fix(mxfp4): Disable monolithic path for TRITON backend with EP (#34270)

Signed-off-by: Elizabeth Thomas <email2eliza@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Michael Goin <mgoin64@gmail.com>

tests/kernels/quantization/test_mxfp4_triton_ep.py

@@ -0,0 +1,194 @@
+# 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
+
+from vllm.model_executor.layers.quantization.mxfp4 import (
+    Mxfp4Backend,
+    Mxfp4MoEMethod,
+)
+
+
+def _make_mock_moe_config(ep_size: int = 1) -> MagicMock:
+    """Create a mock FusedMoEConfig with the given EP size."""
+    parallel_config = MagicMock()
+    parallel_config.ep_size = ep_size
+
+    moe_config = MagicMock()
+    moe_config.ep_size = ep_size
+    moe_config.is_lora_enabled = False
+    moe_config.moe_parallel_config = parallel_config
+    return moe_config
+
+
+class TestMxfp4TritonIsMonolithic:
+    """Verify that is_monolithic is always True for the TRITON backend,
+    regardless of EP size, since triton_kernel_moe_forward now handles
+    expert_map remapping internally."""
+
+    @pytest.mark.parametrize(
+        "backend,ep_size,expected_monolithic",
+        [
+            # TRITON is always monolithic (handles EP via expert_map remapping)
+            (Mxfp4Backend.TRITON, 1, True),
+            (Mxfp4Backend.TRITON, 2, True),
+            (Mxfp4Backend.TRITON, 4, True),
+            # SM100 backends are always monolithic
+            (Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM, 1, True),
+            (Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM, 2, True),
+            (Mxfp4Backend.SM100_FI_MXFP4_BF16, 1, True),
+            (Mxfp4Backend.SM100_FI_MXFP4_BF16, 2, True),
+            # MARLIN is never monolithic
+            (Mxfp4Backend.MARLIN, 1, False),
+            (Mxfp4Backend.MARLIN, 2, False),
+        ],
+        ids=[
+            "triton-no-ep",
+            "triton-ep2",
+            "triton-ep4",
+            "sm100-trtllm-no-ep",
+            "sm100-trtllm-ep2",
+            "sm100-bf16-no-ep",
+            "sm100-bf16-ep2",
+            "marlin-no-ep",
+            "marlin-ep2",
+        ],
+    )
+    @patch(
+        "vllm.model_executor.layers.quantization.mxfp4.get_mxfp4_backend",
+    )
+    @patch(
+        "vllm.model_executor.layers.quantization.mxfp4.get_current_vllm_config",
+    )
+    def test_is_monolithic(
+        self,
+        mock_get_config,
+        mock_get_backend,
+        backend,
+        ep_size,
+        expected_monolithic,
+    ):
+        """is_monolithic should be True for TRITON regardless of EP size."""
+        mock_get_backend.return_value = backend
+
+        mock_compilation_config = MagicMock()
+        mock_compilation_config.max_cudagraph_capture_size = 1024
+        mock_vllm_config = MagicMock()
+        mock_vllm_config.compilation_config = mock_compilation_config
+        mock_get_config.return_value = mock_vllm_config
+
+        moe_config = _make_mock_moe_config(ep_size=ep_size)
+        method = Mxfp4MoEMethod(moe_config)
+
+        assert method.is_monolithic == expected_monolithic, (
+            f"Expected is_monolithic={expected_monolithic} for "
+            f"backend={backend.name}, ep_size={ep_size}, "
+            f"but got {method.is_monolithic}."
+        )
+
+
+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()

vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py

@@ -179,9 +179,35 @@ def triton_kernel_moe_forward(
     global_num_experts: int = -1,
     expert_map: torch.Tensor | None = None,
 ) -> torch.Tensor:
-    routing_data, gather_idx, scatter_idx = legacy_routing(
-        gating_output, topk, sm_first=not renormalize
-    )
+    if expert_map is not None:
+        # With expert parallelism, legacy_routing produces routing data
+        # using global expert IDs which don't correspond to local weight
+        # indices.  Split the routing into topk selection + expert_map
+        # remapping + local routing data construction (matching the
+        # approach used by OAITritonExperts.apply).
+        from triton_kernels.topk import topk as topk_fn
+
+        sm_first = not renormalize
+        logits = gating_output
+        if sm_first:
+            logits = torch.softmax(logits, dim=-1)
+        sparse_logits = topk_fn(logits, topk, apply_softmax=not sm_first)
+        # sparse_logits.indx contains global expert IDs – remap to local.
+        topk_ids = expert_map[sparse_logits.indx.to(torch.long)]
+        topk_weights = sparse_logits.vals
+        local_num_experts = w1.size(0)
+        routing_data, gather_idx, scatter_idx = make_routing_data(
+            topk_ids, topk_weights, local_num_experts
+        )
+        # expert_map already applied; pass None downstream.
+        effective_expert_map = None
+        effective_global_num_experts = local_num_experts
+    else:
+        routing_data, gather_idx, scatter_idx = legacy_routing(
+            gating_output, topk, sm_first=not renormalize
+        )
+        effective_expert_map = expert_map
+        effective_global_num_experts = global_num_experts
 
     output = torch.empty_like(hidden_states)
 
@@ -197,8 +223,8 @@ def triton_kernel_moe_forward(
         activation=activation,
         quant_config=quant_config,
         apply_router_weight_on_input=apply_router_weight_on_input,
-        global_num_experts=global_num_experts,
-        expert_map=expert_map,
+        global_num_experts=effective_global_num_experts,
+        expert_map=effective_expert_map,
     )