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77 lines
3.0 KiB
77 lines
3.0 KiB
import random
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import numpy as np
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import torch
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import torch.distributed as dist
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from torch.utils.data import DataLoader
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from torch.utils.data.distributed import DistributedSampler
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from .plugin_base import Plugin
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class DPPluginBase(Plugin):
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"""This is a base class for all DP plugins. It sets up world size and rank, and provides data loader creation."""
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def __init__(self) -> None:
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super().__init__()
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assert (
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dist.is_initialized()
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), "torch.distributed is not initialized, please use colossalai.launch to create the distributed environment"
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self.rank = dist.get_rank()
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self.world_size = dist.get_world_size()
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def prepare_dataloader(
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self,
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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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distributed_sampler_cls=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 `torch.utils.data.DistributedSampler`.
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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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distributed_sampler_cls = distributed_sampler_cls or DistributedSampler
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sampler = distributed_sampler_cls(dataset, num_replicas=self.world_size, rank=self.rank, shuffle=shuffle)
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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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