[Feature] Add Flashinfer MoE Support for Compressed Tensor NVFP4 (#21639)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

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

@@ -17,9 +17,14 @@ from vllm.model_executor.layers.fused_moe import (
     FusedMoE, FusedMoEActivationFormat, FusedMoEConfig, FusedMoEMethodBase,
     FusedMoEPermuteExpertsUnpermute, FusedMoEPrepareAndFinalize,
     FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize import (  # noqa
+    FlashInferCutlassMoEPrepareAndFinalize)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_wNa16 import (  # noqa
     WNA16_SUPPORTED_BITS, WNA16_SUPPORTED_TYPES_MAP)
 from vllm.model_executor.layers.quantization.utils import replace_parameter
+from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
+    build_flashinfer_fp4_cutlass_moe_kernel,
+    flashinfer_fp4_cutlass_moe_forward, reorder_w1w3_to_w3w1)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     check_moe_marlin_supports_layer, marlin_make_workspace_new,
     marlin_moe_permute_scales)
@@ -28,7 +33,7 @@ from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     prepare_moe_fp8_layer_for_marlin)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    cutlass_fp4_supported, swizzle_blockscale)
+    swizzle_blockscale)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
 from vllm.model_executor.utils import set_weight_attrs
@@ -96,8 +101,14 @@ class CompressedTensorsMoEMethod(FusedMoEMethodBase):
 class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
 
     def __init__(self):
-        self.use_marlin = not cutlass_fp4_supported()
+        from vllm.model_executor.layers.quantization.utils.nvfp4_moe_support import (  # noqa: E501
+            detect_nvfp4_moe_support)
+        _nvfp4 = detect_nvfp4_moe_support(self.__class__.__name__)
+        self.cutlass_nvfp4_supported = _nvfp4.cutlass_supported
+        self.allow_flashinfer_cutlass = _nvfp4.allow_flashinfer_cutlass
+        self.use_marlin = _nvfp4.use_marlin
         self.group_size = 16
+        self.fused_experts = None  # type: ignore[assignment]
 
     def create_weights(self, layer: torch.nn.Module, num_experts: int,
                        hidden_size: int, intermediate_size_per_partition: int,
@@ -200,6 +211,14 @@ class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
         layer.w2_weight = torch.nn.Parameter(layer.w2_weight_packed.data,
                                              requires_grad=False)
 
+        # reorder GEMM1 weights and block scales for FlashInfer CUTLASS kernel.
+        if self.allow_flashinfer_cutlass:
+            w, s = reorder_w1w3_to_w3w1(layer.w13_weight.data,
+                                        layer.w13_weight_scale.data,
+                                        dim=-2)
+            layer.w13_weight = torch.nn.Parameter(w, requires_grad=False)
+            layer.w13_weight_scale = torch.nn.Parameter(s, requires_grad=False)
+
         if not torch.allclose(layer.w13_weight_global_scale[:, 0],
                               layer.w13_weight_global_scale[:, 1]):
             logger.warning_once(
@@ -246,6 +265,21 @@ class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
         layer.w2_input_scale_quant = torch.nn.Parameter(
             (layer.w2_input_global_scale), requires_grad=False)
 
+    def maybe_swap_experts_impl(self, moe_parallel_config):
+        if not self.allow_flashinfer_cutlass:
+            return
+        self.fused_experts = build_flashinfer_fp4_cutlass_moe_kernel(
+            moe_parallel_config)
+
+    def select_gemm_impl(self, prepare_finalize, moe):
+        """Return the appropriate GEMM experts implementation."""
+        assert moe is not None and prepare_finalize is not None
+        from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (  # noqa: E501
+            select_nvfp4_gemm_impl)
+
+        return select_nvfp4_gemm_impl(self.allow_flashinfer_cutlass, moe,
+                                      logger)
+
     def apply(
         self,
         layer: torch.nn.Module,
@@ -303,10 +337,23 @@ class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
                 global_num_experts=global_num_experts,
                 expert_map=expert_map)
 
+        # FlashInfer fused experts path
+        if self.fused_experts is not None:
+            return flashinfer_fp4_cutlass_moe_forward(
+                self.fused_experts,
+                layer,
+                x,
+                topk_weights,
+                topk_ids,
+                activation=activation,
+                global_num_experts=global_num_experts,
+                expert_map=expert_map,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+            )
+
         assert expert_map is None, ("Expert Parallelism / expert_map "
                                     "is currently not supported for "
                                     "CompressedTensorsW4A4MoeMethod.")
-
         from vllm.model_executor.layers.fused_moe.cutlass_moe import (
             cutlass_moe_fp4)
 

vllm/model_executor/layers/quantization/modelopt.py

@@ -10,11 +10,8 @@ from torch.nn.parameter import Parameter
 import vllm.envs as envs
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
-from vllm.distributed import get_ep_group
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.config import FusedMoEParallelConfig
-from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize import (  # noqa: E501
-    FlashInferCutlassMoEPrepareAndFinalize)
 from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
@@ -23,6 +20,9 @@ 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.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
+    build_flashinfer_fp4_cutlass_moe_kernel,
+    flashinfer_fp4_cutlass_moe_forward, reorder_w1w3_to_w3w1)
 from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
     apply_flashinfer_per_tensor_scale_fp8, rotate_flashinfer_fp8_moe_weights,
     swap_w13_to_w31)
@@ -35,7 +35,6 @@ from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     Fp8LinearOp, requantize_with_max_scale)
 from vllm.model_executor.parameter import (ModelWeightParameter,
                                            PerTensorScaleParameter)
-from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 from vllm.utils.flashinfer import has_flashinfer_moe
 
@@ -869,28 +868,12 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
 
     def __init__(self, quant_config: ModelOptNvFp4Config):
         self.quant_config = quant_config
-        self.cutlass_nvfp4_supported = cutlass_fp4_supported()
-        self.use_marlin = False
-        self.allow_flashinfer_cutlass = False
-
-        if envs.VLLM_USE_FLASHINFER_MOE_FP4:
-            if self.cutlass_nvfp4_supported and current_platform.is_cuda() \
-               and current_platform.is_device_capability(100):
-                logger.info_once(
-                    "Using FlashInfer kernels for ModelOptNvFp4FusedMoE.")
-                self.allow_flashinfer_cutlass = True
-            else:
-                logger.warning_once(
-                    "Flashinfer CUTLASS Fused MoE not supported "
-                    "or found on the current platform.")
-
-        if not self.cutlass_nvfp4_supported:
-            if is_fp4_marlin_supported():
-                self.use_marlin = True
-            else:
-                raise ValueError("Current platform does not support NVFP4"
-                                 " quantization. Please use Blackwell and"
-                                 " above.")
+        from vllm.model_executor.layers.quantization.utils.nvfp4_moe_support import (  # noqa: E501
+            detect_nvfp4_moe_support)
+        _nvfp4 = detect_nvfp4_moe_support(self.__class__.__name__)
+        self.cutlass_nvfp4_supported = _nvfp4.cutlass_supported
+        self.allow_flashinfer_cutlass = _nvfp4.allow_flashinfer_cutlass
+        self.use_marlin = _nvfp4.use_marlin
 
         self.fused_experts = None  # type: ignore
 
@@ -900,29 +883,8 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
     ):
         if not self.allow_flashinfer_cutlass:
             return
-
-        logger.debug_once("FlashInferExperts")
-        # default to TP/EP case only
-
-        experts_kwargs: dict[str, Any] = {
-            "use_nvfp4_w4a4": True,
-            "use_dp": moe_parallel_config.dp_size > 1,
-            "ep_rank": moe_parallel_config.ep_rank,
-            "ep_size": moe_parallel_config.ep_size,
-            "tp_rank": moe_parallel_config.tp_rank,
-            "tp_size": moe_parallel_config.tp_size,
-        }
-
-        from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
-            FlashInferExperts)
-        experts = FlashInferExperts(**experts_kwargs)
-        self.fused_experts = mk.FusedMoEModularKernel(
-            FlashInferCutlassMoEPrepareAndFinalize(
-                quant_dtype=torch.uint8,
-                #meaning 2x e2m1 packed in one, kernel requirement
-            ),
-            experts,
-        )
+        self.fused_experts = build_flashinfer_fp4_cutlass_moe_kernel(
+            moe_parallel_config)
 
     # This method update self.fused_experts
     # only prepare_finalize is not None call select_gemm_impl
@@ -931,32 +893,12 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
     def select_gemm_impl(self, prepare_finalize,
                          moe) -> mk.FusedMoEPermuteExpertsUnpermute:
 
-        assert moe is not None
-        assert prepare_finalize is not None
-        experts = None
-        all2all_manager = get_ep_group().device_communicator.all2all_manager
-        assert all2all_manager is not None
-        if self.allow_flashinfer_cutlass:
-            from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
-                FlashInferExperts)
-            logger.debug_once("Using FlashInferExperts")
-            experts = FlashInferExperts(
-                use_nvfp4_w4a4=True,
-                use_dp=moe.moe_parallel_config.dp_size > 1,
-                ep_rank=moe.moe_parallel_config.ep_rank,
-                ep_size=moe.moe_parallel_config.ep_size,
-                tp_rank=moe.moe_parallel_config.tp_rank,
-                tp_size=moe.moe_parallel_config.tp_size,
-            )
-        else:
-            assert moe.dp_size > 1
-            logger.debug_once("Using CutlassExpertsFp4")
-            # Currently CutlassExpertsFp4 doesn't support DP
-            raise ValueError("CutlassExpertsFp4 doesn't support DP. "
-                             "Use flashinfer CUTLASS FusedMoE backend instead "
-                             "(set VLLM_USE_FLASHINFER_MOE_FP4=1)")
+        assert moe is not None and prepare_finalize is not None
+        from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (  # noqa: E501
+            select_nvfp4_gemm_impl)
 
-        return experts
+        return select_nvfp4_gemm_impl(self.allow_flashinfer_cutlass, moe,
+                                      logger)
 
     def uses_weight_scale_2_pattern(self) -> bool:
         """
@@ -1062,18 +1004,8 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
         gemm1_weight_scale = layer.w13_weight_scale.data
 
         if self.allow_flashinfer_cutlass:
-            dim = -2
-            size = gemm1_weight.size(dim)
-            assert size % 2 == 0, f"Expected even size in dim {dim}, got {size}"
-            half = size // 2
-
-            # Reorder weight
-            w1, w3 = gemm1_weight.split(half, dim=dim)
-            gemm1_weight = torch.cat([w3, w1], dim=dim).contiguous()
-
-            # Reorder scale
-            s1, s3 = gemm1_weight_scale.split(half, dim=dim)
-            gemm1_weight_scale = torch.cat([s3, s1], dim=dim).contiguous()
+            gemm1_weight, gemm1_weight_scale = reorder_w1w3_to_w3w1(
+                gemm1_weight, gemm1_weight_scale, dim=-2)
 
         layer.w13_weight = Parameter(gemm1_weight, requires_grad=False)
         layer.w13_weight_scale = Parameter(gemm1_weight_scale,
@@ -1217,49 +1149,15 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
                 expert_map=expert_map,
                 apply_router_weight_on_input=apply_router_weight_on_input)
         else:
-            # TP or DP case
-            from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
-                is_valid_flashinfer_cutlass_fused_moe)
-            assert is_valid_flashinfer_cutlass_fused_moe(
-                x, layer.w13_weight, layer.w2_weight), (
-                    "Flashinfer CUTLASS Fused MoE not applicable!")
-
-            a1_gscale = layer.w13_input_scale_quant
-            a2_gscale = layer.w2_input_scale_quant
-            extra_expert_args = {
-                'g1_alphas': layer.g1_alphas,
-                'g2_alphas': layer.g2_alphas,
-                'out_dtype': x.dtype,
-                # Avoid confusion with a1_scale and a2_scale
-                # where are batch size related.
-                'a1_gscale': a1_gscale,
-                'a2_gscale': a2_gscale,
-            }
-            extra_prepare_args = {
-                'use_dp': layer.dp_size > 1,
-                'local_tokens': x.shape[0],
-                'a1_gscale': a1_gscale,
-            }
-            extra_finalize_args = {
-                'use_dp': layer.dp_size > 1,
-                'local_tokens': x.shape[0],
-            }
-
-            out = self.fused_experts(
-                hidden_states=x,
-                w1=layer.w13_weight,
-                w2=layer.w2_weight,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                inplace=False,  # TODO(shuw): fix later, now output is high prec
+            out = flashinfer_fp4_cutlass_moe_forward(
+                self.fused_experts,
+                layer,
+                x,
+                topk_weights,
+                topk_ids,
                 activation=activation,
                 global_num_experts=global_num_experts,
                 expert_map=expert_map,
-                w1_scale=layer.w13_blockscale_swizzled,
-                w2_scale=layer.w2_blockscale_swizzled,
                 apply_router_weight_on_input=apply_router_weight_on_input,
-                extra_expert_args=extra_expert_args,
-                extra_prepare_args=extra_prepare_args,
-                extra_finalize_args=extra_finalize_args,
             )
         return out

vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py

@@ -0,0 +1,154 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Utility helpers for NVFP4 + FlashInfer fused-MoE path"""
+from __future__ import annotations
+
+from typing import Optional
+
+import torch
+
+import vllm.envs as envs
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.config import FusedMoEParallelConfig
+from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
+    FlashInferExperts, is_valid_flashinfer_cutlass_fused_moe)
+from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize import (  # noqa: E501
+    FlashInferCutlassMoEPrepareAndFinalize)
+from vllm.platforms import current_platform
+
+logger = init_logger(__name__)
+
+__all__ = [
+    "is_flashinfer_fp4_cutlass_moe_available",
+    "reorder_w1w3_to_w3w1",
+    "build_flashinfer_fp4_cutlass_moe_kernel",
+    "flashinfer_fp4_cutlass_moe_forward",
+]
+
+
+def is_flashinfer_fp4_cutlass_moe_available() -> bool:
+    """Return ``True`` when FlashInfer CUTLASS NV-FP4 kernels can be used."""
+    return (envs.VLLM_USE_FLASHINFER_MOE_FP4 and current_platform.is_cuda()
+            and current_platform.is_device_capability(100))
+
+
+def reorder_w1w3_to_w3w1(weight: torch.Tensor,
+                         scale: torch.Tensor,
+                         dim: int = -2) -> tuple[torch.Tensor, torch.Tensor]:
+    """Re-order the concatenated `[w1, w3]` tensors to `[w3, w1]`"""
+    size = weight.size(dim)
+    assert size % 2 == 0, f"Expected even size in dim {dim}, got {size}"
+    half = size // 2
+
+    w1, w3 = weight.split(half, dim=dim)
+    s1, s3 = scale.split(half, dim=dim)
+
+    return (torch.cat([w3, w1],
+                      dim=dim).contiguous(), torch.cat([s3, s1],
+                                                       dim=dim).contiguous())
+
+
+def build_flashinfer_fp4_cutlass_moe_kernel(
+    moe_parallel_config: FusedMoEParallelConfig, ) -> mk.FusedMoEModularKernel:
+    """Create *and return* a FlashInfer CUTLASS fused-MoE modular kernel"""
+    experts = FlashInferExperts(
+        use_nvfp4_w4a4=True,
+        use_dp=moe_parallel_config.dp_size > 1,
+        ep_rank=moe_parallel_config.ep_rank,
+        ep_size=moe_parallel_config.ep_size,
+        tp_rank=moe_parallel_config.tp_rank,
+        tp_size=moe_parallel_config.tp_size,
+    )
+    logger.debug_once("FlashInferExperts (util)")
+    return mk.FusedMoEModularKernel(
+        FlashInferCutlassMoEPrepareAndFinalize(quant_dtype=torch.uint8),
+        experts,
+    )
+
+
+def flashinfer_fp4_cutlass_moe_forward(
+    fused_experts: mk.FusedMoEModularKernel,
+    layer: torch.nn.Module,
+    x: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    activation: str,
+    global_num_experts: int,
+    expert_map: Optional[torch.Tensor],
+    apply_router_weight_on_input: bool,
+) -> torch.Tensor:
+    """Common forward wrapper for FlashInfer NV-FP4 fused-MoE"""
+
+    assert is_valid_flashinfer_cutlass_fused_moe(
+        x, layer.w13_weight,
+        layer.w2_weight), ("FlashInfer CUTLASS fused-MoE not applicable!")
+
+    a1_gscale = layer.w13_input_scale_quant
+    a2_gscale = layer.w2_input_scale_quant
+
+    extra_expert_args = {
+        "g1_alphas": layer.g1_alphas,
+        "g2_alphas": layer.g2_alphas,
+        # Avoid confusion with a1_scale and a2_scale
+        # where are batch size related.
+        "a1_gscale": a1_gscale,
+        "a2_gscale": a2_gscale,
+        "out_dtype": x.dtype,
+    }
+    extra_prepare_args = {
+        "use_dp": layer.dp_size > 1,
+        "local_tokens": x.shape[0],
+        "a1_gscale": a1_gscale,
+    }
+    extra_finalize_args = {
+        "use_dp": layer.dp_size > 1,
+        "local_tokens": x.shape[0],
+    }
+
+    return fused_experts(
+        hidden_states=x,
+        w1=layer.w13_weight,
+        w2=layer.w2_weight,
+        topk_weights=topk_weights,
+        topk_ids=topk_ids,
+        inplace=False,  # TODO(shuw): fix later, now output is high prec
+        activation=activation,
+        global_num_experts=global_num_experts,
+        expert_map=expert_map,
+        w1_scale=layer.w13_blockscale_swizzled,
+        w2_scale=layer.w2_blockscale_swizzled,
+        apply_router_weight_on_input=apply_router_weight_on_input,
+        extra_expert_args=extra_expert_args,
+        extra_prepare_args=extra_prepare_args,
+        extra_finalize_args=extra_finalize_args,
+    )
+
+
+def select_nvfp4_gemm_impl(
+        allow_flashinfer_cutlass: bool,
+        moe,  # FusedMoEConfig
+        logger):
+    """Return a GEMM *experts* implementation for NV-FP4 fused-MoE layers"""
+
+    # lazy import
+    from vllm.distributed import get_ep_group
+
+    all2all_manager = get_ep_group().device_communicator.all2all_manager
+    assert all2all_manager is not None
+
+    if allow_flashinfer_cutlass:
+        logger.debug_once("Using FlashInferExperts")
+        return FlashInferExperts(
+            use_nvfp4_w4a4=True,
+            use_dp=moe.moe_parallel_config.dp_size > 1,
+            ep_rank=moe.moe_parallel_config.ep_rank,
+            ep_size=moe.moe_parallel_config.ep_size,
+            tp_rank=moe.moe_parallel_config.tp_rank,
+            tp_size=moe.moe_parallel_config.tp_size,
+        )
+
+    # native cutlass experts currently don't support DP; TP case won't call this
+    raise ValueError(
+        "CutlassExpertsFp4 doesn't support DP. Use flashinfer CUTLASS "
+        "Fused MoE backend instead (set VLLM_USE_FLASHINFER_MOE_FP4=1)")

vllm/model_executor/layers/quantization/utils/nvfp4_moe_support.py

@@ -0,0 +1,59 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+
+import vllm.envs as envs
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
+    is_flashinfer_fp4_cutlass_moe_available)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
+    is_fp4_marlin_supported)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    cutlass_fp4_supported)
+
+__all__ = ["detect_nvfp4_moe_support", "NvFp4Support"]
+
+_logger = init_logger(__name__)
+
+
+@dataclass(frozen=True)
+class NvFp4Support:
+    """Result container for NV-FP4 capability probing."""
+
+    cutlass_supported: bool
+    allow_flashinfer_cutlass: bool
+    use_marlin: bool
+
+
+def detect_nvfp4_moe_support(class_name: str = "") -> NvFp4Support:
+    """Detect platform support for NV-FP4 fused-MoE path"""
+    cutlass_supported = cutlass_fp4_supported()
+
+    allow_flashinfer = (cutlass_supported
+                        and is_flashinfer_fp4_cutlass_moe_available())
+
+    if allow_flashinfer:
+        _logger.info_once("Using FlashInfer kernels for %s.", class_name
+                          or "NVFP4 path")
+    else:
+        if envs.VLLM_USE_FLASHINFER_MOE_FP4:
+            _logger.warning_once(
+                "FlashInfer kernels unavailable for %s on current platform.",
+                class_name or "NVFP4 path",
+            )
+
+    use_marlin = False
+    if not cutlass_supported:
+        if is_fp4_marlin_supported():
+            use_marlin = True
+            _logger.info_once("Falling back to Marlin FP4 MoE kernel.")
+        else:
+            raise ValueError(
+                "Current platform does not support NVFP4 quantization. "
+                "Please use Blackwell GPUs or enable FlashInfer.")
+
+    return NvFp4Support(
+        cutlass_supported=cutlass_supported,
+        allow_flashinfer_cutlass=allow_flashinfer,
+        use_marlin=use_marlin,
+    )