[Quantization] [Refactor] Create special "GptOssMxfp4MoeMethod"

docs/design/moe_kernel_features.md

@@ -59,7 +59,7 @@ Modular kernels are supported by the following `FusedMoEMethodBase` classes.
 - [`Fp8MoEMethod`][vllm.model_executor.layers.quantization.fp8.Fp8MoEMethod]
 - [`CompressedTensorsW4A4Nvfp4MoEMethod`][vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.compressed_tensors_moe_w4a4_nvfp4.CompressedTensorsW4A4Nvfp4MoEMethod]
 - [`CompressedTensorsW8A8Fp8MoEMethod`][vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.compressed_tensors_moe_w8a8_fp8.CompressedTensorsW8A8Fp8MoEMethod]
-- [`Mxfp4MoEMethod`][vllm.model_executor.layers.quantization.mxfp4.Mxfp4MoEMethod]
+- [`GptOssMxfp4MoEMethod`][vllm.model_executor.layers.quantization.mxfp4.GptOssMxfp4MoEMethod]
 - [`UnquantizedFusedMoEMethod`][vllm.model_executor.layers.fused_moe.layer.UnquantizedFusedMoEMethod]
 
 ## Fused Experts Kernels

vllm/config/model.py

@@ -951,6 +951,7 @@ def _verify_quantization(self) -> None:
                 # Ensure heavy backends are probed last to avoid unnecessary
                 # imports during override detection (e.g., MXFP4 imports Triton)
                 "mxfp4",
+                "gpt_oss_mxfp4",
                 "cpu_awq",
                 "gguf",
             ]
@@ -966,7 +967,7 @@ def _verify_quantization(self) -> None:
             for name in quantization_methods:
                 method = me_quant.get_quantization_config(name)
                 quantization_override = method.override_quantization_method(
-                    quant_cfg, self.quantization
+                    quant_cfg, self.quantization, hf_config=self.hf_config
                 )
                 if quantization_override is not None:
                     # Raise error if the override is not custom (custom would

vllm/model_executor/layers/fused_moe/layer.py

@@ -1063,7 +1063,7 @@ def weight_loader(
         expert_id: int,
         return_success: bool = False,
     ) -> bool | None:
-        if self.quant_config and self.quant_config.get_name() == "mxfp4":
+        if self.quant_config and self.quant_config.get_name() == "gpt_oss_mxfp4":
             # (FIXME) for gpt-oss all experts are combined
             if "bias" in weight_name:
                 dim1 = loaded_weight.shape[1]

vllm/model_executor/layers/fused_moe/oracle/mxfp4.py

@@ -194,7 +194,7 @@ def _backend_activation_key(backend: Mxfp4MoeBackend) -> QuantKey | None:
     return None
 
 
-def select_mxfp4_moe_backend(
+def select_gpt_oss_mxfp4_moe_backend(
     config: FusedMoEConfig,
 ) -> tuple[Mxfp4MoeBackend, type[mk.FusedMoEExperts] | None]:
     """
@@ -400,7 +400,7 @@ def mxfp4_round_up_hidden_size_and_intermediate_size(
     return hidden_size, intermediate_size
 
 
-def convert_to_mxfp4_moe_kernel_format(
+def convert_gpt_oss_weight_to_mxfp4_moe_kernel_format(
     mxfp4_backend: Mxfp4MoeBackend,
     layer: torch.nn.Module,
     w13_weight: torch.Tensor,
@@ -426,7 +426,10 @@ def convert_to_mxfp4_moe_kernel_format(
 
     sf_block_size = 32  # mxfp4 block size
 
-    if mxfp4_backend in (Mxfp4MoeBackend.MARLIN, Mxfp4MoeBackend.BATCHED_MARLIN):
+    if mxfp4_backend in (
+        Mxfp4MoeBackend.MARLIN,
+        Mxfp4MoeBackend.BATCHED_MARLIN,
+    ):
         from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
             prepare_moe_mxfp4_layer_for_marlin,
         )

vllm/model_executor/layers/quantization/__init__.py

@@ -30,6 +30,7 @@
     "torchao",
     "inc",
     "mxfp4",
+    "gpt_oss_mxfp4",
     "mxfp8",
     "cpu_awq",
     "online",
@@ -133,7 +134,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
         ModelOptNvFp4Config,
     )
     from .moe_wna16 import MoeWNA16Config
-    from .mxfp4 import Mxfp4Config
+    from .mxfp4 import GptOssMxfp4Config, Mxfp4Config
     from .mxfp8 import Mxfp8Config
     from .online.base import OnlineQuantizationConfig
     from .torchao import TorchAOConfig
@@ -160,6 +161,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
         "auto-round": INCConfig,
         "inc": INCConfig,
         "mxfp4": Mxfp4Config,
+        "gpt_oss_mxfp4": GptOssMxfp4Config,
         "mxfp8": Mxfp8Config,
         "cpu_awq": CPUAWQConfig,
         "online": OnlineQuantizationConfig,

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -232,7 +232,7 @@ def from_config(cls, config: dict[str, Any]) -> "AWQMarlinConfig":
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg, user_quant
+        cls, hf_quant_cfg, user_quant, hf_config=None
     ) -> "QuantizationMethods | None":
         # Skip override to marlin kernels, as they are not
         # batch invariant

vllm/model_executor/layers/quantization/base_config.py

@@ -110,13 +110,22 @@ def from_config(cls, config: dict[str, Any]) -> "QuantizationConfig":
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg, user_quant
+        cls,
+        hf_quant_cfg: dict[str, Any],
+        user_quant: str | None,
+        hf_config: Any = None,
     ) -> QuantizationMethods | None:
         """
         Detects if this quantization method can support a given checkpoint
         format by overriding the user specified quantization method --
         this method should only be overwritten by subclasses in exceptional
-        circumstances
+        circumstances.
+
+        Args:
+            hf_quant_cfg: The checkpoint's quantization config dict.
+            user_quant: The user-specified quantization method string.
+            hf_config: The HuggingFace model config object (e.g. for
+                model_type checks). May be None if not available.
         """
         return None
 

vllm/model_executor/layers/quantization/cpu_wna16.py

@@ -104,7 +104,7 @@ def from_config(cls, config: dict[str, Any]) -> "CPUAWQConfig":
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg, user_quant
+        cls, hf_quant_cfg, user_quant, hf_config=None
     ) -> "QuantizationMethods | None":
         quant_method = hf_quant_cfg.get("quant_method", "").lower()
         if current_platform.is_cpu() and (quant_method == "awq"):

vllm/model_executor/layers/quantization/gguf.py

@@ -84,7 +84,7 @@ def from_config(cls, config: dict[str, Any]) -> "GGUFConfig":
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg: dict[str, Any], user_quant: str | None
+        cls, hf_quant_cfg: dict[str, Any], user_quant: str | None, hf_config=None
     ) -> "QuantizationMethods | None":
         # When user explicitly specifies --quantization gguf, override
         # whatever quantization method is in the HF model config (e.g. fp8).

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -214,7 +214,7 @@ def from_config(cls, config: dict[str, Any]) -> "GPTQMarlinConfig":
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg, user_quant
+        cls, hf_quant_cfg, user_quant, hf_config=None
     ) -> QuantizationMethods | None:
         can_convert = cls.is_gptq_marlin_compatible(hf_quant_cfg)
 

vllm/model_executor/layers/quantization/inc.py

@@ -453,7 +453,7 @@ def get_quant_method(self, layer: torch.nn.Module, prefix: str):
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg, user_quant
+        cls, hf_quant_cfg, user_quant, hf_config=None
     ) -> "QuantizationMethods | None":
         """Override the `auto-round` method to `inc`."""
         is_auto_round_format = hf_quant_cfg.get("quant_method", None) == "auto-round"

vllm/model_executor/layers/quantization/modelopt.py

@@ -406,7 +406,7 @@ def get_min_capability(cls) -> int:
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg, user_quant
+        cls, hf_quant_cfg, user_quant, hf_config=None
     ) -> QuantizationMethods | None:
         algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
         if algo is not None and algo == "FP8":
@@ -1028,7 +1028,7 @@ def get_min_capability(cls) -> int:
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg, user_quant
+        cls, hf_quant_cfg, user_quant, hf_config=None
     ) -> QuantizationMethods | None:
         algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
         if algo is not None and ("NVFP4" in algo or "FP4" in algo):
@@ -1525,7 +1525,7 @@ def get_min_capability(cls) -> int:
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg, user_quant
+        cls, hf_quant_cfg, user_quant, hf_config=None
     ) -> QuantizationMethods | None:
         algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
         if algo is not None and "MXFP8" in algo:
@@ -2052,7 +2052,7 @@ def get_min_capability(cls) -> int:
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg, user_quant
+        cls, hf_quant_cfg, user_quant, hf_config=None
     ) -> QuantizationMethods | None:
         algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
         if algo is not None and algo == "MIXED_PRECISION":

vllm/model_executor/layers/quantization/moe_wna16.py

@@ -130,7 +130,7 @@ def from_config(cls, config: dict[str, Any]) -> "MoeWNA16Config":
 
     @classmethod
     def override_quantization_method(
-        cls, hf_quant_cfg, user_quant
+        cls, hf_quant_cfg, user_quant, hf_config=None
     ) -> QuantizationMethods | None:
         can_convert = cls.is_moe_wna16_compatible(hf_quant_cfg)
         if can_convert and user_quant == "moe_wna16":

vllm/model_executor/layers/quantization/mxfp4.py

@@ -19,11 +19,11 @@
 from vllm.model_executor.layers.fused_moe.oracle.mxfp4 import (
     TRITON_BACKENDS,
     Mxfp4MoeBackend,
-    convert_to_mxfp4_moe_kernel_format,
+    convert_gpt_oss_weight_to_mxfp4_moe_kernel_format,
     make_mxfp4_moe_kernel,
     make_mxfp4_moe_quant_config,
     mxfp4_round_up_hidden_size_and_intermediate_size,
-    select_mxfp4_moe_backend,
+    select_gpt_oss_mxfp4_moe_backend,
 )
 from vllm.model_executor.layers.linear import LinearBase, UnquantizedLinearMethod
 from vllm.model_executor.layers.quantization import QuantizationMethods
@@ -38,6 +38,12 @@
 
 
 class Mxfp4Config(QuantizationConfig):
+    """Canonical base config for MXFP4 quantization.
+
+    Subclasses override get_name() and override_quantization_method() to
+    register themselves as the handler for a specific checkpoint format.
+    """
+
     def __init__(self, ignored_layers: list[str] | None = None):
         super().__init__()
         self.ignored_layers = ignored_layers
@@ -62,6 +68,8 @@ def get_supported_act_dtypes(cls) -> list[torch.dtype]:
     def get_config_filenames(cls) -> list[str]:
         return []
 
+    # TODO (zyongye) This is only temporaty fallback.
+    # We should have `Mxfp4MoEMethod` after this migration is complete.
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
     ) -> "QuantizeMethodBase | None":
@@ -79,7 +87,7 @@ def get_quant_method(
             )
             return UnquantizedLinearMethod()
         elif isinstance(layer, FusedMoE):
-            return Mxfp4MoEMethod(layer.moe_config)
+            return GptOssMxfp4MoEMethod(layer.moe_config)
         elif isinstance(layer, Attention):
             logger.debug_once(
                 "MXFP4 attention layer is not implemented. "
@@ -93,13 +101,46 @@ def is_mxfp4_quant(self, prefix: str, layer: torch.nn.Module) -> bool:
         return True
 
 
-class Mxfp4MoEMethod(FusedMoEMethodBase):
+class GptOssMxfp4Config(Mxfp4Config):
+    """MXFP4 config for GPT-OSS checkpoints.
+
+    Checkpoints carry ``"quant_method": "mxfp4"`` in their JSON config.
+    override_quantization_method() maps that to the canonical internal name
+    so that the rest of the loading path uses "gpt_oss_mxfp4" consistently.
+    """
+
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "gpt_oss_mxfp4"
+
+    @classmethod
+    def override_quantization_method(
+        cls, hf_quant_cfg, user_quant, hf_config=None
+    ) -> QuantizationMethods | None:
+        # Match both "mxfp4" (original checkpoint value) and "gpt_oss_mxfp4"
+        # (already normalized by verify_and_update_model_config) so that
+        # explicit --quantization mxfp4 from the user doesn't cause a mismatch.
+        if not (
+            isinstance(hf_quant_cfg, dict)
+            and hf_quant_cfg.get("quant_method") in ("mxfp4", "gpt_oss_mxfp4")
+        ):
+            return None
+        # Require explicit confirmation that this is a GPT-OSS model.
+        # Do NOT fall back to returning the override when hf_config is None,
+        # as that would silently claim all mxfp4 checkpoints.
+        model_type = getattr(hf_config, "model_type", None)
+        if model_type != "gpt_oss":
+            return None
+        return "gpt_oss_mxfp4"
+
+
+class GptOssMxfp4MoEMethod(FusedMoEMethodBase):
     """MXFP4 MoE quantization method."""
 
     def __init__(self, moe: FusedMoEConfig):
         super().__init__(moe)
-        self.weight_dtype = "mxfp4"
-        self.mxfp4_backend, self.experts_cls = select_mxfp4_moe_backend(moe)
+        self.weight_dtype = "gpt_oss_mxfp4"
+        self.mxfp4_backend, self.experts_cls = select_gpt_oss_mxfp4_moe_backend(moe)
 
         self.max_capture_size = (
             get_current_vllm_config().compilation_config.max_cudagraph_capture_size
@@ -281,7 +322,7 @@ def _setup_kernel(
 
         # Convert weights to kernel format
         w13, w2, w13_scale, w2_scale, w13_bias, w2_bias = (
-            convert_to_mxfp4_moe_kernel_format(
+            convert_gpt_oss_weight_to_mxfp4_moe_kernel_format(
                 mxfp4_backend=self.mxfp4_backend,
                 layer=layer,
                 w13_weight=w13,

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

@@ -30,11 +30,11 @@
 from vllm.model_executor.layers.fused_moe.oracle.mxfp4 import (
     TRITON_BACKENDS,
     Mxfp4MoeBackend,
-    convert_to_mxfp4_moe_kernel_format,
+    convert_gpt_oss_weight_to_mxfp4_moe_kernel_format,
     make_mxfp4_moe_kernel,
     make_mxfp4_moe_quant_config,
     mxfp4_round_up_hidden_size_and_intermediate_size,
-    select_mxfp4_moe_backend,
+    select_gpt_oss_mxfp4_moe_backend,
 )
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     prepare_fp8_moe_layer_for_marlin,
@@ -995,7 +995,7 @@ def __init__(
         self.w2_precision_config = None
 
         if self.ocp_mx_scheme == "w_mxfp4":
-            self.mxfp4_backend, self.experts_cls = select_mxfp4_moe_backend(moe)
+            self.mxfp4_backend, self.experts_cls = select_gpt_oss_mxfp4_moe_backend(moe)
         elif self.ocp_mx_scheme.startswith("w_mxfp4"):
             # TODO(bowenbao): refactor and introduce backends for other OCP MX schemes.
             self.mxfp4_backend = Mxfp4MoeBackend.NONE
@@ -1300,7 +1300,7 @@ def _setup_kernel_via_oracle(self, layer: FusedMoE):
 
         # Convert weights to kernel format
         w13, w2, w13_scale, w2_scale, w13_bias, w2_bias = (
-            convert_to_mxfp4_moe_kernel_format(
+            convert_gpt_oss_weight_to_mxfp4_moe_kernel_format(
                 mxfp4_backend=self.mxfp4_backend,
                 layer=layer,
                 w13_weight=w13,

vllm/model_executor/models/config.py

@@ -108,6 +108,23 @@ def verify_and_update_config(vllm_config: "VllmConfig") -> None:
 
 
 class GptOssForCausalLMConfig(VerifyAndUpdateConfig):
+    @staticmethod
+    def verify_and_update_model_config(model_config: "ModelConfig") -> None:
+        quant_config = getattr(model_config.hf_config, "quantization_config", None)
+        if quant_config is not None and quant_config.get("quant_method") == "mxfp4":
+            model_config.hf_config.quantization_config["quant_method"] = "gpt_oss_mxfp4"
+
+        hf_text_quant_config = getattr(
+            model_config.hf_text_config, "quantization_config", None
+        )
+        if (
+            hf_text_quant_config is not None
+            and hf_text_quant_config.get("quant_method") == "mxfp4"
+        ):
+            model_config.hf_text_config.quantization_config["quant_method"] = (
+                "gpt_oss_mxfp4"
+            )
+
     @staticmethod
     def verify_and_update_config(vllm_config: "VllmConfig") -> None:
         structured_outputs_config = vllm_config.structured_outputs_config