Datasets:
File size: 2,275 Bytes
453129d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | 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
|