Datasets:
File size: 1,862 Bytes
c2f8cf1 | 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 | from typing import List, Optional, Tuple
import torch
import torch.distributed as dist
def _preprocess_impl(
expert_mask: torch.Tensor,
num_experts: int,
ep_group: dist.ProcessGroup,
) -> Tuple[List[int], List[int], torch.Tensor, torch.Tensor]:
ep_size = ep_group.size()
num_local_experts = num_experts // ep_size
rank = dist.get_rank(ep_group)
num_local_tokens_per_expert = expert_mask.sum(dim=(1, 2))
input_splits = num_local_tokens_per_expert.reshape(ep_size, num_local_experts).sum(dim=1).tolist()
num_global_tokens_per_expert = torch.empty(
ep_size,
num_local_tokens_per_expert.size(0),
dtype=num_local_tokens_per_expert.dtype,
device=num_local_tokens_per_expert.device,
)
dist.all_gather_into_tensor(num_global_tokens_per_expert, num_local_tokens_per_expert, group=ep_group)
start_idx, end_idx = rank * num_local_experts, (rank + 1) * num_local_experts
num_global_tokens_per_local_expert = num_global_tokens_per_expert[:, start_idx:end_idx].contiguous()
output_splits = num_global_tokens_per_local_expert.sum(dim=1).tolist()
num_global_sum_tokens_per_local_expert = num_global_tokens_per_local_expert.sum(dim=0).to(
torch.device("cpu"), non_blocking=True
)
num_global_tokens_per_local_expert = num_global_tokens_per_local_expert.view(-1, num_local_experts).to(
torch.device("cpu"), non_blocking=True
)
return input_splits, output_splits, num_global_tokens_per_local_expert, num_global_sum_tokens_per_local_expert
def solution(
expert_mask: torch.Tensor,
num_experts: int,
group: Optional[dist.ProcessGroup] = None,
) -> Tuple[List[int], List[int], torch.Tensor, torch.Tensor]:
group = group or dist.group.WORLD
return _preprocess_impl(expert_mask=expert_mask, num_experts=num_experts, ep_group=group)
|