[MoE Kernel] Flashinfer nvfp4 cutedsl moe kernel integration (#38050)

tests/kernels/moe/test_cutedsl_moe.py

@@ -17,7 +17,7 @@ from flashinfer import fp4_quantize
 from torch.nn import functional as F
 
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.fused_moe.experts.flashinfer_cutedsl_moe import (
+from vllm.model_executor.layers.fused_moe.experts.flashinfer_cutedsl_batched_moe import (  # noqa: E501
     flashinfer_cutedsl_moe_masked,
 )
 from vllm.utils.flashinfer import (

vllm/model_executor/layers/fused_moe/experts/flashinfer_cutedsl_batched_moe.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm import envs
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
+    FusedMoEQuantConfig,
+)
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    QuantKey,
+    kNvfp4Dynamic,
+    kNvfp4Static,
+)
+from vllm.platforms import current_platform
+from vllm.utils.flashinfer import (
+    flashinfer_cutedsl_grouped_gemm_nt_masked,
+    has_flashinfer_cutedsl_grouped_gemm_nt_masked,
+    scaled_fp4_grouped_quantize,
+    silu_and_mul_scaled_nvfp4_experts_quantize,
+)
+
+logger = init_logger(__name__)
+
+
+class FlashInferCuteDSLBatchedExperts(mk.FusedMoEExpertsModular):
+    def __init__(
+        self,
+        moe_config: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+        max_num_tokens: int,
+        num_dispatchers: int,
+    ):
+        super().__init__(
+            moe_config=moe_config,
+            quant_config=quant_config,
+            max_num_tokens=max_num_tokens,
+            num_dispatchers=num_dispatchers,
+        )
+        assert quant_config.quant_dtype == "nvfp4", (
+            "Only nvfp4 quantization are currently supported."
+        )
+        self.out_dtype = moe_config.in_dtype
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.w13_weight_scale_2.data.mul_(layer.w13_input_scale)
+        layer.w2_weight_scale_2.data.mul_(layer.w2_input_scale)
+
+    @staticmethod
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.BatchedExperts
+
+    @staticmethod
+    def _supports_current_device() -> bool:
+        p = current_platform
+        return (
+            p.is_cuda()
+            and p.is_device_capability_family(100)
+            and has_flashinfer_cutedsl_grouped_gemm_nt_masked()
+        )
+
+    @staticmethod
+    def _supports_no_act_and_mul() -> bool:
+        return False
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        SUPPORTED_W_A = [
+            (kNvfp4Static, kNvfp4Dynamic),
+        ]
+        return (weight_key, activation_key) in SUPPORTED_W_A
+
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        return activation == MoEActivation.SILU
+
+    @staticmethod
+    def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
+        return True
+
+    def supports_expert_map(self) -> bool:
+        return False
+
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        # Let PrepareAndFinalize::finalize() decide the impl.
+        return TopKWeightAndReduceDelegate()
+
+    def workspace_shapes(
+        self,
+        M: int,
+        N: int,
+        K: int,
+        topk: int,
+        global_num_experts: int,
+        local_num_experts: int,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        activation: MoEActivation,
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
+        """
+        Compute the shapes for the temporary and final outputs of the two gemms
+        and activation in the fused expert function.  Since the gemms are
+        independent, the workspace for the first gemm can be shared with the
+        workspace for the last gemm.
+
+        Returns a tuple of:
+        - workspace13 shape tuple: must be large enough to hold the
+          result of either expert gemm.
+        - workspace2 shape tuple: must be large enough to hold the
+          result of the activation function.
+        - output shape tuple: must be exact size of the final gemm output.
+        - Workspace type: The dtype to use for the workspace tensors.
+        - Note: in order for activation chunking to work, the first dimension
+          of each tuple must be the number of tokens.
+        """
+
+        # We use global_num_experts due to how moe_align_block_size handles
+        # expert_maps.
+        K_dim = K * 2 if envs.VLLM_DEEPEPLL_NVFP4_DISPATCH else K
+        output_shape = (local_num_experts, M, K_dim)
+        workspace2 = (local_num_experts, M, N)
+        workspace1 = output_shape
+        return (workspace1, workspace2, output_shape)
+
+    def apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        a2_scale: torch.Tensor | None,  # Not used
+        workspace13: torch.Tensor | None,
+        workspace2: torch.Tensor | None,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        apply_router_weight_on_input: bool | None,
+    ):
+        assert self.quant_dtype == "nvfp4", (
+            "Only nvfp4 quantization are currently supported."
+        )
+        # Ensure w1_scale and w2_scale are not None before calling view
+        assert self.w1_scale is not None and self.w2_scale is not None, (
+            "w1_scale and w2_scale must not be None for FlashInferExperts"
+        )
+        assert expert_tokens_meta is not None
+        expert_num_tokens = expert_tokens_meta.expert_num_tokens
+        assert hidden_states.ndim == 3
+        assert self.w1_scale.ndim == 3
+        assert self.w2_scale.ndim == 3
+
+        input_global_scale = (
+            None if envs.VLLM_DEEPEPLL_NVFP4_DISPATCH else self.a1_gscale
+        )
+        flashinfer_hidden_states = (
+            (hidden_states, a1q_scale)
+            if envs.VLLM_DEEPEPLL_NVFP4_DISPATCH
+            else hidden_states
+        )
+        flashinfer_cutedsl_moe_masked(
+            hidden_states=flashinfer_hidden_states,
+            input_global_scale=input_global_scale,
+            w1=w1,
+            w1_blockscale=self.w1_scale,
+            w1_alpha=self.g1_alphas,
+            w2=w2,
+            a2_global_scale=self.a2_gscale,
+            w2_blockscale=self.w2_scale,
+            w2_alpha=self.g2_alphas,
+            masked_m=expert_num_tokens,
+            workspace=workspace2,
+            out=output,
+        )
+
+
+def get_cute_dtype(input: torch.Tensor) -> str:
+    if input.dtype == torch.bfloat16:
+        return "bfloat16"
+    elif input.dtype == torch.float16:
+        return "float16"
+    elif input.dtype == torch.float32:
+        return "float32"
+    else:
+        raise ValueError(f"Unsupported cute dtype {input.dtype}")
+
+
+def flashinfer_cutedsl_moe_masked(
+    hidden_states: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
+    input_global_scale: torch.Tensor,
+    w1: torch.Tensor,
+    w1_blockscale: torch.Tensor,
+    w1_alpha,
+    w2: torch.Tensor,
+    a2_global_scale: torch.Tensor,
+    w2_blockscale: torch.Tensor,
+    w2_alpha,
+    masked_m: torch.Tensor,
+    workspace: torch.Tensor,
+    out: torch.Tensor,
+):
+    """
+    Perform masked Mixture-of-Experts computation with FlashInfer's CuteDSL
+    kernels.
+
+    Args:
+        hidden_states: Either of the following case
+            * torch.Tensor: [num_experts, m, k], bf16
+            * tuple[torch.Tensor, torch.Tensor]: [num_experts, m, k // 2],
+                  uint8, [num_experts, m, k // 16], float8_e4m3fn
+        input_global_scale (torch.Tensor): (l,)
+        w1 (torch.Tensor): fp4 weights, [l, 2 * n, k // 2], uint8
+        w1_blockscale (torch.Tensor): blockscale factors, e4m3,
+        w1_alpha (torch.Tensor): (l,)
+        w2 (torch.Tensor): fp4 weights, [l, k, n // 2], uint8
+        a2_global_scale (torch.Tensor): (l,)
+        w2_blockscale (torch.Tensor): blockscale factors, e4m3,
+        w2_alpha (torch.Tensor): (l,)
+        masked_m (torch.Tensor): Masked dimension indices
+        workspace (torch.Tensor): For gateup_output
+
+    Notes:
+        - Assumes max(masked_m) <= m.
+    """
+
+    # === Assertions on dtypes ===
+    assert w1.dtype == torch.uint8, f"w1 must be uint8, got {w1.dtype}"
+    assert w1_blockscale.dtype == torch.float8_e4m3fn, (
+        f"w1_blockscale must be float8_e4m3fn, got {w1_blockscale.dtype}"
+    )
+    assert w1_alpha.dtype == torch.float32, (
+        f"w1_alpha must be float32, got {w1_alpha.dtype}"
+    )
+    assert w2.dtype == torch.uint8, f"w2 must be uint8, got {w2.dtype}"
+    assert a2_global_scale.dtype == torch.float32, (
+        f"a2_global_scale must be float32, got {a2_global_scale.dtype}"
+    )
+    assert w2_blockscale.dtype == torch.float8_e4m3fn, (
+        f"w2_blockscale must be float8_e4m3fn, got {w2_blockscale.dtype}"
+    )
+    assert w2_alpha.dtype == torch.float32, (
+        f"w2_alpha must be float32, got {w2_alpha.dtype}"
+    )
+
+    # === Assertions on shapes ===
+    n = w2.shape[-1] * 2  # intermediate dimension
+    if isinstance(hidden_states, tuple):
+        assert input_global_scale is None, (
+            "input_global_scale is needed when input needs quant"
+        )
+
+        aq = hidden_states[0].view(torch.uint8)
+        aq_sf = hidden_states[1].view(torch.float8_e4m3fn)
+        # m, k_by_2, num_experts = aq.shape
+        num_experts, m, k_by_2 = aq.shape
+        k = k_by_2 * 2
+        aq = aq.permute(1, 2, 0)
+    else:
+        num_experts, m, k = hidden_states.shape
+
+        assert input_global_scale.dtype == torch.float32, (
+            f"input_global_scale must be float32, got {input_global_scale.dtype}"
+        )
+        assert input_global_scale.shape == (num_experts,), (
+            f"input_global_scale must be (l,), got {input_global_scale.shape}"
+        )
+
+        aq, aq_sf = scaled_fp4_grouped_quantize(
+            hidden_states,
+            masked_m,
+            input_global_scale,
+        )
+
+    assert w1.shape[-2] == 2 * n, f"w1 last-2 dim must be 2*n, got {w1.shape}"
+    assert w1.shape[-1] * 2 == k, (
+        f"w1 last dim * 2 must equal k, got {w1.shape[-1]} vs k={k}"
+    )
+    assert w2.shape[-2:] == (
+        k,
+        n // 2,
+    ), f"w2 shape mismatch, got {w2.shape[-2:]}, expected {(k, n // 2)}"
+
+    assert w1_alpha.shape == (num_experts,), (
+        f"w1_alpha must be (l,), got {w1_alpha.shape}"
+    )
+    assert a2_global_scale.shape == (num_experts,), (
+        f"a2_global_scale must be (l,), got {a2_global_scale.shape}"
+    )
+    assert w2_alpha.shape == (num_experts,), (
+        f"w2_alpha must be (l,), got {w2_alpha.shape}"
+    )
+
+    workspace = workspace.permute(1, 2, 0)  # requirement of kernel
+    sf_vec_size = 16
+    assert aq_sf.dtype == torch.float8_e4m3fn
+    assert aq.dtype == torch.uint8
+    ab_dtype = "float4_e2m1fn"
+    sf_dtype = "float8_e4m3fn"
+
+    if isinstance(hidden_states, tuple):
+        c_dtype = "bfloat16"
+    else:
+        c_dtype = get_cute_dtype(hidden_states)
+
+    # Gemm1
+    flashinfer_cutedsl_grouped_gemm_nt_masked(
+        (aq, aq_sf),
+        (w1.permute(1, 2, 0), w1_blockscale),
+        workspace,
+        masked_m,
+        ab_dtype=ab_dtype,
+        sf_dtype=sf_dtype,
+        c_dtype=c_dtype,
+        sf_vec_size=sf_vec_size,
+        alpha=w1_alpha.view(1, 1, num_experts),
+        alpha_dtype=get_cute_dtype(w1_alpha),
+    )  # in logical [m, n, l]
+
+    # SILU and quantization
+    diq, diq_sf = silu_and_mul_scaled_nvfp4_experts_quantize(
+        workspace.permute(2, 0, 1),
+        masked_m,
+        a2_global_scale,
+    )
+
+    # Gemm2
+    out = out.permute(1, 2, 0)  # requirement of kernel
+    flashinfer_cutedsl_grouped_gemm_nt_masked(
+        (diq, diq_sf),
+        (w2.permute(1, 2, 0), w2_blockscale),
+        out,
+        masked_m,
+        ab_dtype=ab_dtype,
+        sf_dtype=sf_dtype,
+        c_dtype=c_dtype,
+        sf_vec_size=sf_vec_size,
+        alpha=w2_alpha.view(1, 1, num_experts),
+        alpha_dtype=get_cute_dtype(w2_alpha),
+    )  # in logical [m, k, l]
+    out = out.permute(2, 0, 1)

vllm/model_executor/layers/fused_moe/experts/flashinfer_cutedsl_moe.py

@@ -4,8 +4,6 @@
 import torch
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
-from vllm import envs
-from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
@@ -13,7 +11,7 @@ from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig,
 )
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
-    TopKWeightAndReduceDelegate,
+    TopKWeightAndReduceNoOP,
 )
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
@@ -22,33 +20,42 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 )
 from vllm.platforms import current_platform
 from vllm.utils.flashinfer import (
-    flashinfer_cutedsl_grouped_gemm_nt_masked,
-    has_flashinfer_cutedsl_grouped_gemm_nt_masked,
-    scaled_fp4_grouped_quantize,
-    silu_and_mul_scaled_nvfp4_experts_quantize,
+    flashinfer_cute_dsl_fused_moe_nvfp4,
+    has_flashinfer_cutedsl_moe_nvfp4,
 )
 
-logger = init_logger(__name__)
-
 
 class FlashInferCuteDSLExperts(mk.FusedMoEExpertsModular):
+    """
+    CuteDSL NvFP4 MoE experts using the FlashInfer functional API.
+
+    Uses Standard activation format (non-batched). The kernel handles
+    routing, expert computation, and reduction internally.
+    Supports expert parallelism natively.
+    """
+
     def __init__(
         self,
         moe_config: FusedMoEConfig,
         quant_config: FusedMoEQuantConfig,
-        max_num_tokens: int,
-        num_dispatchers: int,
     ):
         super().__init__(
             moe_config=moe_config,
             quant_config=quant_config,
-            max_num_tokens=max_num_tokens,
-            num_dispatchers=num_dispatchers,
         )
         assert quant_config.quant_dtype == "nvfp4", (
-            "Only nvfp4 quantization are currently supported."
+            "Only nvfp4 quantization is currently supported."
         )
         self.out_dtype = moe_config.in_dtype
+        self.hidden_dim = moe_config.hidden_dim
+        self.intermediate_size_per_partition = (
+            moe_config.intermediate_size_per_partition
+        )
+        self.topk = moe_config.experts_per_token
+        self.local_num_experts = moe_config.num_local_experts
+        self.global_num_experts = moe_config.num_experts
+        self.ep_rank = moe_config.moe_parallel_config.ep_rank
+        self.local_expert_offset = self.ep_rank * self.local_num_experts
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         layer.w13_weight_scale_2.data.mul_(layer.w13_input_scale)
@@ -56,7 +63,7 @@ class FlashInferCuteDSLExperts(mk.FusedMoEExpertsModular):
 
     @staticmethod
     def activation_format() -> mk.FusedMoEActivationFormat:
-        return mk.FusedMoEActivationFormat.BatchedExperts
+        return mk.FusedMoEActivationFormat.Standard
 
     @staticmethod
     def _supports_current_device() -> bool:
@@ -64,7 +71,7 @@ class FlashInferCuteDSLExperts(mk.FusedMoEExpertsModular):
         return (
             p.is_cuda()
             and p.is_device_capability_family(100)
-            and has_flashinfer_cutedsl_grouped_gemm_nt_masked()
+            and has_flashinfer_cutedsl_moe_nvfp4()
         )
 
     @staticmethod
@@ -86,15 +93,16 @@ class FlashInferCuteDSLExperts(mk.FusedMoEExpertsModular):
         return activation == MoEActivation.SILU
 
     @staticmethod
-    def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
+    def _supports_parallel_config(
+        moe_parallel_config: FusedMoEParallelConfig,
+    ) -> bool:
         return True
 
     def supports_expert_map(self) -> bool:
         return False
 
     def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
-        # Let PrepareAndFinalize::finalize() decide the impl.
-        return TopKWeightAndReduceDelegate()
+        return TopKWeightAndReduceNoOP()
 
     def workspace_shapes(
         self,
@@ -107,29 +115,12 @@ class FlashInferCuteDSLExperts(mk.FusedMoEExpertsModular):
         expert_tokens_meta: mk.ExpertTokensMetadata | None,
         activation: MoEActivation,
     ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
-        # We use global_num_experts due to how moe_align_block_size handles
-        # expert_maps.
-        """
-        Compute the shapes for the temporary and final outputs of the two gemms
-        and activation in the fused expert function.  Since the gemms are
-        independent, the workspace for the first gemm can be shared with the
-        workspace for the last gemm.
-
-        Returns a tuple of:
-        - workspace13 shape tuple: must be large enough to hold the
-          result of either expert gemm.
-        - workspace2 shape tuple: must be large enough to hold the
-          result of the activation function.
-        - output shape tuple: must be exact size of the final gemm output.
-        - Workspace type: The dtype to use for the workspace tensors.
-        - Note: in order for activation chunking to work, the first dimension
-          of each tuple must be the number of tokens.
-        """
-        K_dim = K * 2 if envs.VLLM_DEEPEPLL_NVFP4_DISPATCH else K
-        output_shape = (local_num_experts, M, K_dim)
-        workspace2 = (local_num_experts, M, N)
-        workspace1 = output_shape
-        return (workspace1, workspace2, output_shape)
+        workspace1 = (0,)
+        workspace2 = (0,)
+        # K is packed (K//2 for uint8), so output uses hidden_dim.
+        assert self.hidden_dim == K * 2
+        output = (M, self.hidden_dim)
+        return (workspace1, workspace2, output)
 
     def apply(
         self,
@@ -143,210 +134,39 @@ class FlashInferCuteDSLExperts(mk.FusedMoEExpertsModular):
         global_num_experts: int,
         expert_map: torch.Tensor | None,
         a1q_scale: torch.Tensor | None,
-        a2_scale: torch.Tensor | None,  # Not used
+        a2_scale: torch.Tensor | None,
         workspace13: torch.Tensor | None,
         workspace2: torch.Tensor | None,
         expert_tokens_meta: mk.ExpertTokensMetadata | None,
         apply_router_weight_on_input: bool | None,
     ):
-        assert self.quant_dtype == "nvfp4", (
-            "Only nvfp4 quantization are currently supported."
-        )
-        # Ensure w1_scale and w2_scale are not None before calling view
-        assert self.w1_scale is not None and self.w2_scale is not None, (
-            "w1_scale and w2_scale must not be None for FlashInferExperts"
-        )
-        assert expert_tokens_meta is not None
-        expert_num_tokens = expert_tokens_meta.expert_num_tokens
-        assert hidden_states.ndim == 3
-        assert self.w1_scale.ndim == 3
-        assert self.w2_scale.ndim == 3
-
-        input_global_scale = (
-            None if envs.VLLM_DEEPEPLL_NVFP4_DISPATCH else self.a1_gscale
-        )
-        flashinfer_hidden_states = (
-            (hidden_states, a1q_scale)
-            if envs.VLLM_DEEPEPLL_NVFP4_DISPATCH
-            else hidden_states
-        )
-        flashinfer_cutedsl_moe_masked(
-            hidden_states=flashinfer_hidden_states,
-            input_global_scale=input_global_scale,
-            w1=w1,
-            w1_blockscale=self.w1_scale,
-            w1_alpha=self.g1_alphas,
-            w2=w2,
-            a2_global_scale=self.a2_gscale,
-            w2_blockscale=self.w2_scale,
-            w2_alpha=self.g2_alphas,
-            masked_m=expert_num_tokens,
-            workspace=workspace2,
-            out=output,
-        )
-
-
-def get_cute_dtype(input: torch.Tensor) -> str:
-    if input.dtype == torch.bfloat16:
-        return "bfloat16"
-    elif input.dtype == torch.float16:
-        return "float16"
-    elif input.dtype == torch.float32:
-        return "float32"
-    else:
-        raise ValueError(f"Unsupported cute dtype {input.dtype}")
-
-
-def flashinfer_cutedsl_moe_masked(
-    hidden_states: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
-    input_global_scale: torch.Tensor,
-    w1: torch.Tensor,
-    w1_blockscale: torch.Tensor,
-    w1_alpha,
-    w2: torch.Tensor,
-    a2_global_scale: torch.Tensor,
-    w2_blockscale: torch.Tensor,
-    w2_alpha,
-    masked_m: torch.Tensor,
-    workspace: torch.Tensor,
-    out: torch.Tensor,
-):
-    """
-    Perform masked Mixture-of-Experts computation with FlashInfer's CuteDSL
-    kernels.
-
-    Args:
-        hidden_states: Either of the following case
-            * torch.Tensor: [num_experts, m, k], bf16
-            * tuple[torch.Tensor, torch.Tensor]: [num_experts, m, k // 2],
-                  uint8, [num_experts, m, k // 16], float8_e4m3fn
-        input_global_scale (torch.Tensor): (l,)
-        w1 (torch.Tensor): fp4 weights, [l, 2 * n, k // 2], uint8
-        w1_blockscale (torch.Tensor): blockscale factors, e4m3,
-        w1_alpha (torch.Tensor): (l,)
-        w2 (torch.Tensor): fp4 weights, [l, k, n // 2], uint8
-        a2_global_scale (torch.Tensor): (l,)
-        w2_blockscale (torch.Tensor): blockscale factors, e4m3,
-        w2_alpha (torch.Tensor): (l,)
-        masked_m (torch.Tensor): Masked dimension indices
-        workspace (torch.Tensor): For gateup_output
-
-    Notes:
-        - Assumes max(masked_m) <= m.
-    """
-
-    # === Assertions on dtypes ===
-    assert w1.dtype == torch.uint8, f"w1 must be uint8, got {w1.dtype}"
-    assert w1_blockscale.dtype == torch.float8_e4m3fn, (
-        f"w1_blockscale must be float8_e4m3fn, got {w1_blockscale.dtype}"
-    )
-    assert w1_alpha.dtype == torch.float32, (
-        f"w1_alpha must be float32, got {w1_alpha.dtype}"
-    )
-    assert w2.dtype == torch.uint8, f"w2 must be uint8, got {w2.dtype}"
-    assert a2_global_scale.dtype == torch.float32, (
-        f"a2_global_scale must be float32, got {a2_global_scale.dtype}"
-    )
-    assert w2_blockscale.dtype == torch.float8_e4m3fn, (
-        f"w2_blockscale must be float8_e4m3fn, got {w2_blockscale.dtype}"
-    )
-    assert w2_alpha.dtype == torch.float32, (
-        f"w2_alpha must be float32, got {w2_alpha.dtype}"
-    )
-
-    # === Assertions on shapes ===
-    n = w2.shape[-1] * 2  # intermediate dimension
-    if isinstance(hidden_states, tuple):
-        assert input_global_scale is None, (
-            "input_global_scale is needed when input needs quant"
-        )
-
-        aq = hidden_states[0].view(torch.uint8)
-        aq_sf = hidden_states[1].view(torch.float8_e4m3fn)
-        # m, k_by_2, num_experts = aq.shape
-        num_experts, m, k_by_2 = aq.shape
-        k = k_by_2 * 2
-        aq = aq.permute(1, 2, 0)
-    else:
-        num_experts, m, k = hidden_states.shape
-
-        assert input_global_scale.dtype == torch.float32, (
-            f"input_global_scale must be float32, got {input_global_scale.dtype}"
-        )
-        assert input_global_scale.shape == (num_experts,), (
-            f"input_global_scale must be (l,), got {input_global_scale.shape}"
-        )
-
-        aq, aq_sf = scaled_fp4_grouped_quantize(
-            hidden_states,
-            masked_m,
-            input_global_scale,
-        )
-
-    assert w1.shape[-2] == 2 * n, f"w1 last-2 dim must be 2*n, got {w1.shape}"
-    assert w1.shape[-1] * 2 == k, (
-        f"w1 last dim * 2 must equal k, got {w1.shape[-1]} vs k={k}"
-    )
-    assert w2.shape[-2:] == (
-        k,
-        n // 2,
-    ), f"w2 shape mismatch, got {w2.shape[-2:]}, expected {(k, n // 2)}"
-
-    assert w1_alpha.shape == (num_experts,), (
-        f"w1_alpha must be (l,), got {w1_alpha.shape}"
-    )
-    assert a2_global_scale.shape == (num_experts,), (
-        f"a2_global_scale must be (l,), got {a2_global_scale.shape}"
-    )
-    assert w2_alpha.shape == (num_experts,), (
-        f"w2_alpha must be (l,), got {w2_alpha.shape}"
-    )
-
-    workspace = workspace.permute(1, 2, 0)  # requirement of kernel
-    sf_vec_size = 16
-    assert aq_sf.dtype == torch.float8_e4m3fn
-    assert aq.dtype == torch.uint8
-    ab_dtype = "float4_e2m1fn"
-    sf_dtype = "float8_e4m3fn"
-
-    if isinstance(hidden_states, tuple):
-        c_dtype = "bfloat16"
-    else:
-        c_dtype = get_cute_dtype(hidden_states)
-
-    # Gemm1
-    flashinfer_cutedsl_grouped_gemm_nt_masked(
-        (aq, aq_sf),
-        (w1.permute(1, 2, 0), w1_blockscale),
-        workspace,
-        masked_m,
-        ab_dtype=ab_dtype,
-        sf_dtype=sf_dtype,
-        c_dtype=c_dtype,
-        sf_vec_size=sf_vec_size,
-        alpha=w1_alpha.view(1, 1, num_experts),
-        alpha_dtype=get_cute_dtype(w1_alpha),
-    )  # in logical [m, n, l]
-
-    # SILU and quantization
-    diq, diq_sf = silu_and_mul_scaled_nvfp4_experts_quantize(
-        workspace.permute(2, 0, 1),
-        masked_m,
-        a2_global_scale,
-    )
-
-    # Gemm2
-    out = out.permute(1, 2, 0)  # requirement of kernel
-    flashinfer_cutedsl_grouped_gemm_nt_masked(
-        (diq, diq_sf),
-        (w2.permute(1, 2, 0), w2_blockscale),
-        out,
-        masked_m,
-        ab_dtype=ab_dtype,
-        sf_dtype=sf_dtype,
-        c_dtype=c_dtype,
-        sf_vec_size=sf_vec_size,
-        alpha=w2_alpha.view(1, 1, num_experts),
-        alpha_dtype=get_cute_dtype(w2_alpha),
-    )  # in logical [m, k, l]
-    out = out.permute(2, 0, 1)
+        assert self.quant_dtype == "nvfp4"
+        assert a1q_scale is not None
+        assert self.w1_scale is not None
+        assert self.w2_scale is not None
+
+        # a1q_scale is (M, K//16) float8_e4m3fn from fp4_quantize.
+        # The functional API expects x_sf with trailing dim: (M, K//16, 1).
+        x_sf = a1q_scale.unsqueeze(-1)
+
+        from vllm.utils.flashinfer import _is_fi_autotuning, autotune
+
+        with autotune(_is_fi_autotuning):
+            flashinfer_cute_dsl_fused_moe_nvfp4(
+                x=hidden_states,
+                x_sf=x_sf,
+                token_selected_experts=topk_ids.to(torch.int32),
+                token_final_scales=topk_weights.float(),
+                w1_weight=w1,
+                w1_weight_sf=self.w1_scale,
+                w1_alpha=self.g1_alphas,
+                fc2_input_scale=self.a2_gscale,
+                w2_weight=w2,
+                w2_weight_sf=self.w2_scale,
+                w2_alpha=self.g2_alphas,
+                num_experts=self.global_num_experts,
+                top_k=self.topk,
+                num_local_experts=self.local_num_experts,
+                local_expert_offset=self.local_expert_offset,
+                moe_output=output,
+            )

vllm/model_executor/layers/fused_moe/oracle/nvfp4.py

@@ -19,6 +19,7 @@ from vllm.model_executor.layers.fused_moe.config import (
 )
 from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
     prepare_nvfp4_moe_layer_for_fi_or_cutlass,
+    prepare_nvfp4_moe_layer_for_flashinfer_cutedsl,
 )
 from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
     FlashinferMoeBackend,
@@ -38,6 +39,7 @@ class NvFp4MoeBackend(Enum):
     FLASHINFER_TRTLLM = "FLASHINFER_TRTLLM"
     FLASHINFER_CUTLASS = "FLASHINFER_CUTLASS"
     FLASHINFER_CUTEDSL = "FLASHINFER_CUTEDSL"
+    FLASHINFER_CUTEDSL_BATCHED = "FLASHINFER_CUTEDSL_BATCHED"
     VLLM_CUTLASS = "VLLM_CUTLASS"
     MARLIN = "MARLIN"
 
@@ -46,6 +48,7 @@ FLASHINFER_NVFP4_MOE_BACKENDS = [
     NvFp4MoeBackend.FLASHINFER_TRTLLM,
     NvFp4MoeBackend.FLASHINFER_CUTLASS,
     NvFp4MoeBackend.FLASHINFER_CUTEDSL,
+    NvFp4MoeBackend.FLASHINFER_CUTEDSL_BATCHED,
 ]
 
 fi_2_vllm_backend_map: dict[FlashinferMoeBackend, NvFp4MoeBackend] = {
@@ -92,6 +95,13 @@ def backend_to_kernel_cls(
 
         return [FlashInferCuteDSLExperts]
 
+    elif backend == NvFp4MoeBackend.FLASHINFER_CUTEDSL_BATCHED:
+        from vllm.model_executor.layers.fused_moe.experts.flashinfer_cutedsl_batched_moe import (  # noqa: E501
+            FlashInferCuteDSLBatchedExperts,
+        )
+
+        return [FlashInferCuteDSLBatchedExperts]
+
     elif backend == NvFp4MoeBackend.VLLM_CUTLASS:
         from vllm.model_executor.layers.fused_moe.cutlass_moe import (
             CutlassExpertsFp4,
@@ -140,6 +150,7 @@ def select_nvfp4_moe_backend(
     AVAILABLE_BACKENDS = [
         NvFp4MoeBackend.FLASHINFER_TRTLLM,
         NvFp4MoeBackend.FLASHINFER_CUTEDSL,
+        NvFp4MoeBackend.FLASHINFER_CUTEDSL_BATCHED,
         NvFp4MoeBackend.FLASHINFER_CUTLASS,
         NvFp4MoeBackend.VLLM_CUTLASS,
         NvFp4MoeBackend.MARLIN,
@@ -195,6 +206,12 @@ def select_nvfp4_moe_backend(
     runner_backend = config.moe_backend
     if runner_backend != "auto":
         requested_backend = map_nvfp4_backend(runner_backend)
+        # For batched activation format, use batched variant if available.
+        if (
+            activation_format == mk.FusedMoEActivationFormat.BatchedExperts
+            and requested_backend == NvFp4MoeBackend.FLASHINFER_CUTEDSL
+        ):
+            requested_backend = NvFp4MoeBackend.FLASHINFER_CUTEDSL_BATCHED
         return _return_or_raise(
             requested_backend, config, weight_key, activation_key, activation_format
         )
@@ -285,7 +302,28 @@ def convert_to_nvfp4_moe_kernel_format(
     torch.Tensor,
     torch.Tensor,
 ]:
-    if (
+    if nvfp4_backend == NvFp4MoeBackend.FLASHINFER_CUTEDSL:
+        (
+            w13,
+            w13_scale,
+            w13_scale_2,
+            a13_scale,
+            w2,
+            w2_scale,
+            w2_scale_2,
+            a2_scale,
+        ) = prepare_nvfp4_moe_layer_for_flashinfer_cutedsl(
+            layer=layer,
+            w13=w13,
+            w13_scale=w13_scale,
+            w13_scale_2=w13_scale_2,
+            a13_scale=a13_scale,
+            w2=w2,
+            w2_scale=w2_scale,
+            w2_scale_2=w2_scale_2,
+            a2_scale=a2_scale,
+        )
+    elif (
         nvfp4_backend in FLASHINFER_NVFP4_MOE_BACKENDS
         or nvfp4_backend == NvFp4MoeBackend.VLLM_CUTLASS
     ):
@@ -377,7 +415,13 @@ def make_nvfp4_moe_quant_config(
         # NOTE(rob): this is a hack until the MoE kernels
         # create their own quant configs. TRTLLM kernel
         # does not accept swizzled input quant scales.
-        is_nvfp4_scale_swizzled=(backend != NvFp4MoeBackend.FLASHINFER_TRTLLM),
+        is_nvfp4_scale_swizzled=(
+            backend
+            not in (
+                NvFp4MoeBackend.FLASHINFER_TRTLLM,
+                NvFp4MoeBackend.FLASHINFER_CUTEDSL,
+            )
+        ),
     )
 
 

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

@@ -60,6 +60,100 @@ def reorder_w1w3_to_w3w1(
     )
 
 
+def interleave_linear_and_gate(
+    x: torch.Tensor,
+    group_size: int = 64,
+    dim: int = -1,
+) -> torch.Tensor:
+    """Interleave gate and linear weight rows for CuteDSL wrapper."""
+    sizes = x.size()
+    dim = dim % x.dim()
+    assert sizes[dim] % (group_size * 2) == 0, (
+        f"dim {dim} size {sizes[dim]} must be divisible by {group_size * 2}"
+    )
+    prev_sizes = sizes[:dim]
+    post_sizes = sizes[dim + 1 :]
+    x = x.view(*prev_sizes, 2, sizes[dim] // (group_size * 2), group_size, *post_sizes)
+    x = x.transpose(dim, dim + 1).contiguous().view(*sizes)
+    return x
+
+
+def prepare_nvfp4_moe_layer_for_flashinfer_cutedsl(
+    layer: "FusedMoE",
+    w13: torch.Tensor,
+    w13_scale: torch.Tensor,
+    w13_scale_2: torch.Tensor,
+    a13_scale: torch.Tensor,
+    w2: torch.Tensor,
+    w2_scale: torch.Tensor,
+    w2_scale_2: torch.Tensor,
+    a2_scale: torch.Tensor,
+) -> tuple[
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+]:
+    """Prepare weights for the CuteDSL wrapper-based NvFP4 MoE backend.
+
+    Converts weight scale factors to MMA layout expected by CuteDslMoEWrapper,
+    and interleaves w13 gate/linear rows.
+    """
+    from flashinfer.cute_dsl.utils import convert_sf_to_mma_layout
+
+    # Global scaling factors (same as other FlashInfer backends).
+    num_experts = w13.shape[0]
+    a13_scale = a13_scale.max().to(torch.float32).expand(num_experts)
+    a2_scale = a2_scale.max().to(torch.float32).expand(num_experts)
+
+    half = w13.shape[1] // 2
+    w13 = torch.cat([w13[:, half:], w13[:, :half]], dim=1)
+    w13_scale = torch.cat([w13_scale[:, half:], w13_scale[:, :half]], dim=1)
+
+    # Interleave up/gate rows for w13 weights and scales.
+    w13 = interleave_linear_and_gate(w13, group_size=64, dim=1)
+    w13_scale = interleave_linear_and_gate(w13_scale, group_size=64, dim=1)
+
+    # Convert w13 scale factors: linear → swizzled → MMA layout.
+    w13_scale = swizzle_blockscale(w13_scale)
+    E, M_padded, K_sf_padded = w13_scale.shape
+    w13_scale_flat = w13_scale.reshape(E * M_padded, K_sf_padded)
+    w13_scale = convert_sf_to_mma_layout(
+        w13_scale_flat,
+        m=M_padded,
+        k=K_sf_padded * 16,
+        num_groups=E,
+        sf_vec_size=16,
+    )
+
+    # Convert w2 scale factors: linear → swizzled → MMA layout.
+    w2_scale = swizzle_blockscale(w2_scale)
+    E, M_padded, K_sf_padded = w2_scale.shape
+    w2_scale_flat = w2_scale.reshape(E * M_padded, K_sf_padded)
+    w2_scale = convert_sf_to_mma_layout(
+        w2_scale_flat,
+        m=M_padded,
+        k=K_sf_padded * 16,
+        num_groups=E,
+        sf_vec_size=16,
+    )
+
+    return (
+        w13,
+        w13_scale,
+        w13_scale_2,
+        a13_scale,
+        w2,
+        w2_scale,
+        w2_scale_2,
+        a2_scale,
+    )
+
+
 def prepare_static_weights_for_trtllm_fp4_moe(
     # args_dequant,
     # args,
@@ -221,7 +315,7 @@ def prepare_nvfp4_moe_layer_for_fi_or_cutlass(
         NvFp4MoeBackend.VLLM_CUTLASS,
         NvFp4MoeBackend.FLASHINFER_CUTLASS,
         NvFp4MoeBackend.FLASHINFER_TRTLLM,
-        NvFp4MoeBackend.FLASHINFER_CUTEDSL,
+        NvFp4MoeBackend.FLASHINFER_CUTEDSL_BATCHED,
     ]
 
     # Reorder [w1, w3] to [w3, w1] for FI NVFP4 MoE kernels.

vllm/utils/flashinfer.py

@@ -128,6 +128,12 @@ scaled_fp4_grouped_quantize = _lazy_import_wrapper(
 nvfp4_block_scale_interleave = _lazy_import_wrapper(
     "flashinfer.fp4_quantization", "block_scale_interleave"
 )
+flashinfer_cute_dsl_fused_moe_nvfp4 = _lazy_import_wrapper(
+    "flashinfer", "cute_dsl_fused_moe_nvfp4"
+)
+flashinfer_convert_sf_to_mma_layout = _lazy_import_wrapper(
+    "flashinfer.cute_dsl.utils", "convert_sf_to_mma_layout"
+)
 trtllm_fp4_block_scale_moe = _lazy_import_wrapper(
     "flashinfer", "trtllm_fp4_block_scale_moe"
 )
@@ -251,6 +257,15 @@ def has_flashinfer_cutedsl_grouped_gemm_nt_masked() -> bool:
     return True
 
 
+@functools.cache
+def has_flashinfer_cutedsl_moe_nvfp4() -> bool:
+    """Return ``True`` if FlashInfer cute_dsl_fused_moe_nvfp4 is available."""
+    if not has_flashinfer_cutedsl():
+        return False
+    mod = _get_submodule("flashinfer")
+    return mod is not None and hasattr(mod, "cute_dsl_fused_moe_nvfp4")
+
+
 @functools.cache
 def has_nvidia_artifactory() -> bool:
     """Return `True` if NVIDIA's artifactory is accessible.
@@ -767,6 +782,8 @@ __all__ = [
     "silu_and_mul_scaled_nvfp4_experts_quantize",
     "scaled_fp4_grouped_quantize",
     "nvfp4_block_scale_interleave",
+    "flashinfer_cute_dsl_fused_moe_nvfp4",
+    "flashinfer_convert_sf_to_mma_layout",
     "trtllm_fp4_block_scale_moe",
     "autotune",
     "has_flashinfer_moe",
@@ -775,6 +792,7 @@ __all__ = [
     "has_flashinfer_nvlink_one_sided",
     "has_flashinfer_cutlass_fused_moe",
     "has_flashinfer_cutedsl_grouped_gemm_nt_masked",
+    "has_flashinfer_cutedsl_moe_nvfp4",
     "has_flashinfer_fp8_blockscale_gemm",
     "has_nvidia_artifactory",
     "supports_trtllm_attention",