fix: Add SM120 (RTX Blackwell) support for FlashInfer CUTLASS NVFP4 MoE kernels (#33417)

Signed-off-by: mgoin <mgoin64@gmail.com>
Co-authored-by: mgoin <mgoin64@gmail.com>

vllm/model_executor/layers/fused_moe/cutlass_moe.py

@@ -657,7 +657,12 @@ class CutlassExpertsFp4(mk.FusedMoEPermuteExpertsUnpermute):
 
     @staticmethod
     def _supports_current_device() -> bool:
-        return current_platform.has_device_capability((10, 0))
+        p = current_platform
+        return p.is_cuda() and (
+            p.is_device_capability_family(100)
+            or p.is_device_capability_family(110)
+            or p.is_device_capability_family(120)
+        )
 
     @staticmethod
     def _supports_no_act_and_mul() -> bool:

vllm/model_executor/layers/fused_moe/flashinfer_cutedsl_moe.py

@@ -54,7 +54,8 @@ class FlashInferCuteDSLExperts(mk.FusedMoEPermuteExpertsUnpermute):
 
     @staticmethod
     def _supports_current_device() -> bool:
-        return current_platform.is_device_capability_family(100)
+        p = current_platform
+        return p.is_cuda() and p.is_device_capability_family(100)
 
     @staticmethod
     def _supports_no_act_and_mul() -> bool:

vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py

@@ -84,11 +84,14 @@ class FlashInferExperts(mk.FusedMoEPermuteExpertsUnpermute):
 
     @staticmethod
     def _supports_current_device() -> bool:
+        p = current_platform
         return (
-            current_platform.is_cuda()
+            p.is_cuda()
             and (
-                current_platform.is_device_capability((9, 0))
+                p.is_device_capability(90)
-                or current_platform.is_device_capability_family(100)
+                or p.is_device_capability_family(100)
+                or p.is_device_capability_family(110)
+                or p.is_device_capability_family(120)
             )
             and has_flashinfer_cutlass_fused_moe()
         )
@@ -102,29 +105,27 @@ class FlashInferExperts(mk.FusedMoEPermuteExpertsUnpermute):
         weight_key: QuantKey | None,
         activation_key: QuantKey | None,
     ) -> bool:
-        # The following are supported by FlashInferExperts:
-        #   * unquantized
-        #   * fp8 static per-tensor on 9.0+
-        #   * fp8 block on 9.0
-        #   * nvfp4 on 10.0+
-
         p = current_platform
         scheme = (weight_key, activation_key)
+        # The following are supported by FlashInferExperts:
         return (
+            # unquantized and fp8 static per-tensor on 9.0+
             (
                 scheme
                 in [
                     (None, None),
                     (kFp8StaticTensorSym, kFp8StaticTensorSym),
                 ]
+                and p.has_device_capability(90)
             )
+            # fp8 block-scale on 9.0
             or (
-                (scheme == (kFp8Static128BlockSym, kFp8Dynamic128Sym))
+                scheme == (kFp8Static128BlockSym, kFp8Dynamic128Sym)
-                and (p.is_device_capability((9, 0)))
+                and p.is_device_capability(90)
             )
+            # nvfp4 on 10.0+
             or (
-                (scheme == (kNvfp4Static, kNvfp4Dynamic))
+                scheme == (kNvfp4Static, kNvfp4Dynamic) and p.has_device_capability(100)
-                and (p.is_device_capability_family(100))
             )
         )
 

vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py

@@ -30,7 +30,6 @@ from vllm.utils.torch_utils import direct_register_custom_op
 def _supports_current_device() -> bool:
     """Supports only Blackwell-family GPUs."""
     p = current_platform
-    # Add check flashinfer trtllm is available
     return p.is_cuda() and p.is_device_capability_family(100)
 
 

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

@@ -6,7 +6,6 @@ from typing import TYPE_CHECKING
 
 import torch
 
-import vllm.envs as envs
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
@@ -24,10 +23,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     kNvfp4Static,
 )
 from vllm.platforms import current_platform
-from vllm.utils.flashinfer import (
-    has_flashinfer_cutedsl_grouped_gemm_nt_masked,
-    has_flashinfer_cutlass_fused_moe,
-)
 
 if TYPE_CHECKING:
     from vllm.model_executor.layers.fused_moe.oracle.nvfp4 import (
@@ -38,8 +33,6 @@ logger = init_logger(__name__)
 
 
 __all__ = [
-    "is_flashinfer_fp4_cutlass_moe_available",
-    "is_flashinfer_fp4_cutedsl_moe_available",
     "reorder_w1w3_to_w3w1",
 ]
 
@@ -124,26 +117,6 @@ def is_supported_config_trtllm(
     return True, None
 
 
-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 has_flashinfer_cutlass_fused_moe()
-        and current_platform.is_cuda()
-        and current_platform.has_device_capability(100)
-    )
-
-
-def is_flashinfer_fp4_cutedsl_moe_available() -> bool:
-    """Return ``True`` when FlashInfer CUTEDSL NV-FP4 kernels can be used."""
-    return (
-        envs.VLLM_USE_FLASHINFER_MOE_FP4
-        and has_flashinfer_cutedsl_grouped_gemm_nt_masked()
-        and current_platform.is_cuda()
-        and current_platform.is_device_capability_family(100)
-    )
-
-
 def reorder_w1w3_to_w3w1(
     weight: torch.Tensor, scale: torch.Tensor, dim: int = -2
 ) -> tuple[torch.Tensor, torch.Tensor]:

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

@@ -1,67 +0,0 @@
-# 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_cutedsl_moe_available,
-    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.nvfp4_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: 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()
-        or is_flashinfer_fp4_cutedsl_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=allow_flashinfer,
-        use_marlin=use_marlin,
-    )