wehub-resource-sync
183 lines
7.1 KiB
Python
183 lines
7.1 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import division, print_function
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import paddle
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__all__ = ["DistributedBatchSampler"]
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class DistributedBatchSampler(paddle.io.BatchSampler):
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"""Sampler that restricts data loading to a subset of the dataset.
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In such case, each process can pass a DistributedBatchSampler instance
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as a DataLoader sampler, and load a subset of the original dataset that
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is exclusive to it.
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.. note::
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Dataset is assumed to be of constant size.
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Args:
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dataset(paddle.io.Dataset): this could be a `paddle.io.Dataset` implement
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or other python object which implemented
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`__len__` for BatchSampler to get sample
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number of data source.
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batch_size(int): sample indice number in a mini-batch indices.
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num_replicas(int, optional): process number in distributed training.
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If :attr:`num_replicas` is None, :attr:`num_replicas` will be
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retrieved from :code:`paddle.distributed.ParallenEnv`.
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Default None.
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rank(int, optional): the rank of the current process among :attr:`num_replicas`
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processes. If :attr:`rank` is None, :attr:`rank` is retrieved from
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:code:`paddle.distributed.ParallenEnv`. Default None.
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shuffle(bool): whether to shuffle indices order before generating
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batch indices. Default False.
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drop_last(bool): whether drop the last incomplete batch dataset size
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is not divisible by the batch size. Default False
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Examples:
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.. code-block:: python
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import numpy as np
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from paddle.io import Dataset, DistributedBatchSampler
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# init with dataset
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class RandomDataset(Dataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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def __getitem__(self, idx):
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image = np.random.random([784]).astype('float32')
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label = np.random.randint(0, 9, (1, )).astype('int64')
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return image, label
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def __len__(self):
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return self.num_samples
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dataset = RandomDataset(100)
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sampler = DistributedBatchSampler(dataset, batch_size=64)
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for data in sampler:
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# do something
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break
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"""
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def __init__(
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self, dataset, batch_size, num_replicas=None, rank=None, shuffle=False, drop_last=False, consumed_samples=0
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):
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self.dataset = dataset
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assert isinstance(batch_size, int) and batch_size > 0, "batch_size should be a positive integer"
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self.batch_size = batch_size
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assert isinstance(shuffle, bool), "shuffle should be a boolean value"
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self.shuffle = shuffle
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assert isinstance(drop_last, bool), "drop_last should be a boolean number"
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from paddle.distributed import ParallelEnv
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if num_replicas is not None:
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assert isinstance(num_replicas, int) and num_replicas > 0, "num_replicas should be a positive integer"
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self.nranks = num_replicas
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else:
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self.nranks = ParallelEnv().nranks
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if rank is not None:
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assert isinstance(rank, int) and rank >= 0, "rank should be a non-negative integer"
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self.local_rank = rank
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else:
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self.local_rank = ParallelEnv().local_rank
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self.drop_last = drop_last
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self.epoch = 0
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self.consumed_samples = consumed_samples
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if self.dataset is None:
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# In pre-training mode when using distributed dataloader, the input dataset can be None. We should handle this situation.
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self.num_samples = 0
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else:
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self.num_samples = int(len(self.dataset) * 1.0 / self.nranks)
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self.total_size = self.num_samples * self.nranks
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def get_start_end_idx(self):
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start_idx = self.local_rank * self.batch_size
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end_idx = start_idx + self.batch_size
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return start_idx, end_idx
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def __iter__(self):
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assert (
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self.consumed_samples % self.nranks == 0
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), "The consumed_samples should be divided by nranks. consumed_samples=%d, nranks=%s" % (
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self.consumed_samples,
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self.nranks,
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)
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self.remain_num_samples = int((len(self.dataset) - self.consumed_samples) * 1.0 / self.nranks)
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self.remain_total_size = self.remain_num_samples * self.nranks
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self.batch_size_times_rank_size = self.batch_size * self.nranks
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batch_indices = []
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for idx in range(self.consumed_samples, self.total_size):
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batch_indices.append(idx)
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if len(batch_indices) == self.batch_size_times_rank_size:
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start_idx, end_idx = self.get_start_end_idx()
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yield batch_indices[start_idx:end_idx]
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batch_indices = []
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if not self.drop_last and len(batch_indices) > 0:
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yield batch_indices
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def __len__(self):
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num_samples = self.num_samples
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num_samples += int(not self.drop_last) * (self.batch_size - 1)
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return num_samples // self.batch_size
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def set_epoch(self, epoch=0, consumed_samples=0):
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"""
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Sets the epoch number. When :attr:`shuffle=True`, this number is used
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as seeds of random numbers. By default, users may not set this, all
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replicas (workers) use a different random ordering for each epoch.
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If set same number at each epoch, this sampler will yield the same
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ordering at all epoches.
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Arguments:
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epoch (int): Epoch number.
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Examples:
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.. code-block:: python
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from paddle.io import Dataset, DistributedBatchSampler
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# init with dataset
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class RandomDataset(Dataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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def __getitem__(self, idx):
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image = np.random.random([784]).astype('float32')
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label = np.random.randint(0, 9, (1, )).astype('int64')
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return image, label
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def __len__(self):
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return self.num_samples
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dataset = RandomDataset(100)
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sampler = DistributedBatchSampler(dataset, batch_size=64)
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for epoch in range(10):
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sampler.set_epoch(epoch)
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"""
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self.epoch = epoch
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# if we reset the epoch, the consumed_samples should be set to 0.
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self.consumed_samples = consumed_samples
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