[MoE Refactor] Convert mxfp4 moe quant method into oracle

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -40,6 +40,7 @@
     kFp8Static128BlockSym,
     kFp8StaticChannelSym,
     kFp8StaticTensorSym,
+    kMxfp4Static,
     kNvfp4Static,
 )
 from vllm.platforms import current_platform
@@ -581,6 +582,7 @@ def _supports_quant_scheme(
             kFp8StaticChannelSym,
             kFp8StaticTensorSym,
             kNvfp4Static,
+            kMxfp4Static,
         ]
         return weight_key in SUPPORTED_W
 

vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py

@@ -19,6 +19,7 @@
 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
@@ -386,41 +387,32 @@ def make_routing_data(
 class BaseOAITritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
     @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
+        return p.is_cuda_alike() and (
+            p.is_device_capability(90) or p.is_device_capability_family(100)
         )
 
     @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
@@ -477,6 +469,10 @@ def _make_routing_data(
 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
@@ -561,6 +557,15 @@ 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

vllm/model_executor/layers/fused_moe/layer.py

@@ -52,7 +52,6 @@
     QuantizationConfig,
 )
 from vllm.platforms import current_platform
-from vllm.utils.math_utils import round_up
 
 logger = init_logger(__name__)
 
@@ -245,28 +244,6 @@ 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