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fb11af9 | 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 | # 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 TYPE_CHECKING, Callable, List, Optional, Union
from torch.utils.data import 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 .batching_strategy import TextBatchingStrategy
from .data_collator import (
CollatePipeline,
DataCollatorWithPacking,
DataCollatorWithPadding,
DataCollatorWithPositionIDs,
MakeMicroBatchCollator,
TextSequenceShardCollator,
UnpackDataCollator,
)
from .dynamic_batching import DynamicBatchSizeDataLoader
if TYPE_CHECKING:
from torch.utils.data import Dataset
logger = logging.get_logger(__name__)
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_dataloader(
dataset: "Dataset",
micro_batch_size: int,
global_batch_size: int,
dataloader_batch_size: int,
max_seq_len: int,
train_steps: int,
rmpad: bool = True,
rmpad_with_pos_ids: bool = False,
bsz_warmup_ratio: float = 0.02,
bsz_warmup_init_mbtoken: int = 200,
dyn_bsz_buffer_size: int = 500,
dyn_bsz_margin: int = 0,
collate_fn: Optional[Union[Callable, List[Callable]]] = None,
num_workers: int = 8,
drop_last: bool = True,
pin_memory: bool = True,
prefetch_factor: Optional[int] = 2,
seed: int = 0,
) -> "DistributedDataloader":
parallel_state = get_parallel_state()
token_micro_bsz = micro_batch_size * max_seq_len
num_micro_batch = global_batch_size // (
micro_batch_size * parallel_state.dp_size
) # num_micro_batch = num accumulation steps
bsz_warmup_steps = int(train_steps * bsz_warmup_ratio)
use_rmpad = rmpad or rmpad_with_pos_ids
logger.info_rank0(
f"train_steps: {train_steps}, max_seq_len: {max_seq_len}, use_rmpad: {use_rmpad}, "
f"bsz_warmup_steps: {bsz_warmup_steps}, bsz_warmup_init_mbtoken: {bsz_warmup_init_mbtoken}, "
f"token_micro_bsz: {token_micro_bsz}, num_micro_batch: {num_micro_batch}, "
f"micro_batch_size: {micro_batch_size}, global_batch_size: {global_batch_size}, "
f"dp_size: {parallel_state.dp_size}, sp_size: {parallel_state.sp_size}."
)
if collate_fn is None:
collate_fn_list = []
if rmpad_with_pos_ids:
collate_fn_list.append(DataCollatorWithPositionIDs())
elif rmpad:
collate_fn_list.append(DataCollatorWithPacking())
else:
collate_fn_list.append(DataCollatorWithPadding())
if parallel_state.sp_enabled:
collate_fn_list.append(TextSequenceShardCollator(rmpad=rmpad, rmpad_with_pos_ids=rmpad_with_pos_ids))
collate_fn = CollatePipeline(collate_fn_list)
if isinstance(collate_fn, list):
collate_fn = CollatePipeline(collate_fn)
if use_rmpad:
batching_strategy = TextBatchingStrategy(
token_micro_bsz=token_micro_bsz - dyn_bsz_margin * max_seq_len,
buffer_size=dyn_bsz_buffer_size,
bsz_warmup_steps=bsz_warmup_steps if bsz_warmup_steps else -1,
bsz_warmup_init_mbtoken=bsz_warmup_init_mbtoken,
)
dyn_bsz_collate_fn = collate_fn
collate_fn = UnpackDataCollator()
else:
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=True,
seed=seed,
)
dataloader = DistributedDataloader(
dataset,
batch_size=dataloader_batch_size,
sampler=sampler,
num_workers=num_workers,
collate_fn=collate_fn,
pin_memory=pin_memory,
drop_last=drop_last,
prefetch_factor=prefetch_factor,
)
if use_rmpad:
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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