Datasets:
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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 | import torch
import torch.distributed as dist
import torch.nn.functional as F
@torch.no_grad()
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
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