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fed6c68 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 | # Copyright 2025 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 Any, Callable, Dict, Literal, Optional
import torch
from torch.utils.data import Dataset, IterableDataset
from torchdata.stateful_dataloader import StatefulDataLoader
from torchdata.stateful_dataloader.sampler import StatefulDistributedSampler
from ..distributed.parallel_state import get_parallel_state
from ..utils import logging
from ..utils.device import get_device_type
from ..utils.registry import Registry
from .data_collator import (
MainCollator,
MakeMicroBatchCollator,
NoopDataCollator,
UnpackDataCollator,
)
from .dataset import DynamicBatchingSizeDataset, get_length_by_attention_mask_fn
from .dynamic_batching import DynamicBatchSizeDataLoader, TextBatchingStrategy
DATALOADER_REGISTRY = Registry("dataloader")
logger = logging.get_logger(__name__)
def build_dataloader(dataloader_type: str, **kwargs):
return DATALOADER_REGISTRY[dataloader_type](**kwargs)
class DistributedDataloader(StatefulDataLoader):
dataset: "Dataset"
sampler: "StatefulDistributedSampler"
def set_epoch(self, epoch: int) -> None:
if self.sampler is not None and hasattr(self.sampler, "set_epoch"):
self.sampler.set_epoch(epoch)
elif hasattr(self.dataset, "set_epoch"):
self.dataset.set_epoch(epoch)
def _build_worker_init_fn(worker_num_threads: int) -> Callable[[int], None]:
def worker_init_fn(_worker_id: int) -> None:
torch.set_num_threads(worker_num_threads)
return worker_init_fn
@DATALOADER_REGISTRY.register("native")
def build_native_dataloader(
dataset: "Dataset",
micro_batch_size: int,
global_batch_size: int,
dataloader_batch_size: int,
max_seq_len: int,
train_steps: int,
bsz_warmup_ratio: float = 0.02,
bsz_warmup_init_mbtoken: int = 200,
dyn_bsz: bool = True,
dyn_bsz_runtime: Literal["main", "worker"] = "main",
dyn_bsz_dataset_save_by_idx: bool = False, # Whether to save dynamic-batching buffers by index for worker-side checkpoint/resume.
dyn_bsz_buffer_size: int = 200,
num_workers: int = 8,
worker_num_threads: Optional[int] = None,
drop_last: bool = True,
pin_memory: bool = True,
prefetch_factor: int = 2,
shuffle: bool = True,
seed: int = 0,
collate_fn: Optional[Callable] = None,
build_collate_fn: bool = True,
collate_fn_kwargs: Optional[Dict[str, Any]] = None,
multiprocessing_context=None,
) -> "DistributedDataloader":
"""Build the native training dataloader.
Args:
dyn_bsz_runtime: Which process dynamic batching runs in. ``"main"`` keeps the
legacy main-process ``DynamicBatchSizeDataLoader`` path, while ``"worker"``
batches inside each DataLoader worker via ``DynamicBatchingSizeDataset`` so
worker state can participate in ``StatefulDataLoader`` checkpoint/resume.
Data format by stage when ``dyn_bsz=True``:
``dyn_bsz_runtime="main"``
dataset
│ yields: ``list[dict]``
â–¼
DataLoader(batch_size=1, collate_fn=UnpackDataCollator)
│ yields: ``list[dict]``
â–¼
DynamicBatchSizeDataLoader / TextBatchingStrategy
│ flatten each upstream item: ``list[dict]`` -> ``dict``
│ internal buffer entry: ``dict``
│ micro batch from strategy: ``list[dict]``
â–¼
trainer step input
``list[list[dict]]``
(outer list = micro batches in one optimizer step,
inner list = samples in one micro batch)
``dyn_bsz_runtime="worker"``
dataset
│ yields: ``list[dict]``
â–¼
DynamicBatchingSizeDataset (inside each worker)
│ flatten each upstream item: ``list[dict]`` -> ``dict``
│ internal buffer entry: ``dict``
│ micro batch before collate: ``list[dict]``
â–¼
StatefulDataLoader(batch_size=num_micro_batch, collate_fn=NoopDataCollator)
│ ``list[list[dict]]``
â–¼
trainer step input
│ ``list[list[dict]]``
multiprocessing_context: Optional worker start method override.
Use ``"spawn"`` when worker-side code must be pickle-safe and should not
inherit parent-process state; keep ``"fork"`` for the legacy Linux behavior.
Example: ``multiprocessing_context="spawn"``.
"""
if collate_fn_kwargs is None:
collate_fn_kwargs = {}
parallel_state = get_parallel_state()
if collate_fn is None:
if build_collate_fn:
collate_fn = MainCollator(**collate_fn_kwargs)
else:
collate_fn = NoopDataCollator()
num_micro_batch = global_batch_size // (
micro_batch_size * parallel_state.dp_size
) # num_micro_batch = num accumulation steps
if dyn_bsz:
batching_token_len = micro_batch_size * max_seq_len
bsz_warmup_steps = int(train_steps * bsz_warmup_ratio)
logger.info_rank0(
f"Use dynamic_batching -->\n"
f"micro_batch_size: {micro_batch_size}, max_seq_len: {max_seq_len}, "
f"batching_token_len = micro_batch_size * max_seq_len = {batching_token_len}.\n"
f"dp_size: {parallel_state.dp_size}, sp_size: {parallel_state.sp_size}, "
f"global_batch_size: {global_batch_size}, micro_batch_size: {micro_batch_size}, "
f"num_micro_batch: {num_micro_batch}.\n"
f"train_steps: {train_steps}, bsz_warmup_steps: {bsz_warmup_steps}, "
f"bsz_warmup_init_mbtoken: {bsz_warmup_init_mbtoken}."
)
dyn_bsz_collate_fn = collate_fn
if dyn_bsz_runtime == "main":
batching_strategy = TextBatchingStrategy(
token_micro_bsz=batching_token_len,
buffer_size=dyn_bsz_buffer_size,
bsz_warmup_steps=bsz_warmup_steps,
bsz_warmup_init_mbtoken=bsz_warmup_init_mbtoken,
)
collate_fn = UnpackDataCollator()
else:
dataset = DynamicBatchingSizeDataset(
dataset=dataset,
micro_batch_seq_length=batching_token_len,
ready_for_micro_batch_threshold=dyn_bsz_buffer_size,
get_length_fn=get_length_by_attention_mask_fn,
dynamic_batching_collate_fn=dyn_bsz_collate_fn,
save_by_idx=dyn_bsz_dataset_save_by_idx,
)
collate_fn = NoopDataCollator()
else:
logger.info_rank0(
f"Use fixed_sample_batching -->\n"
f"fixed_sample_num in one batch = micro_batch_size: {micro_batch_size}.\n"
f"dp_size: {parallel_state.dp_size}, sp_size: {parallel_state.sp_size}, "
f"global_batch_size: {global_batch_size}, micro_batch_size: {micro_batch_size}, "
f"num_micro_batch: {num_micro_batch}.\n"
f"train_steps: {train_steps}."
)
collate_fn = MakeMicroBatchCollator(num_micro_batch=num_micro_batch, internal_data_collator=collate_fn)
sampler = None
if not isinstance(dataset, IterableDataset):
sampler = StatefulDistributedSampler(
dataset,
num_replicas=parallel_state.dp_size,
rank=parallel_state.dp_rank,
shuffle=shuffle,
seed=seed,
)
worker_init_fn = _build_worker_init_fn(worker_num_threads) if worker_num_threads is not None else None
dataloader = DistributedDataloader(
dataset,
batch_size=dataloader_batch_size,
sampler=sampler,
num_workers=num_workers,
collate_fn=collate_fn,
pin_memory=pin_memory,
pin_memory_device=get_device_type(),
drop_last=drop_last,
prefetch_factor=prefetch_factor,
worker_init_fn=worker_init_fn,
multiprocessing_context=multiprocessing_context,
)
if dyn_bsz and dyn_bsz_runtime == "main":
dataloader = DynamicBatchSizeDataLoader(
dataloader,
batching_strategy=batching_strategy,
collate_fn=dyn_bsz_collate_fn,
num_micro_batch=num_micro_batch,
length=train_steps,
drop_last=drop_last,
)
return dataloader
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