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#
# 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.
from collections import defaultdict
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from .processor_utils import DatasetProcessor, greedy_knapsack, infer_seqlen
if TYPE_CHECKING:
from ..mm_plugin import AudioInput, ImageInput, VideoInput
logger = logging.get_logger(__name__)
MAX_SU_SEQ_IDX = 2**32 # maximum sub-sequence index
@dataclass
class PackingParams:
r"""Metadata for a packed sequence: sub-sequence boundaries and multimodal data indices.
- sequence_boundaries: cumulative token positions, e.g. [0, 100, 250, 512] means 3 sub-seqs
with token ranges [0,100), [100,250), [250,512). Length = num_sub_seqs + 1.
- image_subseq_ids / video_subseq_ids / audio_subseq_ids: for each mm item, the 0-based
sub-sequence index it belongs to. Length = total number of that mm type in the packed sample.
"""
sequence_boundaries: list[int]
image_subseq_ids: list[int]
video_subseq_ids: list[int]
audio_subseq_ids: list[int]
right_padding_length: int
@dataclass
class SupervisedDatasetProcessor(DatasetProcessor):
def _get_media_blacklist(self) -> set[str]:
if not hasattr(self, "_media_blacklist_cache"):
blacklist_path = getattr(self.data_args, "media_blacklist_path", None)
media_blacklist: set[str] = set()
if blacklist_path:
path = Path(blacklist_path)
if path.is_file():
media_blacklist = {
line.strip()
for line in path.read_text(encoding="utf-8").splitlines()
if line.strip() and not line.lstrip().startswith("#")
}
self._media_blacklist_cache = media_blacklist
return self._media_blacklist_cache
def _describe_media_refs(self, refs: list[Any]) -> list[str]:
media_refs: list[str] = []
for ref in refs:
if isinstance(ref, str):
media_refs.append(ref)
elif isinstance(ref, dict):
if ref.get("path"):
media_refs.append(str(ref["path"]))
elif ref.get("bytes") is not None:
media_refs.append("<inline-bytes>")
else:
media_refs.append("<unknown-dict-media>")
else:
media_refs.append(f"<{type(ref).__name__}>")
return media_refs
def _has_blacklisted_media(self, images: list[Any]) -> bool:
if not images:
return False
media_blacklist = self._get_media_blacklist()
if not media_blacklist:
return False
for ref in self._describe_media_refs(images):
if ref in media_blacklist:
return True
return False
def _record_failed_media(self, images: list[Any]) -> None:
blacklist = self._get_media_blacklist()
refs = self._describe_media_refs(images)
new_refs = [ref for ref in refs if ref not in blacklist]
blacklist.update(new_refs)
blacklist_path = getattr(self.data_args, "media_blacklist_path", None)
if blacklist_path and new_refs:
path = Path(blacklist_path)
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("a", encoding="utf-8") as f:
for ref in new_refs:
f.write(ref + "\n")
error_log_path = getattr(self.data_args, "media_error_log_path", None)
if error_log_path:
path = Path(error_log_path)
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("a", encoding="utf-8") as f:
for ref in refs:
f.write(ref + "\n")
def _encode_data_example(
self,
prompt: list[dict[str, str]],
response: list[dict[str, str]],
system: Optional[str],
tools: Optional[str],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
) -> tuple[list[int], list[int]]:
messages = self.template.mm_plugin.process_messages(prompt + response, images, videos, audios, self.processor)
input_ids, labels = self.template.mm_plugin.process_token_ids(
[], [], images, videos, audios, self.tokenizer, self.processor
)
discarding_history_cot = self.data_args.mask_history and not self.template.preserve_thinking
encoded_pairs = self.template.encode_multiturn(self.tokenizer, messages, system, tools, discarding_history_cot)
total_length = len(input_ids) + (1 if self.template.efficient_eos else 0)
if self.data_args.mask_history:
encoded_pairs = encoded_pairs[::-1] # high priority for last turns
for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs):
if total_length >= self.data_args.cutoff_len:
break
source_len, target_len = infer_seqlen(
len(source_ids), len(target_ids), self.data_args.cutoff_len - total_length
)
source_ids = source_ids[:source_len]
target_ids = target_ids[:target_len]
total_length += source_len + target_len
if self.data_args.train_on_prompt:
source_label = source_ids
elif self.template.efficient_eos and turn_idx != 0:
source_label = [self.tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1)
else:
source_label = [IGNORE_INDEX] * source_len
if self.data_args.mask_history and turn_idx != 0: # train on the last turn only
target_label = [IGNORE_INDEX] * target_len
else:
target_label = target_ids
if self.data_args.mask_history: # reversed sequences
input_ids = source_ids + target_ids + input_ids
labels = source_label + target_label + labels
else:
input_ids += source_ids + target_ids
labels += source_label + target_label
if self.template.efficient_eos:
input_ids += [self.tokenizer.eos_token_id]
labels += [self.tokenizer.eos_token_id]
return input_ids, labels
def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
# build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
# for multiturn examples, we only mask the prompt part in each prompt-response pair.
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
images = examples["_images"][i] or []
videos = examples["_videos"][i] or []
audios = examples["_audios"][i] or []
if self._has_blacklisted_media(images):
continue
try:
input_ids, labels = self._encode_data_example(
prompt=examples["_prompt"][i],
response=examples["_response"][i],
system=examples["_system"][i],
tools=examples["_tools"][i],
images=images,
videos=videos,
audios=audios,
)
except Exception as err:
self._record_failed_media(images)
logger.warning_rank0(
"Dropped example due to preprocessing error: {} | images={}".format(
repr(err), self._describe_media_refs(images)
)
)
continue
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
model_inputs["images"].append(examples["_images"][i])
model_inputs["videos"].append(examples["_videos"][i])
model_inputs["audios"].append(examples["_audios"][i])
if not model_inputs: # keep schema consistent when the whole batch is dropped
return {k: [] for k in ("input_ids", "attention_mask", "labels", "images", "videos", "audios")}
return model_inputs
def print_data_example(self, example: dict[str, list[int]]) -> None:
valid_labels = list(filter(lambda x: x != IGNORE_INDEX, example["labels"]))
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(self.tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
print("label_ids:\n{}".format(example["labels"]))
print(f"labels:\n{self.tokenizer.decode(valid_labels, skip_special_tokens=False)}")
@dataclass
class PackedSupervisedDatasetProcessor(SupervisedDatasetProcessor):
def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
# TODO: use `position_ids` to achieve packing
# build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
# and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
valid_num = 0
batch_input_ids, batch_labels, batch_images, batch_videos, batch_audios = [], [], [], [], []
lengths = []
length2indexes = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
images = examples["_images"][i] or []
videos = examples["_videos"][i] or []
audios = examples["_audios"][i] or []
if self._has_blacklisted_media(images):
continue
try:
input_ids, labels = self._encode_data_example(
prompt=examples["_prompt"][i],
response=examples["_response"][i],
system=examples["_system"][i],
tools=examples["_tools"][i],
images=images,
videos=videos,
audios=audios,
)
except Exception as err:
self._record_failed_media(images)
logger.warning_rank0(
"Dropped example due to preprocessing error: {} | images={}".format(
repr(err), self._describe_media_refs(images)
)
)
continue
length = len(input_ids)
if length > self.data_args.cutoff_len:
logger.warning_rank0(f"Dropped lengthy example with length {length} > {self.data_args.cutoff_len}.")
else:
lengths.append(length)
length2indexes[length].append(valid_num)
batch_input_ids.append(input_ids)
batch_labels.append(labels)
batch_images.append(examples["_images"][i] or [])
batch_videos.append(examples["_videos"][i] or [])
batch_audios.append(examples["_audios"][i] or [])
valid_num += 1
model_inputs = defaultdict(list)
requires_packing_params = self.data_args.neat_packing
knapsacks = greedy_knapsack(lengths, self.data_args.cutoff_len)
for knapsack in knapsacks:
packed_input_ids, packed_attention_masks, packed_position_ids, packed_labels = [], [], [], []
packed_images, packed_videos, packed_audios = [], [], []
if requires_packing_params:
sequence_boundaries = [0]
image_subseq_ids: list[int] = []
video_subseq_ids: list[int] = []
audio_subseq_ids: list[int] = []
for i, length in enumerate(knapsack):
index = length2indexes[length].pop()
packed_input_ids += batch_input_ids[index]
packed_position_ids += list(range(len(batch_input_ids[index]))) # NOTE: pad_to_multiple_of ignore this
packed_labels += batch_labels[index]
packed_images += batch_images[index]
packed_videos += batch_videos[index]
packed_audios += batch_audios[index]
if requires_packing_params:
n_img = len(batch_images[index])
n_vid = len(batch_videos[index])
n_aud = len(batch_audios[index])
sequence_boundaries.append(sequence_boundaries[-1] + len(batch_input_ids[index]))
image_subseq_ids.extend([i] * n_img)
video_subseq_ids.extend([i] * n_vid)
audio_subseq_ids.extend([i] * n_aud)
if self.data_args.neat_packing:
packed_attention_masks += [i + 1] * len(batch_input_ids[index]) # start from 1
else:
packed_attention_masks += [1] * len(batch_input_ids[index])
if len(packed_input_ids) < self.data_args.cutoff_len + 1: # avoid flash_attn drops attn mask
pad_length = self.data_args.cutoff_len - len(packed_input_ids) + 1
packed_input_ids += [self.tokenizer.pad_token_id] * pad_length
packed_position_ids += [0] * pad_length
packed_labels += [IGNORE_INDEX] * pad_length
if self.data_args.neat_packing:
packed_attention_masks += [0] * pad_length
else:
packed_attention_masks += [1] * pad_length # more efficient flash_attn
if requires_packing_params:
sequence_boundaries.append(sequence_boundaries[-1] + pad_length)
if len(packed_input_ids) != self.data_args.cutoff_len + 1:
raise ValueError("The length of packed example should be identical to the cutoff length.")
model_inputs["input_ids"].append(packed_input_ids)
if requires_packing_params:
packing_params = PackingParams(
sequence_boundaries=sequence_boundaries,
image_subseq_ids=image_subseq_ids or [MAX_SU_SEQ_IDX], # avoid dataset concat error
video_subseq_ids=video_subseq_ids or [MAX_SU_SEQ_IDX],
audio_subseq_ids=audio_subseq_ids or [MAX_SU_SEQ_IDX],
right_padding_length=pad_length,
)
model_inputs["packing_params"].append(asdict(packing_params))
model_inputs["attention_mask"].append(packed_attention_masks)
model_inputs["position_ids"].append(packed_position_ids)
model_inputs["labels"].append(packed_labels)
model_inputs["images"].append(packed_images or None)
model_inputs["videos"].append(packed_videos or None)
model_inputs["audios"].append(packed_audios or None)
if not model_inputs: # keep schema consistent when the whole batch is dropped
keys = ["input_ids", "attention_mask", "position_ids", "labels", "images", "videos", "audios"]
if requires_packing_params:
keys.append("packing_params")
return {k: [] for k in keys}
return model_inputs
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