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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 | |