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Make foreach faster for SGD Pull Request resolved: https://github.com/pytorch/pytorch/pull/92338 Approved by: https://github.com/albanD
38 lines
2.1 KiB
Python
38 lines
2.1 KiB
Python
from collections import defaultdict
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from typing import List, Dict, Tuple, Optional, Union
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import torch
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from torch import Tensor
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# _group_tensors_by_device_and_dtype is a util function that splits tensors into groups by device and dtype,
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# which is useful before sending tensors off to a foreach implementation, which requires tensors to be on
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# one device and dtype.
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# If tensorlistlist contains more than one tensorlist, the following assumptions are made BUT NOT verified:
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# - tensorlists CAN be None
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# - all tensors in the first specified list cannot be None
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# - given an index i, all specified tensorlist[i]s match in dtype and device
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# with_indices (bool, optional): whether to track previous indices as the last list per dictionary entry.
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# It comes in handy if there are Nones or literals in the tensorlists that are getting scattered out.
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# Whereas mutating a tensor in the resulting split-up tensorlists WILL propagate changes back to the
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# original input tensorlists, changing up Nones/literals WILL NOT propagate, and manual propagation
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# may be necessary. Check out torch/optim/sgd.py for an example.
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@torch.no_grad()
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def _group_tensors_by_device_and_dtype(tensorlistlist: List[List[Tensor]],
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with_indices: Optional[bool] = False) -> \
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Dict[Tuple[str, torch.dtype], List[List[Union[Tensor, int]]]]:
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assert all([not x or len(x) == len(tensorlistlist[0]) for x in tensorlistlist]), (
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"all specified tensorlists must match in length")
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per_device_and_dtype_tensors: Dict[Tuple[str, torch.dtype], List[List[Union[Tensor, int]]]] = defaultdict(
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lambda: [[] for _ in range(len(tensorlistlist) + (1 if with_indices else 0))])
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for i, t in enumerate(tensorlistlist[0]):
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key = (str(t.device), t.dtype)
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for j in range(len(tensorlistlist)):
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# a tensorlist may be empty/None
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if tensorlistlist[j]:
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per_device_and_dtype_tensors[key][j].append(tensorlistlist[j][i])
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if with_indices:
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# tack on previous index
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per_device_and_dtype_tensors[key][j + 1].append(i)
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return per_device_and_dtype_tensors
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