test_moe_permute_unpermute.py

# SPDX-License-Identifier: Apache-2.0
"""Tests for the MOE permute/unpermute kernel

Run `pytest tests/kernels/test_moe_permute_unpermute.py`.
"""

from typing import Optional

import numpy as np
import pytest
import torch

from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
from vllm.model_executor.layers.fused_moe.layer import determine_expert_map
from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
    moe_permute, moe_unpermute)
from vllm.platforms import current_platform

NUM_EXPERTS = [16, 64]
TOP_KS = [2, 4, 6, 8]
EP_SIZE = [1, 4, 16]
current_platform.seed_everything(0)


def torch_permute(hidden_states: torch.Tensor,
                  topk_ids: torch.Tensor,
                  token_expert_indices: torch.Tensor,
                  topk: int,
                  n_expert: int,
                  n_local_expert: int,
                  start_expert: int,
                  expert_map: Optional[torch.Tensor] = None,
                  align_block_size: Optional[int] = None,
                  fill_invalid_expert: int = -1) -> list[torch.Tensor]:
    n_token, n_hidden = hidden_states.shape[0], hidden_states.shape[1]
    if expert_map is not None:
        is_local_expert = (expert_map[topk_ids] != -1)
        not_local_expert = (expert_map[topk_ids] == -1)
        topk_ids = is_local_expert * (
            topk_ids - start_expert) + not_local_expert * (topk_ids + n_expert)

    sorted_topk_ids, sorted_indices = torch.sort(topk_ids.flatten(),
                                                 stable=True)
    dst_row_id2src_row_id_map = token_expert_indices.flatten()[sorted_indices]

    expert_first_token_offset = torch.zeros(n_local_expert + 1,
                                            dtype=torch.int64,
                                            device="cuda")
    idx = 0
    for i in range(0, n_local_expert):
        cnt = 0
        while idx < sorted_topk_ids.numel() and sorted_topk_ids[idx] == i:
            cnt += 1
            idx += 1
        expert_first_token_offset[i + 1] = expert_first_token_offset[i] + cnt

    _, src2dst_idx = torch.sort(dst_row_id2src_row_id_map)
    valid_row_idx = []
    if align_block_size is None:

        permuted_hidden_states = hidden_states[dst_row_id2src_row_id_map %
                                               n_token, ...]
        permuted_row_size = permuted_hidden_states.shape[0]
        m_indices = torch.empty(permuted_row_size,
                                device="cuda",
                                dtype=torch.int32).fill_(fill_invalid_expert)
        for i in range(1, n_local_expert + 1):
            first_token_offset = expert_first_token_offset[i - 1]
            last_token_offset = expert_first_token_offset[i]
            m_indices[first_token_offset:last_token_offset] = i - 1
        src_row_id2dst_row_id_map = torch.arange(
            0, n_token * topk, device="cuda",
            dtype=torch.int32)[src2dst_idx].reshape((n_token, topk))
        valid_row_idx += [i for i in range(expert_first_token_offset[-1])]
        return [
            permuted_hidden_states, expert_first_token_offset,
            src_row_id2dst_row_id_map, m_indices, valid_row_idx
        ]
    else:
        permuted_row_size = (topk * n_token + n_expert *
                             (align_block_size - 1) + align_block_size -
                             1) // align_block_size * align_block_size
        permuted_hidden_states = torch.empty((permuted_row_size, n_hidden),
                                             device="cuda",
                                             dtype=hidden_states.dtype)
        align_src_row_id2dst_row_id = torch.empty(n_token * topk,
                                                  device="cuda",
                                                  dtype=torch.int32)
        align_expert_first_token_offset = torch.zeros_like(
            expert_first_token_offset)
        m_indices = torch.empty(permuted_row_size,
                                device="cuda",
                                dtype=torch.int32).fill_(fill_invalid_expert)
        # get align_permuted_hidden_states,
        # valid row_idx and align_expert_first_token_offset
        for i in range(1, n_local_expert + 1):
            first_token_offset = expert_first_token_offset[i - 1]
            last_token_offset = expert_first_token_offset[i]
            n_token_in_expert = last_token_offset - first_token_offset
            align_expert_first_token_offset[
                i] = align_expert_first_token_offset[
                    i - 1] + (n_token_in_expert + align_block_size -
                              1) // align_block_size * align_block_size
            align_first_token_offset = align_expert_first_token_offset[i - 1]
            align_last_token_offset = align_expert_first_token_offset[i]
            dst_row_id2src_row_id_in_expert = dst_row_id2src_row_id_map[
                first_token_offset:first_token_offset +
                n_token_in_expert] % n_token
            # store token in current expert with align_first_token_offset
            permuted_hidden_states[align_first_token_offset:\
                                   align_first_token_offset+n_token_in_expert,\
                                      ...] = hidden_states[\
                                       dst_row_id2src_row_id_in_expert, ...]
            # set current expert m_indices
            m_indices[align_first_token_offset:align_last_token_offset] = i - 1
            valid_row_idx += [
                i for i in range(align_first_token_offset,
                                 align_first_token_offset + n_token_in_expert)
            ]
        # get align_src_row_id2dst_row_id
        for i in range(n_token * topk):
            eid = sorted_topk_ids[i]
            if (eid >= n_local_expert):
                # check token not in local expert
                align_src_row_id2dst_row_id[
                    i] = align_expert_first_token_offset[-1]
                continue
            first_token_offset = expert_first_token_offset[eid]
            align_first_token_offset = align_expert_first_token_offset[eid]
            token_offset = i - first_token_offset
            align_src_row_id2dst_row_id[
                i] = align_first_token_offset + token_offset
        align_src_row_id2dst_row_id = align_src_row_id2dst_row_id[\
            src2dst_idx].reshape((n_token, topk))
        return [
            permuted_hidden_states, align_expert_first_token_offset,
            align_src_row_id2dst_row_id, m_indices, valid_row_idx
        ]


def torch_unpermute(permuted_hidden_states: torch.Tensor,
                    topk_weights: torch.Tensor, topk_ids: torch.Tensor,
                    token_expert_indices: torch.Tensor,
                    src_row_id2dst_row_id_map: torch.Tensor,
                    valid_row_idx: torch.Tensor, topk: int,
                    n_expert: int) -> torch.Tensor:
    # ignore invalid row
    mask = torch.zeros(permuted_hidden_states.shape[0],
                       dtype=bool,
                       device="cuda")
    mask[valid_row_idx] = True
    permuted_hidden_states[~mask] = 0
    idx = src_row_id2dst_row_id_map.flatten()[
        token_expert_indices.flatten()].reshape(token_expert_indices.shape)
    output = permuted_hidden_states[idx, ...] * topk_weights[..., None]
    output = output.sum(dim=1).to(permuted_hidden_states.dtype)
    return output


@pytest.mark.parametrize("n_token", [1, 33, 64, 222, 1024, 2048, 3000, 5000])
@pytest.mark.parametrize("n_hidden", [2048, 4096, 7168])
@pytest.mark.parametrize("n_expert", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("ep_size", EP_SIZE)
@pytest.mark.parametrize("align_block_size", [None, 128])
def test_moe_permute_unpermute(n_token: int, n_hidden: int, topk: int,
                               n_expert: int, ep_size: int, dtype: torch.dtype,
                               align_block_size: Optional[int]):
    fill_invalid_expert = 0
    ep_rank = np.random.randint(0, ep_size)
    expert_map = None
    n_local_expert = n_expert
    if (ep_size != 1):
        n_local_expert, expert_map = determine_expert_map(
            ep_size, ep_rank, n_expert)
        expert_map = expert_map.cuda()
    start_expert = n_local_expert * ep_rank
    current_platform.seed_everything(0)
    hidden_states = torch.randn((n_token, n_hidden), device="cuda").to(dtype)
    gating_output = torch.randn((n_token, n_expert), device="cuda").to(dtype)
    topk_weights, topk_ids, token_expert_indices = fused_topk(
        hidden_states, gating_output, topk, False)
    gold0, gold1, gold2, gold3, valid_row_idx = torch_permute(
        hidden_states,
        topk_ids,
        token_expert_indices,
        topk,
        n_expert,
        n_local_expert,
        start_expert,
        expert_map=expert_map,
        align_block_size=align_block_size,
        fill_invalid_expert=fill_invalid_expert)

    result0, result1, result2, result3 = moe_permute(
        hidden_states, topk_weights, topk_ids, token_expert_indices, topk,
        n_expert, n_local_expert, expert_map, align_block_size,
        fill_invalid_expert)

    # check expert_first_token_offset
    torch.testing.assert_close(gold1, result1, atol=0, rtol=0)
    # check src_row_id2dst_row_id_map
    torch.testing.assert_close(gold2, result2, atol=0, rtol=0)
    # check mindice
    torch.testing.assert_close(gold3, result3, atol=0, rtol=0)
    # check permuted_hidden_states, only valid token
    torch.testing.assert_close(gold0[valid_row_idx],
                               result0[valid_row_idx],
                               atol=0,
                               rtol=0)

    # add a random tensor to simulate group gemm
    result0 = 0.5 * result0 + torch.randn_like(result0)

    result4 = moe_unpermute(result0, topk_weights, topk_ids, result2, result1,
                            topk, n_expert, n_local_expert)
    gold4 = torch_unpermute(result0, topk_weights, topk_ids,
                            token_expert_indices, result2, valid_row_idx, topk,
                            n_local_expert)

    # check unpermuted hidden
    torch.testing.assert_close(result4, gold4, atol=2e-2, rtol=0)