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from collections.abc import Sequence
import torch
from torch.distributed._shard.metadata import ShardMetadata
DEPRECATE_MSG = "Please use DTensor instead and we are deprecating ShardedTensor."
def narrow_tensor_by_index(
tensor: torch.Tensor,
offsets: Sequence[int],
sizes: Sequence[int],
) -> torch.Tensor:
"""
Narrow the tensor according to ``offsets`` and ``sizes``.
"""
narrowed_tensor = tensor
for idx, (offset, size) in enumerate(zip(offsets, sizes)):
if size < tensor.size(idx):
# Reshape to get shard for this rank and we don't want autograd
# recording here for the narrow op and 'local_shard' should be a
# leaf variable in the autograd graph.
narrowed_tensor = narrowed_tensor.narrow(idx, offset, size)
return narrowed_tensor
def narrow_tensor(tensor: torch.Tensor, metadata: ShardMetadata) -> torch.Tensor:
"""
Narrow the tensor according to the metadata
"""
return narrow_tensor_by_index(tensor, metadata.shard_offsets, metadata.shard_sizes)
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