64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
# mypy: allow-untyped-defs
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from dataclasses import dataclass
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from functools import reduce
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from typing import Optional, Union
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from torch.distributed.remote_device import _remote_device
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@dataclass
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class ShardMetadata:
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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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)
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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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