| import torch |
| import torch.distributed as dist |
|
|
| @torch.no_grad() |
| def solution( |
| indices: torch.Tensor, |
| local_shard: torch.Tensor, |
| ) -> torch.Tensor: |
| rank = dist.get_rank() |
| world_size = dist.get_world_size() |
| shard_size = local_shard.shape[0] |
| embed_dim = local_shard.shape[1] |
| |
| indices = indices.contiguous().to(torch.cuda.current_device()) |
| |
| target_ranks = indices // shard_size |
| |
| send_indices_list = [indices[target_ranks == r] for r in range(world_size)] |
| send_counts = torch.tensor([len(idx) for idx in send_indices_list], dtype=torch.long, device='cuda') |
| |
| recv_counts = torch.zeros(world_size, dtype=torch.long, device='cuda') |
| dist.all_to_all_single(recv_counts, send_counts) |
| |
| non_empty_lists = [idx_list for idx_list in send_indices_list if len(idx_list) > 0] |
| if non_empty_lists: |
| flat_send_indices = torch.cat(non_empty_lists) |
| else: |
| flat_send_indices = torch.empty(0, dtype=torch.long, device='cuda') |
| |
| total_recv = recv_counts.sum().item() |
| total_send = send_counts.sum().item() |
| received_indices = torch.empty(total_recv, dtype=torch.long, device='cuda') |
| |
| if total_recv > 0 or total_send > 0: |
| dist.all_to_all_single( |
| received_indices, |
| flat_send_indices, |
| output_split_sizes=recv_counts.tolist(), |
| input_split_sizes=send_counts.tolist() |
| ) |
| |
| if total_recv > 0: |
| local_lookup_indices = received_indices - (rank * shard_size) |
| local_lookup_indices = torch.clamp(local_lookup_indices, 0, shard_size - 1) |
| retrieved_vectors = local_shard[local_lookup_indices] |
| else: |
| retrieved_vectors = torch.empty((0, embed_dim), dtype=local_shard.dtype, device='cuda') |
| |
| output_vectors = torch.empty((len(indices), embed_dim), dtype=local_shard.dtype, device='cuda') |
| |
| input_split_sizes = recv_counts.cpu().tolist() |
| output_split_sizes = send_counts.cpu().tolist() |
| |
| if len(indices) > 0 or retrieved_vectors.numel() > 0: |
| dist.all_to_all_single( |
| output_vectors, |
| retrieved_vectors, |
| output_split_sizes=output_split_sizes, |
| input_split_sizes=input_split_sizes |
| ) |
| |
| return output_vectors |
|
|
