# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ Tests that triton_kernel_moe_forward correctly applies expert_map remapping when expert parallelism (EP) is enabled. Previously, legacy_routing was always used and it produced routing data with global expert IDs that didn't correspond to local weight indices, causing illegal memory access with EP. The fix splits routing: when expert_map is provided, topk selection is performed first, expert_map is applied to remap global→local IDs, and make_routing_data builds routing structures from the local IDs. """ from unittest.mock import MagicMock, patch import pytest import torch class TestTritonMoeForwardExpertMap: """Test that triton_kernel_moe_forward applies expert_map remapping when expert_map is provided (EP active).""" @pytest.mark.parametrize("expert_map_present", [False, True]) def test_routing_path_selection(self, expert_map_present): """Verify that the EP-aware routing path is taken when expert_map is present, and the legacy_routing path is taken otherwise.""" device = "cuda" if torch.cuda.is_available() else "cpu" # This is a structural test: we mock the routing functions to # verify the correct path is exercised. mock_expert_map = ( torch.tensor([0, -1, 1, -1], device=device) if expert_map_present else None ) with ( patch( "vllm.model_executor.layers.fused_moe." "gpt_oss_triton_kernels_moe.legacy_routing" ) as mock_legacy, patch("triton_kernels.topk.topk") as mock_topk, patch( "vllm.model_executor.layers.fused_moe." "gpt_oss_triton_kernels_moe.make_routing_data" ) as mock_make_routing, patch( "vllm.model_executor.layers.fused_moe." "gpt_oss_triton_kernels_moe.triton_kernel_fused_experts" ) as mock_fused_experts, ): from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import ( # noqa: E501 triton_kernel_moe_forward, ) # Set up return values mock_routing_data = MagicMock() mock_gather = MagicMock() mock_scatter = MagicMock() if expert_map_present: sparse_result = MagicMock() sparse_result.indx = torch.tensor([[0, 2]], dtype=torch.int32) sparse_result.vals = torch.tensor([[0.6, 0.4]]) mock_topk.return_value = sparse_result mock_make_routing.return_value = ( mock_routing_data, mock_gather, mock_scatter, ) else: mock_legacy.return_value = ( mock_routing_data, mock_gather, mock_scatter, ) mock_fused_experts.return_value = torch.zeros((1, 8), device=device) hidden = torch.randn((1, 8), device=device) w1 = torch.randn((2, 8, 16), device=device) w2 = torch.randn((2, 8, 8), device=device) logits = torch.randn((1, 4), device=device) triton_kernel_moe_forward( hidden_states=hidden, w1=w1, w2=w2, gating_output=logits, topk=2, renormalize=True, expert_map=mock_expert_map, ) if expert_map_present: # EP path: should use topk + make_routing_data, NOT # legacy_routing mock_topk.assert_called_once() mock_make_routing.assert_called_once() mock_legacy.assert_not_called() # expert_map should be None in the fused_experts call # (already applied) call_kwargs = mock_fused_experts.call_args assert call_kwargs[1].get("expert_map") is None or ( len(call_kwargs[0]) > 0 ) else: # Non-EP path: should use legacy_routing mock_legacy.assert_called_once() mock_topk.assert_not_called() mock_make_routing.assert_not_called()