# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """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_permute_unpermute_supported, moe_unpermute) from vllm.platforms import current_platform NUM_EXPERTS = [16, 64, 256] TOP_KS = [2, 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) token_expert_indices = torch.arange(0, n_token * topk, dtype=torch.int32, device=hidden_states.device).reshape( (n_token, topk)) 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 // topk, ...] 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])] dst_row_id2src_row_id_map[ expert_first_token_offset[-1]:] = n_token * topk return [ permuted_hidden_states, expert_first_token_offset, src_row_id2dst_row_id_map, dst_row_id2src_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_idx = torch.full((permuted_row_size, ), n_token * topk, dtype=torch.int32, device=hidden_states.device) 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] # 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 // topk,\ ...] permuted_idx[align_first_token_offset:\ align_first_token_offset+\ n_token_in_expert] = 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, permuted_idx, 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 n_hidden = permuted_hidden_states.shape[1] mask = torch.zeros(permuted_hidden_states.shape[0], dtype=bool, device="cuda") mask[valid_row_idx] = True permuted_hidden_states[~mask] = 0 permuted_hidden_states = permuted_hidden_states[ src_row_id2dst_row_id_map.flatten(), ...] permuted_hidden_states = permuted_hidden_states.view(-1, topk, n_hidden) output = (permuted_hidden_states * topk_weights.unsqueeze(2)).sum(1).to( permuted_hidden_states.dtype) return output @pytest.mark.parametrize("n_token", [1, 33, 1024, 5000]) @pytest.mark.parametrize("n_hidden", [2048, 7168]) @pytest.mark.parametrize("n_expert", NUM_EXPERTS) @pytest.mark.parametrize("topk", TOP_KS) @pytest.mark.parametrize("dtype", [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]): if not moe_permute_unpermute_supported(): pytest.skip("moe_permute_unpermute is not supported on this platform.") 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) (gold_permuted_hidden_states, gold_expert_first_token_offset, gold_inv_permuted_idx, gold_permuted_idx, gold_m_indices, 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) (permuted_hidden_states, _, expert_first_token_offset, inv_permuted_idx, m_indices) = moe_permute(hidden_states=hidden_states, a1q_scale=None, topk_ids=topk_ids, n_expert=n_expert, n_local_expert=n_local_expert, expert_map=expert_map, align_block_size=align_block_size, fill_invalid_expert=fill_invalid_expert) # check expert_first_token_offset torch.testing.assert_close(gold_expert_first_token_offset, expert_first_token_offset, atol=0, rtol=0) # check src_row_id2dst_row_id_map torch.testing.assert_close(gold_inv_permuted_idx.flatten(), inv_permuted_idx, atol=0, rtol=0) # check mindice torch.testing.assert_close(gold_m_indices, m_indices, atol=0, rtol=0) # check permuted_hidden_states, only valid token torch.testing.assert_close(gold_permuted_hidden_states[valid_row_idx], permuted_hidden_states[valid_row_idx], atol=0, rtol=0) # add a random tensor to simulate group gemm result0 = 0.5 * permuted_hidden_states + torch.randn_like( permuted_hidden_states) result4 = torch.empty_like(hidden_states) moe_unpermute(result4, result0, topk_weights, inv_permuted_idx, expert_first_token_offset) gold4 = torch_unpermute(result0, topk_weights, topk_ids, token_expert_indices, inv_permuted_idx, valid_row_idx, topk, n_local_expert) # check unpermuted hidden torch.testing.assert_close(result4, gold4, atol=2e-2, rtol=0)