# Copyright 2022 Cerebras Systems.
#
# 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.
import math
import torch
import torch.distributed as dist
[docs]class RepeatedAugSampler(torch.utils.data.Sampler):
"""Sampler that restricts data loading to a subset of the dataset for distributed,
with repeated augmentation.
It ensures that different each augmented version of a sample will be visible to a
different process (GPU).
Heavily based on 'torch.utils.data.DistributedSampler'.
This is borrowed from the DeiT Repo:
https://github.com/facebookresearch/deit/blob/main/samplers.py
"""
def __init__(
self,
dataset,
num_replicas=None,
rank=None,
shuffle=True,
seed=0,
num_repeats=3,
batch_size=256,
):
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available!")
if num_replicas is None:
num_replicas = dist.get_world_size()
if rank is None:
rank = dist.get_rank()
if num_repeats < 1:
raise ValueError(
f"num_repeats is set to {num_repeats}, but it should be an integer greater than 0"
)
if batch_size > len(dataset):
raise RuntimeError(
f"batch size is set to {batch_size}, but it is should be smaller "
f"than the dataset size of {len(dataset)}"
)
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.num_repeats = num_repeats
self.epoch = 0
self.num_samples = int(
math.ceil(
len(self.dataset) * float(num_repeats) / self.num_replicas
)
)
self.total_size = self.num_samples * self.num_replicas
self.num_selected_samples = int(
math.floor(
len(self.dataset) // batch_size * batch_size / self.num_replicas
)
)
self.shuffle = shuffle
self.seed = seed
def __iter__(self):
if self.shuffle:
# Deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.seed + self.epoch)
indices = torch.randperm(len(self.dataset), generator=g)
else:
indices = torch.arange(len(self.dataset))
# Add extra samples to make it evenly divisible
indices = torch.repeat_interleave(
indices, repeats=self.num_repeats, dim=0
).tolist()
indices += indices[: (self.total_size - len(indices))]
assert len(indices) == self.total_size
# Subsample
indices = indices[self.rank : self.total_size : self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices[: self.num_selected_samples])
def __len__(self):
return self.num_selected_samples
def set_epoch(self, epoch):
self.epoch = epoch