Datasets:
| import torch | |
| import torch.distributed as dist | |
| import torch.nn.functional as F | |
| def solution( | |
| x_local: torch.Tensor, | |
| W1: torch.Tensor, | |
| W2: torch.Tensor, | |
| ) -> torch.Tensor: | |
| rank = dist.get_rank() | |
| world_size = dist.get_world_size() | |
| M = x_local.shape[0] | |
| M_local = M // world_size | |
| x_local = x_local.contiguous() | |
| shards = [torch.empty_like(x_local) for _ in range(world_size)] | |
| dist.all_gather(shards, x_local) | |
| x_full = torch.cat(shards, dim=1) | |
| a = F.silu(torch.matmul(x_full, W1)) | |
| a_loc = a[rank * M_local : (rank + 1) * M_local].contiguous() | |
| block = torch.matmul(a_loc, W2) | |
| H = block.shape[1] | |
| buf = block.new_zeros((M, H)) | |
| buf[rank * M_local : (rank + 1) * M_local].copy_(block) | |
| y_local = block.new_empty((M_local, H)) | |
| dist.reduce_scatter_tensor(y_local, buf, op=dist.ReduceOp.SUM) | |
| return y_local | |