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