192 lines
7.1 KiB
Python
192 lines
7.1 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright (c) 2019, NVIDIA CORPORATION. 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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"""batch samplers that work with either random or sequential data samplers"""
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import math
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import os
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import sys
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import numpy as np
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import torch
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from torch.utils import data
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class RandomSampler(data.sampler.Sampler):
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r"""
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Based off of pytorch RandomSampler and DistributedSampler. Essentially a RandomSampler,
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but this class lets the user set an epoch like DistributedSampler
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Samples elements randomly. If without replacement, then sample from a shuffled dataset.
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If with replacement, then user can specify ``num_samples`` to draw.
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Arguments:
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data_source (Dataset): dataset to sample from
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num_samples (int): number of samples to draw, default=len(dataset)
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replacement (bool): samples are drawn with replacement if ``True``, default=False
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"""
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def __init__(self, data_source, replacement=False, num_samples=None):
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super(RandomSampler, self).__init__(data_source)
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self.data_source = data_source
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self.replacement = replacement
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self._num_samples = num_samples
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self.epoch = -1
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if self._num_samples is not None and replacement is False:
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raise ValueError(
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'With replacement=False, num_samples should not be specified, '
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'since a random permute will be performed.')
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if not isinstance(self.num_samples, int) or self.num_samples <= 0:
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raise ValueError('num_samples should be a positive integer '
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'value, but got num_samples={}'.format(
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self.num_samples))
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if not isinstance(self.replacement, bool):
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raise ValueError('replacement should be a boolean value, but got '
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'replacement={}'.format(self.replacement))
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@property
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def num_samples(self):
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# dataset size might change at runtime
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if self._num_samples is None:
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return len(self.data_source)
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return self._num_samples
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def __iter__(self):
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n = len(self.data_source)
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g = torch.Generator()
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if self.epoch >= 0:
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g.manual_seed(self.epoch)
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if self.replacement:
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for _ in range(self.num_samples // 32):
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yield from torch.randint(
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high=n, size=(32, ), dtype=torch.int64,
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generator=g).tolist()
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yield from torch.randint(
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high=n,
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size=(self.num_samples % 32, ),
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dtype=torch.int64,
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generator=g).tolist()
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else:
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yield from torch.randperm(n, generator=self.generator).tolist()
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def __len__(self):
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return self.num_samples
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def set_epoch(self, epoch):
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self.epoch = epoch
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class DistributedSequentialSampler(data.sampler.Sampler):
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def __init__(self,
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num_samples,
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train_iters,
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batch_size,
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rank=-1,
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world_size=2):
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super().__init__(num_samples)
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if rank == -1:
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rank = 0
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world_size = 1
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self.num_samples = num_samples
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self.rank = rank
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self.world_size = world_size
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self.start_iter = 0
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self.train_iters = train_iters
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self.batch_size = batch_size
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self.batch_bias = [
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i * (num_samples // batch_size) for i in range(batch_size)
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]
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def __iter__(self):
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for idx in range(self.start_iter, self.train_iters * 10):
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batch = [(idx + bias) % self.num_samples
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for bias in self.batch_bias]
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tbatch = self._batch(batch)
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yield tbatch
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def __len__(self):
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return self.train_iters
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def _batch(self, batch):
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"""extracts samples only pertaining to this worker's batch"""
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start = self.rank * self.batch_size // self.world_size
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end = (self.rank + 1) * self.batch_size // self.world_size
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return batch[start:end]
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class DistributedBatchSampler(data.sampler.BatchSampler):
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"""
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similar to normal implementation of distributed sampler, except implementation is at the
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batch sampler level, instead of just the sampler level. This allows wrapping of arbitrary
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data samplers (sequential, random, WeightedRandomSampler, etc.) with this batch sampler.
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"""
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def __init__(self,
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sampler,
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batch_size,
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drop_last,
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rank=-1,
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world_size=2,
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wrap_last=False,
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gradient_accumulation_steps=None):
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super(DistributedBatchSampler, self).__init__(sampler, batch_size,
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drop_last)
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if rank == -1:
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assert False, 'should not be here'
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self.rank = rank
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self.world_size = world_size
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self.sampler.wrap_around = 0
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self.wrap_around = 0
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self.wrap_last = wrap_last
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self.start_iter = 0
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self.effective_batch_size = batch_size if gradient_accumulation_steps is None else batch_size * gradient_accumulation_steps # noqa
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def __iter__(self):
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batch = []
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i = 0
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for idx in self.data_iterator(self.sampler, wrap_around=False):
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batch.append(idx)
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if len(batch) == self.batch_size:
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tbatch = self._batch(batch)
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if i >= self.start_iter * self.effective_batch_size:
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yield tbatch
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self.start_iter = 0
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i += len(batch)
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batch = []
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batch_len = len(batch)
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if batch_len > 0 and not self.drop_last:
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if self.wrap_last:
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self.sampler.wrap_around -= (self.batch_size)
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self.wrap_around += (len(batch))
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self.wrap_around %= self.batch_size
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yield self._batch(batch)
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if self.wrap_last:
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self.sampler.wrap_around += self.batch_size
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def data_iterator(self, _iter, wrap_around=False):
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"""iterates through data and handles wrap around"""
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for i, idx in enumerate(_iter):
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if i < self.wrap_around % self.batch_size:
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continue
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if wrap_around:
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self.wrap_around += 1
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self.wrap_around %= self.batch_size
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yield idx
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def _batch(self, batch):
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"""extracts samples only pertaining to this worker's batch"""
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start = self.rank * self.batch_size // self.world_size
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end = (self.rank + 1) * self.batch_size // self.world_size
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return batch[start:end]
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