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from dataclasses import dataclass
from typing import List
import torch
from torch.distributed._shard.metadata import ShardMetadata
from torch.distributed.remote_device import _remote_device
@dataclass
class Shard(object):
"""
Container which holds the data for a shard as a Tensor and also
the associated metadata for that shard.
Args:
tensor(torch.Tensor): Local tensor for the shard.
metadata(:class `torch.distributed._shard.sharded_tensor.ShardMetadata`):
The metadata for the shard, including offsets, lengths and device placement.
"""
__slots__ = ['tensor', 'metadata']
tensor: torch.Tensor
metadata: ShardMetadata
def __post_init__(self):
# verification between local tensor and metadata
if list(self.tensor.size()) != self.metadata.shard_sizes:
raise ValueError(
"Shard tensor size does not match with metadata.shard_lengths! "
f"Found shard tensor size: {list(self.tensor.size())}, "
f"metadata.shard_lengths: {self.metadata.shard_sizes}, "
)
placement_device = self.metadata.placement
if placement_device is not None and placement_device.device() != self.tensor.device:
raise ValueError(
f"Local shard tensor device does not match with local Shard's placement! "
f"Found local shard tensor device: {self.tensor.device}, "
f"local shard metadata placement device: {placement_device.device()}"
)
@classmethod
def from_tensor_and_offsets(cls, tensor: torch.Tensor, shard_offsets: List[int], rank: int):
"""
Creates a Shard of a ShardedTensor from a local torch.Tensor, shard_offsets and rank.
Args:
tensor(torch.Tensor): Local tensor for the shard.
shard_offsets(List[int]): List of integers specify the offset
of the shard on each dimension.
rank(int): Specify the rank for the shard.
"""
shard_sizes = list(tensor.size())
placement = _remote_device(f"rank:{rank}/{str(tensor.device)}")
shard_meta = ShardMetadata(
shard_offsets=shard_offsets,
shard_sizes=shard_sizes,
placement=placement
)
return Shard(tensor, shard_meta)
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