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from dataclasses import dataclass |
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from typing import List, Union, Optional |
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from functools import reduce |
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from torch.distributed.remote_device import _remote_device |
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@dataclass |
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class ShardMetadata(object): |
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""" |
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Represents a shard of the overall Tensor including its |
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offsets, lengths and device placement. |
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Args: |
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shard_offsets(List[int]): Offsets in the original tensor indicating |
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the start offsets for this shard. Should have the same rank as |
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the original tensor. |
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shard_sizes(List[int]): Integers indicating the size of each |
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dimension for this shard. Should have the same rank as the |
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original tensor. |
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placement(:class:`torch.distributed._remote_device`): |
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Specifies the placement of this shard. |
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""" |
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__slots__ = ['shard_offsets', 'shard_sizes', 'placement'] |
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shard_offsets: List[int] |
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shard_sizes: List[int] |
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placement: Optional[_remote_device] |
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def __init__( |
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self, |
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shard_offsets: List[int], |
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shard_sizes: List[int], |
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placement: Optional[Union[str, _remote_device]] = None |
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): |
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self.shard_offsets = shard_offsets |
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self.shard_sizes = shard_sizes |
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if isinstance(placement, str): |
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self.placement = _remote_device(placement) |
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else: |
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self.placement = placement |
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if len(self.shard_offsets) != len(self.shard_sizes): |
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raise ValueError( |
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f'shard_offsets and shard_sizes should have ' |
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f'the same number of elements, found {len(self.shard_offsets)} ' |
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f'and {self.shard_sizes} respectively') |
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for i in range(len(self.shard_offsets)): |
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if self.shard_offsets[i] < 0: |
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raise ValueError('shard_offsets should be >=0') |
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if self.shard_sizes[i] < 0: |
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raise ValueError('shard_sizes should be >= 0') |
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def __hash__(self): |
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def _hash_reduce(a, b): |
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return (a << 8) + hash(b) |
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res = reduce(_hash_reduce, self.shard_offsets, 37) |
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res = reduce(_hash_reduce, self.shard_sizes, res) |
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res = _hash_reduce(res, self.placement) |
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return res |
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