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| from typing import List, Tuple | |
| from torch.distributed.checkpoint.metadata import ChunkStorageMetadata | |
| __all__: List[str] = [] | |
| def _check_shard_metadata_pair_overlap( | |
| shard1: ChunkStorageMetadata, shard2: ChunkStorageMetadata | |
| ): | |
| """Check if two shards overlap.""" | |
| # For each dim of each shard, check if one shard resides on the other | |
| # end of second shard with respect to that dim. As an example for a 2D | |
| # shard, we would check if one shard is above or on the left of the | |
| # other shard. | |
| ndims = len(shard1.offsets) | |
| for i in range(ndims): | |
| if shard1.offsets[i] >= shard2.offsets[i] + shard2.sizes[i]: | |
| return False | |
| if shard2.offsets[i] >= shard1.offsets[i] + shard1.sizes[i]: | |
| return False | |
| return True | |
| def _shards_get_overlap_region_wrt_saved_tensor( | |
| saved_shard: ChunkStorageMetadata, current_shard: ChunkStorageMetadata | |
| ) -> 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.offsets, | |
| current_shard.offsets, | |
| saved_shard.sizes, | |
| current_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 | |