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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Code is copy-pasted exactly as in torch.utils.data.distributed.
# FIXME remove this once c10d fixes the bug it has
import os
import math
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
import random
from uniperceiver.utils import comm
import itertools
class DistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
process can pass a DistributedSampler instance as a DataLoader sampler,
and load a subset of the original dataset that is exclusive to it.
.. note::
Dataset is assumed to be of constant size.
Arguments:
dataset: Dataset used for sampling.
num_replicas (optional): Number of processes participating in
distributed training.
rank (optional): Rank of the current process within num_replicas.
"""
def __init__(self, dataset, num_replicas=None, rank=None, local_rank=None, local_size=None, shuffle=True, dataset_repeat=1):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
self.shuffle = shuffle
self.dataset_repeat = dataset_repeat
def __iter__(self):
if self.shuffle:
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = torch.arange(len(self.dataset)).tolist()
# add extra samples to make it evenly divisible
indices += indices[: (self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
offset = self.num_samples * self.rank
indices = indices[offset : offset + self.num_samples]
assert len(indices) == self.num_samples
repeated_indices = []
for _ in range(self.dataset_repeat):
repeated_indices += torch.tensor(indices)[torch.randperm(len(indices), generator=g)].tolist()
return iter(repeated_indices)
def __len__(self):
return self.num_samples * self.dataset_repeat
def set_epoch(self, epoch):
self.epoch = epoch
class TrainingSampler(Sampler):
"""
In training, we only care about the "infinite stream" of training data.
So this sampler produces an infinite stream of indices and
all workers cooperate to correctly shuffle the indices and sample different indices.
The samplers in each worker effectively produces `indices[worker_id::num_workers]`
where `indices` is an infinite stream of indices consisting of
`shuffle(range(size)) + shuffle(range(size)) + ...` (if shuffle is True)
or `range(size) + range(size) + ...` (if shuffle is False)
"""
def __init__(self, dataset, num_replicas=None, rank=None, local_rank=None, local_size=None, shuffle=True, seed = None):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) -1
self.total_size = len(dataset)
self.shuffle = shuffle
# self.dataset_repeat = dataset_repeat
if seed is None:
seed = comm.shared_random_seed()
self.seed = int(seed)
def __len__(self):
return self.num_samples
def __iter__(self):
start = self.rank
yield from itertools.islice(self._infinite_indices(), start, None, self.num_replicas)
def _infinite_indices(self):
g = torch.Generator()
g.manual_seed(self.seed)
while True:
if self.shuffle:
yield from torch.randperm(self.total_size, generator=g).tolist()
else:
yield from torch.arange(self.total_size).tolist()
class NaiveSampler(Sampler):
"""
In training, we only care about the "infinite stream" of training data.
So this sampler produces an infinite stream of indices and
all workers cooperate to correctly shuffle the indices and sample different indices.
The samplers in each worker effectively produces `indices[worker_id::num_workers]`
where `indices` is an infinite stream of indices consisting of
`shuffle(range(size)) + shuffle(range(size)) + ...` (if shuffle is True)
or `range(size) + range(size) + ...` (if shuffle is False)
for bookswiki node-block cache
"""
def __init__(self, dataset, num_replicas=None, rank=None, local_rank=None, local_size=None, shuffle=True, seed = None):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size() // comm.get_local_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = comm.get_rank() // comm.get_local_size()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.num_samples =int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) -1
self.total_size = len(dataset)
self.shuffle = shuffle
# self.dataset_repeat = dataset_repeat
if seed is None:
seed = comm.shared_random_seed()
self.seed = int(seed)
def __len__(self):
return self.num_samples
def __iter__(self):
start = self.rank
yield from itertools.islice(self._infinite_indices(), start, None, self.num_replicas)
def _infinite_indices(self):
g = torch.Generator()
g.manual_seed(self.seed)
while True:
if self.shuffle:
yield from torch.randperm(self.total_size, generator=g).tolist()
else:
yield from torch.arange(self.total_size).tolist()
class NodeDistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
process can pass a DistributedSampler instance as a DataLoader sampler,
and load a subset of the original dataset that is exclusive to it.
.. note::
Dataset is assumed to be of constant size.
Arguments:
dataset: Dataset used for sampling.
num_replicas (optional): Number of processes participating in
distributed training.
rank (optional): Rank of the current process within num_replicas.
"""
def __init__(self, dataset, num_replicas=None, rank=None, local_rank=None, local_size=None, shuffle=True):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
if local_rank is None:
local_rank = int(os.environ.get('LOCAL_RANK', 0))
if local_size is None:
local_size = int(os.environ.get('LOCAL_SIZE', 1))
self.dataset = dataset
self.shuffle = shuffle
self.num_replicas = num_replicas
self.num_parts = local_size
self.rank = rank
self.local_rank = local_rank
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
self.total_size_parts = self.num_samples * self.num_replicas // self.num_parts
self.indices = [i for i in range(len(self.dataset)) if i % self.num_parts == self.local_rank]
seed = comm.shared_random_seed()
self.seed = int(seed)
def __iter__(self):
start = self.rank // self.num_parts
yield from itertools.islice(self._infinite_indices(), start, None, self.num_replicas // self.num_parts)
def _infinite_indices(self):
g = torch.Generator()
g.manual_seed(self.seed)
while True:
if self.shuffle:
yield from torch.tensor(self.indices)[torch.randperm(len(self.indices), generator=g)].tolist()
else:
yield from self.indices
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
class NodeDistributedSampler_bak(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
process can pass a DistributedSampler instance as a DataLoader sampler,
and load a subset of the original dataset that is exclusive to it.
.. note::
Dataset is assumed to be of constant size.
Arguments:
dataset: Dataset used for sampling.
num_replicas (optional): Number of processes participating in
distributed training.
rank (optional): Rank of the current process within num_replicas.
"""
def __init__(self, dataset, num_replicas=None, rank=None, local_rank=None, local_size=None, shuffle=True):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
if local_rank is None:
local_rank = int(os.environ.get('LOCAL_RANK', 0))
if local_size is None:
local_size = int(os.environ.get('LOCAL_SIZE', 1))
self.dataset = dataset
self.shuffle = shuffle
self.num_replicas = num_replicas
self.num_parts = local_size
self.rank = rank
self.local_rank = local_rank
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
self.total_size_parts = self.num_samples * self.num_replicas // self.num_parts
def __iter__(self):
if self.shuffle:
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = torch.arange(len(self.dataset)).tolist()
indices = [i for i in indices if i % self.num_parts == self.local_rank]
# add extra samples to make it evenly divisible
indices += indices[:(self.total_size_parts - len(indices))]
assert len(indices) == self.total_size_parts
# subsample
indices = indices[self.rank // self.num_parts:self.total_size_parts:self.num_replicas // self.num_parts]
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch |