Datasets:
| import torch | |
| import torch.distributed as dist | |
| def solution( | |
| A: torch.Tensor, | |
| B: torch.Tensor, | |
| ) -> torch.Tensor: | |
| rank = dist.get_rank() | |
| world_size = dist.get_world_size() | |
| M, K = A.shape | |
| K_B, N_local = B.shape | |
| A = A.contiguous() | |
| B = B.contiguous() | |
| C_local = torch.matmul(A, B) | |
| C_gathered = [torch.zeros_like(C_local) for _ in range(world_size)] | |
| dist.all_gather(C_gathered, C_local) | |
| C = torch.cat(C_gathered, dim=1) | |
| return C | |