 4. + num_v_heads (int, Optional): + The number of heads for the value projection, equal to `num_heads` if `None`. + GVA is applied if `num_v_heads` > `num_heads`. Default: `None`. + mode (str, Optional): + Which GLA kernel to use. + Currently available: `chunk` and `fused_recurrent`. + Default: `chunk`. + use_output_gate (bool, Optional): + Whether to use output gate. Default: `True`. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `False`. + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + num_sparse_partition (int, optional): + Number of state partitions. Default: 4. + num_writer (int, optional): + Top-k write size (number of writers). Default: 1. + num_reader (int, optional): + Top-k read size (number of readers). Default: 1. + sse_implementation (str, optional): + SSE implementation to use. One of `"varlen"` or `"mask"`. Default: `"varlen"`. + use_q_softmax (bool, optional): + Whether to apply softmax to the query. Default: `False`. + use_k_softmax (bool, optional): + Whether to apply softmax to the key. Default: `True`. + emulq (bool, optional): + Whether to use a read gate operating on the state output (Q). Default: `True`. + emulk (bool, optional): + Whether to use a write gate operating on the state input (KV). Default: `True`. + gate_logit_normalizer (int, Optional): + The normalizer for the gate logits, appied after `logsigmoid`. Default: 16. + gate_low_rank_dim (int, Optional): + The low rank dim for the gate projection. Default: 16. + layer_idx (int, Optional): + The index of the layer. Default: None. + norm_eps (float, Optional): + The epsilon value for the normalization layer. Default: 1e-5. + """ + + def __init__( + self, + hidden_size: int = 2048, + expand_v: float = 1., + head_dim: int = 256, + num_heads: int = 6, + num_v_heads: int = None, + mode: str = 'chunk', + use_output_gate: bool = True, + use_short_conv: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + num_sparse_partition: int = 4, + num_writer: int = 1, + num_reader: int = 1, + sse_implementation: str = "varlen", + use_q_softmax: bool = False, + use_k_softmax: bool = True, + emulq: bool = True, + emulk: bool = True, + gate_logit_normalizer: int = 16, + gate_low_rank_dim: int = 16, + layer_idx: int = None, + norm_eps: float = 1e-5, + # ---- Camera-Guided SSE-GLA (CGLA) extension ---- + # If pose_dim is None: behaves exactly like vanilla SSEGLA (no pose influence). + # If pose_dim is an int > 0: pose features (per-token) are injected into the + # sparse stream only (shared stream remains view-invariant): + # - routing eta : partition selection becomes viewpoint-aware + # - sparse q2/k2: pose-labeled read/write within partitions + # - sparse gate : pose-aware forgetting + # All pose-injection projections are zero-initialized so at step 0 the model + # is mathematically identical to vanilla SSEGLA; the pose signal is learned + # in from zero to avoid disturbing early optimization. + pose_dim: int = None, + pose_bottleneck: int = 64, + rope=None, + use_pose_rope: bool = False, + use_pose_gate_mod: bool = False, + **kwargs, + ) -> SSEGLA: + super().__init__() + + self.rope = rope + self.use_pose_rope = use_pose_rope + self.mode = mode + self.hidden_size = hidden_size + self.expand_v = expand_v + + assert num_reader < num_sparse_partition and num_writer < num_sparse_partition, \ + "num_reader and num_writer must be less than num_sparse_partition." + assert sse_implementation in ["mask", "varlen"], \ + f"Unknown SSE implementation {sse_implementation}" + + self.num_sparse_partition = num_sparse_partition + self.num_writer = num_writer + self.num_reader = num_reader + self.sse_implementation = { + "mask": self.sse_linear_attention_mask, + "varlen": self.sse_linear_attention_varlen, + }[sse_implementation] + + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.use_q_softmax = use_q_softmax + self.use_k_softmax = use_k_softmax + self.emulq = emulq + self.emulk = emulk + + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads + + self.head_k_dim = head_dim + self.head_v_dim = int(self.head_dim * self.expand_v) + self.key_dim = int(self.num_heads * self.head_k_dim) + self.value_dim = int(self.num_v_heads * self.head_v_dim) + self.layer_idx = layer_idx + + # Consistency check: Ensure expand_v produces integer values + if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. " + f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.", + ) + if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0: + raise ValueError( + f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.", + ) + + if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. " + f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.", + ) + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.lora_q_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.key_dim, bias=False)) + self.lora_k_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.key_dim, bias=False)) + + self.gate_logit_normalizer = gate_logit_normalizer + self.gk_proj = nn.ModuleList([nn.Sequential(nn.Linear(hidden_size, gate_low_rank_dim, bias=False), + nn.Linear(gate_low_rank_dim, self.key_dim, bias=True)) + for _ in range(2)]) + + self.e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d_shared = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + self.k_conv1d_shared = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + + if use_output_gate: + self.g_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.value_dim, bias=False)) + self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps) + else: + self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) + + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + # ---- CGLA: pose injection modules (only when pose_dim is provided) ---- + # Design rationale: + # - pose_encoder: lifts per-token pose (e.g. 6-dim Plucker rays) into the + # model's hidden space so all four injection heads share a common + # pose representation per block. + # - pose_q_proj / pose_k_proj: low-rank projections (bottleneck = head_v_dim + # by default, matching the existing lora_q/k_proj shape) whose outputs + # are added to q2 and k2 BEFORE the activation. This makes the sparse + # stream's read/write pose-aware while leaving q1/k1 (shared stream) + # untouched -> shared stream stays view-invariant. + # - pose_gk_proj: low-rank (bottleneck = gate_low_rank_dim) projection + # added to the sparse gate pre-activation; lets the gate modulate + # forgetting based on viewpoint change. + # - pose_e_proj: direct projection added to the routing logits; this is + # the primary knob that turns sparse partitions into viewpoint buckets. + # + # Zero-init policy (LoRA-style): the "up"-Linear of each injection is + # zeroed so that at step 0 pose contributes exactly 0 to q2/k2/gk2/eta, + # giving behavior identical to vanilla SSEGLA. Gradients on the "up" + # weights are non-zero from step 1 onward, unlocking the "down" weights + # and the encoder in subsequent steps. + self.pose_dim = pose_dim + self.pose_bottleneck = pose_bottleneck + if pose_dim is not None and pose_dim > 0: + self.pose_encoder = nn.Linear(pose_dim, hidden_size, bias=False) + # sparse Q/K injection (matches lora_q/k_proj shape style) + self.pose_q_proj = nn.Sequential( + nn.Linear(hidden_size, pose_bottleneck, bias=False), + nn.Linear(pose_bottleneck, self.key_dim, bias=False), + ) + self.pose_k_proj = nn.Sequential( + nn.Linear(hidden_size, pose_bottleneck, bias=False), + nn.Linear(pose_bottleneck, self.key_dim, bias=False), + ) + # sparse gate injection (matches gk_proj shape: low-rank = gate_low_rank_dim) + self.pose_gk_proj = nn.Sequential( + nn.Linear(hidden_size, gate_low_rank_dim, bias=False), + nn.Linear(gate_low_rank_dim, self.key_dim, bias=False), + ) + # routing injection (direct) + self.pose_e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False) + + # Zero-init the "up" projection of each injection -> at step 0 the + # contribution of pose to q2/k2/gk2/eta is exactly 0. + nn.init.zeros_(self.pose_q_proj[1].weight) + nn.init.zeros_(self.pose_k_proj[1].weight) + nn.init.zeros_(self.pose_gk_proj[1].weight) + nn.init.zeros_(self.pose_e_proj.weight) + self.pose_rope = PoseRoPE(pose_dim, self.head_k_dim) if use_pose_rope else None + self.pose_gate_mod = nn.Linear(hidden_size, self.key_dim, bias=True) if use_pose_gate_mod else None + if self.pose_gate_mod is not None: + nn.init.zeros_(self.pose_gate_mod.weight) + nn.init.zeros_(self.pose_gate_mod.bias) + else: + self.pose_encoder = None + self.pose_rope = None + self.pose_gate_mod = None + + def sse_linear_attention_varlen(self, q1, q2, k1, k2, v, gk1, gk2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None): + """ + q1: [bsz, qlen, nhead, head_dim] + q2: [bsz, qlen, nhead, head_dim] + k1: [bsz, klen, nhead, head_dim] + k2: [bsz, klen, nhead, head_dim] + v: [bsz, klen, nhead, head_dim] + gk1: [bsz, klen, nhead, head_dim] + gk2: [bsz, klen, nhead, head_dim] + eta: [bsz, klen, num_sparse_partition] + """ + assert self.num_writer == self.num_reader, "varlen only support num_writer == num_reader" + bsz, q_len, nhead, _ = q1.shape + if q_len <= 64: + mode = 'fused_recurrent' + else: + mode = self.mode + + v1 = v + v2 = v + if cu_seqlens is None: + cu_seqlens = torch.arange(0, (bsz + 1) * q_len, q_len, dtype=torch.int32, device=q1.device) + q1, k1, gk1, v1 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q1, k1, gk1, v]] + q2, k2, gk2, v2 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q2, k2, gk2, v]] + S = len(cu_seqlens) - 1 + + if use_cache: + recurrent_state1 = recurrent_state[:S] if recurrent_state is not None else \ + torch.zeros(S, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device) + recurrent_state2 = recurrent_state[S:] if recurrent_state is not None else \ + torch.zeros(S*self.num_sparse_partition, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device) + + q2, k2, v2, gk2, _, eta, mask, offsets, state_sizes, global_sorted = sort_along_l(q2, k2, v2, gk2, None, eta, cu_seqlens, self.num_writer, self.emulq, self.emulk) + + aux_loss = torch.zeros(()).to(eta) + if self.training: + p = torch.mean(eta.float(), dim=(0, 1)) + f = torch.mean(mask.float(), dim=(0, 1)) + aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer + # print(f"layer {self.layer_idx}, aux_loss {aux_loss}") + + q, k, gk, v = [torch.cat(pair, dim=1) for pair in zip((q1, k1, gk1, v1), (q2, k2, gk2, v2))] + offsets = torch.cat([cu_seqlens.to(offsets), offsets[1:] + cu_seqlens[-1]]) + + recurrent_state_rec = None + if use_cache: + state_id = torch.nonzero(state_sizes.flatten(), as_tuple=True)[0].cpu() + recurrent_state_rec = torch.cat((recurrent_state1, recurrent_state2[state_id]), dim=0) + + if mode == 'fused_recurrent': + o, recurrent_state_rec = fused_recurrent_gla( + q=q, + k=k, + v=v, + gk=gk, + initial_state=recurrent_state_rec, + output_final_state=use_cache, + cu_seqlens=offsets, + ) + elif mode == 'chunk': + o, recurrent_state_rec = chunk_gla( + q=q, + k=k, + v=v, + g=gk, + initial_state=recurrent_state_rec, + output_final_state=use_cache, + cu_seqlens=offsets, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if recurrent_state_rec is not None: + recurrent_state1 = recurrent_state_rec[:S] + recurrent_state2[state_id] = recurrent_state_rec[S:] + recurrent_state = torch.cat((recurrent_state1, recurrent_state2), dim=0) + else: + recurrent_state = None + + o1, o2 = o[:, :cu_seqlens[-1]], o[:, cu_seqlens[-1]:] + o2_reduce = torch.zeros_like(o1) + o2_reduce.index_add_(dim=1, index=global_sorted, source=o2) + o = o1 + o2_reduce + if bsz > 1: + o = rearrange(o, "1 (b l) h d -> b l h d", b=bsz).contiguous() + + return o, recurrent_state, aux_loss + + def sse_linear_attention_mask(self, q1, q2, k1, k2, v, gk1, gk2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None): + """ + q1: [bsz, qlen, nhead, head_dim] + q2: [bsz, qlen, nhead, head_dim] + k1: [bsz, klen, nhead, head_dim] + k2: [bsz, klen, nhead, head_dim] + v: [bsz, klen, nhead, head_dim] + gk1: [bsz, klen, nhead, head_dim] + gk2: [bsz, klen, nhead, head_dim] + eta: [bsz, klen, num_sparse_partition] + """ + bsz, q_len, nhead, _ = q1.shape + if q_len <= 64: + mode = 'fused_recurrent' + else: + mode = self.mode + + q2, k2, v2, gk2, eta, mask_w, mask_r = softmax_and_mask(q2, k2, v, gk2, eta, self.num_writer, self.num_reader) + + # writer-only auxloss + aux_loss = torch.zeros(()).to(eta) + if self.training: + p = torch.mean(eta.float(), dim=(0, 1)) + f = torch.mean(mask_w.float(), dim=(0, 1)) + aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer + # print(f"layer {self.layer_idx}, aux_loss {aux_loss}") + + q, k, gk, v = [torch.cat(pair, dim=-2) for pair in zip((q1, k1, gk1, v), (q2, k2, gk2, v2))] + + if mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_gla( + q=q, + k=k, + v=v, + gk=gk, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + elif mode == 'chunk': + o, recurrent_state = chunk_gla( + q=q, + k=k, + v=v, + g=gk, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + o = rearrange(o, "b l (n h) d -> b l n h d", n=self.num_sparse_partition+1) + o = o.sum(2) + + return o, recurrent_state, aux_loss + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + # CGLA: optional per-token pose features (B, T, pose_dim). If provided and + # self.pose_dim is set, pose is injected into the sparse stream only. + pose_emb: torch.Tensor | None = None, + write_gate: torch.Tensor | None = None, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + # ---- CGLA: encode pose once per block (shared across injection heads) ---- + pose_feat = None + if self.pose_encoder is not None and pose_emb is not None: + # Cast pose to hidden dtype to stay on one compute dtype through the block. + pose_feat = self.pose_encoder(pose_emb.to(hidden_states.dtype)) + if attention_mask is not None: + # Match the unpadded hidden_states layout: (1, sum_seqlens, hidden_size). + pose_feat = index_first_axis( + rearrange(pose_feat, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + + if write_gate is not None and attention_mask is not None: + write_gate = index_first_axis( + rearrange(write_gate, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + + q1 = self.q_proj(hidden_states) + k1 = self.k_proj(hidden_states) + q2 = q1 + self.lora_q_proj(hidden_states) + k2 = k1 + self.lora_k_proj(hidden_states) + v = self.v_proj(hidden_states) + + gk1 = self.gk_proj[0](hidden_states) + gk2 = self.gk_proj[1](hidden_states) + + eta = self.e_proj(hidden_states) + + # ---- CGLA: pose injection into the sparse stream (pre-activation) ---- + # Shared stream (q1, k1, gk1) is intentionally left untouched: it stays + # a view-invariant global memory. Pose drives (a) which sparse partitions + # a token reads/writes (eta), (b) the pose-labeled address for that + # read/write (q2, k2), and (c) how much history to forget within that + # partition (gk2). Because the "up" weights are zero-initialized, at step + # 0 all four additions contribute exactly 0 and the module is numerically + # identical to vanilla SSEGLA. + if pose_feat is not None: + pose_q = self.pose_q_proj(pose_feat) + q2 = q2 + pose_q + k2 = k2 + self.pose_k_proj(pose_feat) + gk2 = gk2 + self.pose_gk_proj(pose_feat) + eta = eta + self.pose_e_proj(pose_feat) + # behavior-neutral diagnostic: ||pose injection|| / ||q2|| + self._pose_norm = pose_q.detach().norm() / (q2.detach().norm() + 1e-6) + + if self.use_short_conv: + conv_state_q, conv_state_k = None, None + if last_state is not None: + conv_state_q, conv_state_k = last_state['conv_state'] + q1, conv_state_q = self.q_conv1d_shared( + x=q1, + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k1, conv_state_k = self.k_conv1d_shared( + x=k1, + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + + q1, q2, k1, k2, gk1, gk2 = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q1, q2, k1, k2, gk1, gk2)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + + if self.use_q_softmax: + q1 = F.softmax(q1.float(), dim=-1).to(v) + q2 = F.softmax(q2.float(), dim=-1).to(v) + else: + q1 = F.silu(q1) + q2 = F.silu(q2) + if self.use_k_softmax: + k1 = F.softmax(k1.float(), dim=-1).to(v) + k2 = F.softmax(k2.float(), dim=-1).to(v) + else: + k1 = F.silu(k1) + k2 = F.silu(k2) + v = F.silu(v) + + if write_gate is not None: + v = v * write_gate.unsqueeze(-1) + + gk1 = F.logsigmoid(gk1) / self.gate_logit_normalizer + gk2 = F.logsigmoid(gk2) / self.gate_logit_normalizer + + # ---- CGLA: pose gate-rate modulation (camera motion -> forgetting) ---- + # Multiplicative, single zero-init Linear: grad to it is propto gk (nonzero), + # so unlike the additive zero-up-proj injection it does not vanish at start. + if self.pose_gate_mod is not None and pose_feat is not None: + gm = rearrange(self.pose_gate_mod(pose_feat), '... (h d) -> ... h d', d=self.head_k_dim) + gm = torch.tanh(gm) + self._gatemod_norm = gm.detach().abs().mean() + scale = 1.0 + gm + gk1 = gk1 * scale + gk2 = gk2 * scale + + # ---- CGLA: Pose-RoPE (relative camera rotation into q.k bilinear) ---- + if self.pose_rope is not None and pose_emb is not None and attention_mask is None: + q1 = self.pose_rope(q1, pose_emb) + q2 = self.pose_rope(q2, pose_emb) + k1 = self.pose_rope(k1, pose_emb) + k2 = self.pose_rope(k2, pose_emb) + + # ---- CGLA: 3D RoPE on q/k (both shared and sparse streams) ---- + # q/k are [b, l, h, d] with l = T*H*W in (t,h,w) order; rope wants + # the seq dim at -2, so transpose to [b, h, l, d] and back. + if self.rope is not None: + def _rope(x): + return self.rope(x.transpose(1, 2)).transpose(1, 2).to(x.dtype) + q1, q2, k1, k2 = _rope(q1), _rope(q2), _rope(k1), _rope(k2) + + if self.num_v_heads > self.num_heads: + q1, q2, k1, k2, gk1, gk2 = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q1, q2, k1, k2, gk1, gk2)) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + # fp32 stability: plain CGLA (no pose_rope) leaves q/k unbounded, so the + # bf16 recurrence produces nan on a rising fraction of steps. Run the + # kernel in fp32 (same math, more precision); cast the output back. + _sse_dtype = v.dtype + with torch.autocast(device_type='cuda', enabled=False): + o, recurrent_state, aux_loss = self.sse_implementation( + q1.float(), + q2.float(), + k1.float(), + k2.float(), + v.float(), + gk1.float(), + gk2.float(), + eta.float(), + recurrent_state=recurrent_state, + use_cache=use_cache, + cu_seqlens=cu_seqlens, + ) + o = o.to(_sse_dtype) + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + if self.use_output_gate: + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.o_norm(o, g) + else: + o = self.o_norm(o) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, (None, aux_loss), past_key_values + + +class SSEGDN(nn.Module): + """ + The layer implementaion for [SSE: Scaling Linear Attention with Sparse State Expansion](https://arxiv.org/pdf/2507.16577). + + Args: + hidden_size (int, Optional): + The hidden size of the input. Default: 2048. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 2.0. + head_dim (int, Optional): + The dimension of each head. Default: 256. + num_heads (int, Optional): + The number of heads. Default: 4. + num_v_heads (int, Optional): + The number of heads for the value projection, equal to `num_heads` if `None`. + GVA is applied if `num_v_heads` > `num_heads`. Default: `None`. + mode (str, Optional): + Which Gated DeltaNet kernel to use. + Currently available: `chunk` and `fused_recurrent`. + Default: `chunk`. + use_output_gate (bool, Optional): + Whether to use output gate. Default: `True`. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `False`. + allow_neg_eigval (bool, Optional): + Allow negative eigenvalues. Default: `False`. If set to `True`, the beta will be multiplied by 2. + See reference: [Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues](https://arxiv.org/abs/2411.12537) + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + num_sparse_partition (int, optional): + Number of state partitions. Default: 4. + num_writer (int, optional): + Top-k write size (number of writers). Default: 1. + num_reader (int, optional): + Top-k read size (number of readers). Default: 1. + sse_implementation (str, optional): + SSE implementation to use. One of `"varlen"` or `"mask"`. Default: `"varlen"`. + use_q_softmax (bool, optional): + Whether to apply softmax to the query. Default: `False`. + use_k_softmax (bool, optional): + Whether to apply softmax to the key. Default: `True`. + emulq (bool, optional): + Whether to use a read gate operating on the state output (Q). Default: `True`. + emulk (bool, optional): + Whether to use a write gate operating on the state input (KV). Default: `True`. + layer_idx (int, Optional): + The index of the layer. Default: None. + norm_eps (float, Optional): + The epsilon value for the normalization layer. Default: 1e-5. + """ + + def __init__( + self, + hidden_size: int = 2048, + expand_v: float = 1., + head_dim: int = 256, + num_heads: int = 6, + num_v_heads: int = None, + mode: str = 'chunk', + use_output_gate: bool = True, + use_short_conv: bool = False, + allow_neg_eigval: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + num_sparse_partition: int = 4, + num_writer: int = 1, + num_reader: int = 1, + sse_implementation: str = "varlen", + use_q_softmax: bool = False, + use_k_softmax: bool = False, + emulq: bool = True, + emulk: bool = True, + layer_idx: int = None, + norm_eps: float = 1e-5, + # ---- Camera-Guided SSE-GDN (CGLA) extension (mirrors SSEGLA) ---- + # If pose_dim is None: behaves exactly like vanilla SSEGDN (no pose + # influence). If pose_dim is an int > 0: per-token camera-pose features + # are injected into the sparse stream only (shared stream stays + # view-invariant): routing eta, sparse q2/k2, sparse gate g2. With + # use_pose_rope, q/k are additionally rotated by pose-derived angles + # (relative camera PE). All pose-injection "up" projections are + # zero-init so at step 0 the model is identical to vanilla SSEGDN. + pose_dim: int = None, + pose_bottleneck: int = 64, + rope=None, + use_pose_rope: bool = False, + use_pose_gate_mod: bool = False, + **kwargs, + ) -> SSEGDN: + super().__init__() + + self.mode = mode + self.allow_neg_eigval = allow_neg_eigval + self.hidden_size = hidden_size + self.expand_v = expand_v + self.rope = rope + self.use_pose_rope = use_pose_rope + + assert num_reader < num_sparse_partition and num_writer < num_sparse_partition, \ + "num_reader and num_writer must be less than num_sparse_partition." + assert sse_implementation in ["mask", "varlen"], \ + f"Unknown SSE implementation {sse_implementation}" + + self.num_sparse_partition = num_sparse_partition + self.num_writer = num_writer + self.num_reader = num_reader + self.sse_implementation = { + "mask": self.sse_linear_attention_mask, + "varlen": self.sse_linear_attention_varlen, + }[sse_implementation] + + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.use_q_softmax = use_q_softmax + self.use_k_softmax = use_k_softmax + self.emulq = emulq + self.emulk = emulk + + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads + + self.head_k_dim = head_dim + self.head_v_dim = int(self.head_dim * self.expand_v) + self.key_dim = int(self.num_heads * self.head_k_dim) + self.value_dim = int(self.num_v_heads * self.head_v_dim) + self.layer_idx = layer_idx + + # Consistency check: Ensure expand_v produces integer values + if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. " + f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.", + ) + if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0: + raise ValueError( + f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.", + ) + + if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. " + f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.", + ) + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.lora_q_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.key_dim, bias=False)) + self.lora_k_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.key_dim, bias=False)) + + self.a_proj = nn.Linear(hidden_size, self.num_v_heads*2, bias=False) + self.b_proj = nn.Linear(hidden_size, self.num_v_heads*2, bias=False) + + A = torch.empty(self.num_v_heads*2, dtype=torch.float32).uniform_(0, 16) + self.A_log = nn.Parameter(torch.log(A)) + self.A_log._no_weight_decay = True + # hard coded for now + dt_min = 0.001 + dt_max = 0.1 + dt_init_floor = 1e-4 + dt = torch.exp( + torch.rand(self.num_v_heads*2) * (math.log(dt_max) - math.log(dt_min)) + + math.log(dt_min), + ) + dt = torch.clamp(dt, min=dt_init_floor) + # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + self.dt_bias = nn.Parameter(inv_dt) + # Just to be explicit. Without this we already don't put wd on dt_bias because of the check + # name.endswith("bias") in param_grouping.py + self.dt_bias._no_weight_decay = True + + self.e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d_shared = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + self.k_conv1d_shared = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + + if use_output_gate: + self.g_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.value_dim, bias=False)) + self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps) + else: + self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) + + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + # ---- CGLA: pose injection modules (only when pose_dim is provided) ---- + # Mirrors SSEGLA's camera-guided extension. Differences vs SSEGLA: + # - the SSE-GDN sparse gate is the per-head scalar g2 (chunked from g, + # shape (B, L, num_v_heads)), NOT the per-head-dim gk2 of SSE-GLA. + # So pose_gk_proj outputs num_v_heads (added to g2 AFTER chunk => + # sparse half only); pose_gate_mod outputs num_v_heads*2 (applied to + # g BEFORE chunk, modulating both g1 and g2, mirroring SSE-GLA's + # gk1/gk2 modulation). + # - q2/k2/eta injection is identical to SSE-GLA (key_dim / + # num_sparse_partition). + # Zero-init policy (LoRA-style): the "up"-Linear of each injection is + # zeroed so that at step 0 pose contributes exactly 0 to q2/k2/g2/eta. + self.pose_dim = pose_dim + self.pose_bottleneck = pose_bottleneck + if pose_dim is not None and pose_dim > 0: + self.pose_encoder = nn.Linear(pose_dim, hidden_size, bias=False) + # sparse Q/K injection (matches lora_q/k_proj shape style) + self.pose_q_proj = nn.Sequential( + nn.Linear(hidden_size, pose_bottleneck, bias=False), + nn.Linear(pose_bottleneck, self.key_dim, bias=False), + ) + self.pose_k_proj = nn.Sequential( + nn.Linear(hidden_size, pose_bottleneck, bias=False), + nn.Linear(pose_bottleneck, self.key_dim, bias=False), + ) + # sparse gate injection: SSE-GDN's gate is the per-head scalar g + # (num_v_heads*2 pre-chunk -> g1, g2 each (B, L, num_v_heads)). The + # additive injection is applied post-chunk on g2 (sparse half only), + # so the projection outputs num_v_heads. + self.pose_gk_proj = nn.Sequential( + nn.Linear(hidden_size, pose_bottleneck, bias=False), + nn.Linear(pose_bottleneck, self.num_v_heads, bias=False), + ) + # routing injection (direct) + self.pose_e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False) + + # Zero-init the "up" projection of each injection -> at step 0 the + # contribution of pose to q2/k2/g2/eta is exactly 0. + nn.init.zeros_(self.pose_q_proj[1].weight) + nn.init.zeros_(self.pose_k_proj[1].weight) + nn.init.zeros_(self.pose_gk_proj[1].weight) + nn.init.zeros_(self.pose_e_proj.weight) + self.pose_rope = PoseRoPE(pose_dim, self.head_k_dim) if use_pose_rope else None + self.pose_gate_mod = nn.Linear(hidden_size, self.num_v_heads * 2, bias=True) if use_pose_gate_mod else None + if self.pose_gate_mod is not None: + nn.init.zeros_(self.pose_gate_mod.weight) + nn.init.zeros_(self.pose_gate_mod.bias) + else: + self.pose_encoder = None + self.pose_rope = None + self.pose_gate_mod = None + + def sse_linear_attention_varlen(self, q1, q2, k1, k2, v, g1, g2, b1, b2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None): + """ + q1: [bsz, qlen, nhead, head_dim] + q2: [bsz, qlen, nhead, head_dim] + k1: [bsz, klen, nhead, head_dim] + k2: [bsz, klen, nhead, head_dim] + v: [bsz, klen, nhead, head_dim] + g1: [bsz, klen, nhead] + g2: [bsz, klen, nhead] + b1: [bsz, klen, nhead] + b2: [bsz, klen, nhead] + eta: [bsz, klen, num_sparse_partition] + """ + assert self.num_writer == self.num_reader, "varlen only support num_writer == num_reader" + bsz, q_len, nhead, _ = q1.shape + # change to inference mode. + mode = 'fused_recurrent' if q_len // self.num_sparse_partition <= 64 else self.mode + if self.training: + assert mode == 'chunk', "Only chunk mode is supported in training." + + v1 = v + v2 = v + if cu_seqlens is None: + cu_seqlens = torch.arange(0, (bsz + 1) * q_len, q_len, dtype=torch.int32, device=q1.device) + q1, k1, v1 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q1, k1, v]] + q2, k2, v2 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q2, k2, v]] + g1, g2, b1, b2 = [rearrange(src, 'b l h -> 1 (b l) h').contiguous() for src in [g1, g2, b1, b2]] + S = len(cu_seqlens) - 1 + + if use_cache: + recurrent_state1 = recurrent_state[:S] if recurrent_state is not None else \ + torch.zeros(S, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device) + recurrent_state2 = recurrent_state[S:] if recurrent_state is not None else \ + torch.zeros(S*self.num_sparse_partition, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device) + + q2, k2, v2, g2, b2, eta, mask, offsets, state_sizes, global_sorted = sort_along_l(q2, k2, v2, g2, b2, eta, cu_seqlens, self.num_writer, self.emulq, self.emulk) + + aux_loss = torch.zeros(()).to(eta) + if self.training: + p = torch.mean(eta.float(), dim=(0, 1)) + f = torch.mean(mask.float(), dim=(0, 1)) + aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer + # print(f"layer {self.layer_idx}, aux_loss {aux_loss}") + + q, k, g, b, v = [torch.cat(pair, dim=1) for pair in zip((q1, k1, g1, b1, v1), (q2, k2, g2, b2, v2))] + offsets = torch.cat([cu_seqlens.to(offsets), offsets[1:] + cu_seqlens[-1]]) + + recurrent_state_rec = None + if use_cache: + state_id = torch.nonzero(state_sizes.flatten(), as_tuple=True)[0].cpu() + recurrent_state_rec = torch.cat((recurrent_state1, recurrent_state2[state_id]), dim=0) + + if mode == 'fused_recurrent': + o, recurrent_state_rec = fused_recurrent_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=b, + initial_state=recurrent_state_rec, + output_final_state=use_cache, + cu_seqlens=offsets, + use_qk_l2norm_in_kernel=True, + ) + elif mode == 'chunk': + o, recurrent_state_rec = chunk_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=b, + initial_state=recurrent_state_rec, + output_final_state=use_cache, + cu_seqlens=offsets, + use_qk_l2norm_in_kernel=True, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if recurrent_state_rec is not None: + recurrent_state1 = recurrent_state_rec[:S] + recurrent_state2[state_id] = recurrent_state_rec[S:] + recurrent_state = torch.cat((recurrent_state1, recurrent_state2), dim=0) + else: + recurrent_state = None + + o1, o2 = o[:, :cu_seqlens[-1]], o[:, cu_seqlens[-1]:] + o2_reduce = torch.zeros_like(o1) + o2_reduce.index_add_(dim=1, index=global_sorted, source=o2) + o = o1 + o2_reduce + if bsz > 1: + o = rearrange(o, "1 (b l) h d -> b l h d", b=bsz).contiguous() + + return o, recurrent_state, aux_loss + + def sse_linear_attention_mask(self, q1, q2, k1, k2, v, g1, g2, b1, b2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None): + """ + q1: [bsz, qlen, nhead, head_dim] + q2: [bsz, qlen, nhead, head_dim] + k1: [bsz, klen, nhead, head_dim] + k2: [bsz, klen, nhead, head_dim] + v: [bsz, klen, nhead, head_dim] + g1: [bsz, klen, nhead] + g2: [bsz, klen, nhead] + b1: [bsz, klen, nhead] + b2: [bsz, klen, nhead] + eta: [bsz, klen, num_sparse_partition] + """ + bsz, q_len, nhead, _ = q1.shape + # change to inference mode. + mode = 'fused_recurrent' if q_len // self.num_sparse_partition <= 64 else self.mode + if self.training: + assert mode == 'chunk', "Only chunk mode is supported in training." + + q2, k2, v2, _, eta, mask_w, mask_r = softmax_and_mask(q2, k2, v, v, eta, self.num_writer, self.num_reader) + g2, b2 = [repeat(x, "b l h -> b l n h", n=self.num_sparse_partition) for x in (g2, b2)] + mask_r = mask_r[..., None] + g2, b2 = g2 * mask_r, b2 * mask_r + g2, b2 = [rearrange(x, "b l n h -> b l (n h)") for x in (g2, b2)] + + # writer-only auxloss + aux_loss = torch.zeros(()).to(eta) + if self.training: + p = torch.mean(eta.float(), dim=(0, 1)) + f = torch.mean(mask_w.float(), dim=(0, 1)) + aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer + # print(f"layer {self.layer_idx}, aux_loss {aux_loss}") + + q, k, g, b, v = [torch.cat(pair, dim=2) for pair in zip((q1, k1, g1, b1, v), (q2, k2, g2, b2, v2))] + + if mode == 'chunk': + o, recurrent_state = chunk_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=b, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=True, + ) + elif mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=b, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=True, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + o = rearrange(o, "b l (n h) d -> b l n h d", n=self.num_sparse_partition+1) + o = o.sum(2) + + return o, recurrent_state, aux_loss + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + # CGLA: optional per-token pose features (B, T, pose_dim). If provided and + # self.pose_dim is set, pose is injected into the sparse stream only. + pose_emb: torch.Tensor | None = None, + write_gate: torch.Tensor | None = None, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + # ---- CGLA: encode pose once per block (shared across injection heads) ---- + pose_feat = None + if self.pose_encoder is not None and pose_emb is not None: + pose_feat = self.pose_encoder(pose_emb.to(hidden_states.dtype)) + if attention_mask is not None: + pose_feat = index_first_axis( + rearrange(pose_feat, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + + if write_gate is not None and attention_mask is not None: + write_gate = index_first_axis( + rearrange(write_gate, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + + q1 = self.q_proj(hidden_states) + k1 = self.k_proj(hidden_states) + q2 = q1 + self.lora_q_proj(hidden_states) + k2 = k1 + self.lora_k_proj(hidden_states) + v = self.v_proj(hidden_states) + + b = self.b_proj(hidden_states).sigmoid() + if self.allow_neg_eigval: + b = b * 2. + g = -self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias) + # ---- CGLA: pose gate-rate modulation (camera motion -> forgetting) ---- + # Multiplicative, single zero-init Linear on g (pre-chunk, num_v_heads*2). + if self.pose_gate_mod is not None and pose_feat is not None: + gm = torch.tanh(self.pose_gate_mod(pose_feat).float()) + self._gatemod_norm = gm.detach().abs().mean() + g = g * (1.0 + gm.to(g.dtype)) + b1, b2 = torch.chunk(b, 2, dim=-1) + g1, g2 = torch.chunk(g, 2, dim=-1) + + eta = self.e_proj(hidden_states) + + # ---- CGLA: pose injection into the sparse stream (pre-activation) ---- + # Shared stream (q1, k1, g1, b1) is left untouched; pose drives the sparse + # read/write address (q2, k2), the sparse forgetting gate (g2), and the + # viewpoint routing (eta). Zero-init "up" weights => 0 at step 0. + if pose_feat is not None: + pose_q = self.pose_q_proj(pose_feat) + q2 = q2 + pose_q + k2 = k2 + self.pose_k_proj(pose_feat) + # g2 is fp32 (cast above); align the injection dtype to match. + g2 = g2 + self.pose_gk_proj(pose_feat).to(g2.dtype) + eta = eta + self.pose_e_proj(pose_feat) + self._pose_norm = pose_q.detach().norm() / (q2.detach().norm() + 1e-6) + + if self.use_short_conv: + conv_state_q, conv_state_k = None, None + if last_state is not None: + conv_state_q, conv_state_k = last_state['conv_state'] + q1, conv_state_q = self.q_conv1d_shared( + x=q1, + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k1, conv_state_k = self.k_conv1d_shared( + x=k1, + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + + q1, q2, k1, k2 = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q1, q2, k1, k2)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + + if self.use_q_softmax: + q1 = F.softmax(q1.float(), dim=-1).to(v) + q2 = F.softmax(q2.float(), dim=-1).to(v) + else: + q1 = F.silu(q1) + q2 = F.silu(q2) + if self.use_k_softmax: + k1 = F.softmax(k1.float(), dim=-1).to(v) + k2 = F.softmax(k2.float(), dim=-1).to(v) + else: + k1 = F.silu(k1) + k2 = F.silu(k2) + v = F.silu(v) + + # ---- CGLA: write gate on v (dfot noise_write_gate) ---- + if write_gate is not None: + v = v * write_gate.unsqueeze(-1) + + # ---- CGLA: Pose-RoPE (relative camera rotation into q.k bilinear) ---- + if self.pose_rope is not None and pose_emb is not None and attention_mask is None: + q1 = self.pose_rope(q1, pose_emb) + q2 = self.pose_rope(q2, pose_emb) + k1 = self.pose_rope(k1, pose_emb) + k2 = self.pose_rope(k2, pose_emb) + + # ---- CGLA: 3D RoPE on q/k (both shared and sparse streams) ---- + if self.rope is not None: + def _rope(x): + return self.rope(x.transpose(1, 2)).transpose(1, 2).to(x.dtype) + q1, q2, k1, k2 = _rope(q1), _rope(q2), _rope(k1), _rope(k2) + + if self.num_v_heads > self.num_heads: + q1, q2, k1, k2 = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q1, q2, k1, k2)) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + import os as _dbgos + if _dbgos.environ.get('CGLA_NAN_DEBUG'): + import torch as _dt + for _nm, _tn in [('q1',q1),('q2',q2),('k1',k1),('k2',k2),('v',v),('g1',g1),('g2',g2),('b1',b1),('b2',b2),('eta',eta)]: + if _tn is not None and not bool(_dt.isfinite(_tn).all()): + with open('/data1/echo_cgla_runs/nan_debug.log','a') as _f: + _f.write('NONFINITE_INPUT layer=%s tensor=%s\n' % (self.layer_idx, _nm)) + break + o, recurrent_state, aux_loss = self.sse_implementation( + q1, + q2, + k1, + k2, + v, + g1, + g2, + b1, + b2, + eta, + recurrent_state=recurrent_state, + use_cache=use_cache, + cu_seqlens=cu_seqlens, + ) + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + if self.use_output_gate: + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.o_norm(o, g) + else: + o = self.o_norm(o) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, (None, aux_loss), past_key_values diff --git a/code/flash-linear-attention/fla/layers/sse.py.fp32-bak b/code/flash-linear-attention/fla/layers/sse.py.fp32-bak new file mode 100644 index 0000000000000000000000000000000000000000..25f445bdaae7b56c1847b7b99a667d29f121301d --- /dev/null +++ b/code/flash-linear-attention/fla/layers/sse.py.fp32-bak @@ -0,0 +1,1276 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from einops import rearrange, repeat +from rotary_embedding_torch.rotary_embedding_torch import rotate_half +from torch.nn import functional as F + +from fla.layers.utils import get_unpad_data, index_first_axis, pad_input +from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution +from fla.ops.gla import chunk_gla, fused_recurrent_gla +from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule +from fla.ops.sse import prepare_sample_relpos_global_index_flat, softmax_and_mask + + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + from fla.models.utils import Cache + + +def sort_along_l(q, k, v, gk, beta, e, cu_seqlens, K, emulq, emulk): + _, L, H, D = q.shape + N = e.size(-1) + S = len(cu_seqlens) - 1 + + e = F.softmax(e, dim=-1, dtype=torch.float) + topk_value, topk_expert = torch.topk(e, k=K, dim=2) # [1, L, K] + topk_value = topk_value.to(q.dtype) + mask_w = torch.zeros_like(e, dtype=torch.bool).scatter_(dim=-1, index=topk_expert, src=torch.ones_like(topk_expert, dtype=torch.bool)) + experts_flat = topk_expert.reshape(L * K) # [L*K] + values_flat = topk_value.reshape(L * K) # [L*K] + + sample_idx_flat, relpos_flat, global_idx_flat, lengths = prepare_sample_relpos_global_index_flat(cu_seqlens, K) # ([L*K] * 3, S) + assert sample_idx_flat.dtype == torch.long and relpos_flat.dtype == torch.long and global_idx_flat.dtype == torch.long + + bits_pos = int(lengths.max().item()).bit_length() + bits_exp = int((N - 1)).bit_length() + shift_exp = bits_pos + shift_samp = bits_pos + bits_exp + + ## sort by (sample_idx <- expert_idx <- relpos_in_sample) + key = (sample_idx_flat << shift_samp) | (experts_flat << shift_exp) | relpos_flat + order = torch.argsort(key, stable=False) + experts_sorted = experts_flat.take(order) + sample_sorted = sample_idx_flat.take(order) + global_sorted = global_idx_flat.take(order) # gather index + values_sorted = values_flat.take(order) # sorted eta + # pos_sorted = relpos_flat.take(order) + + ## x: [1, L, H, D] -> y: [1, L*K, H, D] + index4gather = global_sorted[None, :, None, None].expand(1, L * K, H, D) + if beta is None: + q, k, v, gk = [torch.gather(x, dim=1, index=index4gather) for x in (q, k, v, gk)] # GLA + else: + q, k, v = [torch.gather(x, dim=1, index=index4gather) for x in (q, k, v)] # GDN + gk, beta = [torch.gather(x, dim=1, index=index4gather[..., 0]) for x in (gk, beta)] + if emulq: + q = q * values_sorted[None, :, None, None] + if emulk: + k = k * values_sorted[None, :, None, None] + + ## calculate offsets (new cu_seqlens) + pair_id = sample_sorted * N + experts_sorted # [L*K] + counts = torch.bincount(pair_id, minlength=S * N) # [S*N] + state_sizes = counts.view(S, N) + offsets = torch.zeros(1 + S * N, dtype=torch.long, device=q.device) + offsets[1:] = counts.cumsum(dim=0) + offsets = torch.unique(offsets) + + return q, k, v, gk, beta, e, mask_w, offsets, state_sizes, global_sorted + + +class PoseRoPE(nn.Module): + """Camera-pose-conditioned rotary embedding for linear attention. + + Maps each token's camera pose to a per-dim-pair rotation angle and rotates + q/k by it. Because rotations compose to their difference, the bilinear form + q_i . k_j then depends on the RELATIVE pose (angle_j - angle_i), giving the + linear attention an explicit relative-camera signal it cannot recover from + additive absolute-pose injection alone. Zero-init -> identity at start. + """ + + def __init__(self, pose_dim: int, head_dim: int, hidden: int = 64): + super().__init__() + assert head_dim % 2 == 0 + self.net = nn.Sequential( + nn.Linear(pose_dim, hidden, bias=True), + nn.SiLU(), + nn.Linear(hidden, head_dim // 2, bias=True), + ) + nn.init.zeros_(self.net[-1].weight) + nn.init.zeros_(self.net[-1].bias) + + def forward(self, x: torch.Tensor, pose: torch.Tensor) -> torch.Tensor: + # x: [b, l, h, d]; pose: [b, l, pose_dim] + ang = self.net(pose) + self._angle_norm = ang.detach().abs().mean() # diagnostic: rotation magnitude + ang = repeat(ang, "b l n -> b l (n r)", r=2) + ang = ang.unsqueeze(2).float() + out = x.float() * ang.cos() + rotate_half(x).float() * ang.sin() + return out.to(x.dtype) + + +class SSEGLA(nn.Module): + """ + The layer implementaion for [SSE: Scaling Linear Attention with Sparse State Expansion](https://arxiv.org/pdf/2507.16577). + + Args: + hidden_size (int, Optional): + The hidden size of the input. Default: 2048. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 2.0. + head_dim (int, Optional): + The dimension of each head. Default: 256. + num_heads (int, Optional): + The number of heads. Default: 4. + num_v_heads (int, Optional): + The number of heads for the value projection, equal to `num_heads` if `None`. + GVA is applied if `num_v_heads` > `num_heads`. Default: `None`. + mode (str, Optional): + Which GLA kernel to use. + Currently available: `chunk` and `fused_recurrent`. + Default: `chunk`. + use_output_gate (bool, Optional): + Whether to use output gate. Default: `True`. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `False`. + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + num_sparse_partition (int, optional): + Number of state partitions. Default: 4. + num_writer (int, optional): + Top-k write size (number of writers). Default: 1. + num_reader (int, optional): + Top-k read size (number of readers). Default: 1. + sse_implementation (str, optional): + SSE implementation to use. One of `"varlen"` or `"mask"`. Default: `"varlen"`. + use_q_softmax (bool, optional): + Whether to apply softmax to the query. Default: `False`. + use_k_softmax (bool, optional): + Whether to apply softmax to the key. Default: `True`. + emulq (bool, optional): + Whether to use a read gate operating on the state output (Q). Default: `True`. + emulk (bool, optional): + Whether to use a write gate operating on the state input (KV). Default: `True`. + gate_logit_normalizer (int, Optional): + The normalizer for the gate logits, appied after `logsigmoid`. Default: 16. + gate_low_rank_dim (int, Optional): + The low rank dim for the gate projection. Default: 16. + layer_idx (int, Optional): + The index of the layer. Default: None. + norm_eps (float, Optional): + The epsilon value for the normalization layer. Default: 1e-5. + """ + + def __init__( + self, + hidden_size: int = 2048, + expand_v: float = 1., + head_dim: int = 256, + num_heads: int = 6, + num_v_heads: int = None, + mode: str = 'chunk', + use_output_gate: bool = True, + use_short_conv: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + num_sparse_partition: int = 4, + num_writer: int = 1, + num_reader: int = 1, + sse_implementation: str = "varlen", + use_q_softmax: bool = False, + use_k_softmax: bool = True, + emulq: bool = True, + emulk: bool = True, + gate_logit_normalizer: int = 16, + gate_low_rank_dim: int = 16, + layer_idx: int = None, + norm_eps: float = 1e-5, + # ---- Camera-Guided SSE-GLA (CGLA) extension ---- + # If pose_dim is None: behaves exactly like vanilla SSEGLA (no pose influence). + # If pose_dim is an int > 0: pose features (per-token) are injected into the + # sparse stream only (shared stream remains view-invariant): + # - routing eta : partition selection becomes viewpoint-aware + # - sparse q2/k2: pose-labeled read/write within partitions + # - sparse gate : pose-aware forgetting + # All pose-injection projections are zero-initialized so at step 0 the model + # is mathematically identical to vanilla SSEGLA; the pose signal is learned + # in from zero to avoid disturbing early optimization. + pose_dim: int = None, + pose_bottleneck: int = 64, + rope=None, + use_pose_rope: bool = False, + use_pose_gate_mod: bool = False, + **kwargs, + ) -> SSEGLA: + super().__init__() + + self.rope = rope + self.use_pose_rope = use_pose_rope + self.mode = mode + self.hidden_size = hidden_size + self.expand_v = expand_v + + assert num_reader < num_sparse_partition and num_writer < num_sparse_partition, \ + "num_reader and num_writer must be less than num_sparse_partition." + assert sse_implementation in ["mask", "varlen"], \ + f"Unknown SSE implementation {sse_implementation}" + + self.num_sparse_partition = num_sparse_partition + self.num_writer = num_writer + self.num_reader = num_reader + self.sse_implementation = { + "mask": self.sse_linear_attention_mask, + "varlen": self.sse_linear_attention_varlen, + }[sse_implementation] + + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.use_q_softmax = use_q_softmax + self.use_k_softmax = use_k_softmax + self.emulq = emulq + self.emulk = emulk + + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads + + self.head_k_dim = head_dim + self.head_v_dim = int(self.head_dim * self.expand_v) + self.key_dim = int(self.num_heads * self.head_k_dim) + self.value_dim = int(self.num_v_heads * self.head_v_dim) + self.layer_idx = layer_idx + + # Consistency check: Ensure expand_v produces integer values + if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. " + f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.", + ) + if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0: + raise ValueError( + f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.", + ) + + if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. " + f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.", + ) + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.lora_q_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.key_dim, bias=False)) + self.lora_k_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.key_dim, bias=False)) + + self.gate_logit_normalizer = gate_logit_normalizer + self.gk_proj = nn.ModuleList([nn.Sequential(nn.Linear(hidden_size, gate_low_rank_dim, bias=False), + nn.Linear(gate_low_rank_dim, self.key_dim, bias=True)) + for _ in range(2)]) + + self.e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d_shared = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + self.k_conv1d_shared = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + + if use_output_gate: + self.g_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.value_dim, bias=False)) + self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps) + else: + self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) + + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + # ---- CGLA: pose injection modules (only when pose_dim is provided) ---- + # Design rationale: + # - pose_encoder: lifts per-token pose (e.g. 6-dim Plucker rays) into the + # model's hidden space so all four injection heads share a common + # pose representation per block. + # - pose_q_proj / pose_k_proj: low-rank projections (bottleneck = head_v_dim + # by default, matching the existing lora_q/k_proj shape) whose outputs + # are added to q2 and k2 BEFORE the activation. This makes the sparse + # stream's read/write pose-aware while leaving q1/k1 (shared stream) + # untouched -> shared stream stays view-invariant. + # - pose_gk_proj: low-rank (bottleneck = gate_low_rank_dim) projection + # added to the sparse gate pre-activation; lets the gate modulate + # forgetting based on viewpoint change. + # - pose_e_proj: direct projection added to the routing logits; this is + # the primary knob that turns sparse partitions into viewpoint buckets. + # + # Zero-init policy (LoRA-style): the "up"-Linear of each injection is + # zeroed so that at step 0 pose contributes exactly 0 to q2/k2/gk2/eta, + # giving behavior identical to vanilla SSEGLA. Gradients on the "up" + # weights are non-zero from step 1 onward, unlocking the "down" weights + # and the encoder in subsequent steps. + self.pose_dim = pose_dim + self.pose_bottleneck = pose_bottleneck + if pose_dim is not None and pose_dim > 0: + self.pose_encoder = nn.Linear(pose_dim, hidden_size, bias=False) + # sparse Q/K injection (matches lora_q/k_proj shape style) + self.pose_q_proj = nn.Sequential( + nn.Linear(hidden_size, pose_bottleneck, bias=False), + nn.Linear(pose_bottleneck, self.key_dim, bias=False), + ) + self.pose_k_proj = nn.Sequential( + nn.Linear(hidden_size, pose_bottleneck, bias=False), + nn.Linear(pose_bottleneck, self.key_dim, bias=False), + ) + # sparse gate injection (matches gk_proj shape: low-rank = gate_low_rank_dim) + self.pose_gk_proj = nn.Sequential( + nn.Linear(hidden_size, gate_low_rank_dim, bias=False), + nn.Linear(gate_low_rank_dim, self.key_dim, bias=False), + ) + # routing injection (direct) + self.pose_e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False) + + # Zero-init the "up" projection of each injection -> at step 0 the + # contribution of pose to q2/k2/gk2/eta is exactly 0. + nn.init.zeros_(self.pose_q_proj[1].weight) + nn.init.zeros_(self.pose_k_proj[1].weight) + nn.init.zeros_(self.pose_gk_proj[1].weight) + nn.init.zeros_(self.pose_e_proj.weight) + self.pose_rope = PoseRoPE(pose_dim, self.head_k_dim) if use_pose_rope else None + self.pose_gate_mod = nn.Linear(hidden_size, self.key_dim, bias=True) if use_pose_gate_mod else None + if self.pose_gate_mod is not None: + nn.init.zeros_(self.pose_gate_mod.weight) + nn.init.zeros_(self.pose_gate_mod.bias) + else: + self.pose_encoder = None + self.pose_rope = None + self.pose_gate_mod = None + + def sse_linear_attention_varlen(self, q1, q2, k1, k2, v, gk1, gk2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None): + """ + q1: [bsz, qlen, nhead, head_dim] + q2: [bsz, qlen, nhead, head_dim] + k1: [bsz, klen, nhead, head_dim] + k2: [bsz, klen, nhead, head_dim] + v: [bsz, klen, nhead, head_dim] + gk1: [bsz, klen, nhead, head_dim] + gk2: [bsz, klen, nhead, head_dim] + eta: [bsz, klen, num_sparse_partition] + """ + assert self.num_writer == self.num_reader, "varlen only support num_writer == num_reader" + bsz, q_len, nhead, _ = q1.shape + if q_len <= 64: + mode = 'fused_recurrent' + else: + mode = self.mode + + v1 = v + v2 = v + if cu_seqlens is None: + cu_seqlens = torch.arange(0, (bsz + 1) * q_len, q_len, dtype=torch.int32, device=q1.device) + q1, k1, gk1, v1 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q1, k1, gk1, v]] + q2, k2, gk2, v2 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q2, k2, gk2, v]] + S = len(cu_seqlens) - 1 + + if use_cache: + recurrent_state1 = recurrent_state[:S] if recurrent_state is not None else \ + torch.zeros(S, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device) + recurrent_state2 = recurrent_state[S:] if recurrent_state is not None else \ + torch.zeros(S*self.num_sparse_partition, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device) + + q2, k2, v2, gk2, _, eta, mask, offsets, state_sizes, global_sorted = sort_along_l(q2, k2, v2, gk2, None, eta, cu_seqlens, self.num_writer, self.emulq, self.emulk) + + aux_loss = torch.zeros(()).to(eta) + if self.training: + p = torch.mean(eta.float(), dim=(0, 1)) + f = torch.mean(mask.float(), dim=(0, 1)) + aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer + # print(f"layer {self.layer_idx}, aux_loss {aux_loss}") + + q, k, gk, v = [torch.cat(pair, dim=1) for pair in zip((q1, k1, gk1, v1), (q2, k2, gk2, v2))] + offsets = torch.cat([cu_seqlens.to(offsets), offsets[1:] + cu_seqlens[-1]]) + + recurrent_state_rec = None + if use_cache: + state_id = torch.nonzero(state_sizes.flatten(), as_tuple=True)[0].cpu() + recurrent_state_rec = torch.cat((recurrent_state1, recurrent_state2[state_id]), dim=0) + + if mode == 'fused_recurrent': + o, recurrent_state_rec = fused_recurrent_gla( + q=q, + k=k, + v=v, + gk=gk, + initial_state=recurrent_state_rec, + output_final_state=use_cache, + cu_seqlens=offsets, + ) + elif mode == 'chunk': + o, recurrent_state_rec = chunk_gla( + q=q, + k=k, + v=v, + g=gk, + initial_state=recurrent_state_rec, + output_final_state=use_cache, + cu_seqlens=offsets, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if recurrent_state_rec is not None: + recurrent_state1 = recurrent_state_rec[:S] + recurrent_state2[state_id] = recurrent_state_rec[S:] + recurrent_state = torch.cat((recurrent_state1, recurrent_state2), dim=0) + else: + recurrent_state = None + + o1, o2 = o[:, :cu_seqlens[-1]], o[:, cu_seqlens[-1]:] + o2_reduce = torch.zeros_like(o1) + o2_reduce.index_add_(dim=1, index=global_sorted, source=o2) + o = o1 + o2_reduce + if bsz > 1: + o = rearrange(o, "1 (b l) h d -> b l h d", b=bsz).contiguous() + + return o, recurrent_state, aux_loss + + def sse_linear_attention_mask(self, q1, q2, k1, k2, v, gk1, gk2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None): + """ + q1: [bsz, qlen, nhead, head_dim] + q2: [bsz, qlen, nhead, head_dim] + k1: [bsz, klen, nhead, head_dim] + k2: [bsz, klen, nhead, head_dim] + v: [bsz, klen, nhead, head_dim] + gk1: [bsz, klen, nhead, head_dim] + gk2: [bsz, klen, nhead, head_dim] + eta: [bsz, klen, num_sparse_partition] + """ + bsz, q_len, nhead, _ = q1.shape + if q_len <= 64: + mode = 'fused_recurrent' + else: + mode = self.mode + + q2, k2, v2, gk2, eta, mask_w, mask_r = softmax_and_mask(q2, k2, v, gk2, eta, self.num_writer, self.num_reader) + + # writer-only auxloss + aux_loss = torch.zeros(()).to(eta) + if self.training: + p = torch.mean(eta.float(), dim=(0, 1)) + f = torch.mean(mask_w.float(), dim=(0, 1)) + aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer + # print(f"layer {self.layer_idx}, aux_loss {aux_loss}") + + q, k, gk, v = [torch.cat(pair, dim=-2) for pair in zip((q1, k1, gk1, v), (q2, k2, gk2, v2))] + + if mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_gla( + q=q, + k=k, + v=v, + gk=gk, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + elif mode == 'chunk': + o, recurrent_state = chunk_gla( + q=q, + k=k, + v=v, + g=gk, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + o = rearrange(o, "b l (n h) d -> b l n h d", n=self.num_sparse_partition+1) + o = o.sum(2) + + return o, recurrent_state, aux_loss + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + # CGLA: optional per-token pose features (B, T, pose_dim). If provided and + # self.pose_dim is set, pose is injected into the sparse stream only. + pose_emb: torch.Tensor | None = None, + write_gate: torch.Tensor | None = None, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + # ---- CGLA: encode pose once per block (shared across injection heads) ---- + pose_feat = None + if self.pose_encoder is not None and pose_emb is not None: + # Cast pose to hidden dtype to stay on one compute dtype through the block. + pose_feat = self.pose_encoder(pose_emb.to(hidden_states.dtype)) + if attention_mask is not None: + # Match the unpadded hidden_states layout: (1, sum_seqlens, hidden_size). + pose_feat = index_first_axis( + rearrange(pose_feat, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + + if write_gate is not None and attention_mask is not None: + write_gate = index_first_axis( + rearrange(write_gate, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + + q1 = self.q_proj(hidden_states) + k1 = self.k_proj(hidden_states) + q2 = q1 + self.lora_q_proj(hidden_states) + k2 = k1 + self.lora_k_proj(hidden_states) + v = self.v_proj(hidden_states) + + gk1 = self.gk_proj[0](hidden_states) + gk2 = self.gk_proj[1](hidden_states) + + eta = self.e_proj(hidden_states) + + # ---- CGLA: pose injection into the sparse stream (pre-activation) ---- + # Shared stream (q1, k1, gk1) is intentionally left untouched: it stays + # a view-invariant global memory. Pose drives (a) which sparse partitions + # a token reads/writes (eta), (b) the pose-labeled address for that + # read/write (q2, k2), and (c) how much history to forget within that + # partition (gk2). Because the "up" weights are zero-initialized, at step + # 0 all four additions contribute exactly 0 and the module is numerically + # identical to vanilla SSEGLA. + if pose_feat is not None: + pose_q = self.pose_q_proj(pose_feat) + q2 = q2 + pose_q + k2 = k2 + self.pose_k_proj(pose_feat) + gk2 = gk2 + self.pose_gk_proj(pose_feat) + eta = eta + self.pose_e_proj(pose_feat) + # behavior-neutral diagnostic: ||pose injection|| / ||q2|| + self._pose_norm = pose_q.detach().norm() / (q2.detach().norm() + 1e-6) + + if self.use_short_conv: + conv_state_q, conv_state_k = None, None + if last_state is not None: + conv_state_q, conv_state_k = last_state['conv_state'] + q1, conv_state_q = self.q_conv1d_shared( + x=q1, + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k1, conv_state_k = self.k_conv1d_shared( + x=k1, + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + + q1, q2, k1, k2, gk1, gk2 = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q1, q2, k1, k2, gk1, gk2)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + + if self.use_q_softmax: + q1 = F.softmax(q1.float(), dim=-1).to(v) + q2 = F.softmax(q2.float(), dim=-1).to(v) + else: + q1 = F.silu(q1) + q2 = F.silu(q2) + if self.use_k_softmax: + k1 = F.softmax(k1.float(), dim=-1).to(v) + k2 = F.softmax(k2.float(), dim=-1).to(v) + else: + k1 = F.silu(k1) + k2 = F.silu(k2) + v = F.silu(v) + + if write_gate is not None: + v = v * write_gate.unsqueeze(-1) + + gk1 = F.logsigmoid(gk1) / self.gate_logit_normalizer + gk2 = F.logsigmoid(gk2) / self.gate_logit_normalizer + + # ---- CGLA: pose gate-rate modulation (camera motion -> forgetting) ---- + # Multiplicative, single zero-init Linear: grad to it is propto gk (nonzero), + # so unlike the additive zero-up-proj injection it does not vanish at start. + if self.pose_gate_mod is not None and pose_feat is not None: + gm = rearrange(self.pose_gate_mod(pose_feat), '... (h d) -> ... h d', d=self.head_k_dim) + gm = torch.tanh(gm) + self._gatemod_norm = gm.detach().abs().mean() + scale = 1.0 + gm + gk1 = gk1 * scale + gk2 = gk2 * scale + + # ---- CGLA: Pose-RoPE (relative camera rotation into q.k bilinear) ---- + if self.pose_rope is not None and pose_emb is not None and attention_mask is None: + q1 = self.pose_rope(q1, pose_emb) + q2 = self.pose_rope(q2, pose_emb) + k1 = self.pose_rope(k1, pose_emb) + k2 = self.pose_rope(k2, pose_emb) + + # ---- CGLA: 3D RoPE on q/k (both shared and sparse streams) ---- + # q/k are [b, l, h, d] with l = T*H*W in (t,h,w) order; rope wants + # the seq dim at -2, so transpose to [b, h, l, d] and back. + if self.rope is not None: + def _rope(x): + return self.rope(x.transpose(1, 2)).transpose(1, 2).to(x.dtype) + q1, q2, k1, k2 = _rope(q1), _rope(q2), _rope(k1), _rope(k2) + + if self.num_v_heads > self.num_heads: + q1, q2, k1, k2, gk1, gk2 = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q1, q2, k1, k2, gk1, gk2)) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + o, recurrent_state, aux_loss = self.sse_implementation( + q1, + q2, + k1, + k2, + v, + gk1, + gk2, + eta, + recurrent_state=recurrent_state, + use_cache=use_cache, + cu_seqlens=cu_seqlens, + ) + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + if self.use_output_gate: + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.o_norm(o, g) + else: + o = self.o_norm(o) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, (None, aux_loss), past_key_values + + +class SSEGDN(nn.Module): + """ + The layer implementaion for [SSE: Scaling Linear Attention with Sparse State Expansion](https://arxiv.org/pdf/2507.16577). + + Args: + hidden_size (int, Optional): + The hidden size of the input. Default: 2048. + expand_v (float, Optional): + The expansion ratio for the value dim. Default: 2.0. + head_dim (int, Optional): + The dimension of each head. Default: 256. + num_heads (int, Optional): + The number of heads. Default: 4. + num_v_heads (int, Optional): + The number of heads for the value projection, equal to `num_heads` if `None`. + GVA is applied if `num_v_heads` > `num_heads`. Default: `None`. + mode (str, Optional): + Which Gated DeltaNet kernel to use. + Currently available: `chunk` and `fused_recurrent`. + Default: `chunk`. + use_output_gate (bool, Optional): + Whether to use output gate. Default: `True`. + use_short_conv (bool, Optional): + Whether to use short convolutions. Default: `False`. + allow_neg_eigval (bool, Optional): + Allow negative eigenvalues. Default: `False`. If set to `True`, the beta will be multiplied by 2. + See reference: [Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues](https://arxiv.org/abs/2411.12537) + conv_size (int, Optional): + The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. + conv_bias (bool, Optional): + Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. + num_sparse_partition (int, optional): + Number of state partitions. Default: 4. + num_writer (int, optional): + Top-k write size (number of writers). Default: 1. + num_reader (int, optional): + Top-k read size (number of readers). Default: 1. + sse_implementation (str, optional): + SSE implementation to use. One of `"varlen"` or `"mask"`. Default: `"varlen"`. + use_q_softmax (bool, optional): + Whether to apply softmax to the query. Default: `False`. + use_k_softmax (bool, optional): + Whether to apply softmax to the key. Default: `True`. + emulq (bool, optional): + Whether to use a read gate operating on the state output (Q). Default: `True`. + emulk (bool, optional): + Whether to use a write gate operating on the state input (KV). Default: `True`. + layer_idx (int, Optional): + The index of the layer. Default: None. + norm_eps (float, Optional): + The epsilon value for the normalization layer. Default: 1e-5. + """ + + def __init__( + self, + hidden_size: int = 2048, + expand_v: float = 1., + head_dim: int = 256, + num_heads: int = 6, + num_v_heads: int = None, + mode: str = 'chunk', + use_output_gate: bool = True, + use_short_conv: bool = False, + allow_neg_eigval: bool = False, + conv_size: int = 4, + conv_bias: bool = False, + num_sparse_partition: int = 4, + num_writer: int = 1, + num_reader: int = 1, + sse_implementation: str = "varlen", + use_q_softmax: bool = False, + use_k_softmax: bool = False, + emulq: bool = True, + emulk: bool = True, + layer_idx: int = None, + norm_eps: float = 1e-5, + # ---- Camera-Guided SSE-GDN (CGLA) extension (mirrors SSEGLA) ---- + # If pose_dim is None: behaves exactly like vanilla SSEGDN (no pose + # influence). If pose_dim is an int > 0: per-token camera-pose features + # are injected into the sparse stream only (shared stream stays + # view-invariant): routing eta, sparse q2/k2, sparse gate g2. With + # use_pose_rope, q/k are additionally rotated by pose-derived angles + # (relative camera PE). All pose-injection "up" projections are + # zero-init so at step 0 the model is identical to vanilla SSEGDN. + pose_dim: int = None, + pose_bottleneck: int = 64, + rope=None, + use_pose_rope: bool = False, + use_pose_gate_mod: bool = False, + **kwargs, + ) -> SSEGDN: + super().__init__() + + self.mode = mode + self.allow_neg_eigval = allow_neg_eigval + self.hidden_size = hidden_size + self.expand_v = expand_v + self.rope = rope + self.use_pose_rope = use_pose_rope + + assert num_reader < num_sparse_partition and num_writer < num_sparse_partition, \ + "num_reader and num_writer must be less than num_sparse_partition." + assert sse_implementation in ["mask", "varlen"], \ + f"Unknown SSE implementation {sse_implementation}" + + self.num_sparse_partition = num_sparse_partition + self.num_writer = num_writer + self.num_reader = num_reader + self.sse_implementation = { + "mask": self.sse_linear_attention_mask, + "varlen": self.sse_linear_attention_varlen, + }[sse_implementation] + + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.conv_bias = conv_bias + + self.use_q_softmax = use_q_softmax + self.use_k_softmax = use_k_softmax + self.emulq = emulq + self.emulk = emulk + + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads + + self.head_k_dim = head_dim + self.head_v_dim = int(self.head_dim * self.expand_v) + self.key_dim = int(self.num_heads * self.head_k_dim) + self.value_dim = int(self.num_v_heads * self.head_v_dim) + self.layer_idx = layer_idx + + # Consistency check: Ensure expand_v produces integer values + if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. " + f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.", + ) + if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0: + raise ValueError( + f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.", + ) + + if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5): + raise ValueError( + f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. " + f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.", + ) + assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`." + + self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) + self.lora_q_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.key_dim, bias=False)) + self.lora_k_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.key_dim, bias=False)) + + self.a_proj = nn.Linear(hidden_size, self.num_v_heads*2, bias=False) + self.b_proj = nn.Linear(hidden_size, self.num_v_heads*2, bias=False) + + A = torch.empty(self.num_v_heads*2, dtype=torch.float32).uniform_(0, 16) + self.A_log = nn.Parameter(torch.log(A)) + self.A_log._no_weight_decay = True + # hard coded for now + dt_min = 0.001 + dt_max = 0.1 + dt_init_floor = 1e-4 + dt = torch.exp( + torch.rand(self.num_v_heads*2) * (math.log(dt_max) - math.log(dt_min)) + + math.log(dt_min), + ) + dt = torch.clamp(dt, min=dt_init_floor) + # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + self.dt_bias = nn.Parameter(inv_dt) + # Just to be explicit. Without this we already don't put wd on dt_bias because of the check + # name.endswith("bias") in param_grouping.py + self.dt_bias._no_weight_decay = True + + self.e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False) + + if use_short_conv: + self.conv_size = conv_size + self.q_conv1d_shared = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + self.k_conv1d_shared = ShortConvolution( + hidden_size=self.key_dim, + kernel_size=conv_size, + bias=conv_bias, + activation=None, + ) + + if use_output_gate: + self.g_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False), + nn.Linear(self.head_v_dim, self.value_dim, bias=False)) + self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps) + else: + self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) + + self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) + + # ---- CGLA: pose injection modules (only when pose_dim is provided) ---- + # Mirrors SSEGLA's camera-guided extension. Differences vs SSEGLA: + # - the SSE-GDN sparse gate is the per-head scalar g2 (chunked from g, + # shape (B, L, num_v_heads)), NOT the per-head-dim gk2 of SSE-GLA. + # So pose_gk_proj outputs num_v_heads (added to g2 AFTER chunk => + # sparse half only); pose_gate_mod outputs num_v_heads*2 (applied to + # g BEFORE chunk, modulating both g1 and g2, mirroring SSE-GLA's + # gk1/gk2 modulation). + # - q2/k2/eta injection is identical to SSE-GLA (key_dim / + # num_sparse_partition). + # Zero-init policy (LoRA-style): the "up"-Linear of each injection is + # zeroed so that at step 0 pose contributes exactly 0 to q2/k2/g2/eta. + self.pose_dim = pose_dim + self.pose_bottleneck = pose_bottleneck + if pose_dim is not None and pose_dim > 0: + self.pose_encoder = nn.Linear(pose_dim, hidden_size, bias=False) + # sparse Q/K injection (matches lora_q/k_proj shape style) + self.pose_q_proj = nn.Sequential( + nn.Linear(hidden_size, pose_bottleneck, bias=False), + nn.Linear(pose_bottleneck, self.key_dim, bias=False), + ) + self.pose_k_proj = nn.Sequential( + nn.Linear(hidden_size, pose_bottleneck, bias=False), + nn.Linear(pose_bottleneck, self.key_dim, bias=False), + ) + # sparse gate injection: SSE-GDN's gate is the per-head scalar g + # (num_v_heads*2 pre-chunk -> g1, g2 each (B, L, num_v_heads)). The + # additive injection is applied post-chunk on g2 (sparse half only), + # so the projection outputs num_v_heads. + self.pose_gk_proj = nn.Sequential( + nn.Linear(hidden_size, pose_bottleneck, bias=False), + nn.Linear(pose_bottleneck, self.num_v_heads, bias=False), + ) + # routing injection (direct) + self.pose_e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False) + + # Zero-init the "up" projection of each injection -> at step 0 the + # contribution of pose to q2/k2/g2/eta is exactly 0. + nn.init.zeros_(self.pose_q_proj[1].weight) + nn.init.zeros_(self.pose_k_proj[1].weight) + nn.init.zeros_(self.pose_gk_proj[1].weight) + nn.init.zeros_(self.pose_e_proj.weight) + self.pose_rope = PoseRoPE(pose_dim, self.head_k_dim) if use_pose_rope else None + self.pose_gate_mod = nn.Linear(hidden_size, self.num_v_heads * 2, bias=True) if use_pose_gate_mod else None + if self.pose_gate_mod is not None: + nn.init.zeros_(self.pose_gate_mod.weight) + nn.init.zeros_(self.pose_gate_mod.bias) + else: + self.pose_encoder = None + self.pose_rope = None + self.pose_gate_mod = None + + def sse_linear_attention_varlen(self, q1, q2, k1, k2, v, g1, g2, b1, b2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None): + """ + q1: [bsz, qlen, nhead, head_dim] + q2: [bsz, qlen, nhead, head_dim] + k1: [bsz, klen, nhead, head_dim] + k2: [bsz, klen, nhead, head_dim] + v: [bsz, klen, nhead, head_dim] + g1: [bsz, klen, nhead] + g2: [bsz, klen, nhead] + b1: [bsz, klen, nhead] + b2: [bsz, klen, nhead] + eta: [bsz, klen, num_sparse_partition] + """ + assert self.num_writer == self.num_reader, "varlen only support num_writer == num_reader" + bsz, q_len, nhead, _ = q1.shape + # change to inference mode. + mode = 'fused_recurrent' if q_len // self.num_sparse_partition <= 64 else self.mode + if self.training: + assert mode == 'chunk', "Only chunk mode is supported in training." + + v1 = v + v2 = v + if cu_seqlens is None: + cu_seqlens = torch.arange(0, (bsz + 1) * q_len, q_len, dtype=torch.int32, device=q1.device) + q1, k1, v1 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q1, k1, v]] + q2, k2, v2 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q2, k2, v]] + g1, g2, b1, b2 = [rearrange(src, 'b l h -> 1 (b l) h').contiguous() for src in [g1, g2, b1, b2]] + S = len(cu_seqlens) - 1 + + if use_cache: + recurrent_state1 = recurrent_state[:S] if recurrent_state is not None else \ + torch.zeros(S, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device) + recurrent_state2 = recurrent_state[S:] if recurrent_state is not None else \ + torch.zeros(S*self.num_sparse_partition, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device) + + q2, k2, v2, g2, b2, eta, mask, offsets, state_sizes, global_sorted = sort_along_l(q2, k2, v2, g2, b2, eta, cu_seqlens, self.num_writer, self.emulq, self.emulk) + + aux_loss = torch.zeros(()).to(eta) + if self.training: + p = torch.mean(eta.float(), dim=(0, 1)) + f = torch.mean(mask.float(), dim=(0, 1)) + aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer + # print(f"layer {self.layer_idx}, aux_loss {aux_loss}") + + q, k, g, b, v = [torch.cat(pair, dim=1) for pair in zip((q1, k1, g1, b1, v1), (q2, k2, g2, b2, v2))] + offsets = torch.cat([cu_seqlens.to(offsets), offsets[1:] + cu_seqlens[-1]]) + + recurrent_state_rec = None + if use_cache: + state_id = torch.nonzero(state_sizes.flatten(), as_tuple=True)[0].cpu() + recurrent_state_rec = torch.cat((recurrent_state1, recurrent_state2[state_id]), dim=0) + + if mode == 'fused_recurrent': + o, recurrent_state_rec = fused_recurrent_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=b, + initial_state=recurrent_state_rec, + output_final_state=use_cache, + cu_seqlens=offsets, + use_qk_l2norm_in_kernel=True, + ) + elif mode == 'chunk': + o, recurrent_state_rec = chunk_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=b, + initial_state=recurrent_state_rec, + output_final_state=use_cache, + cu_seqlens=offsets, + use_qk_l2norm_in_kernel=True, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + if recurrent_state_rec is not None: + recurrent_state1 = recurrent_state_rec[:S] + recurrent_state2[state_id] = recurrent_state_rec[S:] + recurrent_state = torch.cat((recurrent_state1, recurrent_state2), dim=0) + else: + recurrent_state = None + + o1, o2 = o[:, :cu_seqlens[-1]], o[:, cu_seqlens[-1]:] + o2_reduce = torch.zeros_like(o1) + o2_reduce.index_add_(dim=1, index=global_sorted, source=o2) + o = o1 + o2_reduce + if bsz > 1: + o = rearrange(o, "1 (b l) h d -> b l h d", b=bsz).contiguous() + + return o, recurrent_state, aux_loss + + def sse_linear_attention_mask(self, q1, q2, k1, k2, v, g1, g2, b1, b2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None): + """ + q1: [bsz, qlen, nhead, head_dim] + q2: [bsz, qlen, nhead, head_dim] + k1: [bsz, klen, nhead, head_dim] + k2: [bsz, klen, nhead, head_dim] + v: [bsz, klen, nhead, head_dim] + g1: [bsz, klen, nhead] + g2: [bsz, klen, nhead] + b1: [bsz, klen, nhead] + b2: [bsz, klen, nhead] + eta: [bsz, klen, num_sparse_partition] + """ + bsz, q_len, nhead, _ = q1.shape + # change to inference mode. + mode = 'fused_recurrent' if q_len // self.num_sparse_partition <= 64 else self.mode + if self.training: + assert mode == 'chunk', "Only chunk mode is supported in training." + + q2, k2, v2, _, eta, mask_w, mask_r = softmax_and_mask(q2, k2, v, v, eta, self.num_writer, self.num_reader) + g2, b2 = [repeat(x, "b l h -> b l n h", n=self.num_sparse_partition) for x in (g2, b2)] + mask_r = mask_r[..., None] + g2, b2 = g2 * mask_r, b2 * mask_r + g2, b2 = [rearrange(x, "b l n h -> b l (n h)") for x in (g2, b2)] + + # writer-only auxloss + aux_loss = torch.zeros(()).to(eta) + if self.training: + p = torch.mean(eta.float(), dim=(0, 1)) + f = torch.mean(mask_w.float(), dim=(0, 1)) + aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer + # print(f"layer {self.layer_idx}, aux_loss {aux_loss}") + + q, k, g, b, v = [torch.cat(pair, dim=2) for pair in zip((q1, k1, g1, b1, v), (q2, k2, g2, b2, v2))] + + if mode == 'chunk': + o, recurrent_state = chunk_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=b, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=True, + ) + elif mode == 'fused_recurrent': + o, recurrent_state = fused_recurrent_gated_delta_rule( + q=q, + k=k, + v=v, + g=g, + beta=b, + initial_state=recurrent_state, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + use_qk_l2norm_in_kernel=True, + ) + else: + raise NotImplementedError(f"Not supported mode `{mode}`.") + + o = rearrange(o, "b l (n h) d -> b l n h d", n=self.num_sparse_partition+1) + o = o.sum(2) + + return o, recurrent_state, aux_loss + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + # CGLA: optional per-token pose features (B, T, pose_dim). If provided and + # self.pose_dim is set, pose is injected into the sparse stream only. + pose_emb: torch.Tensor | None = None, + write_gate: torch.Tensor | None = None, + **kwargs: Unpack[dict], + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + batch_size, q_len, _ = hidden_states.shape + + last_state = None + if past_key_values is not None and len(past_key_values) > self.layer_idx: + last_state = past_key_values[self.layer_idx] + + cu_seqlens = kwargs.get('cu_seqlens') + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) + + # ---- CGLA: encode pose once per block (shared across injection heads) ---- + pose_feat = None + if self.pose_encoder is not None and pose_emb is not None: + pose_feat = self.pose_encoder(pose_emb.to(hidden_states.dtype)) + if attention_mask is not None: + pose_feat = index_first_axis( + rearrange(pose_feat, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + + if write_gate is not None and attention_mask is not None: + write_gate = index_first_axis( + rearrange(write_gate, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + + q1 = self.q_proj(hidden_states) + k1 = self.k_proj(hidden_states) + q2 = q1 + self.lora_q_proj(hidden_states) + k2 = k1 + self.lora_k_proj(hidden_states) + v = self.v_proj(hidden_states) + + b = self.b_proj(hidden_states).sigmoid() + if self.allow_neg_eigval: + b = b * 2. + g = -self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias) + # ---- CGLA: pose gate-rate modulation (camera motion -> forgetting) ---- + # Multiplicative, single zero-init Linear on g (pre-chunk, num_v_heads*2). + if self.pose_gate_mod is not None and pose_feat is not None: + gm = torch.tanh(self.pose_gate_mod(pose_feat).float()) + self._gatemod_norm = gm.detach().abs().mean() + g = g * (1.0 + gm.to(g.dtype)) + b1, b2 = torch.chunk(b, 2, dim=-1) + g1, g2 = torch.chunk(g, 2, dim=-1) + + eta = self.e_proj(hidden_states) + + # ---- CGLA: pose injection into the sparse stream (pre-activation) ---- + # Shared stream (q1, k1, g1, b1) is left untouched; pose drives the sparse + # read/write address (q2, k2), the sparse forgetting gate (g2), and the + # viewpoint routing (eta). Zero-init "up" weights => 0 at step 0. + if pose_feat is not None: + pose_q = self.pose_q_proj(pose_feat) + q2 = q2 + pose_q + k2 = k2 + self.pose_k_proj(pose_feat) + # g2 is fp32 (cast above); align the injection dtype to match. + g2 = g2 + self.pose_gk_proj(pose_feat).to(g2.dtype) + eta = eta + self.pose_e_proj(pose_feat) + self._pose_norm = pose_q.detach().norm() / (q2.detach().norm() + 1e-6) + + if self.use_short_conv: + conv_state_q, conv_state_k = None, None + if last_state is not None: + conv_state_q, conv_state_k = last_state['conv_state'] + q1, conv_state_q = self.q_conv1d_shared( + x=q1, + cache=conv_state_q, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + k1, conv_state_k = self.k_conv1d_shared( + x=k1, + cache=conv_state_k, + output_final_state=use_cache, + cu_seqlens=cu_seqlens, + ) + + q1, q2, k1, k2 = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q1, q2, k1, k2)) + v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) + + if self.use_q_softmax: + q1 = F.softmax(q1.float(), dim=-1).to(v) + q2 = F.softmax(q2.float(), dim=-1).to(v) + else: + q1 = F.silu(q1) + q2 = F.silu(q2) + if self.use_k_softmax: + k1 = F.softmax(k1.float(), dim=-1).to(v) + k2 = F.softmax(k2.float(), dim=-1).to(v) + else: + k1 = F.silu(k1) + k2 = F.silu(k2) + v = F.silu(v) + + # ---- CGLA: write gate on v (dfot noise_write_gate) ---- + if write_gate is not None: + v = v * write_gate.unsqueeze(-1) + + # ---- CGLA: Pose-RoPE (relative camera rotation into q.k bilinear) ---- + if self.pose_rope is not None and pose_emb is not None and attention_mask is None: + q1 = self.pose_rope(q1, pose_emb) + q2 = self.pose_rope(q2, pose_emb) + k1 = self.pose_rope(k1, pose_emb) + k2 = self.pose_rope(k2, pose_emb) + + # ---- CGLA: 3D RoPE on q/k (both shared and sparse streams) ---- + if self.rope is not None: + def _rope(x): + return self.rope(x.transpose(1, 2)).transpose(1, 2).to(x.dtype) + q1, q2, k1, k2 = _rope(q1), _rope(q2), _rope(k1), _rope(k2) + + if self.num_v_heads > self.num_heads: + q1, q2, k1, k2 = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q1, q2, k1, k2)) + + recurrent_state = last_state['recurrent_state'] if last_state is not None else None + o, recurrent_state, aux_loss = self.sse_implementation( + q1, + q2, + k1, + k2, + v, + g1, + g2, + b1, + b2, + eta, + recurrent_state=recurrent_state, + use_cache=use_cache, + cu_seqlens=cu_seqlens, + ) + + if past_key_values is not None: + past_key_values.update( + recurrent_state=recurrent_state, + conv_state=(conv_state_q, conv_state_k) if self.use_short_conv else None, + layer_idx=self.layer_idx, + offset=q_len, + ) + + if self.use_output_gate: + g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) + o = self.o_norm(o, g) + else: + o = self.o_norm(o) + o = rearrange(o, 'b t h d -> b t (h d)') + o = self.o_proj(o) + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, (None, aux_loss), past_key_values diff --git a/code/flash-linear-attention/fla/layers/utils.py b/code/flash-linear-attention/fla/layers/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e6c95d1ba106e4599dfece9aeb317f2e6616bf01 --- /dev/null +++ b/code/flash-linear-attention/fla/layers/utils.py @@ -0,0 +1,195 @@ +# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang + +# Code is adapted from flash-attn.bert_padding.py + + +import torch +from einops import rearrange, repeat + +from fla.ops.utils.index import prepare_cu_seqlens_from_mask, prepare_lens_from_mask +from fla.utils import tensor_cache + + +class IndexFirstAxis(torch.autograd.Function): + + @staticmethod + def forward(ctx, x, indices): + ctx.save_for_backward(indices) + assert x.ndim >= 2 + ctx.first_axis_dim, other_shape = x.shape[0], x.shape[1:] + second_dim = other_shape.numel() + # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing. + # return x[indices] + return torch.gather( + rearrange(x, "b ... -> b (...)"), 0, repeat(indices, "z -> z d", d=second_dim), + ).reshape(-1, *other_shape) + + @staticmethod + def backward(ctx, do): + (indices,) = ctx.saved_tensors + assert do.ndim >= 2 + other_shape = do.shape[1:] + do = rearrange(do, "b ... -> b (...)") + dx = torch.zeros( + [ctx.first_axis_dim, do.shape[1]], + device=do.device, + dtype=do.dtype, + ) + # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing. + # dx[indices] = do + dx.scatter_(0, repeat(indices, "z -> z d", d=do.shape[1]), do) + return dx.reshape(ctx.first_axis_dim, *other_shape), None + + +index_first_axis = IndexFirstAxis.apply + + +class IndexPutFirstAxis(torch.autograd.Function): + + @staticmethod + def forward(ctx, x, indices, first_axis_dim): + ctx.save_for_backward(indices) + assert indices.ndim == 1 + assert x.ndim >= 2 + y = torch.zeros(first_axis_dim, *x.shape[1:], device=x.device, dtype=x.dtype) + # TODO [2022-03-04] For some reason torch.scatter is a bit faster than indexing. + y[indices] = x + # y.scatter_(0, repeat(indices, 'z -> z d', d=x.shape[1]), x) + return y + + @staticmethod + def backward(ctx, do): + (indices,) = ctx.saved_tensors + # TODO [2022-03-04] For some reason torch.gather is a bit faster than indexing. + dx = do[indices] + # dx = torch.gather(do, 0, repeat(indices, 'z -> z d', d=do.shape[1])) + return dx, None, None + + +index_put_first_axis = IndexPutFirstAxis.apply + + +@tensor_cache +def get_unpad_data( + attention_mask: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor, int]: + """ + Retrieves indexing data required to repad unpadded (ragged) tensors. + + Args: + attention_mask (`torch.Tensor`): + Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid. + + Return: + indices (`torch.Tensor`): + The indices of non-masked tokens from the flattened input sequence. + cu_seqlens (`torch.Tensor`): + The cumulative sequence lengths, used to index into ragged (unpadded) tensors. + `cu_seqlens` shape is [batch_size + 1]. + max_seqlen_in_batch (`int`): + Maximum sequence length in batch. + """ + lens = prepare_lens_from_mask(attention_mask) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = lens.max().item() + cu_seqlens = prepare_cu_seqlens_from_mask(attention_mask) + return indices, cu_seqlens, max_seqlen_in_batch + + +def unpad_input( + q: torch.Tensor, + states: tuple[torch.Tensor], + attention_mask: torch.Tensor, + q_len: int, + keepdim: bool = False, +): + """ + Unpads query, key, and values tensors, using a single dimension for all tokens + even though they belong to different batches. + + + Arguments: + q (`torch.Tensor`): + Query state with padding. Shape: [batch_size, q_len, ...]. + states (`Tuple[torch.Tensor]`): + Attention state with padding. Shape: [batch_size, seq_len, ...]. + attention_mask (`torch.Tensor`): + Boolean or int tensor of shape [batch_size, sequence_length], 1 means valid and 0 means not valid. + q_len (`int`): + Target length. + keepdim (`bool`): + Whether to keep the batch dimension. Default: `False`. + + Return: + q (`torch.Tensor`): + Query state without padding. + Shape: [1, total_target_length, ...] if `keepdim=True` else [total_target_length, ...]. + states (`Tuple[torch.Tensor]`): + Attention state without padding. + Shape: [1, total_source_length, ...] if `keepdim=True` else [total_source_length, ...]. + indices_q (`torch.Tensor`): + The indices of non-masked tokens from the flattened input target sequence. + (cu_seqlens_q, cu_seqlens_k) (`Tuple[int]`): + The cumulative sequence lengths for the target (query) and source (key, value), + used to index into ragged (unpadded) tensors. + `cu_seqlens` shape is [batch_size + 1]. + (max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`Tuple[int]`): + Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence + i.e. query, `max_seqlen_in_batch_k` for the source sequence i.e. key/value). + """ + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = get_unpad_data(attention_mask) + batch_size, seq_len, *_ = states[0].shape + + state = tuple( + index_first_axis(rearrange(s, "b s ... -> (b s) ..."), indices_k) + for s in states + ) + + if q_len == seq_len: + q = index_first_axis(rearrange(q, "b s ... -> (b s) ..."), indices_k) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif q_len == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange(batch_size + 1, dtype=torch.int32, device=q.device) + indices_q = cu_seqlens_q[:-1] + q = q.squeeze(1) + else: + raise NotImplementedError("We only support either q_len == k_len (prefilling) or q_len == 1 (decoding)") + + if keepdim: + q = q.unsqueeze(0) + state = tuple(s.unsqueeze(0) for s in state) + + return ( + q, + state, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +def pad_input( + hidden_states: torch.Tensor, + indices: torch.LongTensor, + batch_size: int, + seq_len: int, +) -> torch.Tensor: + """ + Args: + hidden_states ([total_tokens, ...]): + where total_tokens denotes the number of tokens in selected in attention_mask. + indices ([total_tokens]): + the indices that represent the non-masked tokens of the original padded input sequence. + batch_size (int): + batch_size size for the padded sequence. + seq_len (int): + maximum sequence length for the padded sequence. + + Return: + hidden_states of shape [batch_size, seq_len, ...] + """ + output = index_put_first_axis(hidden_states, indices, batch_size * seq_len) + return rearrange(output, "(b s) ... -> b s ...", b=batch_size) diff --git a/code/flash-linear-attention/fla/models/__init__.py b/code/flash-linear-attention/fla/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dc5212eab6feeac7d1c212bc8597db79d624a0bb --- /dev/null +++ b/code/flash-linear-attention/fla/models/__init__.py @@ -0,0 +1,68 @@ + +from fla.models.abc import ABCConfig, ABCForCausalLM, ABCModel +from fla.models.bitnet import BitNetConfig, BitNetForCausalLM, BitNetModel +from fla.models.comba import CombaConfig, CombaForCausalLM, CombaModel +from fla.models.delta_net import DeltaNetConfig, DeltaNetForCausalLM, DeltaNetModel +from fla.models.deltaformer import DeltaFormerConfig, DeltaFormerForCausalLM, DeltaFormerModel +from fla.models.forgetting_transformer import ( + ForgettingTransformerConfig, + ForgettingTransformerForCausalLM, + ForgettingTransformerModel, +) +from fla.models.gated_deltanet import GatedDeltaNetConfig, GatedDeltaNetForCausalLM, GatedDeltaNetModel +from fla.models.gated_deltaproduct import GatedDeltaProductConfig, GatedDeltaProductForCausalLM, GatedDeltaProductModel +from fla.models.gla import GLAConfig, GLAForCausalLM, GLAModel +from fla.models.gsa import GSAConfig, GSAForCausalLM, GSAModel +from fla.models.hgrn import HGRNConfig, HGRNForCausalLM, HGRNModel +from fla.models.hgrn2 import HGRN2Config, HGRN2ForCausalLM, HGRN2Model +from fla.models.kda import KDAConfig, KDAForCausalLM, KDAModel +from fla.models.lightnet import LightNetConfig, LightNetForCausalLM, LightNetModel +from fla.models.linear_attn import LinearAttentionConfig, LinearAttentionForCausalLM, LinearAttentionModel +from fla.models.log_linear_mamba2 import LogLinearMamba2Config, LogLinearMamba2ForCausalLM, LogLinearMamba2Model +from fla.models.mamba import MambaConfig, MambaForCausalLM, MambaModel +from fla.models.mamba2 import Mamba2Config, Mamba2ForCausalLM, Mamba2Model +from fla.models.mesa_net import MesaNetConfig, MesaNetForCausalLM, MesaNetModel +from fla.models.mla import MLAConfig, MLAForCausalLM, MLAModel +from fla.models.mom import MomConfig, MomForCausalLM, MomModel +from fla.models.nsa import NSAConfig, NSAForCausalLM, NSAModel +from fla.models.path_attn import PaTHAttentionConfig, PaTHAttentionForCausalLM, PaTHAttentionModel +from fla.models.retnet import RetNetConfig, RetNetForCausalLM, RetNetModel +from fla.models.rodimus import RodimusConfig, RodimusForCausalLM, RodimusModel +from fla.models.rwkv6 import RWKV6Config, RWKV6ForCausalLM, RWKV6Model +from fla.models.rwkv7 import RWKV7Config, RWKV7ForCausalLM, RWKV7Model +from fla.models.samba import SambaConfig, SambaForCausalLM, SambaModel +from fla.models.sse import SSEConfig, SSEForCausalLM, SSEModel +from fla.models.transformer import TransformerConfig, TransformerForCausalLM, TransformerModel + +__all__ = [ + 'ABCConfig', 'ABCForCausalLM', 'ABCModel', + 'BitNetConfig', 'BitNetForCausalLM', 'BitNetModel', + 'CombaConfig', 'CombaForCausalLM', 'CombaModel', + 'DeltaNetConfig', 'DeltaNetForCausalLM', 'DeltaNetModel', + 'DeltaFormerConfig', 'DeltaFormerForCausalLM', 'DeltaFormerModel', + 'ForgettingTransformerConfig', 'ForgettingTransformerForCausalLM', 'ForgettingTransformerModel', + 'GatedDeltaNetConfig', 'GatedDeltaNetForCausalLM', 'GatedDeltaNetModel', + 'GatedDeltaProductConfig', 'GatedDeltaProductForCausalLM', 'GatedDeltaProductModel', + 'GLAConfig', 'GLAForCausalLM', 'GLAModel', + 'GSAConfig', 'GSAForCausalLM', 'GSAModel', + 'HGRNConfig', 'HGRNForCausalLM', 'HGRNModel', + 'HGRN2Config', 'HGRN2ForCausalLM', 'HGRN2Model', + 'KDAConfig', 'KDAForCausalLM', 'KDAModel', + 'LightNetConfig', 'LightNetForCausalLM', 'LightNetModel', + 'LinearAttentionConfig', 'LinearAttentionForCausalLM', 'LinearAttentionModel', + 'LogLinearMamba2Config', 'LogLinearMamba2ForCausalLM', 'LogLinearMamba2Model', + 'MambaConfig', 'MambaForCausalLM', 'MambaModel', + 'Mamba2Config', 'Mamba2ForCausalLM', 'Mamba2Model', + 'MesaNetConfig', 'MesaNetForCausalLM', 'MesaNetModel', + 'MomConfig', 'MomForCausalLM', 'MomModel', + 'MLAConfig', 'MLAForCausalLM', 'MLAModel', + 'NSAConfig', 'NSAForCausalLM', 'NSAModel', + 'PaTHAttentionConfig', 'PaTHAttentionForCausalLM', 'PaTHAttentionModel', + 'RetNetConfig', 'RetNetForCausalLM', 'RetNetModel', + 'RodimusConfig', 'RodimusForCausalLM', 'RodimusModel', + 'RWKV6Config', 'RWKV6ForCausalLM', 'RWKV6Model', + 'RWKV7Config', 'RWKV7ForCausalLM', 'RWKV7Model', + 'SambaConfig', 'SambaForCausalLM', 'SambaModel', + 'SSEConfig', 'SSEForCausalLM', 'SSEModel', + 'TransformerConfig', 'TransformerForCausalLM', 'TransformerModel', +] diff --git a/code/flash-linear-attention/fla/models/abc/__init__.py b/code/flash-linear-attention/fla/models/abc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5d67d04284af7a704a7246969ab03bbe6dc7c966 --- /dev/null +++ b/code/flash-linear-attention/fla/models/abc/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.abc.configuration_abc import ABCConfig +from fla.models.abc.modeling_abc import ABCForCausalLM, ABCModel + +AutoConfig.register(ABCConfig.model_type, ABCConfig, exist_ok=True) +AutoModel.register(ABCConfig, ABCModel, exist_ok=True) +AutoModelForCausalLM.register(ABCConfig, ABCForCausalLM, exist_ok=True) + + +__all__ = ['ABCConfig', 'ABCForCausalLM', 'ABCModel'] diff --git a/code/flash-linear-attention/fla/models/abc/configuration_abc.py b/code/flash-linear-attention/fla/models/abc/configuration_abc.py new file mode 100644 index 0000000000000000000000000000000000000000..5aec1042a9c87e10ae8d101232f0110bb6227df0 --- /dev/null +++ b/code/flash-linear-attention/fla/models/abc/configuration_abc.py @@ -0,0 +1,105 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class ABCConfig(PretrainedConfig): + + model_type = 'abc' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + gate_low_rank_dim: int = 16, + clamp_min: float = -32, + clamp_max: float = 32, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + num_hidden_layers: int = 24, + num_heads: int = 4, + num_slots: int | None = 64, + use_short_conv: bool = False, + conv_size: int = 4, + exapnd_k: float = 0.5, + exapnd_v: float = 1, + hidden_act: str = "swish", + max_position_embeddings: int = 2048, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + use_rope: bool = True, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.hidden_size = hidden_size + self.gate_low_rank_dim = gate_low_rank_dim + self.clamp_min = clamp_min + self.clamp_max = clamp_max + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.num_slots = num_slots + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.expand_k = exapnd_k + self.expand_v = exapnd_v + self.hidden_act = hidden_act + self.max_position_embeddings = max_position_embeddings + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.use_rope = use_rope + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/abc/modeling_abc.py b/code/flash-linear-attention/fla/models/abc/modeling_abc.py new file mode 100644 index 0000000000000000000000000000000000000000..c2ef04e2bed4630b997438d29a174ea10c6f2221 --- /dev/null +++ b/code/flash-linear-attention/fla/models/abc/modeling_abc.py @@ -0,0 +1,372 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.abc import ABCAttention +from fla.layers.attn import Attention +from fla.models.abc.configuration_abc import ABCConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as ABCMLP +from fla.modules.l2warp import l2_warp + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +class ABCBlock(GradientCheckpointingLayer): + + def __init__(self, config: ABCConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = ABCAttention( + hidden_size=config.hidden_size, + expand_k=config.expand_k, + expand_v=config.expand_v, + num_heads=config.num_heads, + num_slots=config.num_slots, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + gate_fn=config.hidden_act, + elementwise_affine=config.elementwise_affine, + norm_eps=config.norm_eps, + use_rope=config.use_rope, + clamp_min=config.clamp_min, + clamp_max=config.clamp_max, + fuse_norm=config.fuse_norm, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = ABCMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + + residual = hidden_states + + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class ABCPreTrainedModel(PreTrainedModel): + + config_class = ABCConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['ABCBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class ABCModel(ABCPreTrainedModel): + + def __init__(self, config: ABCConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([ABCBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`ABCModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class ABCForCausalLM(ABCPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = ABCModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/bitnet/__init__.py b/code/flash-linear-attention/fla/models/bitnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3aa44a14248cd17224fe2a40403fb0f4cc6952ba --- /dev/null +++ b/code/flash-linear-attention/fla/models/bitnet/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.bitnet.configuration_bitnet import BitNetConfig +from fla.models.bitnet.modeling_bitnet import BitNetForCausalLM, BitNetModel + +AutoConfig.register(BitNetConfig.model_type, BitNetConfig, exist_ok=True) +AutoModel.register(BitNetConfig, BitNetModel, exist_ok=True) +AutoModelForCausalLM.register(BitNetConfig, BitNetForCausalLM, exist_ok=True) + + +__all__ = ['BitNetConfig', 'BitNetForCausalLM', 'BitNetModel'] diff --git a/code/flash-linear-attention/fla/models/bitnet/configuration_bitnet.py b/code/flash-linear-attention/fla/models/bitnet/configuration_bitnet.py new file mode 100644 index 0000000000000000000000000000000000000000..96309da1be0a6c128b428b86448aa6cbde02b448 --- /dev/null +++ b/code/flash-linear-attention/fla/models/bitnet/configuration_bitnet.py @@ -0,0 +1,81 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class BitNetConfig(PretrainedConfig): + + model_type = 'bitnet' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + num_hidden_layers: int = 24, + num_heads: int = 32, + num_kv_heads: int | None = None, + window_size: int | None = None, + rope_theta: float | None = 10000., + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + initializer_range: float = 0.02, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.window_size = window_size + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + + self.initializer_range = initializer_range + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.use_cache = use_cache + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/bitnet/modeling_bitnet.py b/code/flash-linear-attention/fla/models/bitnet/modeling_bitnet.py new file mode 100644 index 0000000000000000000000000000000000000000..a80b29cfa84f2eed53ec7aae698a52fa40b95e6e --- /dev/null +++ b/code/flash-linear-attention/fla/models/bitnet/modeling_bitnet.py @@ -0,0 +1,396 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Any + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.bitattn import BitAttention +from fla.models.bitnet.configuration_bitnet import BitNetConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules.activations import swiglu +from fla.modules.fused_bitlinear import FusedBitLinear +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class BitNetMLP(nn.Module): + + def __init__( + self, + hidden_size: int, + hidden_ratio: int | None = None, + intermediate_size: int | None = None, + hidden_act: str = 'swish', + fuse_swiglu: bool = True, + ) -> BitNetMLP: + super().__init__() + + self.hidden_size = hidden_size + # the final number of params is `hidden_ratio * hidden_size^2` + # `intermediate_size` is chosen to be a multiple of 256 closest to `2/3 * hidden_size * hidden_ratio` + if hidden_ratio is None: + hidden_ratio = 4 + if intermediate_size is None: + intermediate_size = int(hidden_size * hidden_ratio * 2 / 3) + intermediate_size = 256 * ((intermediate_size + 256 - 1) // 256) + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.fuse_swiglu = fuse_swiglu + + if hidden_act != 'swish': + raise ValueError(f'Unsupported hidden_act: {hidden_act}') + + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + + def forward( + self, + x: torch.Tensor, + **kwargs: Unpack[Any], + ) -> torch.Tensor: + gate, y = self.gate_proj(x), self.up_proj(x) + return self.down_proj(swiglu(gate, y)) + + +class BitNetBlock(GradientCheckpointingLayer): + + def __init__(self, config: BitNetConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.attn = BitAttention( + hidden_size=config.hidden_size, + num_heads=config.num_heads, + num_kv_heads=config.num_kv_heads, + window_size=config.window_size, + rope_theta=config.rope_theta, + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = BitNetMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: tuple[torch.Tensor] | None = None, + output_attentions: bool | None = False, + use_cache: bool | None = False, + **kwargs: Unpack[Any], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attentions,) + + if use_cache: + outputs += (past_key_values,) + + return outputs + + +class BitNetPreTrainedModel(PreTrainedModel): + + config_class = BitNetConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['BitNetBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + rescale_prenorm_residual: bool = False, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, FusedBitLinear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + + +class BitNetModel(BitNetPreTrainedModel): + + def __init__( + self, + config: BitNetConfig, + ) -> BitNetModel: + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([BitNetBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[Any], + ) -> tuple | CausalLMOutputWithPast: + if output_attentions: + warnings.warn( + "`BitNetModel` does not support output attention weights now, so `output_attentions` is set to `False`.", + ) + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + + # embed positions + hidden_states = inputs_embeds + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + next_cache = None + + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + layer_outputs = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attns] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class BitNetForCausalLM(BitNetPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = BitNetModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[Any], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + logits = None if self.config.fuse_linear_cross_entropy else self.lm_head(hidden_states[:, -logits_to_keep:]) + + loss = None + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/comba/__init__.py b/code/flash-linear-attention/fla/models/comba/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..965d9d584cf439f6722fa761b4f9975c4de7c31d --- /dev/null +++ b/code/flash-linear-attention/fla/models/comba/__init__.py @@ -0,0 +1,11 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.comba.configuration_comba import CombaConfig +from fla.models.comba.modeling_comba import CombaForCausalLM, CombaModel + +AutoConfig.register(CombaConfig.model_type, CombaConfig, exist_ok=True) +AutoModel.register(CombaConfig, CombaModel, exist_ok=True) +AutoModelForCausalLM.register(CombaConfig, CombaForCausalLM, exist_ok=True) + +__all__ = ['CombaConfig', 'CombaForCausalLM', 'CombaModel'] diff --git a/code/flash-linear-attention/fla/models/comba/configuration_comba.py b/code/flash-linear-attention/fla/models/comba/configuration_comba.py new file mode 100644 index 0000000000000000000000000000000000000000..6f17f65e0e1bae5aba7e87253c32d122d1f6432a --- /dev/null +++ b/code/flash-linear-attention/fla/models/comba/configuration_comba.py @@ -0,0 +1,105 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class CombaConfig(PretrainedConfig): + model_type = 'comba' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + hidden_size: int = 2048, + conv_size: int = 4, + head_dim: int = 256, + num_heads: int = 6, + num_v_heads: int | None = None, + expand_v: float = 2.0, + use_output_gate: bool = True, + use_short_conv: bool = True, + use_output_correction: bool = True, + use_inner_decay: bool = True, + correction_factor: float = 1., + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + num_hidden_layers: int = 21, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.conv_size = conv_size + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads + self.expand_v = expand_v + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.use_output_correction = use_output_correction + self.correction_factor = correction_factor + self.use_inner_decay = use_inner_decay + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/comba/modeling_comba.py b/code/flash-linear-attention/fla/models/comba/modeling_comba.py new file mode 100644 index 0000000000000000000000000000000000000000..62443caa4e2b98969d008aedca2f03512ed7e9a6 --- /dev/null +++ b/code/flash-linear-attention/fla/models/comba/modeling_comba.py @@ -0,0 +1,379 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.comba import Comba +from fla.models.comba.configuration_comba import CombaConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as CombaMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class CombaBlock(GradientCheckpointingLayer): + + def __init__(self, config: CombaConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = Comba( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_v=config.expand_v, + head_dim=config.head_dim, + num_heads=config.num_heads, + num_v_heads=config.num_v_heads, + use_output_gate=config.use_output_gate, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = CombaMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class CombaPreTrainedModel(PreTrainedModel): + + config_class = CombaConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['CombaBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, Comba) and next(module.parameters()).device.type != 'meta': + with torch.no_grad(): + module.A_log.copy_(nn.init.uniform_(module.A_log, a=0, b=16).log()) + module.A_log._no_weight_decay = True + dt = torch.exp( + nn.init.uniform_(module.dt_bias) * (math.log(0.1) - math.log(0.001)) + math.log(0.001), + ).clamp(min=1e-4) + # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + module.dt_bias.copy_(inv_dt) + module.dt_bias._no_weight_decay = True + + elif isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class CombaModel(CombaPreTrainedModel): + + def __init__(self, config: CombaConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([CombaBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`CombaModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class CombaForCausalLM(CombaPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = CombaModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/delta_net/__init__.py b/code/flash-linear-attention/fla/models/delta_net/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b43ba34156b3b73d78018e0b248ff277b1219a86 --- /dev/null +++ b/code/flash-linear-attention/fla/models/delta_net/__init__.py @@ -0,0 +1,11 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.delta_net.configuration_delta_net import DeltaNetConfig +from fla.models.delta_net.modeling_delta_net import DeltaNetForCausalLM, DeltaNetModel + +AutoConfig.register(DeltaNetConfig.model_type, DeltaNetConfig, exist_ok=True) +AutoModel.register(DeltaNetConfig, DeltaNetModel, exist_ok=True) +AutoModelForCausalLM.register(DeltaNetConfig, DeltaNetForCausalLM, exist_ok=True) + +__all__ = ['DeltaNetConfig', 'DeltaNetForCausalLM', 'DeltaNetModel'] diff --git a/code/flash-linear-attention/fla/models/delta_net/configuration_delta_net.py b/code/flash-linear-attention/fla/models/delta_net/configuration_delta_net.py new file mode 100644 index 0000000000000000000000000000000000000000..e432338b6059cc16aa6e01ec37b48f07fd3007c1 --- /dev/null +++ b/code/flash-linear-attention/fla/models/delta_net/configuration_delta_net.py @@ -0,0 +1,105 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class DeltaNetConfig(PretrainedConfig): + + model_type = 'delta_net' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + hidden_size: int = 2048, + expand_k: float = 1.0, + expand_v: float = 1.0, + use_gate: bool = False, + use_short_conv: bool = True, + conv_size: int = 4, + use_beta: bool = True, + use_output_norm: bool = True, + num_heads: int = 16, + qk_norm: str = 'l2', + qk_activation: str = 'silu', + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + num_hidden_layers: int = 24, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.use_gate = use_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.use_beta = use_beta + self.use_output_norm = use_output_norm + self.num_heads = num_heads + self.qk_norm = qk_norm + self.qk_activation = qk_activation + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/delta_net/modeling_delta_net.py b/code/flash-linear-attention/fla/models/delta_net/modeling_delta_net.py new file mode 100644 index 0000000000000000000000000000000000000000..d1290caf26b82a47174a4720fd4a142fc5209157 --- /dev/null +++ b/code/flash-linear-attention/fla/models/delta_net/modeling_delta_net.py @@ -0,0 +1,369 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.delta_net import DeltaNet +from fla.models.delta_net.configuration_delta_net import DeltaNetConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as DeltaNetMLP +from fla.modules.l2warp import l2_warp + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +class DeltaNetBlock(GradientCheckpointingLayer): + + def __init__(self, config: DeltaNetConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = DeltaNet( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_k=config.expand_k, + expand_v=config.expand_v, + num_heads=config.num_heads, + use_gate=config.use_gate, + use_beta=config.use_beta, + use_short_conv=config.use_short_conv, + use_output_norm=config.use_output_norm, + conv_size=config.conv_size, + qk_norm=config.qk_norm, + qk_activation=config.qk_activation, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = DeltaNetMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class DeltaNetPreTrainedModel(PreTrainedModel): + + config_class = DeltaNetConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['DeltaNetBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class DeltaNetModel(DeltaNetPreTrainedModel): + + def __init__(self, config: DeltaNetConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([DeltaNetBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`DeltaNetModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class DeltaNetForCausalLM(DeltaNetPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = DeltaNetModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/deltaformer/__init__.py b/code/flash-linear-attention/fla/models/deltaformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f8b299b7578fa156cec6798eafe117aa404d5815 --- /dev/null +++ b/code/flash-linear-attention/fla/models/deltaformer/__init__.py @@ -0,0 +1,11 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.deltaformer.configuration_deltaformer import DeltaFormerConfig +from fla.models.deltaformer.modeling_deltaformer import DeltaFormerForCausalLM, DeltaFormerModel + +AutoConfig.register(DeltaFormerConfig.model_type, DeltaFormerConfig, exist_ok=True) +AutoModel.register(DeltaFormerConfig, DeltaFormerModel, exist_ok=True) +AutoModelForCausalLM.register(DeltaFormerConfig, DeltaFormerForCausalLM, exist_ok=True) + +__all__ = ['DeltaFormerConfig', 'DeltaFormerForCausalLM', 'DeltaFormerModel'] diff --git a/code/flash-linear-attention/fla/models/deltaformer/configuration_deltaformer.py b/code/flash-linear-attention/fla/models/deltaformer/configuration_deltaformer.py new file mode 100644 index 0000000000000000000000000000000000000000..205908934061d53e2df0da6663e9136f5f39aac5 --- /dev/null +++ b/code/flash-linear-attention/fla/models/deltaformer/configuration_deltaformer.py @@ -0,0 +1,105 @@ + +from __future__ import annotations + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class DeltaFormerConfig(PretrainedConfig): + model_type = 'deltaformer' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + num_hidden_layers: int = 24, + num_heads: int = 8, + num_kv_heads: int | None = None, + attn_mode: str = "chunk", + hidden_act: str = "swish", + max_position_embeddings: int = 2048, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + qkv_bias: bool = False, + qk_norm: bool = False, + rope_theta: float = 10000., + rope_max_position_embeddings: int | None = None, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + output_attentions: bool = False, + output_hidden_states: bool = False, + **kwargs, + ): + self.hidden_size = hidden_size + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.attn_mode = attn_mode + self.hidden_act = hidden_act + self.max_position_embeddings = max_position_embeddings + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.qkv_bias = qkv_bias + self.qk_norm = qk_norm + self.rope_theta = rope_theta + self.rope_max_position_embeddings = rope_max_position_embeddings + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + self.output_attentions = output_attentions + self.output_hidden_states = output_hidden_states + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/deltaformer/modeling_deltaformer.py b/code/flash-linear-attention/fla/models/deltaformer/modeling_deltaformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ce1e7e2d762eff02b2c43737ed5aeca37b7ddbc2 --- /dev/null +++ b/code/flash-linear-attention/fla/models/deltaformer/modeling_deltaformer.py @@ -0,0 +1,316 @@ + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.deltaformer import DeltaFormerAttention +from fla.models.deltaformer.configuration_deltaformer import DeltaFormerConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as DeltaFormerMLP +from fla.modules.l2warp import l2_warp + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +class DeltaFormerBlock(GradientCheckpointingLayer): + + def __init__(self, config: DeltaFormerConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.attn = DeltaFormerAttention( + hidden_size=config.hidden_size, + num_heads=config.num_heads, + num_kv_heads=config.num_kv_heads, + qkv_bias=config.qkv_bias, + qk_norm=config.qk_norm, + rope_theta=config.rope_theta, + max_position_embeddings=config.rope_max_position_embeddings, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = DeltaFormerMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, _, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + outputs = (hidden_states, None, past_key_values) + return outputs + + +class DeltaFormerPreTrainedModel(PreTrainedModel): + + config_class = DeltaFormerConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['DeltaFormerBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + +class DeltaFormerModel(DeltaFormerPreTrainedModel): + + def __init__(self, config: DeltaFormerConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([DeltaFormerBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn( + "`DeltaFormerModel` does not support output attention weights now, " + "so `output_attentions` is set to `False`.", + ) + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class DeltaFormerForCausalLM(DeltaFormerPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config: DeltaFormerConfig): + super().__init__(config) + self.model = DeltaFormerModel(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + def tie_weights(self): + self._tie_or_clone_weights(self.lm_head, self.get_input_embeddings()) + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + labels: torch.LongTensor | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + # For fused linear cross-entropy we do not materialize logits for the full sequence + logits = None if self.config.fuse_linear_cross_entropy else self.lm_head(hidden_states[:, -logits_to_keep:]) + + loss = None + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/forgetting_transformer/__init__.py b/code/flash-linear-attention/fla/models/forgetting_transformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d9c92eb694b5ae579f4ac19424e7294dcbd34b28 --- /dev/null +++ b/code/flash-linear-attention/fla/models/forgetting_transformer/__init__.py @@ -0,0 +1,15 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.forgetting_transformer.configuration_forgetting_transformer import ForgettingTransformerConfig +from fla.models.forgetting_transformer.modeling_forgetting_transformer import ( + ForgettingTransformerForCausalLM, + ForgettingTransformerModel, +) + +AutoConfig.register(ForgettingTransformerConfig.model_type, ForgettingTransformerConfig, exist_ok=True) +AutoModel.register(ForgettingTransformerConfig, ForgettingTransformerModel, exist_ok=True) +AutoModelForCausalLM.register(ForgettingTransformerConfig, ForgettingTransformerForCausalLM, exist_ok=True) + + +__all__ = ['ForgettingTransformerConfig', 'ForgettingTransformerForCausalLM', 'ForgettingTransformerModel'] diff --git a/code/flash-linear-attention/fla/models/forgetting_transformer/configuration_forgetting_transformer.py b/code/flash-linear-attention/fla/models/forgetting_transformer/configuration_forgetting_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..82ce16cb9e8097bb07f1aaaf1260d1dadcd633f9 --- /dev/null +++ b/code/flash-linear-attention/fla/models/forgetting_transformer/configuration_forgetting_transformer.py @@ -0,0 +1,82 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class ForgettingTransformerConfig(PretrainedConfig): + + model_type = 'forgetting_transformer' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + num_hidden_layers: int = 24, + num_heads: int = 32, + num_kv_heads: int | None = None, + qkv_bias: bool = False, + qk_norm: bool = False, + window_size: int | None = None, + use_output_gate: bool = False, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + initializer_range: float = 0.02, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.qkv_bias = qkv_bias + self.qk_norm = qk_norm + self.window_size = window_size + self.use_output_gate = use_output_gate + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + + self.initializer_range = initializer_range + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.use_cache = use_cache + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/forgetting_transformer/modeling_forgetting_transformer.py b/code/flash-linear-attention/fla/models/forgetting_transformer/modeling_forgetting_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..7836f9413c2bd741ff92284309a0a60c17a49aeb --- /dev/null +++ b/code/flash-linear-attention/fla/models/forgetting_transformer/modeling_forgetting_transformer.py @@ -0,0 +1,359 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Any + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.forgetting_attn import ForgettingAttention +from fla.models.forgetting_transformer.configuration_forgetting_transformer import ForgettingTransformerConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as ForgettingTransformerMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class ForgettingTransformerBlock(GradientCheckpointingLayer): + + def __init__(self, config: ForgettingTransformerConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.attn = ForgettingAttention( + hidden_size=config.hidden_size, + num_heads=config.num_heads, + num_kv_heads=config.num_kv_heads, + qkv_bias=config.qkv_bias, + qk_norm=config.qk_norm, + window_size=config.window_size, + use_output_gate=config.use_output_gate, + layer_idx=layer_idx, + ) + + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = ForgettingTransformerMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: tuple[torch.Tensor] | None = None, + output_attentions: bool | None = False, + use_cache: bool | None = False, + **kwargs: Unpack[Any], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attentions,) + + if use_cache: + outputs += (past_key_values,) + + return outputs + + +class ForgettingTransformerPreTrainedModel(PreTrainedModel): + + config_class = ForgettingTransformerConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['ForgettingTransformerBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + rescale_prenorm_residual: bool = False, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per ForgettingTransformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + + +class ForgettingTransformerModel(ForgettingTransformerPreTrainedModel): + + def __init__( + self, + config: ForgettingTransformerConfig, + ) -> ForgettingTransformerModel: + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([ + ForgettingTransformerBlock(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[Any], + ) -> tuple | CausalLMOutputWithPast: + if output_attentions: + warnings.warn( + "`ForgettingTransformerModel` does not support output attention weights now, " + "so `output_attentions` is set to `False`.", + ) + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + + # embed positions + hidden_states = inputs_embeds + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + next_cache = None + + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + layer_outputs = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attns] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class ForgettingTransformerForCausalLM(ForgettingTransformerPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = ForgettingTransformerModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[Any], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + logits = None if self.config.fuse_linear_cross_entropy else self.lm_head(hidden_states[:, -logits_to_keep:]) + + loss = None + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + # Enable model parallelism + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/gated_deltanet/__init__.py b/code/flash-linear-attention/fla/models/gated_deltanet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..30666194ff07f6041b60cf41ebf269811fa3267c --- /dev/null +++ b/code/flash-linear-attention/fla/models/gated_deltanet/__init__.py @@ -0,0 +1,11 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.gated_deltanet.configuration_gated_deltanet import GatedDeltaNetConfig +from fla.models.gated_deltanet.modeling_gated_deltanet import GatedDeltaNetForCausalLM, GatedDeltaNetModel + +AutoConfig.register(GatedDeltaNetConfig.model_type, GatedDeltaNetConfig, exist_ok=True) +AutoModel.register(GatedDeltaNetConfig, GatedDeltaNetModel, exist_ok=True) +AutoModelForCausalLM.register(GatedDeltaNetConfig, GatedDeltaNetForCausalLM, exist_ok=True) + +__all__ = ['GatedDeltaNetConfig', 'GatedDeltaNetForCausalLM', 'GatedDeltaNetModel'] diff --git a/code/flash-linear-attention/fla/models/gated_deltanet/configuration_gated_deltanet.py b/code/flash-linear-attention/fla/models/gated_deltanet/configuration_gated_deltanet.py new file mode 100644 index 0000000000000000000000000000000000000000..653f80191efe3fc36c9fbf37babc1b93ed3ff48c --- /dev/null +++ b/code/flash-linear-attention/fla/models/gated_deltanet/configuration_gated_deltanet.py @@ -0,0 +1,101 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class GatedDeltaNetConfig(PretrainedConfig): + model_type = 'gated_deltanet' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + hidden_size: int = 2048, + expand_v: float = 2.0, + use_gate: bool = True, + use_short_conv: bool = True, + allow_neg_eigval: bool = False, + conv_size: int = 4, + head_dim: int = 256, + num_heads: int = 6, + num_v_heads: int | None = None, + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + num_hidden_layers: int = 21, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.expand_v = expand_v + self.use_gate = use_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + self.allow_neg_eigval = allow_neg_eigval + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/gated_deltanet/modeling_gated_deltanet.py b/code/flash-linear-attention/fla/models/gated_deltanet/modeling_gated_deltanet.py new file mode 100644 index 0000000000000000000000000000000000000000..0d3d2634a9869b9e23c438f83bb7946043cea0a2 --- /dev/null +++ b/code/flash-linear-attention/fla/models/gated_deltanet/modeling_gated_deltanet.py @@ -0,0 +1,380 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.gated_deltanet import GatedDeltaNet +from fla.models.gated_deltanet.configuration_gated_deltanet import GatedDeltaNetConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as GatedDeltaNetMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class GatedDeltaNetBlock(GradientCheckpointingLayer): + + def __init__(self, config: GatedDeltaNetConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = GatedDeltaNet( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_v=config.expand_v, + head_dim=config.head_dim, + num_heads=config.num_heads, + num_v_heads=config.num_v_heads, + use_gate=config.use_gate, + use_short_conv=config.use_short_conv, + allow_neg_eigval=config.allow_neg_eigval, + conv_size=config.conv_size, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = GatedDeltaNetMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class GatedDeltaNetPreTrainedModel(PreTrainedModel): + + config_class = GatedDeltaNetConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['GatedDeltaNetBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, GatedDeltaNet) and next(module.parameters()).device.type != 'meta': + with torch.no_grad(): + module.A_log.copy_(nn.init.uniform_(module.A_log, a=0, b=16).log()) + module.A_log._no_weight_decay = True + dt = torch.exp( + nn.init.uniform_(module.dt_bias) * (math.log(0.1) - math.log(0.001)) + math.log(0.001), + ).clamp(min=1e-4) + # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + module.dt_bias.copy_(inv_dt) + module.dt_bias._no_weight_decay = True + + elif isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class GatedDeltaNetModel(GatedDeltaNetPreTrainedModel): + + def __init__(self, config: GatedDeltaNetConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([GatedDeltaNetBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`GatedDeltaNetModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class GatedDeltaNetForCausalLM(GatedDeltaNetPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = GatedDeltaNetModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/gated_deltaproduct/__init__.py b/code/flash-linear-attention/fla/models/gated_deltaproduct/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5c2e1a9f29bd23d7d39b4698e0bec0d9b1115b00 --- /dev/null +++ b/code/flash-linear-attention/fla/models/gated_deltaproduct/__init__.py @@ -0,0 +1,14 @@ +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.gated_deltaproduct.configuration_gated_deltaproduct import GatedDeltaProductConfig +from fla.models.gated_deltaproduct.modeling_gated_deltaproduct import GatedDeltaProductForCausalLM, GatedDeltaProductModel + +AutoConfig.register(GatedDeltaProductConfig.model_type, GatedDeltaProductConfig, exist_ok=True) +AutoModel.register(GatedDeltaProductConfig, GatedDeltaProductModel, exist_ok=True) +AutoModelForCausalLM.register(GatedDeltaProductConfig, GatedDeltaProductForCausalLM, exist_ok=True) + +__all__ = [ + "GatedDeltaProductConfig", + "GatedDeltaProductForCausalLM", + "GatedDeltaProductModel", +] diff --git a/code/flash-linear-attention/fla/models/gated_deltaproduct/configuration_gated_deltaproduct.py b/code/flash-linear-attention/fla/models/gated_deltaproduct/configuration_gated_deltaproduct.py new file mode 100644 index 0000000000000000000000000000000000000000..fe40abfb8d5f6628065aa73159252959d96fc4ab --- /dev/null +++ b/code/flash-linear-attention/fla/models/gated_deltaproduct/configuration_gated_deltaproduct.py @@ -0,0 +1,105 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class GatedDeltaProductConfig(PretrainedConfig): + model_type = 'gated_deltaproduct' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + conv_size: int = 4, + head_dim: int = 256, + num_heads: int = 6, + hidden_size: int = 2048, + expand_v: float = 2.0, + use_output_gate: bool = True, + use_short_conv: bool = True, + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + num_hidden_layers: int = 21, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + use_forget_gate: bool = False, + allow_neg_eigval: bool = False, + num_householder: int = 1, + **kwargs, + ): + self.attn_mode = attn_mode + self.conv_size = conv_size + self.head_dim = head_dim + self.num_heads = num_heads + self.hidden_size = hidden_size + self.expand_v = expand_v + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + # DeltaProduct specific + self.allow_neg_eigval = allow_neg_eigval + self.num_householder = num_householder + self.use_forget_gate = use_forget_gate + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/gated_deltaproduct/modeling_gated_deltaproduct.py b/code/flash-linear-attention/fla/models/gated_deltaproduct/modeling_gated_deltaproduct.py new file mode 100644 index 0000000000000000000000000000000000000000..2e66a35f45d9c7ca9edf443e49344e5ad637a285 --- /dev/null +++ b/code/flash-linear-attention/fla/models/gated_deltaproduct/modeling_gated_deltaproduct.py @@ -0,0 +1,372 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.gated_deltaproduct import GatedDeltaProduct +from fla.models.gated_deltaproduct.configuration_gated_deltaproduct import GatedDeltaProductConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as GatedDeltaProductMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class GatedDeltaProductBlock(GradientCheckpointingLayer): + + def __init__(self, config: GatedDeltaProductConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = GatedDeltaProduct( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_v=config.expand_v, + head_dim=config.head_dim, + num_heads=config.num_heads, + use_output_gate=config.use_output_gate, + use_forget_gate=config.use_forget_gate, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + norm_eps=config.norm_eps, + allow_neg_eigval=config.allow_neg_eigval, + num_householder=config.num_householder, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = GatedDeltaProductMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class GatedDeltaProductPreTrainedModel(PreTrainedModel): + + config_class = GatedDeltaProductConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['GatedDeltaProductBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class GatedDeltaProductModel(GatedDeltaProductPreTrainedModel): + + def __init__(self, config: GatedDeltaProductConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([ + GatedDeltaProductBlock(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`GatedDeltaProductModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class GatedDeltaProductForCausalLM(GatedDeltaProductPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = GatedDeltaProductModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/gla/__init__.py b/code/flash-linear-attention/fla/models/gla/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5a680adbb140621c36fdac9a1935e1ab0970af05 --- /dev/null +++ b/code/flash-linear-attention/fla/models/gla/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.gla.configuration_gla import GLAConfig +from fla.models.gla.modeling_gla import GLAForCausalLM, GLAModel + +AutoConfig.register(GLAConfig.model_type, GLAConfig, exist_ok=True) +AutoModel.register(GLAConfig, GLAModel, exist_ok=True) +AutoModelForCausalLM.register(GLAConfig, GLAForCausalLM, exist_ok=True) + + +__all__ = ['GLAConfig', 'GLAForCausalLM', 'GLAModel'] diff --git a/code/flash-linear-attention/fla/models/gla/configuration_gla.py b/code/flash-linear-attention/fla/models/gla/configuration_gla.py new file mode 100644 index 0000000000000000000000000000000000000000..f71d44f06f4a1e01433bb1277f602c734b8c8827 --- /dev/null +++ b/code/flash-linear-attention/fla/models/gla/configuration_gla.py @@ -0,0 +1,109 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class GLAConfig(PretrainedConfig): + + model_type = 'gla' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + expand_k: float = 0.5, + expand_v: float = 1., + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + num_hidden_layers: int = 24, + num_heads: int = 4, + num_kv_heads: int | None = None, + feature_map: str | None = None, + attn_mode: str = "chunk", + use_short_conv: bool = False, + conv_size: int = 4, + use_output_gate: bool = True, + clamp_min: float | None = None, + hidden_act: str = "swish", + max_position_embeddings: int = 2048, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + use_gk: bool = True, + use_gv: bool = False, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.feature_map = feature_map + self.attn_mode = attn_mode + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.use_output_gate = use_output_gate + self.clamp_min = clamp_min + self.hidden_act = hidden_act + self.max_position_embeddings = max_position_embeddings + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.use_gk = use_gk + self.use_gv = use_gv + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/gla/modeling_gla.py b/code/flash-linear-attention/fla/models/gla/modeling_gla.py new file mode 100644 index 0000000000000000000000000000000000000000..4b36d94e37845e4f40bac663f47236a8e6923c5d --- /dev/null +++ b/code/flash-linear-attention/fla/models/gla/modeling_gla.py @@ -0,0 +1,372 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.gla import GatedLinearAttention +from fla.models.gla.configuration_gla import GLAConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as GLAMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class GLABlock(GradientCheckpointingLayer): + + def __init__(self, config: GLAConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = GatedLinearAttention( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_k=config.expand_k, + expand_v=config.expand_v, + num_heads=config.num_heads, + num_kv_heads=config.num_kv_heads, + feature_map=config.feature_map, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + use_output_gate=config.use_output_gate, + gate_fn=config.hidden_act, + elementwise_affine=config.elementwise_affine, + norm_eps=config.norm_eps, + clamp_min=config.clamp_min, + fuse_norm=config.fuse_norm, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = GLAMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class GLAPreTrainedModel(PreTrainedModel): + + config_class = GLAConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['GLABlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class GLAModel(GLAPreTrainedModel): + + def __init__(self, config: GLAConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([GLABlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`GLAModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class GLAForCausalLM(GLAPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = GLAModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/gsa/__init__.py b/code/flash-linear-attention/fla/models/gsa/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..61170f8694ad812c2faa98966caee8007b4faed4 --- /dev/null +++ b/code/flash-linear-attention/fla/models/gsa/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.gsa.configuration_gsa import GSAConfig +from fla.models.gsa.modeling_gsa import GSAForCausalLM, GSAModel + +AutoConfig.register(GSAConfig.model_type, GSAConfig, exist_ok=True) +AutoModel.register(GSAConfig, GSAModel, exist_ok=True) +AutoModelForCausalLM.register(GSAConfig, GSAForCausalLM, exist_ok=True) + + +__all__ = ['GSAConfig', 'GSAForCausalLM', 'GSAModel'] diff --git a/code/flash-linear-attention/fla/models/gsa/configuration_gsa.py b/code/flash-linear-attention/fla/models/gsa/configuration_gsa.py new file mode 100644 index 0000000000000000000000000000000000000000..4d4d893b4bdf81816cffa77eb693c207597ddb1f --- /dev/null +++ b/code/flash-linear-attention/fla/models/gsa/configuration_gsa.py @@ -0,0 +1,111 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class GSAConfig(PretrainedConfig): + + model_type = 'gsa' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + gate_logit_normalizer: int | None = 8, + clamp_min: float | None = None, + clamp_max: float | None = None, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + num_hidden_layers: int = 24, + num_heads: int = 4, + num_kv_heads: int | None = None, + num_slots: int | None = 64, + use_short_conv: bool = False, + conv_size: int = 4, + exapnd_k: float = 1, + exapnd_v: float = 1, + feature_map: str = 'swish', + use_output_gate: bool = False, + use_norm: bool = True, + max_position_embeddings: int = 2048, + hidden_act: str = "swish", + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + initializer_range: float = 0.02, + tie_word_embeddings: bool = False, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.hidden_size = hidden_size + self.gate_logit_normalizer = gate_logit_normalizer + self.clamp_min = clamp_min + self.clamp_max = clamp_max + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.num_slots = num_slots + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.expand_k = exapnd_k + self.expand_v = exapnd_v + self.feature_map = feature_map + self.use_output_gate = use_output_gate + self.use_norm = use_norm + self.max_position_embeddings = max_position_embeddings + self.hidden_act = hidden_act + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/gsa/modeling_gsa.py b/code/flash-linear-attention/fla/models/gsa/modeling_gsa.py new file mode 100644 index 0000000000000000000000000000000000000000..646edd7d4b7d9de78060234d331e9cc42c8cccc4 --- /dev/null +++ b/code/flash-linear-attention/fla/models/gsa/modeling_gsa.py @@ -0,0 +1,375 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.gsa import GatedSlotAttention +from fla.models.gsa.configuration_gsa import GSAConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as GSAMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class GSABlock(GradientCheckpointingLayer): + + def __init__(self, config: GSAConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = GatedSlotAttention( + hidden_size=config.hidden_size, + expand_k=config.expand_k, + expand_v=config.expand_v, + num_heads=config.num_heads, + num_kv_heads=config.num_kv_heads, + num_slots=config.num_slots, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + feature_map=config.feature_map, + use_output_gate=config.use_output_gate, + use_norm=config.use_norm, + gate_fn=config.hidden_act, + gate_logit_normalizer=config.gate_logit_normalizer, + elementwise_affine=config.elementwise_affine, + norm_eps=config.norm_eps, + fuse_norm=config.fuse_norm, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = GSAMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class GSAPreTrainedModel(PreTrainedModel): + + config_class = GSAConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['GSABlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class GSAModel(GSAPreTrainedModel): + + def __init__(self, config: GSAConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([GSABlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`GSAModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class GSAForCausalLM(GSAPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + + super().__init__(config) + self.model = GSAModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + # Enable model parallelism + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/hgrn/__init__.py b/code/flash-linear-attention/fla/models/hgrn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b7ef1f0cd18d1f6aba50f4c8e4410a8a9924d9fe --- /dev/null +++ b/code/flash-linear-attention/fla/models/hgrn/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.hgrn.configuration_hgrn import HGRNConfig +from fla.models.hgrn.modeling_hgrn import HGRNForCausalLM, HGRNModel + +AutoConfig.register(HGRNConfig.model_type, HGRNConfig, exist_ok=True) +AutoModel.register(HGRNConfig, HGRNModel, exist_ok=True) +AutoModelForCausalLM.register(HGRNConfig, HGRNForCausalLM, exist_ok=True) + + +__all__ = ['HGRNConfig', 'HGRNForCausalLM', 'HGRNModel'] diff --git a/code/flash-linear-attention/fla/models/hgrn/configuration_hgrn.py b/code/flash-linear-attention/fla/models/hgrn/configuration_hgrn.py new file mode 100644 index 0000000000000000000000000000000000000000..893e53c63a05054fbfa8421cf90b04a13954e3a5 --- /dev/null +++ b/code/flash-linear-attention/fla/models/hgrn/configuration_hgrn.py @@ -0,0 +1,95 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class HGRNConfig(PretrainedConfig): + + model_type = 'hgrn' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "fused_recurrent", + hidden_size: int = 2048, + num_hidden_layers: int = 24, + expand_ratio: int | None = 1, + use_short_conv: bool = False, + conv_size: int = 4, + use_lower_bound: bool = True, + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.expand_ratio = expand_ratio + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.use_lower_bound = use_lower_bound + self.max_position_embeddings = max_position_embeddings + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.elementwise_affine = elementwise_affine + self.attn = attn + self.norm_eps = norm_eps + self.hidden_act = hidden_act + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/hgrn/modeling_hgrn.py b/code/flash-linear-attention/fla/models/hgrn/modeling_hgrn.py new file mode 100644 index 0000000000000000000000000000000000000000..75c67eb0a4b88b3ad52f7117dd06a09d3bb81a48 --- /dev/null +++ b/code/flash-linear-attention/fla/models/hgrn/modeling_hgrn.py @@ -0,0 +1,374 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.hgrn import HGRNAttention +from fla.models.hgrn.configuration_hgrn import HGRNConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as HGRNMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class HGRNBlock(GradientCheckpointingLayer): + + def __init__(self, config: HGRNConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = HGRNAttention( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_ratio=config.expand_ratio, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + elementwise_affine=config.elementwise_affine, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = HGRNMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + lower_bound: torch.Tensor | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + lower_bound=lower_bound, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class HGRNPreTrainedModel(PreTrainedModel): + + config_class = HGRNConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['HGRNBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class HGRNModel(HGRNPreTrainedModel): + + def __init__(self, config: HGRNConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + if config.use_lower_bound: + self.lower_bounds = nn.Parameter(torch.zeros(config.num_hidden_layers, config.hidden_size)) + self.layers = nn.ModuleList([HGRNBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`HGRNModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + + if self.config.use_lower_bound: + lower_bounds = self.lower_bounds.softmax(0, dtype=torch.float) + lower_bounds = lower_bounds.cumsum(0) - lower_bounds[0] + for i, layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + lower_bound = lower_bounds[i] if self.config.use_lower_bound else None + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + lower_bound=lower_bound, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class HGRNForCausalLM(HGRNPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = HGRNModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/hgrn2/__init__.py b/code/flash-linear-attention/fla/models/hgrn2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e323464a32a430abee216a305e7ea51a813dd207 --- /dev/null +++ b/code/flash-linear-attention/fla/models/hgrn2/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.hgrn2.configuration_hgrn2 import HGRN2Config +from fla.models.hgrn2.modeling_hgrn2 import HGRN2ForCausalLM, HGRN2Model + +AutoConfig.register(HGRN2Config.model_type, HGRN2Config, exist_ok=True) +AutoModel.register(HGRN2Config, HGRN2Model, exist_ok=True) +AutoModelForCausalLM.register(HGRN2Config, HGRN2ForCausalLM, exist_ok=True) + + +__all__ = ['HGRN2Config', 'HGRN2ForCausalLM', 'HGRN2Model'] diff --git a/code/flash-linear-attention/fla/models/hgrn2/configuration_hgrn2.py b/code/flash-linear-attention/fla/models/hgrn2/configuration_hgrn2.py new file mode 100644 index 0000000000000000000000000000000000000000..c05e67adeee6f59550aca70a6590c500dfb83076 --- /dev/null +++ b/code/flash-linear-attention/fla/models/hgrn2/configuration_hgrn2.py @@ -0,0 +1,105 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class HGRN2Config(PretrainedConfig): + + model_type = 'hgrn2' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + num_hidden_layers: int = 24, + attn_mode: str = "chunk", + num_heads: int | None = None, + expand_ratio: int | None = 128, + use_short_conv: bool = False, + conv_size: int = 4, + use_lower_bound: bool = True, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + max_position_embeddings: int = 2048, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.attn_mode = attn_mode + + if expand_ratio is None and num_heads is not None: + expand_ratio = hidden_size // num_heads + elif expand_ratio is not None and num_heads is None: + num_heads = hidden_size // expand_ratio + elif expand_ratio is None and num_heads is None: + raise RuntimeError("One of `expand_ratio` or `num_heads` should be provided.") + self.num_heads = num_heads + self.expand_ratio = expand_ratio + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.use_lower_bound = use_lower_bound + self.max_position_embeddings = max_position_embeddings + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/hgrn2/modeling_hgrn2.py b/code/flash-linear-attention/fla/models/hgrn2/modeling_hgrn2.py new file mode 100644 index 0000000000000000000000000000000000000000..b65fec3fa2f1df7d8d3a2330c922d3a86b74ddd8 --- /dev/null +++ b/code/flash-linear-attention/fla/models/hgrn2/modeling_hgrn2.py @@ -0,0 +1,375 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.hgrn2 import HGRN2Attention +from fla.models.hgrn2.configuration_hgrn2 import HGRN2Config +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as HGRN2MLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class HGRN2Block(GradientCheckpointingLayer): + + def __init__(self, config: HGRN2Config, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = HGRN2Attention( + mode=config.attn_mode, + hidden_size=config.hidden_size, + num_heads=config.num_heads, + expand_ratio=config.expand_ratio, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + elementwise_affine=config.elementwise_affine, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = HGRN2MLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + lower_bound: torch.Tensor | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + lower_bound=lower_bound, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class HGRN2PreTrainedModel(PreTrainedModel): + + config_class = HGRN2Config + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['HGRN2Block'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class HGRN2Model(HGRN2PreTrainedModel): + + def __init__(self, config: HGRN2Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + if config.use_lower_bound: + self.lower_bounds = nn.Parameter(torch.zeros(config.num_hidden_layers, config.hidden_size)) + self.layers = nn.ModuleList([HGRN2Block(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`HGRN2Model` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + + if self.config.use_lower_bound: + lower_bounds = self.lower_bounds.softmax(0, dtype=torch.float) + lower_bounds = lower_bounds.cumsum(0) - lower_bounds[0] + for i, layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + lower_bound = lower_bounds[i] if self.config.use_lower_bound else None + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + lower_bound=lower_bound, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class HGRN2ForCausalLM(HGRN2PreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = HGRN2Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/kda/__init__.py b/code/flash-linear-attention/fla/models/kda/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cd5af56117d7604fc3801d2d7f8c471622f80856 --- /dev/null +++ b/code/flash-linear-attention/fla/models/kda/__init__.py @@ -0,0 +1,11 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.kda.configuration_kda import KDAConfig +from fla.models.kda.modeling_kda import KDAForCausalLM, KDAModel + +AutoConfig.register(KDAConfig.model_type, KDAConfig, exist_ok=True) +AutoModel.register(KDAConfig, KDAModel, exist_ok=True) +AutoModelForCausalLM.register(KDAConfig, KDAForCausalLM, exist_ok=True) + +__all__ = ['KDAConfig', 'KDAForCausalLM', 'KDAModel'] diff --git a/code/flash-linear-attention/fla/models/kda/configuration_kda.py b/code/flash-linear-attention/fla/models/kda/configuration_kda.py new file mode 100644 index 0000000000000000000000000000000000000000..89b162925e49380e089cf2ec0a1a71075bde92a3 --- /dev/null +++ b/code/flash-linear-attention/fla/models/kda/configuration_kda.py @@ -0,0 +1,85 @@ + + +from transformers.configuration_utils import PretrainedConfig + + +class KDAConfig(PretrainedConfig): + model_type = 'kda' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + hidden_size: int = 2048, + expand_v: float = 1.0, + use_short_conv: bool = True, + allow_neg_eigval: bool = False, + conv_size: int = 4, + head_dim: int = 128, + num_heads: int = 16, + num_v_heads: int | None = None, + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + num_hidden_layers: int = 24, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.expand_v = expand_v + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + self.allow_neg_eigval = allow_neg_eigval + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/kda/modeling_kda.py b/code/flash-linear-attention/fla/models/kda/modeling_kda.py new file mode 100644 index 0000000000000000000000000000000000000000..6def9567ee6bb8bd15ea83599f825e58cfe96a98 --- /dev/null +++ b/code/flash-linear-attention/fla/models/kda/modeling_kda.py @@ -0,0 +1,392 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.generation import GenerationMixin +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.kda import KimiDeltaAttention +from fla.models.kda.configuration_kda import KDAConfig +from fla.models.utils import Cache +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as KDAMLP +from fla.modules.l2warp import l2_warp + +try: + from torch.distributed.tensor import DTensor +except (ImportError, AttributeError): + DTensor = None + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +logger = logging.get_logger(__name__) + + +class KDABlock(nn.Module): + def __init__(self, config: KDAConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = KimiDeltaAttention( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_v=config.expand_v, + head_dim=config.head_dim, + num_heads=config.num_heads, + num_v_heads=config.num_v_heads, + use_short_conv=config.use_short_conv, + allow_neg_eigval=config.allow_neg_eigval, + conv_size=config.conv_size, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = KDAMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class KDAPreTrainedModel(PreTrainedModel): + + config_class = KDAConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['KDABlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, KimiDeltaAttention) and next(module.parameters()).device.type != 'meta': + with torch.no_grad(): + module.A_log.copy_(nn.init.uniform_(module.A_log, a=1, b=16).log()) + dt = torch.exp( + nn.init.uniform_(module.dt_bias) * (math.log(0.1) - math.log(0.001)) + math.log(0.001), + ).clamp(min=1e-4) + inv_dt = dt + torch.log(-torch.expm1(-dt)) + module.dt_bias.copy_(inv_dt) + module.dt_bias._is_hf_initialized = True + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None and not getattr(module.bias, '_is_hf_initialized', False): + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class KDAModel(KDAPreTrainedModel): + + def __init__(self, config: KDAConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([KDABlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`KDAModel` does not `output_attentions` now, setting it to `False`.", stacklevel=2) + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + if self.gradient_checkpointing and self.training and use_cache: + logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + hidden_states, attentions, past_key_values = self._gradient_checkpointing_func( + layer.__call__, + hidden_states, + attention_mask, + past_key_values, + use_cache, + output_attentions, + **kwargs, + ) + else: + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class KDAForCausalLM(KDAPreTrainedModel, GenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = KDAModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) from exception + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training and labels is not None + + loss, logits = None, None + if not fuse_linear_and_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if fuse_linear_and_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if fuse_linear_and_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/lightnet/__init__.py b/code/flash-linear-attention/fla/models/lightnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5d3edc95065ce5c16fed4120a1bf9fe34265a08e --- /dev/null +++ b/code/flash-linear-attention/fla/models/lightnet/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.lightnet.configuration_lightnet import LightNetConfig +from fla.models.lightnet.modeling_lightnet import LightNetForCausalLM, LightNetModel + +AutoConfig.register(LightNetConfig.model_type, LightNetConfig, exist_ok=True) +AutoModel.register(LightNetConfig, LightNetModel, exist_ok=True) +AutoModelForCausalLM.register(LightNetConfig, LightNetForCausalLM, exist_ok=True) + + +__all__ = ['LightNetConfig', 'LightNetForCausalLM', 'LightNetModel'] diff --git a/code/flash-linear-attention/fla/models/lightnet/configuration_lightnet.py b/code/flash-linear-attention/fla/models/lightnet/configuration_lightnet.py new file mode 100644 index 0000000000000000000000000000000000000000..8f8c9eefed4c659b6b89f412ea8b8bb66dbcf9f4 --- /dev/null +++ b/code/flash-linear-attention/fla/models/lightnet/configuration_lightnet.py @@ -0,0 +1,97 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class LightNetConfig(PretrainedConfig): + + model_type = 'lightnet' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + num_hidden_layers: int = 24, + attn_mode: str = "chunk", + num_heads: int | None = None, + expand_ratio: int | None = 128, + use_short_conv: bool = False, + conv_size: int = 4, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + max_position_embeddings: int = 2048, + gate_low_rank_dim: int = 128, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.attn_mode = attn_mode + self.num_heads = num_heads + self.expand_ratio = expand_ratio + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.max_position_embeddings = max_position_embeddings + self.gate_low_rank_dim = gate_low_rank_dim + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/lightnet/modeling_lightnet.py b/code/flash-linear-attention/fla/models/lightnet/modeling_lightnet.py new file mode 100644 index 0000000000000000000000000000000000000000..09b7edbbd4403337973d506de14fdd7ce53ef2c9 --- /dev/null +++ b/code/flash-linear-attention/fla/models/lightnet/modeling_lightnet.py @@ -0,0 +1,365 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.lightnet import LightNetAttention +from fla.models.lightnet.configuration_lightnet import LightNetConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as LightNetMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class LightNetBlock(GradientCheckpointingLayer): + + def __init__(self, config: LightNetConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = LightNetAttention( + mode=config.attn_mode, + hidden_size=config.hidden_size, + num_heads=config.num_heads, + expand_ratio=config.expand_ratio, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + gate_low_rank_dim=config.gate_low_rank_dim, + elementwise_affine=config.elementwise_affine, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = LightNetMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class LightNetPreTrainedModel(PreTrainedModel): + + config_class = LightNetConfig + supports_gradient_checkpointing = True + _no_split_modules = ['LightNetBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class LightNetModel(LightNetPreTrainedModel): + + def __init__(self, config: LightNetConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([LightNetBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`LightNetModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + + for i, layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class LightNetForCausalLM(LightNetPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = LightNetModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/linear_attn/__init__.py b/code/flash-linear-attention/fla/models/linear_attn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f54c0f3c49bb94a1260090c7ca73f104a1ecb91f --- /dev/null +++ b/code/flash-linear-attention/fla/models/linear_attn/__init__.py @@ -0,0 +1,11 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.linear_attn.configuration_linear_attn import LinearAttentionConfig +from fla.models.linear_attn.modeling_linear_attn import LinearAttentionForCausalLM, LinearAttentionModel + +AutoConfig.register(LinearAttentionConfig.model_type, LinearAttentionConfig, exist_ok=True) +AutoModel.register(LinearAttentionConfig, LinearAttentionModel, exist_ok=True) +AutoModelForCausalLM.register(LinearAttentionConfig, LinearAttentionForCausalLM, exist_ok=True) + +__all__ = ['LinearAttentionConfig', 'LinearAttentionForCausalLM', 'LinearAttentionModel'] diff --git a/code/flash-linear-attention/fla/models/linear_attn/configuration_linear_attn.py b/code/flash-linear-attention/fla/models/linear_attn/configuration_linear_attn.py new file mode 100644 index 0000000000000000000000000000000000000000..dd7c5a6cd3c019628f5f018098c605b5d503c0e3 --- /dev/null +++ b/code/flash-linear-attention/fla/models/linear_attn/configuration_linear_attn.py @@ -0,0 +1,105 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class LinearAttentionConfig(PretrainedConfig): + + model_type = 'linear_attn' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "fused_chunk", + hidden_size: int = 2048, + expand_k: float = 1.0, + expand_v: float = 1.0, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + num_hidden_layers: int = 24, + num_heads: int = 4, + num_kv_heads: int | None = None, + feature_map: str = "elementwise_product", + tie_feature_map_qk: bool = False, + norm_q: bool = False, + norm_k: bool = False, + norm_feature_map: bool = False, + hidden_act: str = "swish", + max_position_embeddings: int = 2048, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.feature_map = feature_map + self.tie_feature_map_qk = tie_feature_map_qk + self.norm_q = norm_q + self.norm_k = norm_k + self.norm_feature_map = norm_feature_map + self.hidden_act = hidden_act + self.max_position_embeddings = max_position_embeddings + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/linear_attn/modeling_linear_attn.py b/code/flash-linear-attention/fla/models/linear_attn/modeling_linear_attn.py new file mode 100644 index 0000000000000000000000000000000000000000..baea1553287ba3d383038056bc4922589b13f343 --- /dev/null +++ b/code/flash-linear-attention/fla/models/linear_attn/modeling_linear_attn.py @@ -0,0 +1,366 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.linear_attn import LinearAttention +from fla.models.linear_attn.configuration_linear_attn import LinearAttentionConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as LinearAttentionMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class LinearAttentionBlock(GradientCheckpointingLayer): + + def __init__(self, config: LinearAttentionConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = LinearAttention( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_k=config.expand_k, + expand_v=config.expand_v, + num_heads=config.num_heads, + num_kv_heads=config.num_kv_heads, + feature_map=config.feature_map, + tie_feature_map_qk=config.tie_feature_map_qk, + norm_q=config.norm_q, + norm_k=config.norm_k, + do_feature_map_norm=config.norm_feature_map, + elementwise_affine=config.elementwise_affine, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = LinearAttentionMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs, + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + # currently not supported + attentions, past_key_values = None, None + hidden_states = self.attn_norm(hidden_states) + hidden_states = self.attn(hidden_states=hidden_states, **kwargs) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class LinearAttentionPreTrainedModel(PreTrainedModel): + + config_class = LinearAttentionConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['LinearAttentionBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class LinearAttentionModel(LinearAttentionPreTrainedModel): + + def __init__(self, config: LinearAttentionConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([LinearAttentionBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn( + "`LinearAttentionModel` does not support output attention weights now, " + "so `output_attentions` is set to `False`.", + ) + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + + for i, layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class LinearAttentionForCausalLM(LinearAttentionPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = LinearAttentionModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/log_linear_mamba2/__init__.py b/code/flash-linear-attention/fla/models/log_linear_mamba2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..623af8a54eae7a58f1258311fbde1519f26d3150 --- /dev/null +++ b/code/flash-linear-attention/fla/models/log_linear_mamba2/__init__.py @@ -0,0 +1,16 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.log_linear_mamba2.configuration_log_linear_mamba2 import LogLinearMamba2Config +from fla.models.log_linear_mamba2.modeling_log_linear_mamba2 import ( + LogLinearMamba2Block, + LogLinearMamba2ForCausalLM, + LogLinearMamba2Model, +) + +AutoConfig.register(LogLinearMamba2Config.model_type, LogLinearMamba2Config, exist_ok=True) +AutoModel.register(LogLinearMamba2Config, LogLinearMamba2Model, exist_ok=True) +AutoModelForCausalLM.register(LogLinearMamba2Config, LogLinearMamba2ForCausalLM, exist_ok=True) + + +__all__ = ['LogLinearMamba2Config', 'LogLinearMamba2ForCausalLM', 'LogLinearMamba2Model', 'LogLinearMamba2Block'] diff --git a/code/flash-linear-attention/fla/models/log_linear_mamba2/configuration_log_linear_mamba2.py b/code/flash-linear-attention/fla/models/log_linear_mamba2/configuration_log_linear_mamba2.py new file mode 100644 index 0000000000000000000000000000000000000000..dd53f724205eab2799716bb2aa2e1b3764066072 --- /dev/null +++ b/code/flash-linear-attention/fla/models/log_linear_mamba2/configuration_log_linear_mamba2.py @@ -0,0 +1,18 @@ +from fla.models.mamba2 import Mamba2Config + + +class LogLinearMamba2Config(Mamba2Config): + + model_type = "log_linear_mamba2" + + def __init__( + self, + residual_in_fp32: bool = False, + chunk_size: int = 64, + **kwargs, + ): + super().__init__( + residual_in_fp32=residual_in_fp32, + chunk_size=chunk_size, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/log_linear_mamba2/modeling_log_linear_mamba2.py b/code/flash-linear-attention/fla/models/log_linear_mamba2/modeling_log_linear_mamba2.py new file mode 100644 index 0000000000000000000000000000000000000000..cb52e97252c6edf3fd8574914658006d8ea1d2a8 --- /dev/null +++ b/code/flash-linear-attention/fla/models/log_linear_mamba2/modeling_log_linear_mamba2.py @@ -0,0 +1,482 @@ +import math +import warnings + +import torch +from torch import nn +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.log_linear_mamba2 import LogLinearAttentionState, LogLinearMamba2 +from fla.models.log_linear_mamba2.configuration_log_linear_mamba2 import LogLinearMamba2Config +from fla.models.mamba2.modeling_mamba2 import Mamba2Cache, Mamba2CausalLMOutput, Mamba2Output +from fla.models.utils import FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, GatedMLP, RMSNorm + +logger = logging.get_logger(__name__) + + +class LogLinearMamba2Cache(Mamba2Cache): + def __init__( + self, + config: LogLinearMamba2Config, + batch_size: int, + dtype: torch.dtype = torch.float16, + device: str | None = None, + ): + self.dtype = dtype + self.conv_kernel_size = config.conv_kernel + self.n_groups = config.n_groups + self.state_size = config.state_size + self.num_heads = config.num_heads + self.head_dim = config.head_dim + self.intermediate_size = int(config.expand * config.hidden_size) + + self.conv_states = { + i: torch.zeros( + batch_size, + self.intermediate_size + 2 * config.n_groups * config.state_size, + self.conv_kernel_size, + device=device, + dtype=dtype, + ) + for i in range(config.num_hidden_layers) + } + self.hssm_states = dict.fromkeys(range(config.num_hidden_layers)) + + def update_conv_state( + self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False, + ) -> torch.Tensor: + if new_conv_state.dtype != self.conv_states[layer_idx].dtype: + warnings.warn( + f"`new_conv_state.dtype` ({new_conv_state.dtype}) does not match the cache's dtype " + f"({self.conv_states[layer_idx].dtype}), casting.", + stacklevel=2, + ) + new_conv_state = new_conv_state.to(dtype=self.conv_states[layer_idx].dtype) + + if cache_init: + self.conv_states[layer_idx] = new_conv_state.to( + self.conv_states[layer_idx].device, + ) + else: + self.conv_states[layer_idx] = self.conv_states[layer_idx].roll( + shifts=-1, dims=-1, + ) + self.conv_states[layer_idx][:, :, -1] = new_conv_state[:, 0, :].to( + self.conv_states[layer_idx].device, + ) + return self.conv_states[layer_idx] + + def update_hssm_state( + self, layer_idx: int, new_hssm_state: LogLinearAttentionState, + ) -> LogLinearAttentionState: + self.hssm_states[layer_idx] = new_hssm_state + return self.hssm_states[layer_idx] + + def reset(self) -> None: + for k in self.conv_states.keys(): + self.conv_states[k].zero_() + for k in self.hssm_states.keys(): + self.hssm_states[k].reset_states() + + +class LogLinearMamba2Block(nn.Module): + def __init__(self, config: LogLinearMamba2Config, layer_idx: int) -> None: + super().__init__() + if config.residual_in_fp32: + raise NotImplementedError + self.config = config + self.layer_idx = layer_idx + self.mixer_norm = RMSNorm(config.hidden_size, eps=config.norm_eps) + self.mlp_norm = RMSNorm(config.hidden_size, eps=config.norm_eps) + self.mixer = LogLinearMamba2( + num_heads=config.num_heads, + head_dim=config.head_dim, + hidden_size=config.hidden_size, + state_size=config.state_size, + expand=config.expand, + n_groups=config.n_groups, + conv_kernel=config.conv_kernel, + use_conv_bias=config.use_conv_bias, + hidden_act=config.hidden_act, + rms_norm=config.rms_norm, + chunk_size=config.chunk_size, + time_step_rank=config.time_step_rank, + time_step_limit=config.time_step_limit, + time_step_min=config.time_step_min, + time_step_max=config.time_step_max, + use_bias=config.use_bias, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + ) + self.mlp = GatedMLP( + hidden_size=config.hidden_size, + hidden_ratio=4, + intermediate_size=None, + hidden_act="swish", + fuse_swiglu=True, + ) + + def forward( + self, + hidden_states, + cache_params: LogLinearMamba2Cache | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + ): + residual = hidden_states + hidden_states = self.mixer_norm(hidden_states) + hidden_states = self.mixer( + hidden_states, + cache_params=cache_params, + cache_position=cache_position, + attention_mask=attention_mask, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm( + hidden_states, residual=residual, prenorm=True, + ) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +class LogLinearMamba2PreTrainedModel(PreTrainedModel, FLAGenerationMixin): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = LogLinearMamba2Config + base_model_prefix = "backbone" + _no_split_modules = ["LogLinearMamba2Block"] + supports_gradient_checkpointing = True + _is_stateful = True + + def _init_weights( + self, + module: nn.Module, + num_residuals_per_layer: int = 2, # HAttention + MLP + ): + """Initialize the weights.""" + if isinstance(module, LogLinearMamba2): + # --- A_log --- + A = torch.arange(1, module.num_heads + 1) + with torch.no_grad(): + if not isinstance(module.A_log, torch.distributed.tensor.DTensor): + module.A_log.copy_(torch.log(A)) + else: + logger.warning_once("`A_log` is a DTensor, skipping initialization") + module.A_log._no_weight_decay = True + + # --- D --- + nn.init.ones_(module.D) + module.D._no_weight_decay = True + + # --- L --- + nn.init.ones_(module.L) + module.L._no_weight_decay = True + + # --- dt_bias --- + dt = torch.exp( + torch.rand(self.config.num_heads) + * ( + math.log(self.config.time_step_max) + - math.log(self.config.time_step_min) + ) + + math.log(self.config.time_step_min), + ).clamp(min=self.config.time_step_floor) + + # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + with torch.no_grad(): + if not isinstance(module.dt_bias, torch.distributed.tensor.DTensor): + module.dt_bias.copy_(inv_dt) + else: + logger.warning_once( + "`dt_bias` is a DTensor, skipping initialization", + ) + module.dt_bias._no_reinit = True + + elif isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + # guard against deprecated behavior + if hasattr(module.bias, "_no_reinit"): + raise ValueError("This is not supposed to happen") + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, "reset_parameters"): + module.reset_parameters() + + if self.config.rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, "o_proj"): + # p = module.o_proj.weight + # guard against deprecated behavior + raise ValueError("This is not supposed to happen") + elif hasattr(module, "out_proj"): + p = module.out_proj.weight + elif hasattr(module, "down_proj"): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt( + num_residuals_per_layer * self.config.num_hidden_layers, + ) + + +class LogLinearMamba2Model(LogLinearMamba2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size) + self.layers = nn.ModuleList( + [ + LogLinearMamba2Block(config, layer_idx=idx) + for idx in range(config.num_hidden_layers) + ], + ) + + self.gradient_checkpointing = False + self.norm_f = RMSNorm(config.hidden_size, eps=config.norm_eps) + # Initialize weights and apply final processing + self._register_load_state_dict_pre_hook(self.load_hook) + self.post_init() + + def load_hook(self, state_dict, prefix, *args): + for k in state_dict: + if "embedding." in k: + state_dict[k.replace("embedding.", "embeddings.")] = state_dict.pop(k) + break + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, new_embeddings): + self.embeddings = new_embeddings + + def forward( + self, + input_ids: torch.LongTensor | None = None, + inputs_embeds: torch.LongTensor | None = None, + cache_params: LogLinearMamba2Cache | None = None, + use_cache: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple | Mamba2Output: + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + use_cache = ( + use_cache + if use_cache is not None + else (self.config.use_cache if not self.training else False) + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if (input_ids is None) ^ (inputs_embeds is not None): # ^ is python for xor + raise ValueError( + "You must specify exactly one of input_ids or inputs_embeds", + ) + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + + if self.gradient_checkpointing and self.training and use_cache: + use_cache = False + + if use_cache: + if cache_params is None: + cache_params = LogLinearMamba2Cache( + self.config, + inputs_embeds.size(0), + device=inputs_embeds.device, + dtype=inputs_embeds.dtype, + ) + cache_position = torch.arange( + 0, self.config.conv_kernel, device=inputs_embeds.device, + ) + elif cache_position is None: + # cases when we do manual forward instead of using `model.generate` which will initiate + # `cache_position` and makes sure it is not None, throw error here instead of doing some + # hack to conjecture the current cache position + raise ValueError( + "You have to specify the `cache_position` manually when `use_cache=True` and `cache_params` is passed, " + "you don't have to pass a `cache_params` if you are in prefilling stage because in that case it will " + "be initialized for you automatically", + ) + else: + cache_params = None + + hidden_states = inputs_embeds + all_hidden_states = () if output_hidden_states else None + for mixer_block in self.layers: + if self.gradient_checkpointing and self.training: + hidden_states = self._gradient_checkpointing_func( + mixer_block.__call__, + hidden_states, + cache_params, + cache_position, + attention_mask, + ) + else: + hidden_states = mixer_block( + hidden_states, + cache_params=cache_params, + cache_position=cache_position, + attention_mask=attention_mask, + ) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + hidden_states = self.norm_f(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, cache_params, all_hidden_states] + if v is not None + ) + + return Mamba2Output( + last_hidden_state=hidden_states, + cache_params=cache_params if use_cache else None, + hidden_states=all_hidden_states, + ) + + +class LogLinearMamba2ForCausalLM(LogLinearMamba2PreTrainedModel): + _tied_weights_keys = [] + + def __init__(self, config): + super().__init__(config) + self.backbone = LogLinearMamba2Model(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def get_input_embeddings(self): + return self.backbone.get_input_embeddings() + + def set_input_embeddings(self, new_embeddings): + return self.backbone.set_input_embeddings(new_embeddings) + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + cache_params: LogLinearMamba2Cache | None = None, + labels: torch.LongTensor | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + use_cache: bool | None = None, + cache_position: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + logits_to_keep: int | None = 0, + **kwargs, # for now we need this for generation + ) -> tuple | Mamba2CausalLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + outputs = self.backbone( + input_ids, + cache_params=cache_params, + inputs_embeds=inputs_embeds, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + use_cache=use_cache, + cache_position=cache_position, + attention_mask=attention_mask, + ) + hidden_states = outputs[0] + fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training + + loss, logits = None, None + if not fuse_linear_and_cross_entropy or labels is None: + logits = self.lm_head( + hidden_states + if logits_to_keep is None + else hidden_states[:, -logits_to_keep:], + ) + if labels is not None: + if getattr(self, "criterion", None) is None: + if fuse_linear_and_cross_entropy: + criterion = FusedLinearCrossEntropyLoss() + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat( + ( + labels[..., 1:], + torch.full_like(labels[:, :1], criterion.ignore_index), + ), + 1, + ) + if fuse_linear_and_cross_entropy: + loss = criterion( + hidden_states, labels, self.lm_head.weight, self.lm_head.bias, + ) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return Mamba2CausalLMOutput( + loss=loss, + logits=logits, + cache_params=outputs.cache_params, + hidden_states=outputs.hidden_states, + ) diff --git a/code/flash-linear-attention/fla/models/mamba/__init__.py b/code/flash-linear-attention/fla/models/mamba/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..639e8ca23b9f93813c363b64a199069d4d2f6c77 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mamba/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.mamba.configuration_mamba import MambaConfig +from fla.models.mamba.modeling_mamba import MambaBlock, MambaForCausalLM, MambaModel + +AutoConfig.register(MambaConfig.model_type, MambaConfig, exist_ok=True) +AutoModel.register(MambaConfig, MambaModel, exist_ok=True) +AutoModelForCausalLM.register(MambaConfig, MambaForCausalLM, exist_ok=True) + + +__all__ = ['MambaConfig', 'MambaForCausalLM', 'MambaModel', 'MambaBlock'] diff --git a/code/flash-linear-attention/fla/models/mamba/configuration_mamba.py b/code/flash-linear-attention/fla/models/mamba/configuration_mamba.py new file mode 100644 index 0000000000000000000000000000000000000000..c00b16dbc33998add081f3c48327d241aa543ddd --- /dev/null +++ b/code/flash-linear-attention/fla/models/mamba/configuration_mamba.py @@ -0,0 +1,180 @@ +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class MambaConfig(PretrainedConfig): + """ + This is the configuration class to store the configuration of a [`MambaModel`]. It is used to instantiate a MAMBA + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the MAMBA + [state-spaces/mamba-2.8b](https://huggingface.co/state-spaces/mamba-2.8b) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*): + Vocabulary size of the Mamba model. + hidden_size (`int`, *optional*): + Dimensionality of the embeddings and hidden states. Default: 2048. + state_size (`int`, *optional*): + Shape of the state space latents. Default: 16. + num_hidden_layers (`int`, *optional*): + Number of hidden layers in the model. Default: 48. + norm_eps (`float`, *optional*): + The epsilon to use in the layer normalization layers. Default: 1e-5. + pad_token_id (`int`, *optional*): + Padding token id. Default: 0. + bos_token_id (`int`, *optional*): + The id of the beginning of sentence token in the vocabulary. Default: 0. + eos_token_id (`int`, *optional*): + The id of the end of sentence token in the vocabulary. Default: 0. + expand (`int`, *optional*): + Expanding factor used to determine the intermediate size. Default: 2. + conv_kernel (`int`, *optional*): + Size of the convolution kernel. Default: 4. + use_bias (`bool`, *optional*): + Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block. Default: `False`. + use_conv_bias (`bool`, *optional*): + Whether or not to use bias in the convolution layer of the mixer block. Default: `True`. + hidden_act (`str`, *optional*): + The non-linear activation function (function or string) in the decoder. Default: `"silu"`. + initializer_range (`float`, *optional*): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Default: 0.02. + residual_in_fp32 (`bool`, *optional*): + Whether or not residuals should be in `float32`. + If set to `False` residuals will keep the same `dtype` as the rest of the model. Default: `True`. + time_step_rank (`Union[int,str]`, *optional*): + Rank of the the discretization projection matrix. + `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`. Default: `"auto"`. + time_step_scale (`float`, *optional*): + Scale used used to scale `dt_proj.bias`. Default: 1.0. + time_step_min (`float`, *optional*): + Minimum `time_step` used to bound `dt_proj.bias`. Default: 0.001. + time_step_max (`float`, *optional*): + Maximum `time_step` used to bound `dt_proj.bias`. Default: 0.1. + time_step_init_scheme (`float`, *optional*): + Init scheme used for `dt_proj.weight`. Should be one of `["random","uniform"]`. Default: `"random"`. + time_step_floor (`float`, *optional*): + Minimum clamping value of the `dt_proj.bias` layer initialization. Default: 0.0001. + window_size (`int`, *optional*): + The window size used for sliding window attention. Default: 2048. + rescale_prenorm_residual (`bool`, *optional*): + Whether or not to rescale `out_proj` weights when initializing. Default: `False`. + use_cache (`bool`, *optional*): + Whether or not the cache should be used. Default: `True`. + + + Example: + + ```python + >>> from transformers import MambaConfig, MambaModel + + >>> # Initializing a Mamba configuration + >>> configuration = MambaConfig() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = MambaModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "mamba" + + def __init__( + self, + vocab_size: int = 32000, + hidden_size: int = 2048, + state_size: int = 16, + num_hidden_layers: int = 48, + norm_eps=1e-5, + pad_token_id: int = 0, + bos_token_id: int = 1, + eos_token_id: int = 2, + expand: int = 2, + conv_kernel: int = 4, + use_bias: bool = False, + use_conv_bias: bool = True, + hidden_act: str = "silu", + initializer_range: float = 0.02, + residual_in_fp32: bool = False, + time_step_rank: str = "auto", + time_step_scale: float = 1.0, + time_step_min: float = 0.001, + time_step_max: float = 0.1, + time_step_init_scheme: str = "random", + time_step_floor: float = 1e-4, + rescale_prenorm_residual: bool = False, + use_cache: bool = True, + fuse_norm: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + tie_word_embeddings: bool = False, + **kwargs, + ): + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.state_size = state_size + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.conv_kernel = conv_kernel + self.expand = expand + self.intermediate_size = int(expand * self.hidden_size) + self.bos_token_id = bos_token_id + self.eos_token_id = eos_token_id + self.pad_token_id = pad_token_id + self.use_bias = use_bias + self.use_conv_bias = use_conv_bias + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.time_step_rank = math.ceil(self.hidden_size / 16) if time_step_rank == "auto" else time_step_rank + self.time_step_scale = time_step_scale + self.time_step_min = time_step_min + self.time_step_max = time_step_max + self.time_step_init_scheme = time_step_init_scheme + self.time_step_floor = time_step_floor + self.rescale_prenorm_residual = rescale_prenorm_residual + self.residual_in_fp32 = residual_in_fp32 + self.use_cache = use_cache + self.fuse_norm = fuse_norm + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + super().__init__( + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + pad_token_id=pad_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/mamba/modeling_mamba.py b/code/flash-linear-attention/fla/models/mamba/modeling_mamba.py new file mode 100644 index 0000000000000000000000000000000000000000..609eb4d7bd0c23eda5957b5a055e63318c3621c4 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mamba/modeling_mamba.py @@ -0,0 +1,513 @@ +# Copyright 2024 state-spaces/mamba org and HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass +from typing import Any + +import torch +from torch import nn +from transformers.configuration_utils import PretrainedConfig +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ModelOutput, logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.mamba import Mamba +from fla.models.mamba.configuration_mamba import MambaConfig +from fla.models.utils import FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules.l2warp import l2_warp + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class MambaCache: + """ + Cache for mamba model which does not have attention mechanism and key value states. + + Arguments: + config (`PretrainedConfig): + The configuration file defining the shape-related attributes required to initialize the static cache. + batch_size (`int`): + The batch size with which the model will be used. Note that a new instance must be instantiated if a + smaller batch size is used. + dtype (`torch.dtype`, *optional*, defaults to `torch.float16`): + The default `dtype` to use when initializing the layer. + device (`torch.device` or `str`, *optional*): + The device on which the cache should be initialized. Should be the same as the layer. + + Attributes: + dtype: (`torch.dtype`): + The default `dtype` used to initializing the cache. + intermediate_size: (`int`): + Model's intermediate_size taken from config. + ssm_state_size: (`int`): + Model's state_size taken from config. + conv_kernel_size: (`int`): + Model's convolution kernel size taken from config + conv_states: (`torch.Tensor`): + A tensor of shape `[layer_idx, batch_size, intermediate_size, conv_kernel_size]` that holds convolutional states. + ssm_states: (`torch.Tensor`): + A tensor of shape `[layer_idx, batch_size, intermediate_size, ssm_state_size]` that holds ssm states + + Example: + + ```python + >>> from transformers import AutoTokenizer, MambaForCausalLM, MambaCache + + >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf") + + >>> inputs = tokenizer(text="My name is Mamba", return_tensors="pt") + + >>> # Prepare a cache class and pass it to model's forward + >>> past_key_values = MambaCache(config=model.config, batch_size=1, device=model.device, dtype=model.dtype) + >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) + >>> outputs.past_key_values + MambaCache() + ``` + """ + + # TODO (joao): remove `=None` in non-optional arguments in v4.46. Remove from `OBJECTS_TO_IGNORE` as well. + def __init__( + self, + config: PretrainedConfig, + batch_size: int = None, + dtype: torch.dtype = torch.float16, + device: torch.device | str | None = None, + max_batch_size: int | None = None, + ): + if max_batch_size is not None: + logger.warning_once( + f"The 'max_batch_size' argument of {self.__class__.__name__} is deprecated and will be removed in " + "v4.46. Use the more precisely named 'batch_size' argument instead.", + ) + self.dtype = dtype + self.batch_size = batch_size or max_batch_size + self.intermediate_size = config.intermediate_size + self.ssm_state_size = config.state_size + self.conv_kernel_size = config.conv_kernel + + self.conv_states: torch.Tensor = torch.zeros( + config.num_hidden_layers, + self.batch_size, + self.intermediate_size, + self.conv_kernel_size, + device=device, + dtype=dtype, + ) + self.ssm_states: torch.Tensor = torch.zeros( + config.num_hidden_layers, + self.batch_size, + self.intermediate_size, + self.ssm_state_size, + device=device, + dtype=dtype, + ) + + torch._dynamo.mark_static_address(self.conv_states) + torch._dynamo.mark_static_address(self.ssm_states) + + def update_conv_state( + self, layer_idx: int, new_conv_state: torch.Tensor, cache_position: torch.LongTensor, + ) -> torch.Tensor: + conv_state = self.conv_states[layer_idx] + cache_position = cache_position.clamp(0, self.conv_kernel_size - 1) + + conv_state = conv_state.roll(shifts=-1, dims=-1) + conv_state[:, :, cache_position] = new_conv_state.to(conv_state.device) + self.conv_states[layer_idx].zero_() + self.conv_states[layer_idx] += conv_state + return self.conv_states[layer_idx] + + def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor): + self.ssm_states[layer_idx] = new_ssm_state.to(self.ssm_states.device) + return self.ssm_states[layer_idx] + + def reset(self): + self.conv_states.zero_() + self.ssm_states.zero_() + + +class MambaBlock(GradientCheckpointingLayer): + + def __init__(self, config, layer_idx): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.residual_in_fp32 = config.residual_in_fp32 + self.norm = RMSNorm(config.hidden_size, eps=config.norm_eps) + self.mixer = Mamba( + hidden_size=config.hidden_size, + state_size=config.state_size, + conv_kernel=config.conv_kernel, + intermediate_size=config.intermediate_size, + time_step_rank=config.time_step_rank, + use_bias=config.use_bias, + layer_idx=layer_idx, + ) + + def forward( + self, + hidden_states, + cache_params: MambaCache | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + ): + residual = hidden_states + hidden_states = self.norm(hidden_states) + if self.residual_in_fp32: + residual = residual.to(torch.float32) + + hidden_states = self.mixer( + hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=attention_mask, + ) + hidden_states = residual + hidden_states + if self.residual_in_fp32: + hidden_states = hidden_states.to(dtype=self.norm.weight.dtype) + return hidden_states + + +class MambaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = MambaConfig + base_model_prefix = 'backbone' + _no_split_modules = ['Mamba', 'MambaBlock'] + supports_gradient_checkpointing = True + _is_stateful = True + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, nn.Linear): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + if not getattr(module.bias, "_no_reinit", False): + nn.init.zeros_(module.bias) + elif isinstance(module, Mamba): + module.A_log._no_weight_decay = True + module.D._no_weight_decay = True + + dt_init_std = self.config.time_step_rank**-0.5 * self.config.time_step_scale + if self.config.time_step_init_scheme == "constant": + nn.init.constant_(module.dt_proj.weight, dt_init_std) + elif self.config.time_step_init_scheme == "random": + nn.init.uniform_(module.dt_proj.weight, -dt_init_std, dt_init_std) + + dt = torch.exp( + torch.rand(self.config.intermediate_size) + * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min)) + + math.log(self.config.time_step_min), + ).clamp(min=self.config.time_step_floor) + # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + with torch.no_grad(): + module.dt_proj.bias.data = nn.Parameter(inv_dt.to(module.dt_proj.bias.device)) + module.dt_proj.bias._no_reinit = True + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if self.config.rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + for name, p in module.named_parameters(): + if name in ["out_proj.weight"]: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(self.config.num_hidden_layers) + + +@dataclass +class MambaOutput(ModelOutput): + """ + Class for the MAMBA model outputs. + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + cache_params (`MambaCache`): + The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to + avoid providing the old `input_ids`. + + Includes both the State space model state matrices after the selective scan, and the Convolutional states + hidden_states (`tuple(torch.FloatTensor)`, *optional*, + returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + """ + + last_hidden_state: torch.FloatTensor | None = None + cache_params: MambaCache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + + +@dataclass +class MambaCausalLMOutput(ModelOutput): + """ + Base class for causal language model (or autoregressive) outputs. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + cache_params (`MambaCache`): + The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to + avoid providing the old `input_ids`. + + Includes both the State space model state matrices after the selective scan, and the Convolutional states + hidden_states (`tuple(torch.FloatTensor)`, *optional*, + returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + cache_params: MambaCache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + + +class MambaModel(MambaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size) + self.layers = nn.ModuleList([MambaBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)]) + + self.gradient_checkpointing = False + self.norm_f = RMSNorm(config.hidden_size, eps=config.norm_eps) + # Initialize weights and apply final processing + self._register_load_state_dict_pre_hook(self.load_hook) + self.post_init() + + def load_hook(self, state_dict, prefix, *args): + for k in state_dict: + if "embedding." in k: + state_dict[k.replace("embedding.", "embeddings.")] = state_dict.pop(k) + break + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, new_embeddings): + self.embeddings = new_embeddings + + def forward( + self, + input_ids: torch.LongTensor | None = None, + inputs_embeds: torch.LongTensor | None = None, + cache_params: MambaCache | None = None, + use_cache: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + ) -> tuple | MambaOutput: + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if (input_ids is None) ^ (inputs_embeds is not None): # ^ is python for xor + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one", + ) + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + + if use_cache: + if cache_params is None: + cache_params = MambaCache( + self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype, + ) + cache_position = torch.arange(0, self.config.conv_kernel, device=inputs_embeds.device) + elif cache_position is None: + # cases when we do manual forward instead of using `model.generate` which will initiate + # `cache_position` and makes sure it is not None, throw error here instead of doing some + # hack to conjecture the current cache position + raise ValueError( + "You have to specify the `cache_position` manually when `use_cache=True` and `cache_params` is passed, " + "you don't have to pass a `cache_params` if you are in prefilling stage because in that case it will " + "be initialized for you automatically", + ) + else: + cache_params = None + + hidden_states = inputs_embeds + all_hidden_states = () if output_hidden_states else None + for mixer_block in self.layers: + hidden_states = mixer_block( + hidden_states, + cache_params=cache_params, + cache_position=cache_position, + attention_mask=attention_mask, + ) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + hidden_states = self.norm_f(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, cache_params, all_hidden_states] if v is not None) + + return MambaOutput( + last_hidden_state=hidden_states, + cache_params=cache_params if use_cache else None, + hidden_states=all_hidden_states, + ) + + +class MambaForCausalLM(MambaPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.backbone = MambaModel(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def get_input_embeddings(self): + return self.backbone.get_input_embeddings() + + def set_input_embeddings(self, new_embeddings): + return self.backbone.set_input_embeddings(new_embeddings) + + def _update_model_kwargs_for_generation( + self, outputs: ModelOutput, + model_kwargs: dict[str, Any], + num_new_tokens: int = 1, + **kwargs, + ) -> dict[str, Any]: + model_kwargs["cache_params"] = outputs.get("cache_params", None) + if ( + model_kwargs.get("use_cache", True) + and "cache_position" in model_kwargs + and model_kwargs["cache_position"] is not None + ): + model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + num_new_tokens + + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1, + ) + + return model_kwargs + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + cache_params: MambaCache | None = None, + labels: torch.LongTensor | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + use_cache: bool | None = None, + cache_position: torch.Tensor | None = None, + logits_to_keep: int | None = 0, + **kwargs, # for now we need this for generation + ) -> tuple | MambaCausalLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + mamba_outputs = self.backbone( + input_ids, + cache_params=cache_params, + inputs_embeds=inputs_embeds, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + use_cache=use_cache, + cache_position=cache_position, + attention_mask=attention_mask, + ) + hidden_states = mamba_outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + # Enable model parallelism + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + mamba_outputs[1:] + return (loss,) + output if loss is not None else output + + return MambaCausalLMOutput( + loss=loss, + logits=logits, + cache_params=mamba_outputs.cache_params, + hidden_states=mamba_outputs.hidden_states, + ) diff --git a/code/flash-linear-attention/fla/models/mamba2/__init__.py b/code/flash-linear-attention/fla/models/mamba2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..98937da0a71289f596fa6b45721eefb4e7668185 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mamba2/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.mamba2.configuration_mamba2 import Mamba2Config +from fla.models.mamba2.modeling_mamba2 import Mamba2ForCausalLM, Mamba2Model + +AutoConfig.register(Mamba2Config.model_type, Mamba2Config, exist_ok=True) +AutoModel.register(Mamba2Config, Mamba2Model, exist_ok=True) +AutoModelForCausalLM.register(Mamba2Config, Mamba2ForCausalLM, exist_ok=True) + + +__all__ = ['Mamba2Config', 'Mamba2ForCausalLM', 'Mamba2Model'] diff --git a/code/flash-linear-attention/fla/models/mamba2/configuration_mamba2.py b/code/flash-linear-attention/fla/models/mamba2/configuration_mamba2.py new file mode 100644 index 0000000000000000000000000000000000000000..db61364d93a08ab5ef5e11c244557b407b28cb58 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mamba2/configuration_mamba2.py @@ -0,0 +1,182 @@ +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class Mamba2Config(PretrainedConfig): + """ + This is the configuration class to store the configuration of a [`Mamba2Model`]. It is used to instantiate a MAMBA2 + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the MAMBA2 + [state-spaces/mamba2-2.8b](https://huggingface.co/state-spaces/mamba2-2.8b) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + head_dim (`int`, *optional*, defaults to 64): + Dimension of each head. + vocab_size (`int`, *optional*, defaults to 32768): + Vocabulary size of the MAMBA2 model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`Mamba2Model`]. + hidden_size (`int`, *optional*, defaults to 2048): + Dimensionality of the embeddings and hidden states. + state_size (`int`, *optional*, defaults to 128): shape of the state space latents. + num_hidden_layers (`int`, *optional*, defaults to 48): + Number of hidden layers in the model. + norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon to use in the layer normalization layers. + pad_token_id (`int`, *optional*, defaults to 0): + Padding token id. + bos_token_id (`int`, *optional*, defaults to 1): + The id of the beginning of sentence token in the vocabulary. + eos_token_id (`int`, *optional*, defaults to 2): + The id of the end of sentence token in the vocabulary. + expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size. + conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel. + n_groups (`int`, *optional*, defaults to 1): + Number of groups for the evolution matrices of mamba 2. + use_bias (`bool`, *optional*, defaults to `False`): + Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block + use_conv_bias (`bool`, *optional*, defaults to `True`): + Whether or not to use bias in the convolution layer of the mixer block. + hidden_act (`str`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + residual_in_fp32 (`bool`, *optional*, defaults to `True`): + Whether or not residuals should be in `float32`. + If set to `False` residuals will keep the same `dtype` as the rest of the model + time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`): + Rank of the discretization projection matrix. + `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)` + time_step_min (`float`, *optional*, defaults to 0.001): + Minimum `time_step` used to bound `dt_proj.bias`. + time_step_max (`float`, *optional*, defaults to 0.1): + Maximum `time_step` used to bound `dt_proj.bias`. + time_step_floor (`float`, *optional*, defaults to 0.0001): + Minimum clamping value of the `dt_proj.bias` layer initialization. + time_step_limit (`tuple`, *optional*, defaults to `(0.0, inf)`): + Accepted range of time step values. + rescale_prenorm_residual (`bool`, *optional*, defaults to `True`): + Whether or not to rescale `out_proj` weights when initializing. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the cache should be used. + rms_norm (`bool`, *optional*, defaults to `True`): + Whether to use RMS norm or not. + chunk_size (`int`, *optional*, defaults to 256): + Size of the chunks that will comprise the sequence. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether to tie word embeddings or not. + """ + + model_type = "mamba2" + + def __init__( + self, + head_dim: int = 64, + vocab_size: int = 32000, + hidden_size: int = 2048, + state_size: int = 128, + num_hidden_layers: int = 48, + norm_eps: float = 1e-5, + pad_token_id: int = 0, + bos_token_id: int = 1, + eos_token_id: int = 2, + expand: int = 2, + conv_kernel: int = 4, + n_groups: int = 1, + use_bias: bool = False, + use_conv_bias: bool = True, + hidden_act: str = "silu", + initializer_range: float = 0.02, + residual_in_fp32: bool = True, + time_step_rank: str = "auto", + time_step_min: float = 0.001, + time_step_max: float = 0.1, + time_step_floor: float = 1e-4, + time_step_limit=(0.0, float("inf")), + rescale_prenorm_residual: bool = True, + use_cache: bool = True, + rms_norm: bool = True, + chunk_size: int = 256, + fuse_norm: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + tie_word_embeddings: bool = False, + **kwargs, + ): + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.state_size = state_size + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.conv_kernel = conv_kernel + self.expand = expand + + self.bos_token_id = bos_token_id + self.eos_token_id = eos_token_id + self.pad_token_id = pad_token_id + self.use_bias = use_bias + self.use_conv_bias = use_conv_bias + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.time_step_rank = ( + math.ceil(self.hidden_size / 16) + if time_step_rank == "auto" + else time_step_rank + ) + self.time_step_min = time_step_min + self.time_step_max = time_step_max + self.time_step_floor = time_step_floor + self.rescale_prenorm_residual = rescale_prenorm_residual + self.residual_in_fp32 = residual_in_fp32 + self.use_cache = use_cache + self.n_groups = n_groups + self.head_dim = head_dim + self.num_heads = int(self.expand * self.hidden_size / self.head_dim) + self.rms_norm = rms_norm + self.state_size = state_size + self.chunk_size = chunk_size + self.time_step_limit = time_step_limit + self.fuse_norm = fuse_norm + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.tie_word_embeddings = tie_word_embeddings + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + super().__init__( + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + pad_token_id=pad_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/mamba2/modeling_mamba2.py b/code/flash-linear-attention/fla/models/mamba2/modeling_mamba2.py new file mode 100644 index 0000000000000000000000000000000000000000..cac8421eabafc72aaaf218ede7c74387a305a833 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mamba2/modeling_mamba2.py @@ -0,0 +1,512 @@ +# Copyright 2024 state-spaces/mamba2 org and HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass + +import torch +from torch import nn +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ModelOutput, logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.mamba2 import Mamba2 +from fla.models.mamba2.configuration_mamba2 import Mamba2Config +from fla.models.utils import FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules.l2warp import l2_warp + +try: + from torch.distributed.tensor import DTensor +except (ImportError, AttributeError): + DTensor = None + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + + +logger = logging.get_logger(__name__) + + +class Mamba2Cache: + """ + Arguments: + config: Mamba2Config + batch_size: int + dtype: torch.dtype + device: torch.device + + Attributes: + dtype: (`torch.dtype`): + The default `dtype` used to initializing the cache. + conv_kernel_size: (`int`): + Model's convolution kernel size taken from config. + n_groups: (`int`): + Model's number of groups taken from the config - similar to tensor parallel in Transformer. + state_size: (`int`): + Model's SSM state size taken from config. + num_heads: (`int`): + The number of heads used in the linear attention / SSM. + head_dim: (`int`): + The respective dimension of the heads used in the linear attention / SSM. + intermediate_size: (`int`): + Model's intermediate_size based on (expand * hidden_dim) from config. + conv_states: (`torch.Tensor`): + A tensor of shape `[num_layers, batch_size, conv_kernel_size, intermediate_size + 2 * n_groups * state_size]` + that holds convolutional states. + ssm_states: (`torch.Tensor`): + A tensor of shape `[num_layers, batch_size, num_heads, head_dim, state_size]` that holds ssm states. + """ + + def __init__( + self, + config: Mamba2Config, + batch_size: int, + dtype: torch.dtype = torch.float16, + device: str | None = None, + ): + self.dtype = dtype + self.conv_kernel_size = config.conv_kernel + self.n_groups = config.n_groups + self.state_size = config.state_size + self.num_heads = config.num_heads + self.head_dim = config.head_dim + self.intermediate_size = int(config.expand * config.hidden_size) + + self.conv_states = torch.zeros( + config.num_hidden_layers, + batch_size, + self.intermediate_size + 2 * self.n_groups * self.state_size, + self.conv_kernel_size, + device=device, + dtype=dtype, + ) + self.ssm_states = torch.zeros( + config.num_hidden_layers, + batch_size, + self.num_heads, + self.head_dim, + self.state_size, + device=device, + dtype=dtype, + ) + + def update_conv_state( + self, + layer_idx: int, + new_conv_state: torch.Tensor, + cache_init: bool = False, + ) -> torch.Tensor: + if cache_init: + self.conv_states[layer_idx] = new_conv_state.to(self.conv_states.device) + else: + self.conv_states[layer_idx] = self.conv_states[layer_idx].roll(shifts=-1, dims=-1) + self.conv_states[layer_idx][:, :, -1] = new_conv_state[:, 0, :].to(self.conv_states.device) + return self.conv_states[layer_idx] + + def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor): + self.ssm_states[layer_idx] = new_ssm_state.to(self.ssm_states.device) + return self.ssm_states[layer_idx] + + def reset(self): + self.conv_states.zero_() + self.ssm_states.zero_() + + +class Mamba2Block(GradientCheckpointingLayer): + + def __init__(self, config, layer_idx): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.residual_in_fp32 = config.residual_in_fp32 + self.norm = RMSNorm(config.hidden_size, eps=config.norm_eps) + self.mixer = Mamba2( + num_heads=config.num_heads, + head_dim=config.head_dim, + hidden_size=config.hidden_size, + state_size=config.state_size, + expand=config.expand, + n_groups=config.n_groups, + conv_kernel=config.conv_kernel, + use_conv_bias=config.use_conv_bias, + hidden_act=config.hidden_act, + rms_norm=config.rms_norm, + chunk_size=config.chunk_size, + time_step_rank=config.time_step_rank, + time_step_limit=config.time_step_limit, + time_step_min=config.time_step_min, + time_step_max=config.time_step_max, + use_bias=config.use_bias, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + ) + + def forward( + self, + hidden_states, + cache_params: Mamba2Cache | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + ): + residual = hidden_states + hidden_states = self.norm(hidden_states) + if self.residual_in_fp32: + residual = residual.to(torch.float32) + + hidden_states = self.mixer( + hidden_states, + cache_params=cache_params, + cache_position=cache_position, + attention_mask=attention_mask, + ) + hidden_states = residual + hidden_states + if self.residual_in_fp32: + hidden_states = hidden_states.to(dtype=self.norm.weight.dtype) + return hidden_states + + +class Mamba2PreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = Mamba2Config + base_model_prefix = "backbone" + _no_split_modules = ["Mamba2Block"] + supports_gradient_checkpointing = True + _is_stateful = True + + def _init_weights( + self, + module: nn.Module, + num_residuals_per_layer: int = 1, + ): + """Initialize the weights.""" + if isinstance(module, Mamba2): + + # --- A_log --- + A = torch.arange(1, module.num_heads + 1) + with torch.no_grad(): + if not isinstance(module.A_log, DTensor): + module.A_log.copy_(torch.log(A)) + else: + logger.warning_once("`A_log` is a DTensor, skipping initialization") + module.A_log._no_weight_decay = True + + # --- D --- + nn.init.ones_(module.D) + module.D._no_weight_decay = True + + # --- dt_bias --- + dt = torch.exp( + torch.rand(self.config.num_heads) + * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min)) + + math.log(self.config.time_step_min), + ).clamp(min=self.config.time_step_floor) + + # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + with torch.no_grad(): + if not isinstance(module.dt_bias, DTensor): + module.dt_bias.copy_(inv_dt) + else: + logger.warning_once("`dt_bias` is a DTensor, skipping initialization") + module.dt_bias._no_reinit = True + + elif isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + # guard against deprecated behavior + if hasattr(module.bias, "_no_reinit"): + raise ValueError("This is not supposed to happen") + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if self.config.rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + # p = module.o_proj.weight + # guard against deprecated behavior + raise ValueError("This is not supposed to happen") + elif hasattr(module, 'out_proj'): + p = module.out_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + + +@dataclass +# Copied from transformers.models.mamba.modeling_mamba.MambaOutput with MAMBA->MAMBA2,Mamba->Mamba2 +class Mamba2Output(ModelOutput): + """ + Class for the MAMBA2 model outputs. + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + cache_params (`Mamba2Cache`): + The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to + avoid providing the old `input_ids`. + + Includes both the State space model state matrices after the selective scan, and the Convolutional states + hidden_states (`tuple(torch.FloatTensor)`, *optional*, + returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + """ + + last_hidden_state: torch.FloatTensor | None = None + cache_params: Mamba2Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + + +@dataclass +# Copied from transformers.models.mamba.modeling_mamba.MambaCausalLMOutput with Mamba->Mamba2 +class Mamba2CausalLMOutput(ModelOutput): + """ + Base class for causal language model (or autoregressive) outputs. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + cache_params (`Mamba2Cache`): + The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to + avoid providing the old `input_ids`. + + Includes both the State space model state matrices after the selective scan, and the Convolutional states + hidden_states (`tuple(torch.FloatTensor)`, *optional*, + returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + cache_params: Mamba2Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + + +class Mamba2Model(Mamba2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size) + self.layers = nn.ModuleList([Mamba2Block(config, layer_idx=idx) for idx in range(config.num_hidden_layers)]) + + self.gradient_checkpointing = False + self.norm_f = RMSNorm(config.hidden_size, eps=config.norm_eps) + # Initialize weights and apply final processing + self._register_load_state_dict_pre_hook(self.load_hook) + self.post_init() + + def load_hook(self, state_dict, prefix, *args): + for k in state_dict: + if "embedding." in k: + state_dict[k.replace("embedding.", "embeddings.")] = state_dict.pop(k) + break + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, new_embeddings): + self.embeddings = new_embeddings + + def forward( + self, + input_ids: torch.LongTensor | None = None, + inputs_embeds: torch.LongTensor | None = None, + cache_params: Mamba2Cache | None = None, + use_cache: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + cache_position: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple | Mamba2Output: + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if (input_ids is None) ^ (inputs_embeds is not None): # ^ is python for xor + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + + if use_cache: + if cache_params is None: + cache_params = Mamba2Cache( + self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype, + ) + cache_position = torch.arange(0, self.config.conv_kernel, device=inputs_embeds.device) + elif cache_position is None: + # cases when we do manual forward instead of using `model.generate` which will initiate + # `cache_position` and makes sure it is not None, throw error here instead of doing some + # hack to conjecture the current cache position + raise ValueError( + "You have to specify the `cache_position` manually when `use_cache=True` and `cache_params` is passed, " + "you don't have to pass a `cache_params` if you are in prefilling stage because in that case it will " + "be initialized for you automatically", + ) + else: + cache_params = None + + hidden_states = inputs_embeds + all_hidden_states = () if output_hidden_states else None + for mixer_block in self.layers: + hidden_states = mixer_block( + hidden_states, + cache_params=cache_params, + cache_position=cache_position, + attention_mask=attention_mask, + ) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + hidden_states = self.norm_f(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, cache_params, all_hidden_states] if v is not None) + + return Mamba2Output( + last_hidden_state=hidden_states, + cache_params=cache_params if use_cache else None, + hidden_states=all_hidden_states, + ) + + +class Mamba2ForCausalLM(Mamba2PreTrainedModel, FLAGenerationMixin): + _tied_weights_keys = [] + + def __init__(self, config): + super().__init__(config) + self.backbone = Mamba2Model(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def get_input_embeddings(self): + return self.backbone.get_input_embeddings() + + def set_input_embeddings(self, new_embeddings): + return self.backbone.set_input_embeddings(new_embeddings) + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + cache_params: Mamba2Cache | None = None, + labels: torch.LongTensor | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + use_cache: bool | None = None, + cache_position: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + logits_to_keep: int | None = 0, + **kwargs, # for now we need this for generation + ) -> tuple | Mamba2CausalLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.backbone( + input_ids, + cache_params=cache_params, + inputs_embeds=inputs_embeds, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + use_cache=use_cache, + cache_position=cache_position, + attention_mask=attention_mask, + ) + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return Mamba2CausalLMOutput( + loss=loss, + logits=logits, + cache_params=outputs.cache_params, + hidden_states=outputs.hidden_states, + ) diff --git a/code/flash-linear-attention/fla/models/mesa_net/__init__.py b/code/flash-linear-attention/fla/models/mesa_net/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..65907264e54ab47c6c410c4fac3b23587eaf1846 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mesa_net/__init__.py @@ -0,0 +1,11 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.mesa_net.configuration_mesa_net import MesaNetConfig +from fla.models.mesa_net.modeling_mesa_net import MesaNetForCausalLM, MesaNetModel + +AutoConfig.register(MesaNetConfig.model_type, MesaNetConfig, exist_ok=True) +AutoModel.register(MesaNetConfig, MesaNetModel, exist_ok=True) +AutoModelForCausalLM.register(MesaNetConfig, MesaNetForCausalLM, exist_ok=True) + +__all__ = ['MesaNetConfig', 'MesaNetForCausalLM', 'MesaNetModel'] diff --git a/code/flash-linear-attention/fla/models/mesa_net/configuration_mesa_net.py b/code/flash-linear-attention/fla/models/mesa_net/configuration_mesa_net.py new file mode 100644 index 0000000000000000000000000000000000000000..0c9549fd638c4b23a1b363fdec35b9f9f14a96ce --- /dev/null +++ b/code/flash-linear-attention/fla/models/mesa_net/configuration_mesa_net.py @@ -0,0 +1,101 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class MesaNetConfig(PretrainedConfig): + model_type = 'mesa_net' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + hidden_size: int = 2048, + use_output_gate: bool = False, + use_short_conv: bool = True, + conv_size: int = 4, + num_heads: int = 16, + head_dim: int = 128, + lambda_lower_bound: float = 0.25, + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + num_hidden_layers: int = 24, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + max_cg_step_training: int = 30, + max_cg_step_decoding: int = 30, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.num_heads = num_heads + self.head_dim = head_dim + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + self.lambda_lower_bound = lambda_lower_bound + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + self.max_cg_step_training = max_cg_step_training + self.max_cg_step_decoding = max_cg_step_decoding + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/mesa_net/modeling_mesa_net.py b/code/flash-linear-attention/fla/models/mesa_net/modeling_mesa_net.py new file mode 100644 index 0000000000000000000000000000000000000000..18fe23ebe4189a5a8e34b35e3d71ca6e6186fbc5 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mesa_net/modeling_mesa_net.py @@ -0,0 +1,368 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.mesa_net import MesaNet +from fla.models.mesa_net.configuration_mesa_net import MesaNetConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as MesaNetMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class MesaNetBlock(GradientCheckpointingLayer): + + def __init__(self, config: MesaNetConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = MesaNet( + mode=config.attn_mode, + hidden_size=config.hidden_size, + num_heads=config.num_heads, + head_dim=config.head_dim, + use_output_gate=config.use_output_gate, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + norm_eps=config.norm_eps, + lambda_lower_bound=config.lambda_lower_bound, + layer_idx=layer_idx, + max_cg_step_training=config.max_cg_step_training, + max_cg_step_decoding=config.max_cg_step_decoding, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = MesaNetMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class MesaNetPreTrainedModel(PreTrainedModel): + + config_class = MesaNetConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['MesaNetBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class MesaNetModel(MesaNetPreTrainedModel): + + def __init__(self, config: MesaNetConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([MesaNetBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`MesaNetModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class MesaNetForCausalLM(MesaNetPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = MesaNetModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/mla/__init__.py b/code/flash-linear-attention/fla/models/mla/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cf043bfcf12dff1fc994bc0119bce1e5cc49ee58 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mla/__init__.py @@ -0,0 +1,14 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.mla.configuration_mla import MLAConfig +from fla.models.mla.modeling_mla import MLAForCausalLM, MLAModel + +AutoConfig.register(MLAConfig.model_type, MLAConfig, exist_ok=True) +AutoModel.register(MLAConfig, MLAModel, exist_ok=True) +AutoModelForCausalLM.register(MLAConfig, MLAForCausalLM, exist_ok=True) + + +__all__ = [ + 'MLAConfig', 'MLAModel', 'MLAForCausalLM', +] diff --git a/code/flash-linear-attention/fla/models/mla/configuration_mla.py b/code/flash-linear-attention/fla/models/mla/configuration_mla.py new file mode 100644 index 0000000000000000000000000000000000000000..516749adb1474b932389c725135d0fac7734fc97 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mla/configuration_mla.py @@ -0,0 +1,96 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class MLAConfig(PretrainedConfig): + + model_type = 'mla' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + num_hidden_layers: int = 24, + num_heads: int = 16, + q_lora_rank: int | None = 64, + qk_rope_head_dim: int = 64, + kv_lora_rank: int = 512, # following the original Deepseek paper + v_head_dim: int = 128, + qk_nope_head_dim: int = 128, + qk_head_dim: int | None = 192, # qk_nope_head_dim + qk_rope_head_dim + window_size: int | None = None, + rope_theta: float | None = 10000., + max_position_embeddings: int = 2048, + rope_scaling: dict | None = None, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + initializer_range: float = 0.02, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + use_cache: bool = True, + pad_token_id: int = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + + # MLA specific args + self.q_lora_rank = q_lora_rank + self.qk_rope_head_dim = qk_rope_head_dim + self.kv_lora_rank = kv_lora_rank + self.v_head_dim = v_head_dim + self.qk_nope_head_dim = qk_nope_head_dim + self.qk_head_dim = qk_head_dim + self.rope_scaling = rope_scaling + + self.window_size = window_size + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + + self.initializer_range = initializer_range + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.use_cache = use_cache + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/mla/modeling_mla.py b/code/flash-linear-attention/fla/models/mla/modeling_mla.py new file mode 100644 index 0000000000000000000000000000000000000000..9504c724d12c90d6f51b0ca34c930b963b075779 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mla/modeling_mla.py @@ -0,0 +1,356 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.mla import MultiheadLatentAttention +from fla.models.mla.configuration_mla import MLAConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as MLAMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class MLABlock(GradientCheckpointingLayer): + + def __init__(self, config: MLAConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.attn = MultiheadLatentAttention( + hidden_size=config.hidden_size, + num_heads=config.num_heads, + q_lora_rank=config.q_lora_rank, + qk_rope_head_dim=config.qk_rope_head_dim, + kv_lora_rank=config.kv_lora_rank, + v_head_dim=config.v_head_dim, + qk_nope_head_dim=config.qk_nope_head_dim, + qk_head_dim=config.qk_head_dim, + window_size=config.window_size, + rope_theta=config.rope_theta, + max_position_embeddings=config.max_position_embeddings, + rope_scaling=config.rope_scaling, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = MLAMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class MLAPreTrainedModel(PreTrainedModel): + + config_class = MLAConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['MLABlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class MLAModel(MLAPreTrainedModel): + + def __init__(self, config: MLAConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([MLABlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`MLAModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class MLAForCausalLM(MLAPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config: MLAConfig): + super().__init__(config) + self.model = MLAModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/modeling_layers.py b/code/flash-linear-attention/fla/models/modeling_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..24727191a3c478838df4aadba2b579a58f39d5ca --- /dev/null +++ b/code/flash-linear-attention/fla/models/modeling_layers.py @@ -0,0 +1,70 @@ + +from functools import partial + +from torch import nn +from transformers.utils import logging + +logger = logging.get_logger(__name__) + + +class GradientCheckpointingLayer(nn.Module): + """Base class for layers with gradient checkpointing. + + This class enables gradient checkpointing functionality for a layer. + By default, gradient checkpointing is disabled (`gradient_checkpointing = False`). + When `model.set_gradient_checkpointing()` is called, gradient checkpointing is enabled + by setting `gradient_checkpointing = True` and assigning a checkpointing function to `_gradient_checkpointing_func`. + + Important: + + When using gradient checkpointing with `use_reentrant=True`, inputs that require gradients (e.g. hidden states) + must be passed as positional arguments (`*args`) rather than keyword arguments to properly propagate gradients. + + Example: + + ```python + >>> # Correct - hidden_states passed as positional arg + >>> out = self.layer(hidden_states, attention_mask=attention_mask) + + >>> # Incorrect - hidden_states passed as keyword arg + >>> out = self.layer(hidden_states=hidden_states, attention_mask=attention_mask) + ``` + """ + + gradient_checkpointing = False + + def __call__(self, *args, **kwargs): + if self.gradient_checkpointing and self.training: + do_warn = False + layer_name = self.__class__.__name__ + message = f"Caching is incompatible with gradient checkpointing in {layer_name}. Setting" + + if "use_cache" in kwargs and kwargs["use_cache"]: + kwargs["use_cache"] = False + message += " `use_cache=False`," + do_warn = True + + # different names for the same thing in different layers + # TODO cyril: this one without `S` can be removed after deprection cycle + if "past_key_value" in kwargs and kwargs["past_key_value"] is not None: + kwargs["past_key_value"] = None + message += " `past_key_value=None`," + do_warn = True + + if "past_key_values" in kwargs and kwargs["past_key_values"] is not None: + kwargs["past_key_values"] = None + message += " `past_key_values=None`," + do_warn = True + + if "layer_past" in kwargs and kwargs["layer_past"] is not None: + kwargs["layer_past"] = None + message += " `layer_past=None`," + do_warn = True + + # warn if anything was changed + if do_warn: + message = message.rstrip(",") + "." + logger.warning_once(message) + + return self._gradient_checkpointing_func(partial(super().__call__, **kwargs), *args) + return super().__call__(*args, **kwargs) diff --git a/code/flash-linear-attention/fla/models/mom/__init__.py b/code/flash-linear-attention/fla/models/mom/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..76f5d081b55c3095b1a2ff3046eca07fe40ced25 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mom/__init__.py @@ -0,0 +1,11 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.mom.configuration_mom import MomConfig +from fla.models.mom.modeling_mom import MomForCausalLM, MomModel + +AutoConfig.register(MomConfig.model_type, MomConfig, exist_ok=True) +AutoModel.register(MomConfig, MomModel, exist_ok=True) +AutoModelForCausalLM.register(MomConfig, MomForCausalLM, exist_ok=True) + +__all__ = ['MomConfig', 'MomForCausalLM', 'MomModel'] diff --git a/code/flash-linear-attention/fla/models/mom/configuration_mom.py b/code/flash-linear-attention/fla/models/mom/configuration_mom.py new file mode 100644 index 0000000000000000000000000000000000000000..e16931d9f500814139a5038e94ef82442fdf4feb --- /dev/null +++ b/code/flash-linear-attention/fla/models/mom/configuration_mom.py @@ -0,0 +1,99 @@ + + +from transformers.configuration_utils import PretrainedConfig + + +class MomConfig(PretrainedConfig): + model_type = 'mom' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + hidden_size: int = 2048, + conv_size: int = 4, + num_heads: int = 4, + head_dim: int = 256, + expand_v: float = 1., + use_output_gate: bool = True, + use_short_conv: bool = True, + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + num_hidden_layers: int = 24, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + num_memories: int = 4, + topk: int = 2, + capacity: float = 1.0, + use_layer_wise_balance: bool = True, + aux_loss_scale: float = 0.01, + shared_mem: bool = True, + single_kv_proj: bool = False, + mom_backend: str = 'gated_deltanet', + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + vocab_size: int = 32000, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.num_heads = num_heads + self.head_dim = head_dim + self.expand_v = expand_v + self.conv_size = conv_size + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.num_memories = num_memories + self.topk = topk + self.capacity = capacity + self.use_layer_wise_balance = use_layer_wise_balance + self.aux_loss_scale = aux_loss_scale + self.shared_mem = shared_mem + self.single_kv_proj = single_kv_proj + self.mom_backend = mom_backend + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.vocab_size = vocab_size + + if self.mom_backend not in ['gated_deltanet']: + raise NotImplementedError(f"The MoM backend {mom_backend} is not currently supported.") + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['window_size'] = attn.get('window_size', None) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/mom/modeling_mom.py b/code/flash-linear-attention/fla/models/mom/modeling_mom.py new file mode 100644 index 0000000000000000000000000000000000000000..cac1e99a7a8f3764dff957fc7230b7382b7b8366 --- /dev/null +++ b/code/flash-linear-attention/fla/models/mom/modeling_mom.py @@ -0,0 +1,472 @@ + +from __future__ import annotations + +import math +import warnings +from dataclasses import dataclass +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging + +from fla.layers import MomAttention +from fla.layers.attn import Attention +from fla.models.mom.configuration_mom import MomConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as MomMLP + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +def load_balancing_loss_func( + gate_logits: torch.Tensor | tuple[torch.Tensor] | None, + num_experts: int | None = None, + top_k=2, + attention_mask: torch.Tensor | None = None, +) -> torch.Tensor | int: + r""" + Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. + + See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss + function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between + experts is too unbalanced. + + Args: + gate_logits: + Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of + shape [batch_size X sequence_length, num_experts]. + num_experts: + Number of experts + top_k: + The number of experts to route per-token, can be also interpreted as the `top-k` routing + parameter. + attention_mask (`torch.Tensor`, *optional*): + The attention_mask used in forward function + shape [batch_size X sequence_length] if not None. + + Returns: + The auxiliary loss. + """ + if gate_logits is None or not isinstance(gate_logits, tuple): + return 0 + + if isinstance(gate_logits, tuple): + compute_device = gate_logits[0].device + concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0) + + routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) + + _, selected_experts = torch.topk(routing_weights, top_k, dim=-1) + + expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) + + if attention_mask is None: + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.mean(expert_mask.float(), dim=0) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.mean(routing_weights, dim=0) + else: + batch_size, sequence_length = attention_mask.shape + num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length) + + # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask + expert_attention_mask = ( + attention_mask[None, :, :, None, None] + .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) + .reshape(-1, top_k, num_experts) + .to(compute_device) + ) + + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( + expert_attention_mask, dim=0, + ) + + # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert + router_per_expert_attention_mask = ( + attention_mask[None, :, :, None] + .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) + .reshape(-1, num_experts) + .to(compute_device) + ) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum( + router_per_expert_attention_mask, dim=0, + ) + + overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) + return overall_loss * num_experts + + +class MomBlock(GradientCheckpointingLayer): + + def __init__(self, config: MomConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.attn_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + window_size=config.attn['window_size'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + if config.mom_backend == 'gated_deltanet': + self.attn = MomAttention( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_v=config.expand_v, + head_dim=config.head_dim, + num_heads=config.num_heads, + use_output_gate=config.use_output_gate, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + norm_eps=config.norm_eps, + layer_idx=layer_idx, + num_memories=config.num_memories, + topk=config.topk, + capacity=config.capacity, + shared_mem=config.shared_mem, + single_kv_proj=config.single_kv_proj, + ) + else: + raise NotImplementedError(f"The MoM backend {config.mom_backend} is not currently supported.") + self.mlp_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps) + self.mlp = MomMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + if hasattr(self, 'attn_norm'): + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values, router_logits = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if hasattr(self, 'mlp_norm'): + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values, router_logits) + + return outputs + + +class MomPreTrainedModel(PreTrainedModel): + + config_class = MomConfig + supports_gradient_checkpointing = True + _no_split_modules = ['MomBlock'] + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + rescale_prenorm_residual: bool = False, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + for name, p in module.named_parameters(): + if name in ["o_proj.weight", "down_proj.weight"]: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + + +@dataclass +class MomOutputWithPast(BaseModelOutputWithPast): + router_logits: tuple[torch.FloatTensor, ...] | None = None + + +class MomModel(MomPreTrainedModel): + + def __init__(self, config: MomConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([MomBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = RMSNorm(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`MomModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + all_router_logits = () + + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values, router_logits = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + all_router_logits += (router_logits,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return MomOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + router_logits=all_router_logits, + ) + + +@dataclass +class MomCausalLMOutputWithPast(CausalLMOutputWithPast): + aux_loss: torch.FloatTensor | None = None + router_logits: tuple[torch.FloatTensor, ...] | None = None + + +class MomForCausalLM(MomPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = MomModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.num_memories = config.num_memories + self.topk = config.topk + self.aux_loss_scale = config.aux_loss_scale + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + num_logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training + logits = None if fuse_linear_and_cross_entropy else self.lm_head(hidden_states[:, -num_logits_to_keep:]) + + loss = None + aux_loss = None + if labels is not None: + if self.config.fuse_cross_entropy: + if fuse_linear_and_cross_entropy: + loss_fct = FusedLinearCrossEntropyLoss() + else: + loss_fct = FusedCrossEntropyLoss(inplace_backward=True) + else: + loss_fct = nn.CrossEntropyLoss() + # Enable model parallelism + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], loss_fct.ignore_index)), 1) + if fuse_linear_and_cross_entropy: + loss = loss_fct(hidden_states.view(-1, self.config.hidden_size), + labels.view(-1), + self.lm_head.weight, + self.lm_head.bias) + else: + loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) + + aux_loss = load_balancing_loss_func( + outputs.router_logits, + self.num_memories, + self.topk, + attention_mask, + ) + + # print(aux_loss) + + loss += aux_loss.to(loss.device) * self.aux_loss_scale + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return MomCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + router_logits=outputs.router_logits, + aux_loss=aux_loss, + ) diff --git a/code/flash-linear-attention/fla/models/nsa/__init__.py b/code/flash-linear-attention/fla/models/nsa/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e5ae8856eab22da6ae38e2f021532865e36914b8 --- /dev/null +++ b/code/flash-linear-attention/fla/models/nsa/__init__.py @@ -0,0 +1,14 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.nsa.configuration_nsa import NSAConfig +from fla.models.nsa.modeling_nsa import NSAForCausalLM, NSAModel + +AutoConfig.register(NSAConfig.model_type, NSAConfig, exist_ok=True) +AutoModel.register(NSAConfig, NSAModel, exist_ok=True) +AutoModelForCausalLM.register(NSAConfig, NSAForCausalLM, exist_ok=True) + + +__all__ = [ + 'NSAConfig', 'NSAModel', 'NSAForCausalLM', +] diff --git a/code/flash-linear-attention/fla/models/nsa/configuration_nsa.py b/code/flash-linear-attention/fla/models/nsa/configuration_nsa.py new file mode 100644 index 0000000000000000000000000000000000000000..56c1002b3f1d7d34180d87ee62599ba10882394c --- /dev/null +++ b/code/flash-linear-attention/fla/models/nsa/configuration_nsa.py @@ -0,0 +1,89 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class NSAConfig(PretrainedConfig): + + model_type = 'nsa' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + num_hidden_layers: int = 24, + num_heads: int = 64, + num_kv_heads: int = 4, + head_dim: int = 32, + qkv_bias: bool = False, + block_size: int = 64, + block_counts: int | None = 16, + window_size: int | None = 512, + rope_theta: float | None = 10000., + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + initializer_range: float = 0.02, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = head_dim + self.qkv_bias = qkv_bias + self.block_size = block_size + self.block_counts = block_counts + self.window_size = window_size + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + + self.initializer_range = initializer_range + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.use_cache = use_cache + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/nsa/modeling_nsa.py b/code/flash-linear-attention/fla/models/nsa/modeling_nsa.py new file mode 100644 index 0000000000000000000000000000000000000000..7bf07e5c40ee10b82466e72651135c9a789e2f83 --- /dev/null +++ b/code/flash-linear-attention/fla/models/nsa/modeling_nsa.py @@ -0,0 +1,353 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.nsa import NativeSparseAttention +from fla.models.nsa.configuration_nsa import NSAConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as NSAMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class NSABlock(GradientCheckpointingLayer): + + def __init__(self, config: NSAConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.attn = NativeSparseAttention( + hidden_size=config.hidden_size, + num_heads=config.num_heads, + num_kv_heads=config.num_kv_heads, + qkv_bias=config.qkv_bias, + block_size=config.block_size, + block_counts=config.block_counts, + window_size=config.window_size, + rope_theta=config.rope_theta, + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = NSAMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class NSAPreTrainedModel(PreTrainedModel): + + config_class = NSAConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['NSABlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class NSAModel(NSAPreTrainedModel): + + def __init__(self, config: NSAConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([NSABlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`NSAModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class NSAForCausalLM(NSAPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = NSAModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/path_attn/__init__.py b/code/flash-linear-attention/fla/models/path_attn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3d641116033b06d5f907c821d5bc6e42d02cd449 --- /dev/null +++ b/code/flash-linear-attention/fla/models/path_attn/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.path_attn.configuration_path_attention import PaTHAttentionConfig +from fla.models.path_attn.modeling_path_attention import PaTHAttentionForCausalLM, PaTHAttentionModel + +AutoConfig.register(PaTHAttentionConfig.model_type, PaTHAttentionConfig, exist_ok=True) +AutoModel.register(PaTHAttentionConfig, PaTHAttentionModel, exist_ok=True) +AutoModelForCausalLM.register(PaTHAttentionConfig, PaTHAttentionForCausalLM, exist_ok=True) + + +__all__ = ['PaTHAttentionConfig', 'PaTHAttentionForCausalLM', 'PaTHAttentionModel'] diff --git a/code/flash-linear-attention/fla/models/path_attn/configuration_path_attention.py b/code/flash-linear-attention/fla/models/path_attn/configuration_path_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..0fad6f5c16e4a00bd1aeaf02f61bba58649a6eb5 --- /dev/null +++ b/code/flash-linear-attention/fla/models/path_attn/configuration_path_attention.py @@ -0,0 +1,81 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class PaTHAttentionConfig(PretrainedConfig): + + model_type = 'path_attn' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + hidden_size: int = 2048, + num_hidden_layers: int = 24, + num_heads: int = 32, + num_kv_heads: int | None = None, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + initializer_range: float = 0.02, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + use_forget_gate: bool = False, + use_w_shortconv: bool = True, + use_low_rank_w: bool = True, + **kwargs, + ): + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + + self.initializer_range = initializer_range + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.use_cache = use_cache + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + self.use_forget_gate = use_forget_gate + self.use_w_shortconv = use_w_shortconv + self.use_low_rank_w = use_low_rank_w + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/path_attn/modeling_path_attention.py b/code/flash-linear-attention/fla/models/path_attn/modeling_path_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..fbd4e4d2a970a913da227f2c0b30fba2da819a39 --- /dev/null +++ b/code/flash-linear-attention/fla/models/path_attn/modeling_path_attention.py @@ -0,0 +1,358 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Any + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.path_attn import PaTHAttention +from fla.models.path_attn.configuration_path_attention import PaTHAttentionConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as PaTHAttentionMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class PaTHAttentionBlock(GradientCheckpointingLayer): + + def __init__(self, config: PaTHAttentionConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.attn = PaTHAttention( + hidden_size=config.hidden_size, + num_heads=config.num_heads, + num_kv_heads=config.num_kv_heads, + use_forget_gate=config.use_forget_gate, + use_w_shortconv=config.use_w_shortconv, + use_low_rank_w=config.use_low_rank_w, + layer_idx=layer_idx, + ) + + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = PaTHAttentionMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: tuple[torch.Tensor] | None = None, + output_attentions: bool | None = False, + use_cache: bool | None = False, + **kwargs: Unpack[Any], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + + residual = hidden_states + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attentions,) + + if use_cache: + outputs += (past_key_values,) + + return outputs + + +class PaTHAttentionPreTrainedModel(PreTrainedModel): + + config_class = PaTHAttentionConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['PaTHAttentionBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + rescale_prenorm_residual: bool = False, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per PaTHAttention Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + + +class PaTHAttentionModel(PaTHAttentionPreTrainedModel): + + def __init__( + self, + config: PaTHAttentionConfig, + ) -> PaTHAttentionModel: + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([ + PaTHAttentionBlock(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[Any], + ) -> tuple | CausalLMOutputWithPast: + if output_attentions: + warnings.warn( + "`PaTHAttentionModel` does not support output attention weights now, " + "so `output_attentions` is set to `False`.", + ) + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + + # embed positions + hidden_states = inputs_embeds + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + next_cache = None + + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + layer_outputs = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attns] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class PaTHAttentionForCausalLM(PaTHAttentionPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = PaTHAttentionModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[Any], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + logits = None if self.config.fuse_linear_cross_entropy else self.lm_head(hidden_states[:, -logits_to_keep:]) + + loss = None + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + # Enable model parallelism + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/retnet/__init__.py b/code/flash-linear-attention/fla/models/retnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9727d610b133c651061840e129de20d3284d5b1e --- /dev/null +++ b/code/flash-linear-attention/fla/models/retnet/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.retnet.configuration_retnet import RetNetConfig +from fla.models.retnet.modeling_retnet import RetNetForCausalLM, RetNetModel + +AutoConfig.register(RetNetConfig.model_type, RetNetConfig, exist_ok=True) +AutoModel.register(RetNetConfig, RetNetModel, exist_ok=True) +AutoModelForCausalLM.register(RetNetConfig, RetNetForCausalLM, exist_ok=True) + + +__all__ = ['RetNetConfig', 'RetNetForCausalLM', 'RetNetModel'] diff --git a/code/flash-linear-attention/fla/models/retnet/configuration_retnet.py b/code/flash-linear-attention/fla/models/retnet/configuration_retnet.py new file mode 100644 index 0000000000000000000000000000000000000000..9b9d383a0c45b3ac3d0ff4441c39ba6fd33e0ce8 --- /dev/null +++ b/code/flash-linear-attention/fla/models/retnet/configuration_retnet.py @@ -0,0 +1,106 @@ + +from __future__ import annotations + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class RetNetConfig(PretrainedConfig): + + model_type = 'retnet' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + hidden_size: int = 2048, + expand_k: float = 1.0, + expand_v: float = 2.0, + hidden_ratio: int | None = 2, + intermediate_size: int | None = None, + num_hidden_layers: int = 24, + num_heads: int = 8, + num_kv_heads: int | None = None, + feature_map: str | None = None, + hidden_act: str = "swish", + use_short_conv: bool = False, + conv_size: int = 4, + use_output_gate: bool = True, + max_position_embeddings: int = 2048, + elementwise_affine: bool | None = True, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ) -> RetNetConfig: + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.feature_map = feature_map + self.hidden_act = hidden_act + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.use_output_gate = use_output_gate + self.hidden_act = hidden_act + self.max_position_embeddings = max_position_embeddings + self.elementwise_affine = elementwise_affine + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/retnet/modeling_retnet.py b/code/flash-linear-attention/fla/models/retnet/modeling_retnet.py new file mode 100644 index 0000000000000000000000000000000000000000..83d2f0c16aae3c2b4334f08aa14e41ba6c2290c5 --- /dev/null +++ b/code/flash-linear-attention/fla/models/retnet/modeling_retnet.py @@ -0,0 +1,379 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.multiscale_retention import MultiScaleRetention +from fla.models.retnet.configuration_retnet import RetNetConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as RetNetMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class RetNetBlock(GradientCheckpointingLayer): + + def __init__(self, config: RetNetConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = MultiScaleRetention( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_k=config.expand_k, + expand_v=config.expand_v, + num_heads=config.num_heads, + num_kv_heads=config.num_kv_heads, + feature_map=config.feature_map, + use_output_gate=config.use_output_gate, + gate_fn=config.hidden_act, + elementwise_affine=config.elementwise_affine, + norm_eps=config.norm_eps, + fuse_norm=config.fuse_norm, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = RetNetMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + + residual = hidden_states + + hidden_states = self.attn_norm(hidden_states) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class RetNetPreTrainedModel(PreTrainedModel): + + config_class = RetNetConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['RetNetBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class RetNetModel(RetNetPreTrainedModel): + + def __init__(self, config: RetNetConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [RetNetBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)], + ) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn( + "`RetNetModel` does not support output attention weights now, so `output_attentions` is set to `False`.", + ) + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class RetNetForCausalLM(RetNetPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = RetNetModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + # Expected exception: "AttributeError: '(object name)' object has no attribute 'past_key_values'" + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/rodimus/__init__.py b/code/flash-linear-attention/fla/models/rodimus/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0aa7da945f9115d28562203a2087f4d181897bf8 --- /dev/null +++ b/code/flash-linear-attention/fla/models/rodimus/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.rodimus.configuration_rodimus import RodimusConfig +from fla.models.rodimus.modeling_rodimus import RodimusForCausalLM, RodimusModel + +AutoConfig.register(RodimusConfig.model_type, RodimusConfig, exist_ok=True) +AutoModel.register(RodimusConfig, RodimusModel, exist_ok=True) +AutoModelForCausalLM.register(RodimusConfig, RodimusForCausalLM, exist_ok=True) + + +__all__ = ['RodimusConfig', 'RodimusForCausalLM', 'RodimusModel'] diff --git a/code/flash-linear-attention/fla/models/rodimus/configuration_rodimus.py b/code/flash-linear-attention/fla/models/rodimus/configuration_rodimus.py new file mode 100644 index 0000000000000000000000000000000000000000..a389f75356ac3c5f3de75e947df83c86a5118e38 --- /dev/null +++ b/code/flash-linear-attention/fla/models/rodimus/configuration_rodimus.py @@ -0,0 +1,116 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class RodimusConfig(PretrainedConfig): + + model_type = 'rodimus' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + block_type: str = 'rodimus_plus', + hidden_size: int = 2048, + num_hidden_layers: int = 24, + attn_mode: str = "chunk", + residual_in_fp32: bool = True, + block_residual_in_fp32: bool = False, + expand_ratio: int | None = 64, + input_gate_low_rank: float | str | None = 'auto', + use_short_conv: bool = True, + conv_size: int = 4, + hidden_ratio: float | None = 4/3, + intermediate_size: int | None = None, + hidden_act: str = "swish", + max_position_embeddings: int = 2048, + norm_eps: float = 1e-5, + k_norm_eps: float | None = None, + attn: dict | None = None, + ska_attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 126080, + eos_token_id: int = 126081, + tie_word_embeddings: bool = True, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 126464, + **kwargs, + ): + self.block_type = block_type + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.attn_mode = attn_mode + self.residual_in_fp32 = residual_in_fp32 + self.block_residual_in_fp32 = block_residual_in_fp32 + self.expand_ratio = expand_ratio + self.input_gate_low_rank = input_gate_low_rank + + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.max_position_embeddings = max_position_embeddings + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.norm_eps = norm_eps + self.k_norm_eps = k_norm_eps + + self.attn = attn + self.ska_attn = ska_attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['qk_norm'] = attn.get('qk_norm', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + if ska_attn is not None: + if not isinstance(ska_attn, dict): + raise ValueError("attn must be a dictionary") + if 'num_heads' not in ska_attn: + raise ValueError("Number of heads must be provided to initialize shared-key attention layers") + ska_attn['qkv_bias'] = ska_attn.get('qkv_bias', False) + ska_attn['qk_norm'] = ska_attn.get('qk_norm', False) + ska_attn['window_size'] = ska_attn.get('window_size', 1024) + ska_attn['rope_theta'] = ska_attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/rodimus/modeling_rodimus.py b/code/flash-linear-attention/fla/models/rodimus/modeling_rodimus.py new file mode 100644 index 0000000000000000000000000000000000000000..4dc48066c2780323b1ee690b1e00b4d842810c12 --- /dev/null +++ b/code/flash-linear-attention/fla/models/rodimus/modeling_rodimus.py @@ -0,0 +1,561 @@ + +from __future__ import annotations + +import math +import warnings +from functools import partial +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.rodimus import RodimusAttention, SlidingWindowSharedKeyAttention, align_multiple +from fla.models.rodimus.configuration_rodimus import RodimusConfig +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as RodimusMLP +from fla.modules.l2warp import l2_warp + +try: + from torch.distributed.tensor import DTensor +except (ImportError, AttributeError): + DTensor = None + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class RodimusBlock(GradientCheckpointingLayer): + + def __init__(self, config: RodimusConfig, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + self.block_type = config.block_type + self.block_residual_in_fp32 = config.residual_in_fp32 + self.residual_in_fp32 = config.residual_in_fp32 + self.fuse_norm = config.fuse_norm + + self._is_ori_attn = False + + if config.intermediate_size is None: + intermediate_size = align_multiple(int(config.hidden_ratio * config.hidden_size), 8) + else: + intermediate_size = config.intermediate_size + + mlp_cls = partial( + RodimusMLP, + hidden_size=config.hidden_size, + hidden_ratio=None, + intermediate_size=intermediate_size, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + norm_cls = partial( + RMSNorm if self.fuse_norm else nn.RMSNorm, + config.hidden_size, + eps=config.norm_eps, + ) + + if config.attn is not None and layer_idx in config.attn['layers']: + self._is_ori_attn = True + self.attn_norm = norm_cls() + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + + self.mlp_norm = norm_cls() + self.mlp = mlp_cls() + else: + self.mixer_norm = norm_cls() + self.mixer = RodimusAttention( + block_type=config.block_type, + mode=config.attn_mode, + hidden_size=config.hidden_size, + input_gate_low_rank=config.input_gate_low_rank, + expand_ratio=config.expand_ratio, + use_short_conv=config.use_short_conv, + conv_size=config.conv_size, + norm_eps=config.norm_eps, + k_norm_eps=config.k_norm_eps, + residual_in_fp32=config.residual_in_fp32, + layer_idx=layer_idx, + ) + + if self.block_type == "rodimus_plus": + self.ska_attn_norm = norm_cls() + self.ska_attn = SlidingWindowSharedKeyAttention( + hidden_size=config.hidden_size, + num_heads=config.ska_attn['num_heads'], + qkv_bias=config.ska_attn['qkv_bias'], + qk_norm=config.ska_attn['qk_norm'], + window_size=config.ska_attn['window_size'], + rope_theta=config.ska_attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + + self.mlp_norm = norm_cls() + self.mlp = mlp_cls() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + residual: torch.Tensor | None = None, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + + if self.block_residual_in_fp32 and self.layer_idx > 0: + assert residual is not None, 'Residual must be passed in when setting `block_residual_in_fp32=True`' + + if self._is_ori_attn: + if self.block_residual_in_fp32: + hidden_states, residual = self.attn_norm( + hidden_states, + residual=residual, + prenorm=True, + residual_in_fp32=self.residual_in_fp32, + ) + else: + residual = hidden_states.float() if self.residual_in_fp32 else hidden_states + hidden_states = self.attn_norm(hidden_states) + + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + if self.fuse_norm: + hidden_states, residual = self.mlp_norm( + hidden_states, + residual, + prenorm=True, + residual_in_fp32=self.residual_in_fp32, + ) + else: + hidden_states = residual + hidden_states + residual = hidden_states.float() if self.residual_in_fp32 else hidden_states + hidden_states = self.mlp_norm(hidden_states.to(self.mlp_norm.weight.dtype)) + + hidden_states = self.mlp(hidden_states, **kwargs) + else: + if self.block_residual_in_fp32: + hidden_states, residual = self.mixer_norm( + hidden_states, + residual=residual, + prenorm=True, + residual_in_fp32=self.residual_in_fp32, + ) + else: + residual = hidden_states.float() if self.residual_in_fp32 else hidden_states + hidden_states = self.mixer_norm(hidden_states) + + hidden_states, attentions, past_key_values = self.mixer( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + **kwargs, + ) + + if self.block_type == "rodimus_plus": + past_key_values, rodimus_caches = past_key_values + + if self.fuse_norm: + hidden_states, residual = self.ska_attn_norm( + hidden_states, + residual, + prenorm=True, + residual_in_fp32=self.residual_in_fp32, + ) + else: + hidden_states = residual + hidden_states + residual = hidden_states.float() if self.residual_in_fp32 else hidden_states + hidden_states = self.ska_attn_norm(hidden_states.to(dtype=self.ska_attn_norm.weight.dtype)) + + hidden_states, attentions, past_key_values = self.ska_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + rodimus_caches=rodimus_caches, + **kwargs, + ) + + if self.fuse_norm: + hidden_states = self.mlp_norm( + hidden_states, + residual=residual, + prenorm=False, + residual_in_fp32=self.residual_in_fp32, + ) + else: + hidden_states = residual + hidden_states + hidden_states = self.mlp_norm(hidden_states.to(dtype=self.mlp_norm.weight.dtype)) + + hidden_states = self.mlp(hidden_states, **kwargs) + + if self.block_residual_in_fp32: + hidden_states = (hidden_states, residual) + else: + hidden_states = (residual + hidden_states).to(dtype=hidden_states.dtype) + + outputs = (hidden_states, attentions, past_key_values) + return outputs + + +class RodimusPreTrainedModel(PreTrainedModel): + + config_class = RodimusConfig + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['RodimusBlock'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + if self.config.block_type == "rodimus": + self.num_residuals_per_layer = 1 + elif self.config.block_type == "rodimus_plus": + self.num_residuals_per_layer = 3 + else: + raise NotImplementedError() + + def _init_weights( + self, + module: nn.Module, + prenorm_residual_strategy: str | None = None, + ): + num_residuals_per_layer = self.num_residuals_per_layer + + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + sigmoid_bias_max = 0.999 + sigmoid_bias_min = 0.9 + max_ = 1 - sigmoid_bias_min + min_ = 1 - sigmoid_bias_max + g_gate_bias = torch.exp( + torch.rand(self.config.expand_ratio) * (math.log(max_) - math.log(min_)) + + math.log(min_), + ).clamp(min=1e-4) + g_gate_bias = g_gate_bias + torch.log(-torch.expm1(-g_gate_bias)) + tau_gate_bias = torch.logit(torch.empty((self.config.expand_ratio, )).uniform_(1/16, 0.9)) + + if hasattr(module, 'i_gate_proj'): + nn.init.xavier_uniform_(module.i_gate_proj[0].weight, gain=2 ** -2.5) + nn.init.xavier_uniform_(module.i_gate_proj[1].weight, gain=2 ** -2.5) + nn.init.zeros_(module.i_gate_proj[1].bias) + if hasattr(module, 'g_gate_proj'): + nn.init.xavier_uniform_(module.g_gate_proj.weight, gain=2 ** -2.5) + with torch.no_grad(): + if not isinstance(module.g_gate_proj.bias, DTensor): + module.g_gate_proj.bias.copy_(g_gate_bias) + else: + logger.warning_once("`g_gate_proj.bias` is a DTensor, skipping initialization") + + if hasattr(module, 'tau_gate_proj'): + nn.init.xavier_uniform_(module.tau_gate_proj.weight, gain=2 ** -2.5) + with torch.no_grad(): + if not isinstance(module.tau_gate_proj.bias, DTensor): + module.tau_gate_proj.bias.copy_(tau_gate_bias) + else: + logger.warning_once("`tau_gate_proj.bias` is a DTensor, skipping initialization") + + if prenorm_residual_strategy is not None: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + if prenorm_residual_strategy == 'rescale': + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + elif prenorm_residual_strategy == 'zero': + nn.init.zeros_(p) + else: + raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}") + + +class RodimusModel(RodimusPreTrainedModel): + + def __init__(self, config: RodimusConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + self.block_residual_in_fp32 = config.block_residual_in_fp32 + + if config.block_residual_in_fp32: + if not config.residual_in_fp32: + warning_message = ( + "`residual_in_fp32=False` is incompatible with `block_residual_in_fp32=True`. " + "Setting `residual_in_fp32=True`..." + ) + logger.warning_once(warning_message) + config.residual_in_fp32 = True + if not config.fuse_norm: + logger.warning_once( + '`fuse_norm=False` is incompatible with `block_residual_in_fp32=True` Setting `fuse_norm=True`...') + config.fuse_norm = True + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([RodimusBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + + if output_attentions: + warnings.warn("`RodimusModel` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + residual = None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + residual=residual, + **kwargs, + ) + + if self.block_residual_in_fp32: + hidden_states, residual = hidden_states + else: + residual = None + + if output_attentions: + all_attns += (attentions,) + + if self.block_residual_in_fp32: + hidden_states = self.norm( + hidden_states, + residual=residual, + prenorm=False, + residual_in_fp32=True, + ) + else: + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class RodimusForCausalLM(RodimusPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = RodimusModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/rwkv6/__init__.py b/code/flash-linear-attention/fla/models/rwkv6/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bde1972830019cfb6c8f7f7f38507f85c939d3df --- /dev/null +++ b/code/flash-linear-attention/fla/models/rwkv6/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.rwkv6.configuration_rwkv6 import RWKV6Config +from fla.models.rwkv6.modeling_rwkv6 import RWKV6ForCausalLM, RWKV6Model + +AutoConfig.register(RWKV6Config.model_type, RWKV6Config, exist_ok=True) +AutoModel.register(RWKV6Config, RWKV6Model, exist_ok=True) +AutoModelForCausalLM.register(RWKV6Config, RWKV6ForCausalLM, exist_ok=True) + + +__all__ = ['RWKV6Config', 'RWKV6ForCausalLM', 'RWKV6Model'] diff --git a/code/flash-linear-attention/fla/models/rwkv6/configuration_rwkv6.py b/code/flash-linear-attention/fla/models/rwkv6/configuration_rwkv6.py new file mode 100644 index 0000000000000000000000000000000000000000..56559b091ed8789da30c4eadf393bd7002b8fe34 --- /dev/null +++ b/code/flash-linear-attention/fla/models/rwkv6/configuration_rwkv6.py @@ -0,0 +1,96 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class RWKV6Config(PretrainedConfig): + + model_type = 'rwkv6' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + hidden_size: int = 2048, + expand_k: float = 0.5, + expand_v: float = 1.0, + hidden_ratio: float | None = 3.5, + intermediate_size: int | None = None, + num_hidden_layers: int = 24, + num_heads: int = 4, + proj_low_rank_dim: int = 32, + gate_low_rank_dim: int = 64, + hidden_act: str = "sqrelu", + max_position_embeddings: int = 2048, + norm_first: bool = True, + norm_bias: bool = True, + norm_eps: float = 1e-5, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.expand_k = expand_k + self.expand_v = expand_v + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.norm_first = norm_first + self.num_hidden_layers = num_hidden_layers + self.num_heads = num_heads + self.proj_low_rank_dim = proj_low_rank_dim + self.gate_low_rank_dim = gate_low_rank_dim + self.hidden_act = hidden_act + self.max_position_embeddings = max_position_embeddings + self.norm_bias = norm_bias + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + self.fuse_norm = fuse_norm + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/rwkv6/modeling_rwkv6.py b/code/flash-linear-attention/fla/models/rwkv6/modeling_rwkv6.py new file mode 100644 index 0000000000000000000000000000000000000000..864cfc028b7c6e2bb4f63034637ab60b355b7384 --- /dev/null +++ b/code/flash-linear-attention/fla/models/rwkv6/modeling_rwkv6.py @@ -0,0 +1,448 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.rwkv6 import LerpLinear, RWKV6Attention +from fla.models.rwkv6.configuration_rwkv6 import RWKV6Config +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, LayerNorm +from fla.modules.activations import ACT2FN +from fla.modules.l2warp import l2_warp +from fla.modules.token_shift import token_shift + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class RWKV6FeedForward(nn.Module): + + def __init__( + self, + hidden_size: int, + hidden_ratio: int | None = None, + intermediate_size: int | None = None, + hidden_act: str = 'sqrelu', + layer_idx: int = None, + ) -> RWKV6FeedForward: + super().__init__() + + self.hidden_size = hidden_size + if hidden_ratio is None: + hidden_ratio = 3.5 + if intermediate_size is None: + intermediate_size = int(hidden_size * hidden_ratio) + intermediate_size = 32 * ((intermediate_size + 32 - 1) // 32) + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + + self.time_shift = nn.ZeroPad2d((0, 0, 1, -1)) + + self.key = LerpLinear(hidden_size, intermediate_size) + self.value = nn.Linear(intermediate_size, hidden_size, bias=False) + self.receptance = LerpLinear(hidden_size, hidden_size) + self.act_fn = ACT2FN[hidden_act] + + self.layer_idx = layer_idx + + def forward( + self, + x: torch.Tensor, + attention_mask: torch.Tensor | None = None, + state: Cache | None = None, + cu_seqlens: torch.LongTensor | None = None, + **kwargs, + ) -> torch.Tensor: + if attention_mask is not None: + x = x.mul_(attention_mask[:, -x.shape[-2]:, None]) + if x.shape[1] == 1 and state is not None and state[self.layer_idx]['ffn_state'] is not None: + shifted = state[self.layer_idx]['ffn_state'].unsqueeze(1) + delta = shifted - x + elif state is not None and state[self.layer_idx]['ffn_state'] is not None: + shifted = self.time_shift(x) + if state is not None and state[self.layer_idx]['ffn_state'] is not None: + shifted[:, 0] = state[self.layer_idx]['ffn_state'] + delta = shifted - x + else: + delta = token_shift(x, cu_seqlens) + key = self.act_fn(self.key(x, delta)) + value = self.value(key) + receptance = self.receptance(x, delta) + + if state is not None: + # no need to update the offset twice + state.update(ffn_state=x[:, -1], layer_idx=self.layer_idx, offset=0) + return receptance.sigmoid() * value, state + + +class RWKV6Block(GradientCheckpointingLayer): + + def __init__(self, config: RWKV6Config, layer_idx: int): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + if config.norm_first and layer_idx == 0: + self.pre_norm = (LayerNorm if config.fuse_norm else nn.LayerNorm)( + config.hidden_size, + bias=config.norm_bias, + eps=config.norm_eps, + ) + self.attn_norm = (LayerNorm if config.fuse_norm else nn.LayerNorm)( + config.hidden_size, + bias=config.norm_bias, + eps=config.norm_eps, + ) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = RWKV6Attention( + mode=config.attn_mode, + hidden_size=config.hidden_size, + expand_k=config.expand_k, + expand_v=config.expand_v, + num_heads=config.num_heads, + proj_low_rank_dim=config.proj_low_rank_dim, + gate_low_rank_dim=config.gate_low_rank_dim, + norm_eps=config.norm_eps, + fuse_norm=config.fuse_norm, + layer_idx=layer_idx, + ) + self.ffn_norm = (LayerNorm if config.fuse_norm else nn.LayerNorm)( + config.hidden_size, + bias=config.norm_bias, + eps=config.norm_eps, + ) + self.ffn = RWKV6FeedForward( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + layer_idx=layer_idx, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + cu_seqlens: torch.LongTensor | None = None, + **kwargs, + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = self.pre_norm(hidden_states) if hasattr(self, 'pre_norm') else hidden_states + hidden_states = self.attn_norm(residual) + hidden_states, attentions, past_key_values = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + cu_seqlens=cu_seqlens, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.ffn_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.ffn_norm(hidden_states) + hidden_states, past_key_values = self.ffn( + hidden_states, attention_mask, past_key_values, cu_seqlens, **kwargs, + ) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values) + + return outputs + + +class RWKV6PreTrainedModel(PreTrainedModel): + + config_class = RWKV6Config + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['RWKV6Block'] + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights( + self, + module: nn.Module, + rescale_prenorm_residual: bool = True, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, (nn.Linear, nn.Conv1d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Parameter): + nn.init.normal_(module, mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + + +class RWKV6Model(RWKV6PreTrainedModel): + + def __init__(self, config: RWKV6Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([RWKV6Block(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (LayerNorm if config.fuse_norm else nn.LayerNorm)( + config.hidden_size, + bias=config.norm_bias, + eps=config.norm_eps, + ) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + cu_seqlens: torch.LongTensor | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`RWKV6Model` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + cu_seqlens=cu_seqlens, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class RWKV6ForCausalLM(RWKV6PreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = RWKV6Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/rwkv7/__init__.py b/code/flash-linear-attention/fla/models/rwkv7/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2b3fff4d18b1ec37fa440a349fdcd74b4202ed52 --- /dev/null +++ b/code/flash-linear-attention/fla/models/rwkv7/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.rwkv7.configuration_rwkv7 import RWKV7Config +from fla.models.rwkv7.modeling_rwkv7 import RWKV7ForCausalLM, RWKV7Model + +AutoConfig.register(RWKV7Config.model_type, RWKV7Config, exist_ok=True) +AutoModel.register(RWKV7Config, RWKV7Model, exist_ok=True) +AutoModelForCausalLM.register(RWKV7Config, RWKV7ForCausalLM, exist_ok=True) + + +__all__ = ['RWKV7Config', 'RWKV7ForCausalLM', 'RWKV7Model'] diff --git a/code/flash-linear-attention/fla/models/rwkv7/configuration_rwkv7.py b/code/flash-linear-attention/fla/models/rwkv7/configuration_rwkv7.py new file mode 100644 index 0000000000000000000000000000000000000000..c6bd8a429c627f534a0bf589c6b5994e2ee6bb18 --- /dev/null +++ b/code/flash-linear-attention/fla/models/rwkv7/configuration_rwkv7.py @@ -0,0 +1,119 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class RWKV7Config(PretrainedConfig): + + model_type = 'rwkv7' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + hidden_size: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + num_hidden_layers: int = 24, + head_dim: int | None = 64, + num_heads: int | None = None, + decay_low_rank_dim: int = 64, + gate_low_rank_dim: int = 128, + a_low_rank_dim: int = 64, + v_low_rank_dim: int = 16, + hidden_act: str = "sqrelu", + max_position_embeddings: int = 2048, + norm_first: bool = True, + norm_bias: bool = True, + norm_eps: float = 1e-5, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = True, + vocab_size: int = 32000, + value_dim: int | list[int] | None = None, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.norm_first = norm_first + self.num_hidden_layers = num_hidden_layers + + if head_dim is None and num_heads is not None: + head_dim = int(hidden_size // num_heads) + elif head_dim is not None and num_heads is None: + num_heads = int(hidden_size // head_dim) + + if value_dim is None: + value_dim = [hidden_size] * num_hidden_layers + elif isinstance(value_dim, int): + assert value_dim >= hidden_size, "value_dim must be greater than hidden_size" + assert value_dim % hidden_size == 0, "value_dim must be divisible by hidden_size" + value_dim = [value_dim] * num_hidden_layers + else: + assert len(value_dim) == num_hidden_layers, "value_dim must have the same length as num_hidden_layers" + for v in value_dim: + assert v >= hidden_size, "value_dim must be greater than hidden_size" + assert v % hidden_size == 0, "value_dim must be divisible by hidden_size" + + self.head_dim = head_dim + self.num_heads = num_heads + self.value_dim = value_dim + + self.decay_low_rank_dim = decay_low_rank_dim + self.gate_low_rank_dim = gate_low_rank_dim + self.a_low_rank_dim = a_low_rank_dim + self.v_low_rank_dim = v_low_rank_dim + self.hidden_act = hidden_act + self.max_position_embeddings = max_position_embeddings + self.norm_bias = norm_bias + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + self.fuse_norm = fuse_norm + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/rwkv7/modeling_rwkv7.py b/code/flash-linear-attention/fla/models/rwkv7/modeling_rwkv7.py new file mode 100644 index 0000000000000000000000000000000000000000..6694abb683ac24efdef41dd181451b5f9b954cc1 --- /dev/null +++ b/code/flash-linear-attention/fla/models/rwkv7/modeling_rwkv7.py @@ -0,0 +1,546 @@ + +from __future__ import annotations + +import math +import warnings +from typing import TYPE_CHECKING, Optional + +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.rwkv7 import RWKV7Attention +from fla.models.rwkv7.configuration_rwkv7 import RWKV7Config +from fla.models.utils import Cache, FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, LayerNorm +from fla.modules.activations import ACT2FN +from fla.modules.l2warp import l2_warp +from fla.modules.token_shift import token_shift + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class RWKV7FeedForward(nn.Module): + + def __init__( + self, + hidden_size: int, + hidden_ratio: int | None = None, + intermediate_size: int | None = None, + hidden_act: str = 'sqrelu', + layer_idx: int = None, + num_hidden_layers: int = None, + ) -> RWKV7FeedForward: + super().__init__() + + self.hidden_size = hidden_size + if hidden_ratio is None: + hidden_ratio = 4 + if intermediate_size is None: + intermediate_size = int(hidden_size * hidden_ratio) + intermediate_size = 32 * ((intermediate_size + 32 - 1) // 32) + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + + self.time_shift = nn.ZeroPad2d((0, 0, 1, -1)) + + self.x_k = nn.Parameter(torch.zeros(hidden_size)) + + self.key = nn.Linear(hidden_size, intermediate_size, bias=False) + self.value = nn.Linear(intermediate_size, hidden_size, bias=False) + self.act_fn = ACT2FN[hidden_act] + + self.layer_idx = layer_idx + self.num_hidden_layers = num_hidden_layers + + try: + from transformers.modeling_utils import _init_weights + except ImportError: + _init_weights = True + if _init_weights: + self.apply(self._initialize_weights) + for name, module in self.named_modules(): + module._in_rwkv_module = True + + def _initialize_weights(self, module: nn.Module): + if isinstance(module, RWKV7FeedForward): + with torch.no_grad(): + ratio_1_to_almost0 = 1.0 - (module.layer_idx / module.num_hidden_layers) # 1 to ~0 + ddd = torch.ones(1, 1, module.hidden_size) + for i in range(module.hidden_size): + ddd[0, 0, i] = i / module.hidden_size + module.x_k.data = 1.0 - torch.pow(ddd, ratio_1_to_almost0**4).squeeze() + + # Initialize key and value weights as in CMix_x070 + original_dtype = module.key.weight.dtype + module.key.weight.data = nn.init.orthogonal_(module.key.weight.data.to(torch.float32)).to(original_dtype) + module.value.weight.data.zero_() + + def forward( + self, + x: torch.Tensor, + attention_mask: torch.Tensor | None = None, + state: Cache | None = None, + cu_seqlens: torch.LongTensor | None = None, + **kwargs, + ) -> torch.Tensor: + if attention_mask is not None: + x = x.mul(attention_mask[:, -x.shape[-2]:, None]) + if state is not None: + delta, ffn_state = token_shift(x, cu_seqlens, cache=state[self.layer_idx]['ffn_state'], output_cache=True) + else: + delta, ffn_state = token_shift(x, cu_seqlens, output_cache=True) + if state is not None: + # no need to update the offset twice + state.update(ffn_state=ffn_state, layer_idx=self.layer_idx, offset=0) + return self.value(self.act_fn(self.key(x.addcmul(delta, self.x_k)))), state + + +class RWKV7Block(GradientCheckpointingLayer): + + def __init__( + self, + config: RWKV7Config, + layer_idx: int, + ) -> RWKV7Block: + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + if config.norm_first and layer_idx == 0: + self.pre_norm = (LayerNorm if config.fuse_norm else nn.LayerNorm)( + config.hidden_size, + bias=config.norm_bias, + eps=config.norm_eps, + ) + self.attn_norm = (LayerNorm if config.fuse_norm else nn.LayerNorm)( + config.hidden_size, + bias=config.norm_bias, + eps=config.norm_eps, + ) + if config.attn is not None and layer_idx in config.attn['layers']: + self.attn = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.attn = RWKV7Attention( + mode=config.attn_mode, + hidden_size=config.hidden_size, + head_dim=config.head_dim, + num_heads=config.num_heads, + decay_low_rank_dim=config.decay_low_rank_dim, + gate_low_rank_dim=config.gate_low_rank_dim, + a_low_rank_dim=config.a_low_rank_dim, + v_low_rank_dim=config.v_low_rank_dim, + norm_eps=config.norm_eps, + fuse_norm=config.fuse_norm, + layer_idx=layer_idx, + value_dim=config.value_dim[layer_idx], + num_hidden_layers=config.num_hidden_layers, + ) + self.ffn_norm = (LayerNorm if config.fuse_norm else nn.LayerNorm)( + config.hidden_size, + bias=config.norm_bias, + eps=config.norm_eps, + ) + self.ffn = RWKV7FeedForward( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + layer_idx=layer_idx, + num_hidden_layers=config.num_hidden_layers, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + output_attentions: bool | None = False, + v_first: torch.Tensor = None, + cu_seqlens: torch.LongTensor | None = None, + **kwargs, + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = self.pre_norm(hidden_states) if hasattr(self, 'pre_norm') else hidden_states + hidden_states = self.attn_norm(residual) + hidden_states, attentions, past_key_values, v_first = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + v_first=v_first, + cu_seqlens=cu_seqlens, + **kwargs, + ) + if self.config.fuse_norm: + hidden_states, residual = self.ffn_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.ffn_norm(hidden_states) + hidden_states, past_key_values = self.ffn( + hidden_states, attention_mask, past_key_values, cu_seqlens, **kwargs, + ) + hidden_states = residual + hidden_states + + outputs = (hidden_states, attentions, past_key_values, v_first) + + return outputs + + +class RWKV7PreTrainedModel(PreTrainedModel): + + config_class = RWKV7Config + base_model_prefix = 'model' + supports_gradient_checkpointing = True + _no_split_modules = ['RWKV7Block'] + _supports_cache_class = True + _skip_keys_device_placement = ["past_key_values"] + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + @torch.no_grad() + def _init_weights( + self, + module: nn.Module, + rescale_prenorm_residual: bool = True, + num_residuals_per_layer: int = 2, + ): + if isinstance(module, nn.Embedding): + # https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v7/train_temp/src/model.py#L396C12-L399C58 + scale = -1e-4 + nn.init.uniform_(module.weight, a=scale, b=-scale) + elif isinstance(module, nn.Linear) and hasattr(self, 'lm_head') and module is self.lm_head: + # https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v7/train_temp/src/model.py#L403 + if self.config.vocab_size > self.config.hidden_size: + scale = 0.5 * math.sqrt(self.config.vocab_size / self.config.hidden_size) + else: + scale = 0.5 + original_dtype = module.weight.dtype + module.weight.data = nn.init.orthogonal_(module.weight.data.to(torch.float32), gain=scale).to(original_dtype) + # Init Attention parameters + elif isinstance(module, (nn.Linear, nn.Conv1d)) and getattr(module, '_in_rwkv_module', False) is False: + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Parameter): + nn.init.normal_(module, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters') and getattr(module, '_in_rwkv_module', False) is False: + module.reset_parameters() + + if rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + p = None + if hasattr(module, 'o_proj'): + p = module.o_proj.weight + elif hasattr(module, 'down_proj'): + p = module.down_proj.weight + if p is not None: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) + + +class RWKV7Model(RWKV7PreTrainedModel): + + def __init__(self, config: RWKV7Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([RWKV7Block(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) + self.norm = (LayerNorm if config.fuse_norm else nn.LayerNorm)( + config.hidden_size, + bias=config.norm_bias, + eps=config.norm_eps, + ) + + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + def load_state_dict(self, state_dict, strict=True, assign=False): + """ + Override the load_state_dict method to handle migration from version 1 to version 2. + Handles hierarchical keys like 'model.layers.0.attn.x_x'. + """ + # Collect all layer indices from the state_dict keys + layer_indices = set() + for key in state_dict.keys(): + if key.startswith("model.layers."): + # Extract the layer index from the key + try: + layer_idx = int(key.split(".")[2]) # Extract the number after 'model.layers.' + layer_indices.add(layer_idx) + except ValueError: + # Skip keys that don't match the expected format + continue + + # Sort the layer indices to process them in order + sorted_layer_indices = sorted(layer_indices) + + # Migration logic for each layer + for layer_idx in sorted_layer_indices: + layer_prefix = f"model.layers.{layer_idx}" + attn_prefix = f"{layer_prefix}.attn" + + # Check if the layer contains the old 'x_x' parameter + if f"{attn_prefix}.x_x" in state_dict: + logger.info(f"Migrating weights for layer {layer_idx} from RWKV7Attention version 1 to version 2...") + # Extract the x_x parameter + x_x = state_dict[f"{attn_prefix}.x_x"] + with torch.no_grad(): + # Create new parameters for version 2 + state_dict[f"{attn_prefix}.x_r"] = x_x[0].unsqueeze(0).unsqueeze(0) + state_dict[f"{attn_prefix}.x_w"] = x_x[1].unsqueeze(0).unsqueeze(0) + state_dict[f"{attn_prefix}.x_k"] = x_x[2].unsqueeze(0).unsqueeze(0) + state_dict[f"{attn_prefix}.x_v"] = x_x[3].unsqueeze(0).unsqueeze(0) + state_dict[f"{attn_prefix}.x_a"] = x_x[4].unsqueeze(0).unsqueeze(0) + state_dict[f"{attn_prefix}.x_g"] = x_x[5].unsqueeze(0).unsqueeze(0) + + # Call the parent method to load the modified state_dict + try: + super().load_state_dict(state_dict, strict=strict, assign=assign) + except TypeError: + # If the parent method does not support `assign`, fall back to strict loading + logger.warning( + "`assign` parameter is not supported by the parent `load_state_dict` method. " + "Falling back to default behavior.", + ) + super().load_state_dict(state_dict, strict=strict) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + cu_seqlens: torch.LongTensor | None = None, + **kwargs: Unpack[dict], + ) -> tuple | BaseModelOutputWithPast: + if output_attentions: + warnings.warn("`RWKV7Model` does not `output_attentions` now, setting it to `False`.") + output_attentions = False + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + if input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + hidden_states = inputs_embeds + + if use_cache and not isinstance(past_key_values, Cache): + past_key_values = Cache.from_legacy_cache(past_key_values) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + + v_first = torch.zeros_like(hidden_states) + for layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + hidden_states, attentions, past_key_values, v_first = layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + v_first=v_first, + cu_seqlens=cu_seqlens, + **kwargs, + ) + + if output_attentions: + all_attns += (attentions,) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attns, + ) + + +class RWKV7ForCausalLM(RWKV7PreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = RWKV7Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embeddings + + def set_input_embeddings(self, value): + self.model.embeddings = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def generate(self, *args, **kwargs): + try: + return super().generate(*args, **kwargs) + except AttributeError as exception: + if 'past_key_values' in str(exception): + raise AttributeError( + f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " + f"which is not supported for {self.__class__.__name__}. " + f"Try another generation strategy instead. " + f"For the available generation strategies, check this doc: " + f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies", + ) + else: + raise exception + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | None = None, + labels: torch.LongTensor | None = None, + shift_labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | CausalLMOutputWithPast: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + + loss, logits = None, None + has_labels = (labels is not None) or (shift_labels is not None) + if not (self.config.fuse_linear_cross_entropy and has_labels): + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if has_labels: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + + # shift_labels: See https://github.com/huggingface/transformers/pull/36607/files. + if shift_labels is None: + shift_labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + shift_labels = shift_labels.to(hidden_states.device) + + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, shift_labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(shift_labels.numel(), -1), shift_labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/code/flash-linear-attention/fla/models/samba/__init__.py b/code/flash-linear-attention/fla/models/samba/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2f4ed2f74d0cce8495fd6c87ada7eef17a6641fa --- /dev/null +++ b/code/flash-linear-attention/fla/models/samba/__init__.py @@ -0,0 +1,12 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.samba.configuration_samba import SambaConfig +from fla.models.samba.modeling_samba import SambaBlock, SambaForCausalLM, SambaModel + +AutoConfig.register(SambaConfig.model_type, SambaConfig, exist_ok=True) +AutoModel.register(SambaConfig, SambaModel, exist_ok=True) +AutoModelForCausalLM.register(SambaConfig, SambaForCausalLM, exist_ok=True) + + +__all__ = ['SambaConfig', 'SambaForCausalLM', 'SambaModel', 'SambaBlock'] diff --git a/code/flash-linear-attention/fla/models/samba/configuration_samba.py b/code/flash-linear-attention/fla/models/samba/configuration_samba.py new file mode 100644 index 0000000000000000000000000000000000000000..1ada5edb4680b9523664af9d17d7da38f9e61f85 --- /dev/null +++ b/code/flash-linear-attention/fla/models/samba/configuration_samba.py @@ -0,0 +1,106 @@ + +import math +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class SambaConfig(PretrainedConfig): + + model_type = "samba" + + def __init__( + self, + hidden_size: int = 2304, + state_size: int = 16, + num_hidden_layers: int = 18, + norm_eps=1e-5, + pad_token_id: int = 0, + bos_token_id: int = 1, + eos_token_id: int = 2, + expand: int = 2, + conv_kernel: int = 4, + use_bias: bool = False, + use_conv_bias: bool = True, + hidden_act: str = "swish", + initializer_range: float = 0.02, + residual_in_fp32: bool = False, + time_step_rank: str = "auto", + time_step_scale: float = 1.0, + time_step_min: float = 0.001, + time_step_max: float = 0.1, + time_step_init_scheme: str = "random", + time_step_floor: float = 1e-4, + max_position_embeddings: int = 2048, + attn: dict | None = { + 'layers': (1, 3, 5, 7, 9, 11, 13, 15, 17), + 'num_heads': 18, + 'num_kv_heads': 18, + 'qkv_bias': False, + 'window_size': 2048, + 'rope_theta': 10000., + }, + hidden_ratio: int | None = 4, + rescale_prenorm_residual: bool = False, + use_cache: bool = True, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + tie_word_embeddings: bool = False, + **kwargs, + ): + self.hidden_size = hidden_size + self.state_size = state_size + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.conv_kernel = conv_kernel + self.expand = expand + self.intermediate_size = int(expand * self.hidden_size) + self.bos_token_id = bos_token_id + self.eos_token_id = eos_token_id + self.pad_token_id = pad_token_id + self.use_bias = use_bias + self.use_conv_bias = use_conv_bias + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.time_step_rank = math.ceil(self.hidden_size / 16) if time_step_rank == "auto" else time_step_rank + self.time_step_scale = time_step_scale + self.time_step_min = time_step_min + self.time_step_max = time_step_max + self.time_step_init_scheme = time_step_init_scheme + self.time_step_floor = time_step_floor + self.max_position_embeddings = max_position_embeddings + self.attn = attn + self.hidden_ratio = hidden_ratio + self.rescale_prenorm_residual = rescale_prenorm_residual + self.residual_in_fp32 = residual_in_fp32 + self.use_cache = use_cache + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + super().__init__( + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + pad_token_id=pad_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/code/flash-linear-attention/fla/models/samba/modeling_samba.py b/code/flash-linear-attention/fla/models/samba/modeling_samba.py new file mode 100644 index 0000000000000000000000000000000000000000..0c84a060b9ad9195326cfbc73178d489cdea0b8d --- /dev/null +++ b/code/flash-linear-attention/fla/models/samba/modeling_samba.py @@ -0,0 +1,384 @@ + +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Optional + +import torch +from torch import nn +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ModelOutput, logging +from transformers.utils.deprecation import deprecate_kwarg + +from fla.layers.attn import Attention +from fla.layers.mamba import Mamba +from fla.models.mamba.modeling_mamba import MambaCache +from fla.models.samba.configuration_samba import SambaConfig +from fla.models.utils import FLAGenerationMixin +from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm +from fla.modules import GatedMLP as SambaMLP +from fla.modules.l2warp import l2_warp + +if TYPE_CHECKING: + from transformers.processing_utils import Unpack + + +try: + from transformers.modeling_layers import GradientCheckpointingLayer +except ImportError: + from fla.models.modeling_layers import GradientCheckpointingLayer + +logger = logging.get_logger(__name__) + + +class SambaBlock(GradientCheckpointingLayer): + + def __init__(self, config, layer_idx): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + + self.mixer_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps) + if config.attn is not None and layer_idx in config.attn['layers']: + self.mixer = Attention( + hidden_size=config.hidden_size, + num_heads=config.attn['num_heads'], + num_kv_heads=config.attn['num_kv_heads'], + qkv_bias=config.attn['qkv_bias'], + window_size=config.attn['window_size'], + rope_theta=config.attn['rope_theta'], + max_position_embeddings=config.max_position_embeddings, + layer_idx=layer_idx, + ) + else: + self.mixer = Mamba( + hidden_size=config.hidden_size, + state_size=config.state_size, + conv_kernel=config.conv_kernel, + intermediate_size=config.intermediate_size, + time_step_rank=config.time_step_rank, + use_bias=config.use_bias, + layer_idx=layer_idx, + ) + self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps) + self.mlp = SambaMLP( + hidden_size=config.hidden_size, + hidden_ratio=config.hidden_ratio, + hidden_act=config.hidden_act, + fuse_swiglu=config.fuse_swiglu, + ) + + def forward( + self, + hidden_states: torch.Tensor, + cache_params: tuple[torch.Tensor] | None = None, + **kwargs: Unpack[dict], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + + residual = hidden_states + hidden_states = self.mixer_norm(hidden_states) + if isinstance(self.mixer, Mamba): + hidden_states = self.mixer(hidden_states, cache_params=cache_params, **kwargs) + else: + hidden_states, _, cache_params = self.mixer(hidden_states=hidden_states, past_key_values=cache_params, **kwargs) + if self.config.fuse_norm: + hidden_states, residual = self.mlp_norm(hidden_states, residual, True) + else: + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.mlp_norm(hidden_states) + hidden_states = self.mlp(hidden_states, **kwargs) + hidden_states = residual + hidden_states + return hidden_states + + +class SambaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = SambaConfig + base_model_prefix = "backbone" + _no_split_modules = ["SambaBlock"] + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, nn.Linear): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + if not getattr(module.bias, "_no_reinit", False): + nn.init.zeros_(module.bias) + elif isinstance(module, Mamba): + module.A_log._no_weight_decay = True + module.D._no_weight_decay = True + + dt_init_std = self.config.time_step_rank**-0.5 * self.config.time_step_scale + if self.config.time_step_init_scheme == "constant": + nn.init.constant_(module.dt_proj.weight, dt_init_std) + elif self.config.time_step_init_scheme == "random": + nn.init.uniform_(module.dt_proj.weight, -dt_init_std, dt_init_std) + + dt = torch.exp( + torch.rand(self.config.intermediate_size) + * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min)) + + math.log(self.config.time_step_min), + ).clamp(min=self.config.time_step_floor) + # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + with torch.no_grad(): + module.dt_proj.bias.data = nn.Parameter(inv_dt.to(module.dt_proj.bias.device)) + module.dt_proj.bias._no_reinit = True + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, std=self.config.initializer_range) + elif hasattr(module, 'reset_parameters'): + module.reset_parameters() + + if self.config.rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + for name, p in module.named_parameters(): + if name in ["out_proj.weight"]: + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + # We need to reinit p since this code could be called multiple times + # Having just p *= scale would repeatedly scale it down + nn.init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p /= math.sqrt(self.config.num_layers) + + +@dataclass +class SambaOutput(ModelOutput): + """ + Class for the Samba model outputs. + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + cache_params (`MambaCache`): + The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to + avoid providing the old `input_ids`. + + Includes both the State space model state matrices after the selective scan, and the Convolutional states + hidden_states (`tuple(torch.FloatTensor)`, *optional*, + returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + """ + + last_hidden_state: torch.FloatTensor | None = None + cache_params: MambaCache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + + +@dataclass +class SambaCausalLMOutput(ModelOutput): + """ + Base class for causal language model (or autoregressive) outputs. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + cache_params (`MambaCache`): + The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to + avoid providing the old `input_ids`. + + Includes both the State space model state matrices after the selective scan, and the Convolutional states + hidden_states (`tuple(torch.FloatTensor)`, *optional*, + returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + cache_params: MambaCache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + + +class SambaModel(SambaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size) + self.layers = nn.ModuleList([SambaBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)]) + + self.gradient_checkpointing = False + self.norm_f = RMSNorm(config.hidden_size, eps=config.norm_eps) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, new_embeddings): + self.embeddings = new_embeddings + + def forward( + self, + input_ids: torch.LongTensor | None = None, + inputs_embeds: torch.LongTensor | None = None, + cache_params: MambaCache | None = None, + use_cache: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs: Unpack[dict], + ) -> tuple | SambaOutput: + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if (input_ids is None) ^ (inputs_embeds is not None): # ^ is python for xor + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one", + ) + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + + if cache_params is None and use_cache: + cache_params = MambaCache( + self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype, + ) + + hidden_states = inputs_embeds + all_hidden_states = () if output_hidden_states else None + for mixer_block in self.layers: + hidden_states = mixer_block( + hidden_states, + cache_params=cache_params, + **kwargs, + ) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if use_cache: + cache_params.seqlen_offset += inputs_embeds.shape[1] + + hidden_states = self.norm_f(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, cache_params, all_hidden_states] if v is not None) + + return SambaOutput( + last_hidden_state=hidden_states, + cache_params=cache_params if use_cache else None, + hidden_states=all_hidden_states, + ) + + +class SambaForCausalLM(SambaPreTrainedModel, FLAGenerationMixin): + + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.backbone = SambaModel(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.criterion = None + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def get_input_embeddings(self): + return self.backbone.get_input_embeddings() + + def set_input_embeddings(self, new_embeddings): + return self.backbone.set_input_embeddings(new_embeddings) + + def _update_model_kwargs_for_generation( + self, outputs: ModelOutput, model_kwargs: dict[str, Any], **kwargs, + ) -> dict[str, Any]: + model_kwargs["cache_params"] = outputs.get("cache_params", None) + return model_kwargs + + @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: Optional[torch.Tensor] = None, # noqa + inputs_embeds: torch.FloatTensor | None = None, + cache_params: MambaCache | None = None, + labels: torch.LongTensor | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + use_cache: bool | None = None, + logits_to_keep: int | None = 0, + **kwargs: Unpack[dict], + ) -> tuple | SambaCausalLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.backbone( + input_ids, + cache_params=cache_params, + inputs_embeds=inputs_embeds, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + use_cache=use_cache, + **kwargs, + ) + hidden_states = outputs[0] + + loss, logits = None, None + if not self.config.fuse_linear_cross_entropy or labels is None: + logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:]) + if labels is not None: + if getattr(self, 'criterion', None) is None: + if self.config.fuse_linear_cross_entropy: + criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp) + elif self.config.fuse_cross_entropy: + criterion = FusedCrossEntropyLoss(inplace_backward=True) + else: + criterion = nn.CrossEntropyLoss() + else: + criterion = self.criterion + labels = labels.to(hidden_states.device) + labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1) + if self.config.fuse_linear_cross_entropy: + loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias) + else: + loss = criterion(logits.view(labels.numel(), -1), labels.view(-1)) + loss = l2_warp(loss, logits) if self.config.use_l2warp else loss + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return SambaCausalLMOutput( + loss=loss, + logits=logits, + cache_params=outputs.cache_params, + hidden_states=outputs.hidden_states, + ) diff --git a/code/flash-linear-attention/fla/models/sse/__init__.py b/code/flash-linear-attention/fla/models/sse/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..598e949994c942bec5a1441ff2977d5b2c877b09 --- /dev/null +++ b/code/flash-linear-attention/fla/models/sse/__init__.py @@ -0,0 +1,11 @@ + +from transformers import AutoConfig, AutoModel, AutoModelForCausalLM + +from fla.models.sse.configuration_sse import SSEConfig +from fla.models.sse.modeling_sse import SSEForCausalLM, SSEModel + +AutoConfig.register(SSEConfig.model_type, SSEConfig, exist_ok=True) +AutoModel.register(SSEConfig, SSEModel, exist_ok=True) +AutoModelForCausalLM.register(SSEConfig, SSEForCausalLM, exist_ok=True) + +__all__ = ['SSEConfig', 'SSEForCausalLM', 'SSEModel'] diff --git a/code/flash-linear-attention/fla/models/sse/configuration_sse.py b/code/flash-linear-attention/fla/models/sse/configuration_sse.py new file mode 100644 index 0000000000000000000000000000000000000000..234aa67f1c0053077dcf0f4fc0df65dd46e2d280 --- /dev/null +++ b/code/flash-linear-attention/fla/models/sse/configuration_sse.py @@ -0,0 +1,113 @@ + +import warnings + +from transformers.configuration_utils import PretrainedConfig + + +class SSEConfig(PretrainedConfig): + model_type = 'sse' + keys_to_ignore_at_inference = ['past_key_values'] + + def __init__( + self, + attn_mode: str = "chunk", + hidden_size: int = 2048, + expand_v: float = 1.0, + use_output_gate: bool = True, + use_short_conv: bool = False, + allow_neg_eigval: bool = False, + conv_size: int = 4, + head_dim: int = 256, + num_heads: int = 6, + num_v_heads: int | None = None, + num_sparse_partition: int = 4, + num_writer: int = 2, + num_reader: int = 2, + linear_attn_type: str = "gla", + sse_implementation: str = "varlen", + aux_loss_coef: float = 0.01, + max_position_embeddings: int = 2048, + hidden_ratio: int | None = 4, + intermediate_size: int | None = None, + hidden_act: str = "swish", + num_hidden_layers: int = 24, + norm_eps: float = 1e-6, + attn: dict | None = None, + use_cache: bool = True, + pad_token_id: int | None = None, + bos_token_id: int = 1, + eos_token_id: int = 2, + tie_word_embeddings: bool = False, + initializer_range: float = 0.02, + fuse_norm: bool = True, + fuse_swiglu: bool = True, + fuse_cross_entropy: bool = True, + fuse_linear_cross_entropy: bool = False, + use_l2warp: bool = False, + vocab_size: int = 32000, + **kwargs, + ): + self.attn_mode = attn_mode + self.hidden_size = hidden_size + self.expand_v = expand_v + self.use_output_gate = use_output_gate + self.use_short_conv = use_short_conv + self.conv_size = conv_size + self.head_dim = head_dim + self.num_heads = num_heads + self.num_v_heads = num_v_heads + self.num_sparse_partition = num_sparse_partition + self.num_writer = num_writer + self.num_reader = num_reader + self.linear_attn_type = linear_attn_type + self.sse_implementation = sse_implementation + self.aux_loss_coef = aux_loss_coef + self.max_position_embeddings = max_position_embeddings + + self.hidden_ratio = hidden_ratio + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_hidden_layers = num_hidden_layers + self.norm_eps = norm_eps + self.attn = attn + self.use_cache = use_cache + self.initializer_range = initializer_range + + self.fuse_norm = fuse_norm + self.fuse_swiglu = fuse_swiglu + self.fuse_cross_entropy = fuse_cross_entropy + self.fuse_linear_cross_entropy = fuse_linear_cross_entropy + self.use_l2warp = use_l2warp + self.vocab_size = vocab_size + self.allow_neg_eigval = allow_neg_eigval + + if fuse_cross_entropy and fuse_linear_cross_entropy: + raise ValueError( + "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", + ) + if fuse_linear_cross_entropy: + warnings.warn( + "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " + "at the potential cost of reduced precision. " + "If you observe issues like loss divergence, consider disabling this setting.", + ) + + if attn is not None: + if not isinstance(attn, dict): + raise ValueError("attn must be a dictionary") + if 'layers' not in attn: + raise ValueError("Layer indices must be provided to initialize hybrid attention layers") + if 'num_heads' not in attn: + raise ValueError("Number of heads must be provided to initialize hybrid attention layers") + attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) + attn['qkv_bias'] = attn.get('qkv_bias', False) + attn['window_size'] = attn.get('window_size', None) + attn['rope_theta'] = attn.get('rope_theta', 10000.) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + )