[gpt-oss] flashinfer mxfp4 (#22339)

Signed-off-by: simon-mo <xmo@berkeley.edu>
Signed-off-by: Yongye Zhu <zyy1102000@gmail.com>
Co-authored-by: simon-mo <xmo@berkeley.edu>

vllm/envs.py

@@ -154,6 +154,8 @@ if TYPE_CHECKING:
     VLLM_ENABLE_RESPONSES_API_STORE: bool = False
     VLLM_USE_TRTLLM_CONTEXT_ATTENTION: bool = False
     VLLM_USE_TRTLLM_DECODE_ATTENTION: bool = False
+    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8: bool = False
+    VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
 
 
 def get_default_cache_root():
@@ -932,6 +934,16 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_USE_FLASHINFER_MOE_FP4":
     lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP4", "0"))),
 
+    # If set to 1, use the FlashInfer
+    # MXFP8 (activation) x MXFP4 (weight) MoE backend.
+    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8":
+    lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "0"))),
+
+    # If set to 1, use the FlashInfer
+    # BF16 (activation) x MXFP4 (weight) MoE backend.
+    "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16":
+    lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "0"))),
+
     # Control the cache sized used by the xgrammar compiler. The default
     # of 512 MB should be enough for roughly 1000 JSON schemas.
     # It can be changed with this variable if needed for some reason.

vllm/model_executor/layers/fused_moe/layer.py

@@ -33,7 +33,8 @@ from vllm.model_executor.layers.quantization.base_config import (
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.platforms.interface import CpuArchEnum
-from vllm.utils import direct_register_custom_op, has_deep_ep, has_pplx
+from vllm.utils import (direct_register_custom_op, has_deep_ep, has_pplx,
+                        round_up)
 from vllm.utils.flashinfer import has_flashinfer
 
 if current_platform.is_cuda_alike():
@@ -719,6 +720,12 @@ class FusedMoE(torch.nn.Module):
 
         self.global_num_experts = num_experts + num_redundant_experts
 
+        # we padding globally so EP buffer allocation works
+        if quant_config and quant_config.get_name() == "mxfp4" and (
+                envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
+                or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
+            hidden_size = round_up(hidden_size, 256)
+
         # For smuggling this layer into the fused moe custom op
         compilation_config = vllm_config.compilation_config
         if prefix in compilation_config.static_forward_context:
@@ -1064,6 +1071,18 @@ class FusedMoE(torch.nn.Module):
                       shard_id: str,
                       expert_id: int,
                       return_success: bool = False) -> Optional[bool]:
+
+        if self.quant_config and self.quant_config.get_name() == "mxfp4":
+            # (FIXME) for gpt-oss all experts are combined
+            if "bias" in weight_name:
+                dim1 = loaded_weight.shape[1]
+                param.data[:, :dim1].copy_(loaded_weight)
+            else:
+                dim1 = loaded_weight.shape[1]
+                dim2 = loaded_weight.shape[2]
+                param.data[:, :dim1, :dim2].copy_(loaded_weight)
+            return True if return_success else None
+
         expert_id = self._map_global_expert_id_to_local_expert_id(expert_id)
         if expert_id == -1:
             # Failed to load this param since it's not local to this rank
@@ -1476,13 +1495,20 @@ class FusedMoE(torch.nn.Module):
 
     def forward(self, hidden_states: torch.Tensor,
                 router_logits: torch.Tensor):
+        og_hidden_states = hidden_states.shape[-1]
+        if self.hidden_size != og_hidden_states:
+            hidden_states = F.pad(hidden_states,
+                                  (0, self.hidden_size - og_hidden_states),
+                                  mode='constant',
+                                  value=0.0)
         # TODO: Once the OOM issue for the TPU backend is resolved, we will
         # switch to using the moe_forward custom op.
         if current_platform.is_tpu():
             return self.forward_impl(hidden_states, router_logits)
         else:
-            return torch.ops.vllm.moe_forward(hidden_states, router_logits,
+            return torch.ops.vllm.moe_forward(
-                                              self.layer_name)
+                hidden_states, router_logits,
+                self.layer_name)[..., :og_hidden_states]
 
     def forward_impl_chunked(self, full_hidden_states: torch.Tensor,
                              full_router_logits: torch.Tensor):

vllm/model_executor/layers/quantization/__init__.py

@@ -37,6 +37,7 @@ QuantizationMethods = Literal[
     "auto-round",
     "rtn",
     "inc",
+    "mxfp4",
 ]
 QUANTIZATION_METHODS: list[str] = list(get_args(QuantizationMethods))
 
@@ -110,6 +111,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
     from .marlin import MarlinConfig
     from .modelopt import ModelOptFp8Config, ModelOptNvFp4Config
     from .moe_wna16 import MoeWNA16Config
+    from .mxfp4 import Mxfp4Config
     from .neuron_quant import NeuronQuantConfig
     from .ptpc_fp8 import PTPCFp8Config
     from .qqq import QQQConfig
@@ -148,6 +150,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
         "auto-round": AutoRoundConfig,
         "rtn": RTNConfig,
         "inc": INCConfig,
+        "mxfp4": Mxfp4Config,
     }
     # Update the `method_to_config` with customized quantization methods.
     method_to_config.update(_CUSTOMIZED_METHOD_TO_QUANT_CONFIG)

vllm/model_executor/layers/quantization/mxfp4.py

@@ -0,0 +1,387 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Callable, Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm import envs
+from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEConfig,
+                                                  FusedMoEMethodBase)
+from vllm.model_executor.layers.linear import (LinearBase,
+                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
+    _can_support_mxfp4)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    is_layer_skipped)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.utils import next_power_of_2, round_up
+
+if (envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
+        or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
+    # from flashinfer.fused_moe import cutlass_fused_moe
+    from flashinfer import (mxfp8_quantize, shuffle_matrix_a,
+                            shuffle_matrix_sf_a, trtllm_fp4_block_scale_moe)
+
+
+class Mxfp4Config(QuantizationConfig):
+
+    def __init__(self, ignored_layers: Optional[list[str]] = None):
+        super().__init__()
+        self.ignored_layers = ignored_layers
+
+    @classmethod
+    def from_config(cls, config):
+        return cls()
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 100
+
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "mxfp4"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.bfloat16]
+
+    @classmethod
+    def get_config_filenames(cls) -> list[str]:
+        return []
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+
+        if isinstance(layer, LinearBase):
+            if self.ignored_layers and is_layer_skipped(
+                    prefix=prefix,
+                    ignored_layers=self.ignored_layers,
+                    fused_mapping=self.packed_modules_mapping):
+                return UnquantizedLinearMethod()
+            raise NotImplementedError("Mxfp4 linear layer is not implemented")
+        elif isinstance(layer, FusedMoE):
+            return Mxfp4MoEMethod(layer.moe_config)
+        elif isinstance(layer, Attention):
+            raise NotImplementedError(
+                "Mxfp4 attention layer is not implemented")
+        return None
+
+
+class Mxfp4MoEMethod(FusedMoEMethodBase):
+
+    def __init__(self, moe: FusedMoEConfig):
+        super().__init__()
+        self.topk_indices_dtype = None
+        self.moe = moe
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+        self.num_experts = num_experts
+        weight_dtype = torch.uint8
+        scale_dtype = torch.uint8
+
+        # FIXME (zyongye): ship after torch and safetensors support mxfp4
+        # is_torch_mxfp4_available = (
+        #     hasattr(torch, "float4_e2m1fn_x2") and
+        #     hasattr(torch, "float8_e8m0fnu"))
+        # if is_torch_mxfp4_available:
+        #     weight_dtype = torch.float4_e2m1fn_x2
+        #     scale_dtype = torch.float8_e8m0fnu
+
+        mxfp4_block = 32
+
+        intermediate_size_per_partition_after_pad = \
+            intermediate_size_per_partition
+        # pad the intermediate size to be a multiple of 2 * mxfp4_block
+        # for to hold non-uniform sharded tensor as well as swizzling
+        if (envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
+                or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
+            intermediate_size_per_partition_after_pad = round_up(
+                intermediate_size_per_partition, 256)
+            hidden_size = round_up(hidden_size, 256)
+
+        self.intermediate_size = intermediate_size_per_partition_after_pad
+        self.hidden_size = hidden_size
+        # Fused gate_up_proj (column parallel)
+        w13_weight = torch.nn.Parameter(torch.zeros(
+            num_experts,
+            2 * intermediate_size_per_partition_after_pad,
+            hidden_size // 2,
+            dtype=weight_dtype),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w13_weight_scale = torch.nn.Parameter(torch.zeros(
+            num_experts,
+            2 * intermediate_size_per_partition_after_pad,
+            hidden_size // mxfp4_block,
+            dtype=scale_dtype),
+                                              requires_grad=False)
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+
+        w13_bias = torch.nn.Parameter(torch.zeros(
+            num_experts,
+            2 * intermediate_size_per_partition_after_pad,
+            dtype=torch.bfloat16),
+                                      requires_grad=False)
+        layer.register_parameter("w13_bias", w13_bias)
+        set_weight_attrs(w13_bias, extra_weight_attrs)
+
+        # down_proj (row parallel)
+        w2_weight = torch.nn.Parameter(torch.zeros(
+            num_experts,
+            hidden_size,
+            intermediate_size_per_partition_after_pad // 2,
+            dtype=weight_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        w2_weight_scale = torch.nn.Parameter(torch.zeros(
+            num_experts,
+            hidden_size,
+            intermediate_size_per_partition_after_pad // mxfp4_block,
+            dtype=scale_dtype),
+                                             requires_grad=False)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        w2_bias = torch.nn.Parameter(torch.zeros(num_experts,
+                                                 hidden_size,
+                                                 dtype=torch.bfloat16),
+                                     requires_grad=False)
+        layer.register_parameter("w2_bias", w2_bias)
+        set_weight_attrs(w2_bias, extra_weight_attrs)
+
+    def process_weights_after_loading(self, layer):
+        if (envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
+                or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
+            layer.gemm1_alpha = Parameter(torch.tensor(
+                [1.702] * self.num_experts, dtype=torch.float32).cuda(),
+                                          requires_grad=False)
+            layer.gemm1_beta = Parameter(torch.tensor(
+                [1.0] * self.num_experts, dtype=torch.float32).cuda(),
+                                         requires_grad=False)
+            layer.gemm1_clamp_limit = Parameter(torch.tensor(
+                [7.0] * self.num_experts, dtype=torch.float32).cuda(),
+                                                requires_grad=False)
+            sf_block_size = 32  # mxfp4 block size
+
+            assert (layer.w13_weight.dim() == 3
+                    and layer.w13_weight.shape[0] == self.num_experts
+                    and layer.w13_weight.shape[1] == self.intermediate_size * 2
+                    and layer.w13_weight.shape[2] == self.hidden_size // 2)
+            assert (layer.w13_weight_scale.dim() == 3
+                    and layer.w13_weight_scale.shape[0] == self.num_experts
+                    and layer.w13_weight_scale.shape[1]
+                    == self.intermediate_size * 2
+                    and layer.w13_weight_scale.shape[2]
+                    == self.hidden_size // sf_block_size)
+            assert (layer.w2_weight.dim() == 3
+                    and layer.w2_weight.shape[0] == self.num_experts
+                    and layer.w2_weight.shape[1] == self.hidden_size and
+                    layer.w2_weight.shape[2] == self.intermediate_size // 2)
+            assert (layer.w2_weight_scale.dim() == 3
+                    and layer.w2_weight_scale.shape[1] == self.hidden_size
+                    and layer.w2_weight_scale.shape[2]
+                    == self.intermediate_size // sf_block_size)
+            assert (layer.w13_bias.dim() == 2
+                    and layer.w13_bias.shape[0] == self.num_experts
+                    and layer.w13_bias.shape[1] == self.intermediate_size * 2)
+            assert (layer.w2_bias.dim() == 2
+                    and layer.w2_bias.shape[0] == self.num_experts
+                    and layer.w2_bias.shape[1] == self.hidden_size)
+
+            w13_weight_scale = layer.w13_weight_scale.data
+            w2_weight_scale = layer.w2_weight_scale.data
+            w13_weight = layer.w13_weight.data
+            w2_weight = layer.w2_weight.data
+            w13_bias = layer.w13_bias.data.to(torch.float32)
+            w2_bias = layer.w2_bias.data.to(torch.float32)
+
+            # Swap w1 and w3 as the defenition of
+            # swiglu is different in the trtllm-gen
+            def swap_every_two_rows(x, axis=-1):
+                shape = x.shape
+                if axis < 0:
+                    axis = len(shape) + axis
+
+                # Create a new shape with pairs swapped along specified axis
+                new_shape = list(shape)
+                new_shape[axis] = shape[axis] // 2
+                new_shape.insert(axis + 1, 2)
+
+                # Reshape to expose pairs, swap them, and reshape back
+                x = x.reshape(*new_shape)
+                x = x.flip(axis + 1)
+                new_shape = list(shape)
+                return x.reshape(*new_shape)
+
+            w13_weight_scale = swap_every_two_rows(w13_weight_scale, -2)
+            w13_weight = swap_every_two_rows(w13_weight, -2)
+            w13_bias = swap_every_two_rows(w13_bias, -1)
+
+            # Do not interleave as the checkpoint is already interleaved
+
+            # Shuffle weights and scaling factors for transposed mma output
+            gemm1_weights_mxfp4_shuffled = []
+            gemm1_scales_mxfp4_shuffled = []
+            gemm2_weights_mxfp4_shuffled = []
+            gemm2_scales_mxfp4_shuffled = []
+            gemm1_bias_shuffled = []
+            gemm2_bias_shuffled = []
+            epilogue_tile_m = 128  # FIXME: this depends on the kernel internals
+            for i in range(self.num_experts):
+                gemm1_weights_mxfp4_shuffled.append(
+                    shuffle_matrix_a(w13_weight[i].view(torch.uint8),
+                                     epilogue_tile_m))
+                gemm1_scales_mxfp4_shuffled.append(
+                    shuffle_matrix_sf_a(w13_weight_scale[i].view(torch.uint8),
+                                        epilogue_tile_m))
+                gemm1_bias_shuffled.append(
+                    shuffle_matrix_a(w13_bias[i].clone().reshape(-1, 1),
+                                     epilogue_tile_m))
+
+                gemm2_weights_mxfp4_shuffled.append(
+                    shuffle_matrix_a(w2_weight[i].view(torch.uint8),
+                                     epilogue_tile_m))
+                gemm2_scales_mxfp4_shuffled.append(
+                    shuffle_matrix_sf_a(w2_weight_scale[i].view(torch.uint8),
+                                        epilogue_tile_m))
+                gemm2_bias_shuffled.append(
+                    shuffle_matrix_a(w2_bias[i].clone().reshape(-1, 1),
+                                     epilogue_tile_m))
+
+            w13_weight = torch.stack(gemm1_weights_mxfp4_shuffled)
+            w13_weight_scale = torch.stack(
+                gemm1_scales_mxfp4_shuffled).reshape(
+                    self.num_experts, 2 * self.intermediate_size,
+                    self.hidden_size // sf_block_size).view(
+                        torch.float8_e4m3fn)
+
+            w2_weight = torch.stack(gemm2_weights_mxfp4_shuffled)
+            w2_weight_scale = torch.stack(gemm2_scales_mxfp4_shuffled).reshape(
+                self.num_experts, self.hidden_size, self.intermediate_size //
+                sf_block_size).view(torch.float8_e4m3fn)
+
+            layer.w13_weight = Parameter(w13_weight, requires_grad=False)
+            layer.w13_weight_scale = Parameter(w13_weight_scale,
+                                               requires_grad=False)
+            layer.w2_weight = Parameter(w2_weight, requires_grad=False)
+            layer.w2_weight_scale = Parameter(w2_weight_scale,
+                                              requires_grad=False)
+            layer.w13_bias = Parameter(
+                torch.stack(gemm1_bias_shuffled).reshape(self.num_experts, -1),
+                requires_grad=False)
+            layer.w2_bias = Parameter(torch.stack(gemm2_bias_shuffled).reshape(
+                self.num_experts, -1),
+                                      requires_grad=False)
+            return
+
+    def _get_tile_tokens_dim(self, x: torch.Tensor, top_k: int):
+        # Number of tokens in the input tensor.
+        num_tokens = x.shape[0]
+        # Factor to account for the imbalance of the experts.
+        # factor equals to the
+        # max_real_num_tokens_per_expert / perfect_num_tokens_per_expert
+        # - 1.0 means perfect expert distribution.
+        # - > 1.0 means some experts have more
+        #     tokens than the perfect distribution.
+        # - < 1.0 does not make sense.
+        imbalance_factor = 1.3
+        # Calculate the number of tokens per expert
+        # assuming perfect distribution.
+        num_tokens_per_expert = (num_tokens * top_k) // self.num_experts
+        # Apply the imbalance factor.
+        num_tokens_per_expert = int(num_tokens_per_expert * imbalance_factor)
+        # And pad the number to the next power of 2.
+        tile_tokens_dim = next_power_of_2(num_tokens_per_expert)
+        # Cap to 8-64 tokens per CTA tile
+        # as it's the range supported by the kernel.
+        tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
+
+        return tile_tokens_dim
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        if enable_eplb:
+            raise NotImplementedError("EPLB is not supported for mxfp4")
+
+        assert _can_support_mxfp4(
+            use_grouped_topk, topk_group, num_expert_group, expert_map,
+            custom_routing_function, e_score_correction_bias,
+            apply_router_weight_on_input, scoring_func, activation,
+            expert_load_view, logical_to_physical_map,
+            logical_replica_count), ("MXFP4 are not supported\
+                                      with this configuration.")
+
+        if (envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
+                or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
+            assert not self.moe.use_ep, (
+                "EP is not supported for flashinfer mxfp4 moe backend yet.")
+            if envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16:
+                assert x.dtype == torch.bfloat16
+                x_quant = x
+                x_scale = None
+            else:
+                x_quant, x_scale = mxfp8_quantize(x, False)  # to mxfp8
+                x_scale = x_scale.view(torch.float8_e4m3fn).reshape(-1)
+            trtllm_gen_output = trtllm_fp4_block_scale_moe(
+                router_logits.to(torch.bfloat16),
+                None,  # routing_bias
+                x_quant,
+                x_scale,
+                layer.w13_weight,  # uint8 (e2m1 x 2)
+                layer.w13_weight_scale,  # uint8 (e4m3 x 2)
+                layer.w13_bias,  # fp32 per expert per channel
+                layer.gemm1_alpha,  # fp32 per expert
+                layer.gemm1_beta,  # fp32 per expert
+                layer.gemm1_clamp_limit,  # fp32 per expert
+                layer.w2_weight,  # uint8 (e2m1 x 2)
+                layer.w2_weight_scale,  # ue8m0
+                layer.w2_bias,  # fp32 per expert per channel
+                None,  # output1_scale_scalar
+                None,  # output1_scale_gate_scalar
+                None,  # output2_scale_scalar
+                self.num_experts,
+                top_k,
+                None,  # n_group
+                None,  # topk_group
+                self.intermediate_size,  # padded to multiple of 256
+                0,  # local_expert_offset
+                self.num_experts,  # local num experts
+                None,
+                self._get_tile_tokens_dim(x, top_k),
+                1 if renormalize else 0,  # routing_method_type, renormalize
+                True,  # do finalize
+            )[0]
+            return trtllm_gen_output

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

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Callable, Optional
+
 import torch
 
 from vllm.utils import direct_register_custom_op
@@ -7,6 +9,26 @@ from vllm.utils import direct_register_custom_op
 OCP_MX_BLOCK_SIZE = 32
 
 
+def _can_support_mxfp4(use_grouped_topk: bool = False,
+                       topk_group: Optional[int] = None,
+                       num_expert_group: Optional[int] = None,
+                       expert_map: Optional[torch.Tensor] = None,
+                       custom_routing_function: Optional[Callable] = None,
+                       e_score_correction_bias: Optional[torch.Tensor] = None,
+                       apply_router_weight_on_input: bool = False,
+                       scoring_func: str = "softmax",
+                       activation: str = "silu",
+                       expert_load_view: Optional[torch.Tensor] = None,
+                       logical_to_physical_map: Optional[torch.Tensor] = None,
+                       logical_replica_count: Optional[torch.Tensor] = None):
+    return not (use_grouped_topk or topk_group or num_expert_group
+                or expert_map or custom_routing_function
+                or e_score_correction_bias or apply_router_weight_on_input
+                or scoring_func != "softmax" or activation != "silu"
+                or expert_load_view or logical_to_physical_map
+                or logical_replica_count)
+
+
 def _dequant_mxfp4(x: torch.Tensor, scale: torch.Tensor,
                    float_dtype: torch.dtype) -> torch.Tensor:
     try: