| from typing import List, Optional, Union |
|
|
| import torch |
| import torch.distributed as dist |
|
|
|
|
| def solution( |
| local_tensor: torch.Tensor, |
| input_split_sizes: Optional[Union[List[int], torch.Tensor]] = None, |
| output_split_sizes: Optional[Union[List[int], torch.Tensor]] = None, |
| group: Optional[dist.ProcessGroup] = None, |
| ) -> torch.Tensor: |
| group = group or dist.group.WORLD |
| world_size = dist.get_world_size(group) |
| if world_size == 1: |
| return local_tensor.contiguous() |
|
|
| local_tensor = local_tensor.contiguous() |
| if output_split_sizes is None: |
| output = torch.empty_like(local_tensor) |
| else: |
| out_size = sum(output_split_sizes) if isinstance(output_split_sizes, list) else int(output_split_sizes.sum().item()) |
| output = torch.empty( |
| (out_size, local_tensor.size(1)), |
| dtype=local_tensor.dtype, |
| device=local_tensor.device, |
| ) |
| dist.all_to_all_single( |
| output, |
| local_tensor, |
| output_split_sizes=output_split_sizes, |
| input_split_sizes=input_split_sizes, |
| group=group, |
| ) |
| return output |
|
|
