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Zero
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from typing import *
import math
import torch
import numpy as np
from torch.utils.data import Sampler, Dataset, DataLoader, DistributedSampler
import torch.distributed as dist
def recursive_to_device(
data: Any,
device: torch.device,
non_blocking: bool = False,
) -> Any:
"""
Recursively move all tensors in a data structure to a device.
"""
if hasattr(data, "to"):
return data.to(device, non_blocking=non_blocking)
elif isinstance(data, (list, tuple)):
return type(data)(recursive_to_device(d, device, non_blocking) for d in data)
elif isinstance(data, dict):
return {k: recursive_to_device(v, device, non_blocking) for k, v in data.items()}
else:
return data
def load_balanced_group_indices(
load: List[int],
num_groups: int,
equal_size: bool = False,
) -> List[List[int]]:
"""
Split indices into groups with balanced load.
"""
if equal_size:
group_size = len(load) // num_groups
indices = np.argsort(load)[::-1]
groups = [[] for _ in range(num_groups)]
group_load = np.zeros(num_groups)
for idx in indices:
min_group_idx = np.argmin(group_load)
groups[min_group_idx].append(idx)
if equal_size and len(groups[min_group_idx]) == group_size:
group_load[min_group_idx] = float('inf')
else:
group_load[min_group_idx] += load[idx]
return groups
def cycle(data_loader: DataLoader) -> Iterator:
while True:
for data in data_loader:
if isinstance(data_loader.sampler, ResumableSampler):
data_loader.sampler.idx += data_loader.batch_size # type: ignore[attr-defined]
yield data
if isinstance(data_loader.sampler, DistributedSampler):
data_loader.sampler.epoch += 1
if isinstance(data_loader.sampler, ResumableSampler):
data_loader.sampler.epoch += 1
data_loader.sampler.idx = 0
class ResumableSampler(Sampler):
"""
Distributed sampler that is resumable.
Args:
dataset: Dataset used for sampling.
rank (int, optional): Rank of the current process within :attr:`num_replicas`.
By default, :attr:`rank` is retrieved from the current distributed
group.
shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
indices.
seed (int, optional): random seed used to shuffle the sampler if
:attr:`shuffle=True`. This number should be identical across all
processes in the distributed group. Default: ``0``.
drop_last (bool, optional): if ``True``, then the sampler will drop the
tail of the data to make it evenly divisible across the number of
replicas. If ``False``, the sampler will add extra indices to make
the data evenly divisible across the replicas. Default: ``False``.
"""
def __init__(
self,
dataset: Dataset,
shuffle: bool = True,
seed: int = 0,
drop_last: bool = False,
) -> None:
self.dataset = dataset
self.epoch = 0
self.idx = 0
self.drop_last = drop_last
self.world_size = dist.get_world_size() if dist.is_initialized() else 1
self.rank = dist.get_rank() if dist.is_initialized() else 0
# If the dataset length is evenly divisible by # of replicas, then there
# is no need to drop any data, since the dataset will be split equally.
if self.drop_last and len(self.dataset) % self.world_size != 0: # type: ignore[arg-type]
# Split to nearest available length that is evenly divisible.
# This is to ensure each rank receives the same amount of data when
# using this Sampler.
self.num_samples = math.ceil(
(len(self.dataset) - self.world_size) / self.world_size # type: ignore[arg-type]
)
else:
self.num_samples = math.ceil(len(self.dataset) / self.world_size) # type: ignore[arg-type]
self.total_size = self.num_samples * self.world_size
self.shuffle = shuffle
self.seed = seed
def __iter__(self) -> Iterator:
if self.shuffle:
# deterministically shuffle based on epoch and seed
g = torch.Generator()
g.manual_seed(self.seed + self.epoch)
indices = torch.randperm(len(self.dataset), generator=g).tolist() # type: ignore[arg-type]
else:
indices = list(range(len(self.dataset))) # type: ignore[arg-type]
if not self.drop_last:
# add extra samples to make it evenly divisible
padding_size = self.total_size - len(indices)
if padding_size <= len(indices):
indices += indices[:padding_size]
else:
indices += (indices * math.ceil(padding_size / len(indices)))[
:padding_size
]
else:
# remove tail of data to make it evenly divisible.
indices = indices[: self.total_size]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank : self.total_size : self.world_size]
# resume from previous state
indices = indices[self.idx:]
return iter(indices)
def __len__(self) -> int:
return self.num_samples
def state_dict(self) -> dict[str, int]:
return {
'epoch': self.epoch,
'idx': self.idx,
}
def load_state_dict(self, state_dict):
self.epoch = state_dict['epoch']
self.idx = state_dict['idx']
class BalancedResumableSampler(ResumableSampler):
"""
Distributed sampler that is resumable and balances the load among the processes.
Args:
dataset: Dataset used for sampling.
rank (int, optional): Rank of the current process within :attr:`num_replicas`.
By default, :attr:`rank` is retrieved from the current distributed
group.
shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
indices.
seed (int, optional): random seed used to shuffle the sampler if
:attr:`shuffle=True`. This number should be identical across all
processes in the distributed group. Default: ``0``.
drop_last (bool, optional): if ``True``, then the sampler will drop the
tail of the data to make it evenly divisible across the number of
replicas. If ``False``, the sampler will add extra indices to make
the data evenly divisible across the replicas. Default: ``False``.
"""
def __init__(
self,
dataset: Dataset,
shuffle: bool = True,
seed: int = 0,
drop_last: bool = False,
batch_size: int = 1,
) -> None:
assert hasattr(dataset, 'loads'), 'Dataset must have "loads" attribute to use BalancedResumableSampler'
super().__init__(dataset, shuffle, seed, drop_last)
self.batch_size = batch_size
self.loads = dataset.loads
def __iter__(self) -> Iterator:
if self.shuffle:
# deterministically shuffle based on epoch and seed
g = torch.Generator()
g.manual_seed(self.seed + self.epoch)
indices = torch.randperm(len(self.dataset), generator=g).tolist() # type: ignore[arg-type]
else:
indices = list(range(len(self.dataset))) # type: ignore[arg-type]
if not self.drop_last:
# add extra samples to make it evenly divisible
padding_size = self.total_size - len(indices)
if padding_size <= len(indices):
indices += indices[:padding_size]
else:
indices += (indices * math.ceil(padding_size / len(indices)))[
:padding_size
]
else:
# remove tail of data to make it evenly divisible.
indices = indices[: self.total_size]
assert len(indices) == self.total_size
# balance load among processes
num_batches = len(indices) // (self.batch_size * self.world_size)
balanced_indices = []
for i in range(num_batches):
start_idx = i * self.batch_size * self.world_size
end_idx = (i + 1) * self.batch_size * self.world_size
batch_indices = indices[start_idx:end_idx]
batch_loads = [self.loads[idx] for idx in batch_indices]
groups = load_balanced_group_indices(batch_loads, self.world_size, equal_size=True)
balanced_indices.extend([batch_indices[j] for j in groups[self.rank]])
# resume from previous state
indices = balanced_indices[self.idx:]
return iter(indices)
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