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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import itertools
from typing import Any, Optional
import warnings
import numpy as np
import torch
from torch.utils.data.sampler import Sampler
import dinov2.distributed as distributed
class EpochSampler(Sampler):
def __init__(
self,
*,
size: int,
sample_count: int,
shuffle: bool = False,
seed: int = 0,
start: Optional[int] = None,
step: Optional[int] = None,
):
self._size = size
self._sample_count = sample_count
self._shuffle = shuffle
self._seed = seed
self._start = distributed.get_global_rank() if start is None else start
self._step = distributed.get_global_size() if step is None else step
self._epoch = 0
def __iter__(self):
count = (self._size + self._sample_count - 1) // self._sample_count
tiled_indices = np.tile(np.arange(self._sample_count), count)
if self._shuffle:
seed = self._seed * self._epoch if self._seed != 0 else self._epoch
rng = np.random.default_rng(seed)
iterable = rng.choice(tiled_indices, self._size, replace=False)
else:
iterable = tiled_indices[: self._size]
yield from itertools.islice(iterable, self._start, None, self._step)
def __len__(self):
return (self._size - self._start + self._step - 1) // self._step
def set_epoch(self, epoch):
self._epoch = epoch
def _get_numpy_dtype(size: int) -> Any:
return np.int32 if size <= 2**31 else np.int64
def _get_torch_dtype(size: int) -> Any:
return torch.int32 if size <= 2**31 else torch.int64
def _generate_randperm_indices(*, size: int, generator: torch.Generator):
"""Generate the indices of a random permutation."""
dtype = _get_torch_dtype(size)
# This is actually matching PyTorch's CPU implementation, see: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorFactories.cpp#L900-L921
perm = torch.arange(size, dtype=dtype)
for i in range(size):
j = torch.randint(i, size, size=(1,), generator=generator).item()
# Always swap even if no-op
value = perm[j].item()
perm[j] = perm[i].item()
perm[i] = value
yield value
class InfiniteSampler(Sampler):
def __init__(
self,
*,
sample_count: int,
shuffle: bool = False,
seed: int = 0,
start: Optional[int] = None,
step: Optional[int] = None,
advance: int = 0,
):
self._sample_count = sample_count
self._seed = seed
self._shuffle = shuffle
self._start = distributed.get_global_rank() if start is None else start
self._step = distributed.get_global_size() if step is None else step
self._advance = advance
def __iter__(self):
if self._shuffle:
iterator = self._shuffled_iterator()
else:
iterator = self._iterator()
yield from itertools.islice(iterator, self._advance, None)
def _iterator(self):
assert not self._shuffle
while True:
iterable = range(self._sample_count)
yield from itertools.islice(iterable, self._start, None, self._step)
def _shuffled_iterator(self):
assert self._shuffle
# Instantiate a generator here (rather than in the ctor) to keep the class
# picklable (requirement of mp.spawn)
generator = torch.Generator().manual_seed(self._seed)
while True:
iterable = _generate_randperm_indices(size=self._sample_count, generator=generator)
yield from itertools.islice(iterable, self._start, None, self._step)
# The following function is somewhat equivalent to _new_shuffle_tensor_slice below,
# but avoids a full in-place random permutation generation.
def _shuffle_tensor_slice(
*, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator
) -> np.ndarray:
stop = len(tensor)
count = stop // step
drop_count = stop - step * count
if drop_count:
warnings.warn(f"# of dropped samples: {drop_count}")
dtype = _get_numpy_dtype(stop)
result = np.empty(count, dtype=dtype)
for i in range(count):
j = torch.randint(0, i + 1, size=(1,), generator=generator).item() if i > 0 else 0
result[i] = result[j]
result[j] = tensor[start + i * step].item()
return result
def _new_shuffle_tensor_slice(
*, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator
) -> np.ndarray:
stop = len(tensor)
count = stop // step
dtype = torch.int64 # Needed for using randperm result as indices
count = stop // step
drop_count = stop - step * count
if drop_count:
warnings.warn(f"# of dropped samples: {drop_count}")
indices = torch.randperm(count, dtype=dtype, generator=generator)
return tensor[start::step][indices].numpy()
def _make_seed(seed: int, start: int, iter_count: int) -> int:
# NOTE: Tried a few variants (including iter_count << 32), this one worked best.
return seed + start + (iter_count << 24)
class ShardedInfiniteSampler(Sampler):
def __init__(
self,
*,
sample_count: int,
shuffle: bool = False,
seed: int = 0,
start: Optional[int] = None,
step: Optional[int] = None,
advance: int = 0,
use_new_shuffle_tensor_slice: bool = False,
):
self._sample_count = sample_count
self._seed = seed
self._shuffle = shuffle
self._start = distributed.get_global_rank() if start is None else start
self._step = distributed.get_global_size() if step is None else step
self._advance = advance
self._iter_count = 0
self._shuffle_tensor_slice_fn = (
_new_shuffle_tensor_slice if use_new_shuffle_tensor_slice else _shuffle_tensor_slice
)
def __iter__(self):
iter_count = self._advance // self._sample_count
if iter_count > 0:
self._advance -= iter_count * self._sample_count
self._iter_count += iter_count
if self._shuffle:
iterator = self._shuffled_iterator()
else:
iterator = self._iterator()
yield from itertools.islice(iterator, self._advance, None)
def _iterator(self):
assert not self._shuffle
while True:
iterable = range(self._sample_count)
yield from itertools.islice(iterable, self._start, None, self._step)
def _shuffled_iterator(self):
assert self._shuffle
# Instantiate a generator here (rather than in the ctor) to be keep the class
# picklable (requirement of mp.spawn)
generator = torch.Generator()
# Always shuffle everything first
generator.manual_seed(self._seed)
dtype = _get_torch_dtype(self._sample_count)
perm = torch.randperm(self._sample_count, dtype=dtype, generator=generator)
while True:
# Re-seed on each iteration to allow skipping whole permutations
seed = _make_seed(self._seed, self._start, self._iter_count)
generator.manual_seed(seed)
iterable = self._shuffle_tensor_slice_fn(
tensor=perm, start=self._start, step=self._step, generator=generator
)
yield from iterable
self._iter_count += 1
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