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53c10a4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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 typing import Optional
import torch
from torch.utils.data import RandomSampler, SequentialSampler
from torchdata.stateful_dataloader import StatefulDataLoader
from transformers import AutoConfig, PreTrainedTokenizer, ProcessorMixin
from ..utils.dataset import RLHFDataset, TaskGroupedBatchSampler, collate_fn
from .config import DataConfig
def create_dataloader(
config: DataConfig,
tokenizer: PreTrainedTokenizer,
processor: Optional[ProcessorMixin],
model_path: Optional[str] = None,
) -> None:
# Auto-detect model_type from model config for proper position_ids routing
model_type = None
if model_path is not None:
try:
auto_config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model_type = getattr(auto_config, "model_type", None)
except Exception:
pass
train_dataset = RLHFDataset(
data_path=config.train_files,
tokenizer=tokenizer,
processor=processor,
prompt_key=config.prompt_key,
answer_key=config.answer_key,
image_key=config.image_key,
video_key=config.video_key,
image_dir=config.image_dir,
video_fps=config.video_fps,
video_max_frames=config.video_max_frames,
max_prompt_length=config.max_prompt_length,
truncation="right",
format_prompt=config.format_prompt,
image_min_pixels=config.image_min_pixels,
image_max_pixels=config.image_max_pixels,
video_min_pixels=config.video_min_pixels,
video_max_pixels=config.video_max_pixels,
video_total_pixels=config.video_total_pixels,
filter_overlong_prompts=config.filter_overlong_prompts,
filter_overlong_prompts_workers=config.filter_overlong_prompts_workers,
use_preprocessed_videos=config.use_preprocessed_videos,
video_source_mode=config.video_source_mode,
preprocessed_video_dir=config.preprocessed_video_dir,
inline_video_tensors=config.inline_video_tensors,
enable_thinking=config.enable_thinking,
response_prefix=config.response_prefix,
model_type=model_type,
)
if config.mini_rollout_batch_size is not None:
train_batch_size = config.mini_rollout_batch_size
else:
train_batch_size = config.rollout_batch_size
if config.group_by_task:
batch_sampler = TaskGroupedBatchSampler(
dataset=train_dataset,
batch_size=train_batch_size,
task_key=config.group_by_task_key,
shuffle=config.shuffle,
seed=config.seed,
drop_last=True,
)
train_dataloader = StatefulDataLoader(
dataset=train_dataset,
batch_sampler=batch_sampler,
num_workers=config.dataloader_num_workers,
collate_fn=collate_fn,
pin_memory=False,
)
else:
if config.shuffle:
train_dataloader_generator = torch.Generator()
train_dataloader_generator.manual_seed(config.seed)
sampler = RandomSampler(data_source=train_dataset, generator=train_dataloader_generator)
else:
sampler = SequentialSampler(data_source=train_dataset)
train_dataloader = StatefulDataLoader(
dataset=train_dataset,
batch_size=train_batch_size,
sampler=sampler,
num_workers=config.dataloader_num_workers,
collate_fn=collate_fn,
pin_memory=False,
drop_last=True,
)
val_dataset = RLHFDataset(
data_path=config.val_files,
tokenizer=tokenizer,
processor=processor,
prompt_key=config.prompt_key,
answer_key=config.answer_key,
image_key=config.image_key,
video_key=config.video_key,
image_dir=config.image_dir,
video_fps=config.val_video_fps,
video_max_frames=config.val_video_max_frames,
max_prompt_length=config.max_prompt_length,
truncation="right",
format_prompt=config.format_prompt,
image_min_pixels=config.image_min_pixels,
image_max_pixels=config.image_max_pixels,
video_min_pixels=config.val_video_min_pixels,
video_max_pixels=config.val_video_max_pixels,
video_total_pixels=config.val_video_total_pixels,
filter_overlong_prompts=config.filter_overlong_prompts,
use_preprocessed_videos=config.use_preprocessed_videos,
video_source_mode=config.val_video_source_mode,
preprocessed_video_dir=config.val_preprocessed_video_dir,
inline_video_tensors=config.inline_video_tensors,
enable_thinking=config.enable_thinking,
response_prefix=config.response_prefix,
model_type=model_type,
)
if config.val_batch_size == -1:
val_batch_size = len(val_dataset)
else:
val_batch_size = config.val_batch_size
val_dataloader = StatefulDataLoader(
dataset=val_dataset,
batch_size=val_batch_size,
shuffle=False,
num_workers=config.dataloader_num_workers,
collate_fn=collate_fn,
pin_memory=False,
drop_last=False,
)
assert len(train_dataloader) >= 1
assert len(val_dataloader) >= 1
print(f"Size of train dataloader: {len(train_dataloader)}")
print(f"Size of val dataloader: {len(val_dataloader)}")
return train_dataloader, val_dataloader
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