[MoE Refactor] Mxfp4 oracle rebased (#37128)

Signed-off-by: Yongye Zhu <zyy1102000@gmail.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

docs/design/moe_kernel_features.md

@@ -88,8 +88,8 @@ To be used with a particular `FusedMoEPrepareAndFinalizeModular` subclass, MoE k
 | flashinfer | standard | nvfp4,</br>fp8 | T | <sup>5</sup> | N | Y | [`FlashInferExperts`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe.FlashInferExperts] |
 | gpt oss triton | standard | N/A | N/A | <sup>5</sup> | Y | Y | [`triton_kernel_fused_experts`][vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe.triton_kernel_fused_experts],</br>[`OAITritonExperts`][vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe.OAITritonExperts] |
 | marlin | standard,</br>batched | <sup>3</sup> / N/A | <sup>3</sup> / N/A | silu,</br>swigluoai | Y | Y | [`fused_marlin_moe`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.fused_marlin_moe],</br>[`MarlinExperts`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.MarlinExperts],</br>[`BatchedMarlinExperts`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.BatchedMarlinExperts] |
-| trtllm | standard | mxfp4,</br>nvfp4 | G(16),G(32) | <sup>5</sup> | N | Y | [`TrtLlmGenExperts`][vllm.model_executor.layers.fused_moe.trtllm_moe.TrtLlmGenExperts] |
-| rocm aiter moe | standard | fp8 | G(128),A,T | silu, gelu | Y | N | [`rocm_aiter_fused_experts`][vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe.rocm_aiter_fused_experts] |
+| trtllm | standard | mxfp4,</br>nvfp4 | G(16),G(32) | <sup>5</sup> | N | Y | [`TrtLlmMxfp4ExpertsMonolithic`][vllm.model_executor.layers.fused_moe.experts.trtllm_mxfp4_moe.TrtLlmMxfp4ExpertsMonolithic],</br>[`TrtLlmMxfp4ExpertsModular`][vllm.model_executor.layers.fused_moe.experts.trtllm_mxfp4_moe.TrtLlmMxfp4ExpertsModular],</br>[`TrtLlmNvFp4ExpertsMonolithic`][vllm.model_executor.layers.fused_moe.experts.trtllm_nvfp4_moe.TrtLlmNvFp4ExpertsMonolithic],</br>[`TrtLlmNvfp4ExpertsModular`][vllm.model_executor.layers.fused_moe.experts.trtllm_nvfp4_moe.TrtLlmNvFp4ExpertsModular] |
+| rocm aiter moe | standard | mxfp4,</br>fp8 | G(32),G(128),A,T | silu, gelu,</br>swigluoai | Y | N | `rocm_aiter_fused_experts`,</br>`AiterExperts` |
 | cpu_fused_moe | standard | N/A | N/A | silu | N | N | [`CPUFusedMOE`][vllm.model_executor.layers.fused_moe.cpu_fused_moe.CPUFusedMOE] |
 | naive batched<sup>4</sup> | batched | int8,</br>fp8 | G,A,T | silu, gelu | <sup>6</sup> | Y | [`NaiveBatchedExperts`][vllm.model_executor.layers.fused_moe.fused_batched_moe.NaiveBatchedExperts] |
 

tests/compile/fusions_e2e/conftest.py

@@ -84,7 +84,10 @@ def run_e2e_fusion_test(monkeypatch, caplog_mp_spawn):
 
         # TODO: remove this after finishing migration from envs to model kwargs
         if model_name == "openai/gpt-oss-20b":
-            monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "1")
+            from .common import is_blackwell
+
+            if is_blackwell():
+                monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "1")
 
         # Disable, compile cache to make sure custom passes run.
         # Otherwise, we can't verify fusion happened through the logs.

tests/kernels/moe/test_gpt_oss_triton_kernels.py

@@ -6,6 +6,7 @@ import pytest
 import torch
 import torch.nn.functional as F
 
+from vllm.platforms import current_platform
 from vllm.utils.import_utils import has_triton_kernels
 
 if not has_triton_kernels():
@@ -14,6 +15,7 @@ if not has_triton_kernels():
         allow_module_level=True,
     )
 
+import triton_kernels.matmul_ogs_details.opt_flags as opt_flags
 import triton_kernels.swiglu
 from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig
 from triton_kernels.numerics import InFlexData
@@ -303,6 +305,12 @@ def test_equiv(num_token, a_dtype, w_dtype, tp, workspace_init):
         pc2,
     ) = init_compute_data(M, K, N, E, a_dtype, w_dtype, num_warps=8)
 
+    if current_platform.is_device_capability_family(100):
+        constraints = {
+            "is_persistent": True,
+        }
+        opt_flags.update_opt_flags_constraints(constraints)
+
     if a_dtype == "bf16" and w_dtype == "mx4":
         quant_config = mxfp4_w4a16_moe_quant_config(
             w1_scale=pc1,

tests/kernels/moe/test_ocp_mx_moe.py

@@ -82,7 +82,7 @@ def test_mxfp4_loading_and_execution_moe(vllm_runner, model_case: ModelCase):
         model_case.model_id,
         tensor_parallel_size=model_case.tp,
         load_format="dummy",
-        cudagraph_capture_sizes=[16],
+        compilation_config={"cudagraph_capture_sizes": [16]},
     ) as llm:
         # Disabled as check_model is broken: https://github.com/vllm-project/vllm/pull/18465#issuecomment-3329880562
         # def check_model(model):

tests/kernels/quantization/test_mxfp4_triton_ep.py

@@ -17,89 +17,6 @@ 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

vllm/model_executor/layers/fused_moe/trtllm_moe.py → vllm/model_executor/layers/fused_moe/experts/trtllm_mxfp4_moe.py

@@ -9,80 +9,236 @@ from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEParallelConfig,
     FusedMoEQuantConfig,
+    RoutingMethodType,
 )
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceNoOP,
 )
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
+    kMxfp4Static,
+    kMxfp8Dynamic,
 )
+from vllm.platforms import current_platform
+from vllm.utils.flashinfer import has_flashinfer
 
 
-class TrtLlmGenExperts(mk.FusedMoEExpertsModular):
-    """TensorRT-LLM-based fused MoE expert implementation."""
+class TrtLlmMxfp4ExpertsBase:
+    """
+    MXFP4 TRTLLM-Gen MoE kernels. Shared base for modular and monolithic.
+    """
 
     def __init__(
         self,
         moe_config: FusedMoEConfig,
         quant_config: FusedMoEQuantConfig,
-        max_capture_size,
     ):
-        super().__init__(moe_config, quant_config)
-        self.device = torch.accelerator.current_device_index()
-        self.num_experts = moe_config.num_local_experts
+        # NOTE: FusedMoEExperts.__init__ is called by the concrete subclass
+        # (Monolithic/Modular) via MRO, not here, to avoid mypy issues with
+        # multiple inheritance. This matches the NvFP4 expert pattern.
+        self.moe_config = moe_config
+        self.quant_config = quant_config
+
+        self.routing_method_type = moe_config.routing_method
+        self.topk = moe_config.experts_per_token
+        self.intermediate_size_per_partition = (
+            moe_config.intermediate_size_per_partition
+        )
+        self.hidden_dim = moe_config.hidden_dim
+        self.local_num_experts = moe_config.num_local_experts
+        self.ep_rank = moe_config.moe_parallel_config.ep_rank
+
+        # MXFP4-specific TRTLLM parameters
+        device = torch.accelerator.current_device_index()
         self.gemm1_alpha = torch.tensor(
-            [1.702] * self.num_experts, dtype=torch.float32, device=self.device
+            [1.702] * self.local_num_experts,
+            dtype=torch.float32,
+            device=device,
         )
         self.gemm1_beta = torch.tensor(
-            [1.0] * self.num_experts, dtype=torch.float32, device=self.device
+            [1.0] * self.local_num_experts,
+            dtype=torch.float32,
+            device=device,
         )
         self.gemm1_clamp_limit = torch.tensor(
-            [7.0] * self.num_experts, dtype=torch.float32, device=self.device
+            [7.0] * self.local_num_experts,
+            dtype=torch.float32,
+            device=device,
         )
-        self.max_capture_size = max_capture_size
 
-    @staticmethod
-    def activation_format() -> mk.FusedMoEActivationFormat:
-        return mk.FusedMoEActivationFormat.Standard
+        from vllm.config import get_current_vllm_config
+
+        self.max_capture_size = (
+            get_current_vllm_config().compilation_config.max_cudagraph_capture_size
+        )
+
+        # P1-5 fix: use public quant_dtype property instead of private _a1
+        self.use_mxfp8_input = quant_config.quant_dtype == "mxfp8"
 
     @staticmethod
     def _supports_current_device() -> bool:
-        raise NotImplementedError(
-            "TrtLlmGenExperts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
+        p = current_platform
+        return p.is_cuda() and p.is_device_capability_family(100) and has_flashinfer()
 
     @staticmethod
     def _supports_no_act_and_mul() -> bool:
-        raise NotImplementedError(
-            "TrtLlmGenExperts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
+        return False
 
     @staticmethod
     def _supports_quant_scheme(
         weight_key: QuantKey | None,
         activation_key: QuantKey | None,
     ) -> bool:
-        raise NotImplementedError(
-            "TrtLlmGenExperts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
+        SUPPORTED_W_A = [
+            (kMxfp4Static, None),
+            (kMxfp4Static, kMxfp8Dynamic),
+        ]
+        return (weight_key, activation_key) in SUPPORTED_W_A
 
     @staticmethod
     def _supports_activation(activation: MoEActivation) -> bool:
-        raise NotImplementedError(
-            "TrtLlmGenExperts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
+        return activation == MoEActivation.SWIGLUOAI
 
     @staticmethod
-    def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
-        raise NotImplementedError(
-            "TrtLlmGenExperts is not yet used by an Oracle. "
-            "This method should not be called."
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    def supports_chunking(self) -> bool:
+        return False
+
+    def supports_expert_map(self) -> bool:
+        return False
+
+    @property
+    def expects_unquantized_inputs(self) -> bool:
+        # Expert handles MXFP8 quantization internally if needed
+        return True
+
+
+class TrtLlmMxfp4ExpertsMonolithic(
+    TrtLlmMxfp4ExpertsBase, mk.FusedMoEExpertsMonolithic
+):
+    """
+    Monolithic version of the MXFP4 TRTLLM kernel (router + experts).
+    Wraps flashinfer.trtllm_fp4_block_scale_moe().
+    """
+
+    @staticmethod
+    def _supports_parallel_config(
+        moe_parallel_config: FusedMoEParallelConfig,
+    ) -> bool:
+        return (
+            not moe_parallel_config.use_all2all_kernels
+            and not moe_parallel_config.enable_eplb
+            and moe_parallel_config.dp_size <= 1
         )
 
+    @staticmethod
+    def _supports_routing_method(
+        routing_method: RoutingMethodType,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        return routing_method in [
+            RoutingMethodType.Renormalize,
+            RoutingMethodType.RenormalizeNaive,
+        ]
+
+    @staticmethod
+    def _supports_router_logits_dtype(
+        router_logits_dtype: torch.dtype | None,
+        routing_method: RoutingMethodType,
+    ) -> bool:
+        # Kernel converts to bfloat16 internally
+        return True
+
+    def apply(
+        self,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        router_logits: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        apply_router_weight_on_input: bool,
+        # grouped topk + fused topk bias parameters
+        num_expert_group: int | None = None,
+        e_score_correction_bias: torch.Tensor | None = None,
+        routed_scaling_factor: float | None = None,
+        topk_group: int | None = None,
+    ) -> torch.Tensor:
+        from flashinfer import trtllm_fp4_block_scale_moe
+
+        # Handle input quantization
+        if self.use_mxfp8_input:
+            from flashinfer import mxfp8_quantize
+
+            x_quant, x_scale = mxfp8_quantize(
+                hidden_states,
+                is_sf_swizzled_layout=False,
+                alignment=256,
+            )
+            x_scale = x_scale.view(torch.float8_e4m3fn).reshape(
+                *hidden_states.shape[:-1], -1
+            )
+        else:
+            assert hidden_states.dtype == torch.bfloat16
+            x_quant = hidden_states
+            x_scale = None
+
+        output = torch.empty_like(hidden_states)
+
+        return trtllm_fp4_block_scale_moe(
+            routing_logits=router_logits.to(torch.bfloat16),
+            routing_bias=None,
+            hidden_states=x_quant,
+            hidden_states_scale=x_scale,
+            gemm1_weights=w1,
+            gemm1_weights_scale=self.w1_scale,
+            gemm1_bias=self.w1_bias,
+            gemm1_alpha=self.gemm1_alpha,
+            gemm1_beta=self.gemm1_beta,
+            gemm1_clamp_limit=self.gemm1_clamp_limit,
+            gemm2_weights=w2,
+            gemm2_weights_scale=self.w2_scale,
+            gemm2_bias=self.w2_bias,
+            output1_scale_scalar=None,
+            output1_scale_gate_scalar=None,
+            output2_scale_scalar=None,
+            num_experts=global_num_experts,
+            top_k=self.topk,
+            n_group=None,
+            topk_group=None,
+            intermediate_size=self.intermediate_size_per_partition,
+            local_expert_offset=self.ep_rank * self.local_num_experts,
+            local_num_experts=self.local_num_experts,
+            routed_scaling_factor=None,
+            routing_method_type=self.routing_method_type,
+            do_finalize=True,
+            tune_max_num_tokens=max(self.max_capture_size, 1),
+            output=output,
+        )[0]
+
+
+class TrtLlmMxfp4ExpertsModular(TrtLlmMxfp4ExpertsBase, mk.FusedMoEExpertsModular):
+    """
+    Modular version of the MXFP4 TRTLLM kernel (just the experts).
+    Wraps flashinfer.trtllm_fp4_block_scale_routed_moe().
+    Moved from trtllm_moe.py.
+    """
+
+    @property
+    def expects_unquantized_inputs(self) -> bool:
+        return True
+
+    @staticmethod
+    def _supports_parallel_config(
+        moe_parallel_config: FusedMoEParallelConfig,
+    ) -> bool:
+        return True
+
     def supports_expert_map(self) -> bool:
         return True
 
@@ -129,10 +285,22 @@ class TrtLlmGenExperts(mk.FusedMoEExpertsModular):
         intermediate_size = w2.size(1)
         local_expert_offset = self.moe_config.ep_rank * local_num_experts
 
-        x_quant = hidden_states
-        x_scale = a1q_scale
-        if x_scale is not None:
-            x_scale = x_scale.view(torch.float8_e4m3fn).reshape(*x_quant.shape[:-1], -1)
+        # Handle input quantization
+        if self.use_mxfp8_input:
+            from flashinfer import mxfp8_quantize
+
+            x_quant, x_scale = mxfp8_quantize(
+                hidden_states,
+                is_sf_swizzled_layout=False,
+                alignment=256,
+            )
+            x_scale = x_scale.view(torch.float8_e4m3fn).reshape(
+                *hidden_states.shape[:-1], -1
+            )
+        else:
+            assert hidden_states.dtype == torch.bfloat16
+            x_quant = hidden_states
+            x_scale = None
 
         packed_tensor = (topk_ids.to(torch.int32) << 16) | topk_weights.to(
             torch.bfloat16
@@ -165,7 +333,7 @@ class TrtLlmGenExperts(mk.FusedMoEExpertsModular):
             "local_expert_offset": local_expert_offset,
             "local_num_experts": local_num_experts,
             "routed_scaling_factor": None,
-            "routing_method_type": 1,
+            "routing_method_type": self.routing_method_type,
             "do_finalize": True,
             "output": output,
             "tune_max_num_tokens": max(self.max_capture_size, 1),

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -40,6 +40,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     kFp8Static128BlockSym,
     kFp8StaticChannelSym,
     kFp8StaticTensorSym,
+    kMxfp4Static,
     kNvfp4Static,
 )
 from vllm.platforms import current_platform
@@ -574,12 +575,13 @@ class MarlinExpertsBase(mk.FusedMoEExpertsModular):
         weight_key: QuantKey | None,
         activation_key: QuantKey | None,
     ) -> bool:
-        # TODO(rob): add int4, mxfp4, int8 as integrations
+        # TODO(rob): add int4, int8 as integrations
         # are migrated to use the oracle one-by-one.
         SUPPORTED_W = [
             kFp8Static128BlockSym,
             kFp8StaticChannelSym,
             kFp8StaticTensorSym,
+            kMxfp4Static,
             kNvfp4Static,
         ]
         return weight_key in SUPPORTED_W

vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py

@@ -11,8 +11,10 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
     FUSED_MOE_UNQUANTIZED_CONFIG,
+    FusedMoEConfig,
     FusedMoEParallelConfig,
     FusedMoEQuantConfig,
+    RoutingMethodType,
 )
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceNoOP,
@@ -20,6 +22,7 @@ from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
 from vllm.model_executor.layers.fused_moe.utils import _resize_cache
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
+    kMxfp4Static,
 )
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
@@ -537,43 +540,43 @@ def make_routing_data(
 
 
 class BaseOAITritonExperts(mk.FusedMoEExpertsModular):
+    @property
+    def expects_unquantized_inputs(self) -> bool:
+        return True
+
     @staticmethod
     def _supports_current_device() -> bool:
-        raise NotImplementedError(
-            "OAITritonExperts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
+        p = current_platform
+        if not p.is_cuda_alike():
+            return False
+        cap = p.get_device_capability()
+        if cap is None:
+            return False
+        # (9,0) <= cap < (11,0) covers CUDA SM90 (Hopper), SM100+ (Blackwell)
+        # and ROCm gfx942/gfx950 (which map to 9.4/9.5).
+        return (9, 0) <= (cap.major, cap.minor) < (11, 0)
 
     @staticmethod
     def _supports_no_act_and_mul() -> bool:
-        raise NotImplementedError(
-            "OAITritonExperts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
+        return False
 
     @staticmethod
     def _supports_quant_scheme(
         weight_key: QuantKey | None,
         activation_key: QuantKey | None,
     ) -> bool:
-        raise NotImplementedError(
-            "OAITritonExperts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
+        SUPPORTED_W_A = [
+            (kMxfp4Static, None),
+        ]
+        return (weight_key, activation_key) in SUPPORTED_W_A
 
     @staticmethod
     def _supports_activation(activation: MoEActivation) -> bool:
-        raise NotImplementedError(
-            "OAITritonExperts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
+        raise NotImplementedError
 
     @staticmethod
     def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
-        raise NotImplementedError(
-            "OAITritonExperts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
+        return True
 
     def supports_expert_map(self) -> bool:
         return True
@@ -630,6 +633,10 @@ class BaseOAITritonExperts(mk.FusedMoEExpertsModular):
 class OAITritonExperts(BaseOAITritonExperts):
     """OAI Triton-based fused MoE expert implementation."""
 
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        return activation == MoEActivation.SWIGLUOAI
+
     @staticmethod
     def activation_format() -> mk.FusedMoEActivationFormat:
         return mk.FusedMoEActivationFormat.Standard
@@ -714,6 +721,15 @@ class UnfusedOAITritonExperts(BaseOAITritonExperts):
     One use case for it is to inject LoRA modules on the activation and moe_sum.
     """
 
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        return activation in [
+            MoEActivation.SILU,
+            MoEActivation.GELU,
+            MoEActivation.SWIGLUOAI,
+            MoEActivation.SWIGLUSTEP,
+        ]
+
     @staticmethod
     def activation_format() -> mk.FusedMoEActivationFormat:
         return mk.FusedMoEActivationFormat.Standard
@@ -839,3 +855,118 @@ class UnfusedOAITritonExperts(BaseOAITritonExperts):
         )
 
         self.moe_sum(intermediate_cache3.view(-1, topk, K), output)
+
+
+class OAITritonMxfp4ExpertsMonolithic(mk.FusedMoEExpertsMonolithic):
+    """Monolithic Triton MXFP4 expert. Wraps triton_kernel_moe_forward()."""
+
+    def __init__(
+        self,
+        moe_config: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+    ):
+        super().__init__(moe_config, quant_config)
+        self.topk = moe_config.experts_per_token
+        self.renormalize = moe_config.routing_method in (
+            RoutingMethodType.Renormalize,
+            RoutingMethodType.RenormalizeNaive,
+        )
+
+    @staticmethod
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    @staticmethod
+    def _supports_current_device() -> bool:
+        p = current_platform
+        if not p.is_cuda_alike():
+            return False
+        cap = p.get_device_capability()
+        if cap is None:
+            return False
+        # (9,0) <= cap < (11,0) covers CUDA SM90 (Hopper), SM100+ (Blackwell)
+        # and ROCm gfx942/gfx950 (which map to 9.4/9.5).
+        return (9, 0) <= (cap.major, cap.minor) < (11, 0)
+
+    @staticmethod
+    def _supports_no_act_and_mul() -> bool:
+        return False
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        SUPPORTED_W_A = [
+            (kMxfp4Static, None),
+        ]
+        return (weight_key, activation_key) in SUPPORTED_W_A
+
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        return activation == MoEActivation.SWIGLUOAI
+
+    @staticmethod
+    def _supports_parallel_config(
+        moe_parallel_config: FusedMoEParallelConfig,
+    ) -> bool:
+        return (
+            not moe_parallel_config.use_all2all_kernels
+            and not moe_parallel_config.enable_eplb
+            and moe_parallel_config.dp_size <= 1
+        )
+
+    @staticmethod
+    def _supports_routing_method(
+        routing_method: RoutingMethodType,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        return routing_method in [
+            RoutingMethodType.Renormalize,
+            RoutingMethodType.RenormalizeNaive,
+        ]
+
+    @staticmethod
+    def _supports_router_logits_dtype(
+        router_logits_dtype: torch.dtype | None,
+        routing_method: RoutingMethodType,
+    ) -> bool:
+        return True
+
+    def supports_expert_map(self) -> bool:
+        return True
+
+    @property
+    def expects_unquantized_inputs(self) -> bool:
+        return True
+
+    def apply(
+        self,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        router_logits: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        apply_router_weight_on_input: bool,
+        # grouped topk + fused topk bias parameters
+        num_expert_group: int | None = None,
+        e_score_correction_bias: torch.Tensor | None = None,
+        routed_scaling_factor: float | None = None,
+        topk_group: int | None = None,
+    ) -> torch.Tensor:
+        return triton_kernel_moe_forward(
+            hidden_states=hidden_states,
+            w1=w1,
+            w2=w2,
+            gating_output=router_logits,
+            topk=self.topk,
+            renormalize=self.renormalize,
+            global_num_experts=global_num_experts,
+            expert_map=expert_map,
+            quant_config=self.quant_config,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+        )

vllm/model_executor/layers/fused_moe/layer.py

@@ -52,7 +52,6 @@ from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig,
 )
 from vllm.platforms import current_platform
-from vllm.utils.math_utils import round_up
 
 logger = init_logger(__name__)
 
@@ -218,7 +217,6 @@ def maybe_roundup_hidden_size(
     moe_parallel_config: FusedMoEParallelConfig,
     is_lora_enabled: bool,
     model_type: str | None,
-    is_mxfp4_quant: bool,
 ) -> int:
     """
     Given layer hidden size and MoE configurations, round up hidden_size
@@ -232,7 +230,6 @@ def maybe_roundup_hidden_size(
             is used in the case of mxfp4 quantization in selecting the
             MxFP4Backend.
         model_type: for checking if gpt-oss
-        is_mxfp4_quant: whether the layer is quantized with mxfp4
 
     Return:
         Rounded up hidden_size if rounding up is required based on the configs.
@@ -246,28 +243,6 @@ def maybe_roundup_hidden_size(
         hidden_size, act_dtype, moe_parallel_config
     )
 
-    # we are padding globally so EP buffer allocation works
-    if model_type == "gpt_oss" and is_mxfp4_quant:
-        from vllm.model_executor.layers.quantization.mxfp4 import (
-            Mxfp4Backend,
-            get_mxfp4_backend,
-        )
-
-        current_mxfp4_backend = get_mxfp4_backend(is_lora_enabled)
-
-        if (
-            current_mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16
-            or current_mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS
-        ):
-            hidden_size = round_up(hidden_size, 128)
-        elif (
-            current_platform.is_rocm()
-            or current_mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
-            or current_mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_BF16
-            or current_mxfp4_backend == Mxfp4Backend.MARLIN
-        ):
-            hidden_size = round_up(hidden_size, 256)
-
     return hidden_size
 
 
@@ -540,9 +515,6 @@ class FusedMoE(CustomOp):
             moe_parallel_config=self.moe_parallel_config,
             is_lora_enabled=vllm_config.lora_config is not None,
             model_type=self.model_type,
-            is_mxfp4_quant=(
-                quant_config is not None and quant_config.is_mxfp4_quant(prefix, self)
-            ),
         )
         self.hidden_size = hidden_size
 

vllm/model_executor/layers/fused_moe/oracle/nvfp4.py

@@ -14,7 +14,6 @@ from vllm.model_executor.layers.fused_moe.all2all_utils import (
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEQuantConfig,
-    mxfp4_w4a16_moe_quant_config,
     nvfp4_moe_quant_config,
     nvfp4_w4a16_moe_quant_config,
 )
@@ -347,16 +346,6 @@ def convert_to_nvfp4_moe_kernel_format(
     )
 
 
-def make_mxfp4_moe_quant_config(
-    w13_scale: torch.Tensor,
-    w2_scale: torch.Tensor,
-) -> FusedMoEQuantConfig:
-    return mxfp4_w4a16_moe_quant_config(
-        w1_scale=w13_scale,
-        w2_scale=w2_scale,
-    )
-
-
 def make_nvfp4_moe_quant_config(
     backend: NvFp4MoeBackend,
     w13_scale: torch.Tensor,

vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py

@@ -24,6 +24,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     kFp8Static128BlockSym,
     kFp8StaticChannelSym,
     kFp8StaticTensorSym,
+    kMxfp4Static,
 )
 
 
@@ -201,6 +202,8 @@ def rocm_aiter_fused_experts(
         activation_method = ActivationMethod.SILU
     elif activation == MoEActivation.GELU:
         activation_method = ActivationMethod.GELU
+    elif activation == MoEActivation.SWIGLUOAI:
+        activation_method = rocm_aiter_ops.get_aiter_activation_type("swiglu")
     else:
         raise ValueError(f"Unsupported activation: {activation}")
 
@@ -247,8 +250,8 @@ def rocm_aiter_fused_experts(
 
     else:
         quant_method = QuantMethod.NO.value
-        # quark moe for mxfp4 w_dtype mxfp4 a_dtype
-        if quant_config.use_mxfp4_w4a4:
+        # mxfp4: both w4a4 (quark) and w4a16 (oracle CK) use BLOCK_1X32
+        if quant_config.use_mxfp4_w4a4 or quant_config.use_mxfp4_w4a16:
             quant_method = QuantMethod.BLOCK_1X32.value
         # w8a8 block-scaled
         if quant_config.block_shape is not None and quant_config.use_fp8_w8a8:
@@ -289,6 +292,8 @@ def rocm_aiter_fused_experts(
             doweight_stage1=apply_router_weight_on_input,
             num_local_tokens=num_local_tokens,
             output_dtype=output_dtype,
+            bias1=quant_config.w1_bias if quant_config.use_mxfp4_w4a16 else None,
+            bias2=quant_config.w2_bias if quant_config.use_mxfp4_w4a16 else None,
         )
 
 
@@ -319,21 +324,23 @@ class AiterExperts(mk.FusedMoEExpertsModular):
         weight_key: QuantKey | None,
         activation_key: QuantKey | None,
     ) -> bool:
-        # TODO(rob): AITER also supports MXFP4, which is not
-        # yet supported via an Oracle. Once it is, we will add
-        # MXFP4 to this list.
         SUPPORTED_W_A = [
             (None, None),
             (kFp8Static128BlockSym, kFp8Dynamic128Sym),
             (kFp8StaticTensorSym, kFp8StaticTensorSym),
             (kFp8StaticTensorSym, kFp8DynamicTensorSym),
             (kFp8StaticChannelSym, kFp8DynamicTokenSym),
+            (kMxfp4Static, None),
         ]
         return (weight_key, activation_key) in SUPPORTED_W_A
 
     @staticmethod
     def _supports_activation(activation: MoEActivation) -> bool:
-        return activation in [MoEActivation.SILU, MoEActivation.GELU]
+        return activation in [
+            MoEActivation.SILU,
+            MoEActivation.GELU,
+            MoEActivation.SWIGLUOAI,
+        ]
 
     @staticmethod
     def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -45,11 +45,14 @@ from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
     make_fp8_moe_quant_config,
     select_fp8_moe_backend,
 )
+from vllm.model_executor.layers.fused_moe.oracle.mxfp4 import (
+    Mxfp4MoeBackend,
+    make_mxfp4_moe_kernel,
+    make_mxfp4_moe_quant_config,
+)
 from vllm.model_executor.layers.fused_moe.oracle.nvfp4 import (
-    NvFp4MoeBackend,
     convert_to_nvfp4_moe_kernel_format,
     is_global_sf_supported_for_nvfp4_backend,
-    make_mxfp4_moe_quant_config,
     make_nvfp4_moe_kernel,
     make_nvfp4_moe_quant_config,
     select_nvfp4_moe_backend,
@@ -235,7 +238,7 @@ class CompressedTensorsW4A4Mxfp4MoEMethod(CompressedTensorsMoEMethod):
     def __init__(self, moe):
         super().__init__(moe)
         self.group_size = 32
-        self.mxfp4_backend = NvFp4MoeBackend.MARLIN
+        self.mxfp4_backend = Mxfp4MoeBackend.MARLIN
         self.experts_cls = MarlinExperts
 
     def create_weights(
@@ -310,7 +313,9 @@ class CompressedTensorsW4A4Mxfp4MoEMethod(CompressedTensorsMoEMethod):
         self, layer: torch.nn.Module
     ) -> FusedMoEQuantConfig | None:
         return make_mxfp4_moe_quant_config(
-            w13_scale=layer.w13_weight_scale, w2_scale=layer.w2_weight_scale
+            mxfp4_backend=self.mxfp4_backend,
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
         )
 
     def process_weights_after_loading(self, layer: FusedMoE) -> None:
@@ -334,10 +339,11 @@ class CompressedTensorsW4A4Mxfp4MoEMethod(CompressedTensorsMoEMethod):
 
         self.moe_quant_config = self.get_fused_moe_quant_config(layer)
         if self.moe_quant_config is not None:
-            self.moe_kernel = make_nvfp4_moe_kernel(
+            self.moe_kernel = make_mxfp4_moe_kernel(
                 moe_quant_config=self.moe_quant_config,
                 moe_config=self.moe,
                 experts_cls=self.experts_cls,
+                mxfp4_backend=self.mxfp4_backend,
                 shared_experts=layer.shared_experts,
                 routing_tables=layer._maybe_init_expert_routing_tables(),
             )

vllm/model_executor/layers/quantization/quark/quark_moe.py

@@ -25,9 +25,9 @@ from vllm.model_executor.layers.fused_moe.config import (
     ocp_mx_moe_quant_config,
 )
 from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe
-from vllm.model_executor.layers.quantization.mxfp4 import (
-    Mxfp4Backend,
-    get_mxfp4_backend,
+from vllm.model_executor.layers.fused_moe.oracle.mxfp4 import (
+    Mxfp4MoeBackend,
+    select_mxfp4_moe_backend,
 )
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     prepare_fp8_moe_layer_for_marlin,
@@ -699,9 +699,9 @@ class QuarkOCP_MX_MoEMethod(QuarkMoEMethod):
                 f"Please check that the combination is supported in OCP_MX_Scheme."
             )
 
-        self.mxfp4_backend: Mxfp4Backend | None = None
+        self.mxfp4_backend: Mxfp4MoeBackend | None = None
         if self.ocp_mx_scheme == "w_mxfp4":
-            self.mxfp4_backend = get_mxfp4_backend(moe.is_lora_enabled)
+            self.mxfp4_backend, _ = select_mxfp4_moe_backend(moe)
 
         if self.input_quant is not None:
             self.static_input_scales = not self.input_quant.get("is_dynamic")

vllm/model_executor/layers/quantization/utils/marlin_utils_fp4.py

@@ -389,9 +389,9 @@ def prepare_moe_fp4_layer_for_marlin(
 
     group_size = 16 if is_nvfp4 else 32
 
-    e = layer.num_experts
-    k = layer.hidden_size
-    n = layer.intermediate_size_per_partition
+    e = layer.moe_config.num_experts
+    k = layer.moe_config.hidden_dim
+    n = layer.moe_config.intermediate_size_per_partition
 
     # WORKSPACE
     device = layer.w13_weight.device
@@ -500,6 +500,120 @@ def prepare_moe_fp4_layer_for_marlin(
         setattr(layer, name, bias)
 
 
+def prepare_moe_mxfp4_layer_for_marlin(
+    layer: torch.nn.Module,
+    w13: torch.Tensor,
+    w2: torch.Tensor,
+    w13_scale: torch.Tensor,
+    w2_scale: torch.Tensor,
+    w13_bias: torch.Tensor | None,
+    w2_bias: torch.Tensor | None,
+) -> tuple[
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor | None,
+    torch.Tensor | None,
+]:
+    """Pure-function version of prepare_moe_fp4_layer_for_marlin for MXFP4.
+
+    Takes weight tensors as inputs and returns transformed tensors.
+    Does NOT modify the layer in-place.
+    """
+    input_dtype = get_marlin_input_dtype()
+    if (
+        input_dtype is not None
+        and input_dtype.itemsize == 1
+        and input_dtype != torch.float8_e4m3fn
+    ):
+        raise RuntimeError("MXFP4 weight + INT8 activation is not supported.")
+
+    group_size = 32  # MXFP4 block size
+
+    # Derive dimensions from actual weight shapes to handle rounded/padded
+    # sizes correctly (e.g., Mxfp4MoEMethod rounds up hidden_dim).
+    # w13 shape: (E, 2*N, K//2)
+    e = w13.shape[0]
+    n = w13.shape[1] // 2  # intermediate_size_per_partition
+    k = w13.shape[2] * 2  # hidden_size
+
+    device = w13.device
+    param_dtype = layer.params_dtype
+    is_a_8bit = input_dtype is not None and input_dtype.itemsize == 1
+    perm = torch.empty(0, dtype=torch.int, device=device)
+
+    # WEIGHT: Repack weights to marlin format
+    def repack_weight(weight: torch.Tensor, name: str) -> torch.Tensor:
+        tensor_list = []
+        if "w13" in name:
+            size_n, size_k = n * 2, k
+        else:
+            size_n, size_k = k, n
+
+        assert weight.shape == (e, size_n, size_k // 2)
+
+        for i in range(e):
+            qweight = weight[i].view(torch.int32).T.contiguous()
+            marlin_qweight = ops.gptq_marlin_repack(
+                b_q_weight=qweight,
+                perm=perm,
+                size_k=size_k,
+                size_n=size_n,
+                num_bits=4,
+                is_a_8bit=is_a_8bit,
+            )
+            tensor_list.append(marlin_qweight)
+        return torch.cat([x.unsqueeze(0) for x in tensor_list], 0)
+
+    w13 = repack_weight(w13, "w13")
+    w2 = repack_weight(w2, "w2")
+
+    # WEIGHT SCALES: Permute scales
+    def permute_scales(scales: torch.Tensor, name: str) -> torch.Tensor:
+        scales = scales.view(torch.float8_e8m0fnu)
+        scales = scales.to(param_dtype)
+
+        tensor_list = []
+        if "w13" in name:
+            size_n, size_k = n * 2, k
+        else:
+            size_n, size_k = k, n
+
+        for i in range(e):
+            scale = scales[i].T
+            marlin_scales = marlin_permute_scales(
+                s=scale,
+                size_k=size_k,
+                size_n=size_n,
+                group_size=group_size,
+                is_a_8bit=is_a_8bit,
+            )
+            marlin_scales = mxfp4_marlin_process_scales(
+                marlin_scales, input_dtype=input_dtype
+            )
+            tensor_list.append(marlin_scales)
+        return torch.cat([x.unsqueeze(0) for x in tensor_list], 0)
+
+    w13_scale = permute_scales(w13_scale, "w13")
+    w2_scale = permute_scales(w2_scale, "w2")
+
+    # BIAS: Permute bias
+    def permute_bias(bias: torch.Tensor | None) -> torch.Tensor | None:
+        if bias is None:
+            return None
+        bias = bias.to(param_dtype)
+        tensor_list = []
+        for i in range(e):
+            tensor_list.append(marlin_permute_bias(bias[i]))
+        return torch.cat([x.unsqueeze(0) for x in tensor_list], 0)
+
+    w13_bias = permute_bias(w13_bias)
+    w2_bias = permute_bias(w2_bias)
+
+    return w13, w2, w13_scale, w2_scale, w13_bias, w2_bias
+
+
 def rand_marlin_weight_nvfp4_like(weight, group_size, input_dtype=None):
     is_a_8bit = input_dtype is not None and input_dtype.itemsize == 1
 

vllm/model_executor/layers/quantization/utils/mxfp4_utils.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from collections.abc import Callable
 from typing import Any
 
 import torch
 
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
 from vllm.utils.import_utils import has_triton_kernels
@@ -22,7 +20,7 @@ logger = init_logger(__name__)
 CK_MXFP4_MOE_DIM_ALIGNMENT = 256
 
 
-def _swizzle_mxfp4(quant_tensor, scale, num_warps):
+def _swizzle_mxfp4(quant_tensor, scale, num_warps=8):
     """weight swizzle for mxfp4 moe, used for OAI mxfp4 kernel"""
     assert has_triton_kernels()
     import triton_kernels.matmul_ogs_details.opt_flags as opt_flags
@@ -87,35 +85,6 @@ def _swizzle_mxfp4(quant_tensor, scale, num_warps):
     return quant_tensor, InFlexData(), scale
 
 
-def _can_support_mxfp4(
-    use_grouped_topk: bool = False,
-    topk_group: int | None = None,
-    num_expert_group: int | None = None,
-    expert_map: torch.Tensor | None = None,
-    custom_routing_function: Callable | None = None,
-    e_score_correction_bias: torch.Tensor | None = None,
-    apply_router_weight_on_input: bool = False,
-    scoring_func: str = "softmax",
-    activation: MoEActivation = MoEActivation.SWIGLUOAI,
-    expert_load_view: torch.Tensor | None = None,
-    logical_to_physical_map: torch.Tensor | None = None,
-    logical_replica_count: torch.Tensor | None = None,
-):
-    return not (
-        use_grouped_topk
-        or topk_group
-        or num_expert_group
-        or custom_routing_function
-        or e_score_correction_bias
-        or apply_router_weight_on_input
-        or scoring_func != "softmax"
-        or activation != MoEActivation.SWIGLUOAI
-        or expert_load_view
-        or logical_to_physical_map
-        or logical_replica_count
-    )
-
-
 def get_padding_alignment():
     return (
         256