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>
(cherry picked from commit 079781177a)

vllm/model_executor/layers/fused_moe/cutlass_moe.py

@@ -649,7 +649,12 @@ 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

@@ -91,11 +91,14 @@ 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))
-                or current_platform.is_device_capability_family(100)
+                p.is_device_capability(90)
+                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()
         )
@@ -109,29 +112,27 @@ 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))
-                and (p.is_device_capability((9, 0)))
+                scheme == (kFp8Static128BlockSym, kFp8Dynamic128Sym)
+                and p.is_device_capability(90)
             )
+            # nvfp4 on 10.0+
             or (
-                (scheme == (kNvfp4Static, kNvfp4Dynamic))
-                and (p.is_device_capability_family(100))
+                scheme == (kNvfp4Static, kNvfp4Dynamic) and p.has_device_capability(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
@@ -25,10 +24,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     swizzle_blockscale,
 )
 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 (
@@ -39,8 +34,6 @@ logger = init_logger(__name__)
 
 
 __all__ = [
-    "is_flashinfer_fp4_cutlass_moe_available",
-    "is_flashinfer_fp4_cutedsl_moe_available",
     "reorder_w1w3_to_w3w1",
     "build_flashinfer_fp4_cutlass_moe_prepare_finalize",
 ]
@@ -126,26 +119,6 @@ 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]: