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https://github.com/hpcaitech/Open-Sora.git
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069ea0d687
* [wip] bucket * [bug] not parallel * update eval * update sample.sh * accelerate bucket build with pandarallel
142 lines
4.4 KiB
Python
142 lines
4.4 KiB
Python
import random
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from typing import Iterator, Optional
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import numpy as np
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import torch
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from torch.distributed import ProcessGroup
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from torch.distributed.distributed_c10d import _get_default_group
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from torch.utils.data import DataLoader, Dataset
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from torch.utils.data.distributed import DistributedSampler
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from .sampler import VariableVideoBatchSampler
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class StatefulDistributedSampler(DistributedSampler):
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def __init__(
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self,
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dataset: Dataset,
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num_replicas: Optional[int] = None,
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rank: Optional[int] = None,
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shuffle: bool = True,
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seed: int = 0,
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drop_last: bool = False,
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) -> None:
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super().__init__(dataset, num_replicas, rank, shuffle, seed, drop_last)
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self.start_index: int = 0
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def __iter__(self) -> Iterator:
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iterator = super().__iter__()
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indices = list(iterator)
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indices = indices[self.start_index :]
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return iter(indices)
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def __len__(self) -> int:
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return self.num_samples - self.start_index
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def set_start_index(self, start_index: int) -> None:
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self.start_index = start_index
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def prepare_dataloader(
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dataset,
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batch_size,
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shuffle=False,
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seed=1024,
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drop_last=False,
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pin_memory=False,
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num_workers=0,
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process_group: Optional[ProcessGroup] = None,
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**kwargs,
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):
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r"""
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Prepare a dataloader for distributed training. The dataloader will be wrapped by
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`torch.utils.data.DataLoader` and `StatefulDistributedSampler`.
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Args:
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dataset (`torch.utils.data.Dataset`): The dataset to be loaded.
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shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False.
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seed (int, optional): Random worker seed for sampling, defaults to 1024.
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add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True.
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drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size
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is not divisible by the batch size. If False and the size of dataset is not divisible by
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the batch size, then the last batch will be smaller, defaults to False.
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pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False.
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num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0.
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kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in
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`DataLoader <https://pytorch.org/docs/stable/_modules/torch/utils/data/dataloader.html#DataLoader>`_.
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Returns:
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:class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing.
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"""
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_kwargs = kwargs.copy()
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process_group = process_group or _get_default_group()
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sampler = StatefulDistributedSampler(
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dataset,
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num_replicas=process_group.size(),
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rank=process_group.rank(),
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shuffle=shuffle,
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)
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# Deterministic dataloader
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def seed_worker(worker_id):
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worker_seed = seed
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np.random.seed(worker_seed)
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torch.manual_seed(worker_seed)
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random.seed(worker_seed)
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return DataLoader(
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dataset,
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batch_size=batch_size,
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sampler=sampler,
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worker_init_fn=seed_worker,
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drop_last=drop_last,
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pin_memory=pin_memory,
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num_workers=num_workers,
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**_kwargs,
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)
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def prepare_variable_dataloader(
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dataset,
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batch_size,
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bucket_config,
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shuffle=False,
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seed=1024,
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drop_last=False,
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pin_memory=False,
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num_workers=0,
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process_group=None,
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num_bucket_build_workers=1,
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**kwargs,
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):
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_kwargs = kwargs.copy()
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process_group = process_group or _get_default_group()
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batch_sampler = VariableVideoBatchSampler(
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dataset,
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bucket_config,
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num_replicas=process_group.size(),
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rank=process_group.rank(),
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shuffle=shuffle,
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seed=seed,
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drop_last=drop_last,
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verbose=True,
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num_bucket_build_workers=num_bucket_build_workers,
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)
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# Deterministic dataloader
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def seed_worker(worker_id):
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worker_seed = seed
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np.random.seed(worker_seed)
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torch.manual_seed(worker_seed)
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random.seed(worker_seed)
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return torch.utils.data.DataLoader(
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dataset,
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batch_sampler=batch_sampler,
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worker_init_fn=seed_worker,
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pin_memory=pin_memory,
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num_workers=num_workers,
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**_kwargs,
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)
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