Vehicle-cpp/hkpc/software/torch/distributed/checkpoint/resharding.py

from typing import List, Tuple

from torch.distributed._shard.sharding_spec import (
    ShardMetadata,
)

__all__: List[str] = []


def _shards_get_overlap_region_wrt_saved_tensor(
    saved_shard: ShardMetadata, current_shard: ShardMetadata
) -> List[Tuple[int, int, int, int]]:
    """
    Return the overlapping region between saved_shard and current_shard.
    There returned list has the same number of elements as the tensor's dimension.
    For each element, we produce a tuple with the following contents:
        (dimension, `saved_shard` offset, `current_shard` offset, length)

    Offsets are relative to each shard.
    """
    narrows = []
    for dim, (
        saved_shard_offset,
        current_shard_offset,
        saved_shard_size,
        current_shard_size,
    ) in enumerate(
        zip(
            saved_shard.shard_offsets,
            current_shard.shard_offsets,
            saved_shard.shard_sizes,
            current_shard.shard_sizes,
        )
    ):
        min_range_end = min(
            saved_shard_offset + saved_shard_size,
            current_shard_offset + current_shard_size,
        )

        length = min_range_end - max(current_shard_offset, saved_shard_offset)

        if saved_shard_offset > current_shard_offset:
            offset_for_saved_tensor = 0
            offset_for_current_tensor = (
                saved_shard_offset - current_shard_offset
            )
        else:
            offset_for_saved_tensor = current_shard_offset - saved_shard_offset
            offset_for_current_tensor = 0

        narrows.append(
            (dim, offset_for_saved_tensor, offset_for_current_tensor, length)
        )

    return narrows