code/reference/26_moe_token_preprocess.py

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)