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# Copyright (c) 2024, EleutherAI
# This file is based on code by the authors denoted below and has been modified from its original version.
#
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
#
# 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.
"""Batch samplers that work with either random or sequential data samplers."""
import torch
from torch.utils import data
class RandomSampler(data.sampler.Sampler):
"""Based off of pytorch RandomSampler and DistributedSampler. Essentially
a RandomSampler, but this class lets the user set an epoch like
DistributedSampler Samples elements randomly. If without replacement, then
sample from a shuffled dataset. If with replacement, then user can
specify ``num_samples`` to draw.
Arguments:
data_source (Dataset): dataset to sample from
num_samples (int): number of samples to draw, default=len(dataset)
replacement (bool): samples are drawn with replacement if ``True``,
default=False
"""
def __init__(self, data_source, replacement=False, num_samples=None):
self.data_source = data_source
self.replacement = replacement
self._num_samples = num_samples
self.epoch = -1
if self._num_samples is not None and replacement is False:
raise ValueError(
"With replacement=False, num_samples should not "
"be specified, since a random permute will be "
"performed."
)
if not isinstance(self.num_samples, int) or self.num_samples <= 0:
raise ValueError(
"num_samples should be a positive integer "
"value, but got num_samples={}".format(self.num_samples)
)
if not isinstance(self.replacement, bool):
raise ValueError(
"replacement should be a boolean value, but got "
"replacement={}".format(self.replacement)
)
@property
def num_samples(self):
# dataset size might change at runtime
if self._num_samples is None:
return len(self.data_source)
return self._num_samples
def __iter__(self):
n = len(self.data_source)
g = torch.Generator()
if self.epoch >= 0:
g.manual_seed(self.epoch)
if self.replacement:
return iter(
torch.randint(
high=n, size=(self.num_samples,), dtype=torch.int64, generator=g
).tolist()
)
return iter(torch.randperm(n, generator=g).tolist())
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
class DistributedBatchSampler(data.sampler.BatchSampler):
"""Similar to normal implementation of distributed sampler, except
implementation is at the batch sampler level, instead of just the
sampler level. This allows wrapping of arbitrary data samplers
(sequential, random, WeightedRandomSampler, etc.) with this batch
sampler.
The `interleave` argument specifies how to distribute a batch. A value
of True combined with the above random sampler is equivalent to pytorch's
torch.utils.data.distributed.DistributedSampler.
For the following batch [0,1,2,3,4,5,6,7] and data parallelism of 2
specifying True will result in the following samples for each gpu:
GPU0: [0,2,4,6] GPU1: [1,3,5,7]
specifying False will result in the following samples:
GPU0: [0,1,2,3] GPU1: [4,5,6,7]
The `infinite_loop` parameter allows the sampler to yield batches indefinitely,
restarting from the beginning of the dataset when all samples have been iterated over.
"""
def __init__(
self,
sampler,
batch_size,
drop_last,
rank=-1,
world_size=2,
wrap_last=False,
interleave=False,
):
super(DistributedBatchSampler, self).__init__(sampler, batch_size, drop_last)
if rank == -1:
assert False, "should not be here"
rank = torch.distributed.get_rank()
self.rank = rank
self.world_size = world_size
self.sampler.wrap_around = 0
self.wrap_around = 0
self.wrap_last = wrap_last
self.start_iter = 0
self.interleave = interleave
def __iter__(self):
batch = []
i = 0
for idx in self.data_iterator(self.sampler, wrap_around=False):
batch.append(idx)
if len(batch) == self.batch_size:
tbatch = self._batch(batch)
if i >= self.start_iter:
yield tbatch
self.start_iter = 0
i += 1
batch = []
batch_len = len(batch)
if batch_len > 0 and not self.drop_last:
if self.wrap_last:
self.sampler.wrap_around -= self.batch_size
self.wrap_around += len(batch)
self.wrap_around %= self.batch_size
yield self._batch(batch)
if self.wrap_last:
self.sampler.wrap_around += self.batch_size
def data_iterator(self, _iter, wrap_around=False):
"""iterates through data and handles wrap around"""
for i, idx in enumerate(_iter):
if i < self.wrap_around % self.batch_size:
continue
if wrap_around:
self.wrap_around += 1
self.wrap_around %= self.batch_size
yield idx
def _batch(self, batch):
"""extracts samples only pertaining to this worker's batch"""
if self.interleave:
return batch[self.rank : self.batch_size : self.world_size]
start = self.rank * self.batch_size // self.world_size
end = (self.rank + 1) * self.batch_size // self.world_size
return batch[start:end]
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