[NVIDIA] [3/N] Nvfp4 Masked Gemm: Add flashinfer grouped_gemm_nt_masked (#9199)

docs/references/environment_variables.md

@@ -40,6 +40,11 @@ SGLang supports various environment variables that can be used to configure its
 | `SGL_DG_USE_NVRTC` | Use NVRTC (instead of Triton) for JIT compilation (Experimental) | `"0"` |
 | `SGL_USE_DEEPGEMM_BMM` | Use DeepGEMM for Batched Matrix Multiplication (BMM) operations | `"false"` |
 
+## DeepEP Configuration
+
+| Environment Variable | Description | Default Value |
+| `SGLANG_DEEPEP_BF16_DISPATCH` | Use Bfloat16 for dispatch | `"false"` |
+
 ## Memory Management
 
 | Environment Variable | Description | Default Value |

python/sglang/srt/layers/moe/ep_moe/layer.py

@@ -459,6 +459,8 @@ class DeepEPMoE(EPMoE):
             assert deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM and self.use_fp8_w8a8
             return self.forward_deepgemm_contiguous(dispatch_output)
         elif DispatchOutputChecker.format_is_deepep_ll(dispatch_output):
+            if get_moe_runner_backend().is_flashinfer_cutedsl():
+                return self.forward_flashinfer_cutedsl(dispatch_output)
             assert deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM and self.use_fp8_w8a8
             return self.forward_deepgemm_masked(dispatch_output)
         else:
@@ -638,6 +640,22 @@ class DeepEPMoE(EPMoE):
 
         return gather_out
 
+    def forward_flashinfer_cutedsl(
+        self,
+        dispatch_output: DeepEPLLOutput,
+    ):
+        hidden_states, _, _, masked_m, _ = dispatch_output
+        assert self.quant_method is not None
+        assert self.moe_runner_config.activation == "silu"
+
+        output = self.quant_method.apply_without_routing_weights(
+            layer=self,
+            x=hidden_states,
+            masked_m=masked_m,
+            moe_runner_config=self.moe_runner_config,
+        )
+        return output
+
     def forward_deepgemm_masked(
         self,
         dispatch_output: DeepEPLLOutput,

python/sglang/srt/layers/moe/flashinfer_cutedsl_moe.py

+from typing import Any, Dict, Optional
+
+import torch
+from flashinfer.cute_dsl.blockscaled_gemm import grouped_gemm_nt_masked
+from sgl_kernel.gemm import (
+    scaled_fp4_grouped_quant,
+    silu_and_mul_scaled_fp4_grouped_quant,
+)
+
+
+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,
+    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,
+):
+    """
+    Perform masked Mixture-of-Experts computation with FlashInfer's CuteDSL
+    kernels.
+
+    Args:
+        hidden_states (torch.Tensor): [num_experts, m, k], bf16
+        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
+
+    Notes:
+        - Assumes max(masked_m) <= m.
+    """
+
+    # === Assertions on dtypes ===
+    assert (
+        input_global_scale.dtype == torch.float32
+    ), f"input_global_scale must be float32, got {input_global_scale.dtype}"
+    assert w1.dtype == torch.uint8, f"w1 must be uint8 (fp4 packed), 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 (fp4 packed), 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
+    num_experts, m, k = hidden_states.shape
+
+    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 input_global_scale.shape == (
+        num_experts,
+    ), f"input_global_scale must be (l,), got {input_global_scale.shape}"
+    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}"
+
+    aq, aq_sf = scaled_fp4_grouped_quant(
+        hidden_states,
+        input_global_scale,
+        masked_m,
+    )
+    gateup_output = torch.empty(
+        (num_experts, m, n * 2), dtype=hidden_states.dtype, device=aq.device
+    )
+    gateup_output = gateup_output.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"
+
+    c_dtype = get_cute_dtype(hidden_states)
+
+    # Gemm1
+
+    grouped_gemm_nt_masked(
+        (aq, aq_sf),
+        (w1.permute(1, 2, 0), w1_blockscale),
+        gateup_output,
+        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_fp4_grouped_quant(
+        gateup_output.permute(2, 0, 1),
+        a2_global_scale,
+        masked_m,
+    )
+
+    # Gemm2
+    out = torch.empty_like(hidden_states)
+    out = out.permute(1, 2, 0)  # requirement of kernel
+    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]
+    return out.permute(2, 0, 1)

python/sglang/srt/layers/moe/token_dispatcher/deepep.py

@@ -508,7 +508,8 @@ class _DeepEPDispatcherImplLowLatency(_DeepEPDispatcherImplBase):
         hidden_states, masked_m, event, hook = self._dispatch_core(
             hidden_states,
             topk_idx,
-            use_fp8=True,
+            # TODO(shuw): pending https://github.com/deepseek-ai/DeepEP/pull/341
+            use_fp8=not get_bool_env_var("SGLANG_DEEPEP_BF16_DISPATCH"),
         )
         return (
             hidden_states,

python/sglang/srt/layers/moe/utils.py

@@ -49,6 +49,7 @@ class MoeRunnerBackend(Enum):
     FLASHINFER = "flashinfer_trtllm"
     FLASHINFER_CUTLASS = "flashinfer_cutlass"
     FLASHINFER_MXFP4 = "flashinfer_mxfp4"
+    FLASHINFER_CUTEDSL = "flashinfer_cutedsl"
 
     def is_auto(self):
         return self == MoeRunnerBackend.AUTO
@@ -65,6 +66,9 @@ class MoeRunnerBackend(Enum):
     def is_flashinfer_cutlass(self):
         return self == MoeRunnerBackend.FLASHINFER_CUTLASS
 
+    def is_flashinfer_cutedsl(self):
+        return self == MoeRunnerBackend.FLASHINFER_CUTEDSL
+
     def is_flashinfer_mxfp4(self):
         return self == MoeRunnerBackend.FLASHINFER_MXFP4
 

python/sglang/srt/layers/quantization/modelopt_quant.py

@@ -878,6 +878,13 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
         """Access the global enable_flashinfer_cutlass_moe setting."""
         return get_moe_runner_backend().is_flashinfer_cutlass()
 
+    @property
+    def enable_flashinfer_cutedsl_moe(self) -> bool:
+        from sglang.srt.layers.moe import get_moe_runner_backend
+
+        """Access the global enable_flashinfer_cutedsl_moe setting."""
+        return get_moe_runner_backend().is_flashinfer_cutedsl()
+
     def create_weights(
         self,
         layer: torch.nn.Module,
@@ -1398,5 +1405,38 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
             apply_router_weight_on_input=moe_runner_config.apply_router_weight_on_input,
         ).to(x.dtype)
         # Scale by routed_scaling_factor is fused into select_experts.
-
         return StandardCombineInput(hidden_states=output)
+
+    def apply_without_routing_weights(
+        self,
+        layer: FusedMoE,
+        x: torch.Tensor,
+        masked_m: torch.Tensor,
+        moe_runner_config: MoeRunnerConfig,
+    ) -> torch.Tensor:
+        assert (
+            moe_runner_config.activation == "silu"
+        ), "Only SiLU activation is supported."
+
+        assert self.enable_flashinfer_cutedsl_moe, "only support flashinfer cutedsl moe"
+        assert (
+            not moe_runner_config.apply_router_weight_on_input
+        ), "apply_router_weight_on_input is not supported for Flashinfer"
+
+        from sglang.srt.layers.moe.flashinfer_cutedsl_moe import (
+            flashinfer_cutedsl_moe_masked,
+        )
+
+        out = flashinfer_cutedsl_moe_masked(
+            hidden_states=x,
+            input_global_scale=layer.w13_input_scale_quant,
+            w1=layer.w13_weight,
+            w1_blockscale=layer.w13_blockscale_swizzled,
+            w1_alpha=layer.g1_alphas,
+            w2=layer.w2_weight,
+            a2_global_scale=layer.w2_input_scale_quant,
+            w2_blockscale=layer.w2_blockscale_swizzled,
+            w2_alpha=layer.g2_alphas,
+            masked_m=masked_m,
+        )
+        return out

python/sglang/srt/models/deepseek_v2.py

@@ -673,10 +673,14 @@ class DeepseekV2MoE(nn.Module):
 
         if shared_output is not None:
             x = shared_output
-            x.add_(final_hidden_states, alpha=self.routed_scaling_factor)
+            if self.experts.should_fuse_routed_scaling_factor_in_topk():
+                x.add_(final_hidden_states)
+            else:
+                x.add_(final_hidden_states, alpha=self.routed_scaling_factor)
             final_hidden_states = x
         else:
-            final_hidden_states *= self.routed_scaling_factor
+            if not self.experts.should_fuse_routed_scaling_factor_in_topk():
+                final_hidden_states *= self.routed_scaling_factor
 
         return final_hidden_states
 

python/sglang/srt/server_args.py

@@ -399,6 +399,7 @@ class ServerArgs:
     enable_ep_moe: bool = False
     enable_deepep_moe: bool = False
     enable_flashinfer_cutlass_moe: bool = False
+    enable_flashinfer_cutedsl_moe: bool = False
     enable_flashinfer_trtllm_moe: bool = False
     enable_triton_kernel_moe: bool = False
     enable_flashinfer_mxfp4_moe: bool = False
@@ -420,6 +421,11 @@ class ServerArgs:
             print_deprecated_warning(
                 "NOTE: --enable-triton-kernel-moe is deprecated. Please set `--moe-runner-backend` to 'triton_kernel' instead."
             )
+        if self.enable_flashinfer_cutedsl_moe:
+            self.moe_runner_backend = "flashinfer_cutedsl"
+            print_deprecated_warning(
+                "NOTE: --enable-flashinfer-cutedsl-moe is deprecated. Please set `--moe-runner-backend` to 'flashinfer_cutedsl' instead."
+            )
         if self.enable_flashinfer_cutlass_moe:
             self.moe_runner_backend = "flashinfer_cutlass"
             print_deprecated_warning(
@@ -1622,6 +1628,7 @@ class ServerArgs:
                 "flashinfer_trtllm",
                 "flashinfer_cutlass",
                 "flashinfer_mxfp4",
+                "flashinfer_cutedsl",
             ],
             default=ServerArgs.moe_runner_backend,
             help="Choose the runner backend for MoE.",
@@ -2204,6 +2211,11 @@ class ServerArgs:
             action="store_true",
             help="(Deprecated) Enable FlashInfer CUTLASS MoE backend for modelopt_fp4 quant on Blackwell. Supports MoE-EP",
         )
+        parser.add_argument(
+            "--enable-flashinfer-cutedsl-moe",
+            action="store_true",
+            help="(Deprecated) Enable FlashInfer CuteDSL MoE backend for modelopt_fp4 quant on Blackwell. Supports MoE-EP",
+        )
         parser.add_argument(
             "--enable-flashinfer-trtllm-moe",
             action="store_true",

python/sglang/test/test_fp4_moe.py

@@ -3,12 +3,15 @@ from typing import Callable
 
 import pytest
 import torch
+from flashinfer import fp4_quantize
 from flashinfer.fused_moe import cutlass_fused_moe as flashinfer_cutlass_fused_moe
-from sgl_kernel import scaled_fp4_quant
+from sgl_kernel import scaled_fp4_grouped_quant, scaled_fp4_quant
+from torch.nn import functional as F
 
 from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.moe.cutlass_moe import cutlass_moe_fp4
 from sglang.srt.layers.moe.cutlass_moe_params import CutlassMoEParams, CutlassMoEType
+from sglang.srt.layers.moe.flashinfer_cutedsl_moe import flashinfer_cutedsl_moe_masked
 from sglang.srt.layers.moe.topk import TopKConfig, select_experts
 
 if torch.cuda.get_device_capability() < (10, 0):
@@ -78,6 +81,37 @@ def break_fp4_bytes(a, dtype):
     return values.reshape(m, n * 2).to(dtype=dtype)
 
 
+def compute_routing(router_logits: torch.Tensor, top_k: int):
+    routing_weights = torch.softmax(router_logits, dim=1, dtype=torch.float)
+    routing_weights, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+    routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+    routing_weights = routing_weights.float()
+    return routing_weights, selected_experts
+
+
+def prepare_inputs(
+    hidden_states: torch.Tensor,
+    router_logits: torch.Tensor,
+    num_experts: int,
+    topk: int,
+):
+    routing_weights, topk_idx = compute_routing(router_logits, topk)
+
+    masked_m = []
+    for i in range(num_experts):
+        mask = topk_idx.view(-1) == i
+        masked_m.append(mask.sum())
+
+    masked_m = torch.tensor(masked_m, dtype=torch.int32)
+    hidden_states_3d = torch.empty(
+        (num_experts, max(masked_m), hidden_states.shape[1]), dtype=hidden_states.dtype
+    )
+    for i in range(num_experts):
+        hidden_states_3d[i, : masked_m[i], :] = hidden_states[topk_idx.view(-1) == i]
+
+    return hidden_states_3d, masked_m, topk_idx, routing_weights
+
+
 MNK_FACTORS = [
     (2, 1024, 1024),
     (2, 1024, 1536),
@@ -114,6 +148,99 @@ def torch_moe(a, w1, w2, score, topk, expert_map):
     ).sum(dim=1)
 
 
+def torch_moe_nvfp4(a, w1, w2, topk, topk_weight, topk_ids):
+    B, D = a.shape
+    a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
+    out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
+
+    topk_weight = topk_weight.view(-1)
+    topk_ids = topk_ids.view(-1)
+
+    for i in range(w1.shape[0]):
+        mask = topk_ids == i
+        if mask.sum():
+            m = w1[i].shape[0]
+            assert m % 2 == 0
+            # Note: w1 and w3 are swapped!
+            w3_expert, w1_expert = w1[i][m // 2 :, :], w1[i][: m // 2, :]
+            inter = F.silu(a[mask] @ w1_expert.t()) * (a[mask] @ w3_expert.t())
+            inter_gs = torch.tensor(1.0).cuda()
+            inter_q, inter_blockscale = fp4_quantize(inter, inter_gs)
+            inter = dequantize_nvfp4_to_dtype(
+                inter_q,
+                inter_blockscale,
+                inter_gs,
+                dtype=inter.dtype,
+                device=inter.device,
+                block_size=16,
+            ).cuda()
+            out[mask] = inter @ w2[i].transpose(0, 1)
+    return (
+        out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
+    ).sum(dim=1)
+
+
+def flashinfer_cutedsl_grouped_gemm_nt_masked(
+    hidden_states: torch.Tensor,  # 3d
+    input_global_scale: torch.Tensor,  # (l,)
+    weights: torch.Tensor,
+    w_global_scale: torch.Tensor,  # (l,)
+    masked_m: torch.Tensor,
+):
+    from flashinfer.cute_dsl.blockscaled_gemm import grouped_gemm_nt_masked
+
+    # hidden_states: [l, m, k]
+    # weights: [l, n, k]
+    aq, aq_sf = scaled_fp4_grouped_quant(
+        hidden_states,
+        input_global_scale,
+        masked_m.to(hidden_states.device),
+    )
+    num_experts, n, k = weights.shape
+    bq, bq_sf = scaled_fp4_grouped_quant(
+        weights,
+        w_global_scale,
+        torch.ones(num_experts, device=weights.device, dtype=torch.int32) * n,
+    )
+
+    out = torch.zeros(
+        (num_experts, max(masked_m), n), dtype=weights.dtype, device=aq.device
+    )
+    out = out.permute(1, 2, 0)  # requirement of kernel
+    sf_vec_size = 16
+    ab_dtype = "float4_e2m1fn"
+    sf_dtype = "float8_e4m3fn"
+    c_dtype = "bfloat16"
+    alpha = 1.0 / (input_global_scale * w_global_scale).to(out.dtype).view(
+        1, 1, num_experts
+    )
+
+    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}")
+
+    grouped_gemm_nt_masked(
+        (aq, aq_sf),
+        (bq, bq_sf),
+        out,
+        masked_m.to(aq.device),
+        ab_dtype=ab_dtype,
+        sf_dtype=sf_dtype,
+        c_dtype=c_dtype,
+        sf_vec_size=sf_vec_size,
+        alpha=alpha,
+        alpha_dtype=get_cute_dtype(alpha),
+    )
+
+    return out
+
+
 def check_moe(
     m: int,
     n: int,
@@ -324,6 +451,248 @@ def test_flashinfer_fp4_moe_no_graph(
     check_moe(m, n, k, e, topk, dtype, flashinfer_moe_impl, flip_w13=True)
 
 
+@pytest.mark.parametrize("bs, hidden_dim, inter_dim", [(2, 128, 256), (16, 128, 512)])
+@pytest.mark.parametrize("topk", [1, 2, 4])
+@torch.inference_mode()
+def test_flashinfer_cutedsl_moe_masked(
+    bs: int, hidden_dim: int, inter_dim: int, topk: int
+):
+    torch.manual_seed(42)
+    device = "cuda"
+    dtype = torch.bfloat16
+    num_experts = 8
+    hidden_states = (
+        torch.randn(bs, hidden_dim, dtype=torch.bfloat16, device=device) / 5.0
+    )
+    w1 = (
+        torch.randn(
+            num_experts, 2 * inter_dim, hidden_dim, dtype=torch.bfloat16, device=device
+        )
+        / 10.0
+    )
+    w2 = (
+        torch.randn(
+            num_experts, hidden_dim, inter_dim, dtype=torch.bfloat16, device=device
+        )
+        / 10.0
+    )
+    router_logits = torch.randn(bs, num_experts, dtype=torch.float32)
+
+    hidden_states_expanded = (
+        hidden_states.view(bs, -1, hidden_dim)
+        .repeat(1, topk, 1)
+        .reshape(-1, hidden_dim)
+    )
+    hidden_states_3d, masked_m, topk_idx, routing_weights = prepare_inputs(
+        hidden_states_expanded, router_logits, num_experts, topk
+    )
+
+    w1_amax = w1.abs().amax(dim=(1, 2)).to(torch.float32).to(w1.device)
+    w2_amax = w2.abs().amax(dim=(1, 2)).to(torch.float32).to(w2.device)
+    input_global_scale = torch.ones(
+        (num_experts,), dtype=torch.float32, device=hidden_states.device
+    )
+
+    w1_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w1_amax
+    w2_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w2_amax
+    a2_global_scale = torch.ones(
+        (num_experts,), dtype=torch.float32, device=hidden_states.device
+    )  # assume intermediate scale is 1.0
+
+    w1_fp4, w1_blockscale = scaled_fp4_grouped_quant(
+        w1,
+        w1_global_scale,
+        torch.ones(num_experts, dtype=torch.int32, device=w1.device) * 2 * inter_dim,
+    )
+    w2_fp4, w2_blockscale = scaled_fp4_grouped_quant(
+        w2,
+        w2_global_scale,
+        torch.ones(num_experts, dtype=torch.int32, device=w2.device) * hidden_dim,
+    )
+
+    w1_alpha = 1.0 / (input_global_scale * w1_global_scale)
+    w2_alpha = 1.0 / (a2_global_scale * w2_global_scale)
+
+    out = flashinfer_cutedsl_moe_masked(
+        hidden_states_3d.to(hidden_states.device),
+        input_global_scale,
+        w1_fp4.permute(2, 0, 1),
+        w1_blockscale,
+        w1_alpha,
+        w2_fp4.permute(2, 0, 1),
+        a2_global_scale,
+        w2_blockscale,
+        w2_alpha,
+        masked_m.to(hidden_states.device),
+    )
+
+    # reference
+    a_fp4, a_scale_interleaved = fp4_quantize(hidden_states, input_global_scale)
+    a_in_dtype = dequantize_nvfp4_to_dtype(
+        a_fp4,
+        a_scale_interleaved,
+        input_global_scale,
+        dtype=hidden_states.dtype,
+        device=hidden_states.device,
+        block_size=16,
+    )
+    w1_d = torch.empty(
+        (num_experts, 2 * inter_dim, hidden_dim), device=w1.device, dtype=w1.dtype
+    )
+    w2_d = torch.empty(
+        (num_experts, hidden_dim, inter_dim), device=w2.device, dtype=w2.dtype
+    )
+
+    for idx in range(0, num_experts):
+        w1_fp4_sliced, w1_blockscale_sliced = fp4_quantize(
+            w1[idx], w1_global_scale[idx]
+        )
+        w2_fp4_sliced, w2_blockscale_sliced = fp4_quantize(
+            w2[idx], w2_global_scale[idx]
+        )
+        w1_d[idx] = dequantize_nvfp4_to_dtype(
+            w1_fp4_sliced,
+            w1_blockscale_sliced,
+            w1_global_scale[idx],
+            dtype=w1.dtype,
+            device=w1.device,
+            block_size=16,
+        )
+        w2_d[idx] = dequantize_nvfp4_to_dtype(
+            w2_fp4_sliced,
+            w2_blockscale_sliced,
+            w2_global_scale[idx],
+            dtype=w2.dtype,
+            device=w2.device,
+            block_size=16,
+        )
+
+    ref_output = torch_moe_nvfp4(
+        a_in_dtype,
+        w1_d,
+        w2_d,
+        topk,
+        routing_weights.to(a_in_dtype.device),
+        topk_idx.to(a_in_dtype.device),
+    )
+    out_weighted = torch.zeros_like(ref_output, device=out.device, dtype=out.dtype)
+
+    positions = torch.nonzero(masked_m[topk_idx], as_tuple=False)
+    rows, cols = positions[:, 0], positions[:, 1]
+    experts = topk_idx[rows, cols]
+    for i in range(num_experts):
+        mask = experts == i
+        if mask.any():
+            idx = torch.nonzero(mask, as_tuple=False).squeeze(-1)
+            r, c = rows[idx], cols[idx]
+            out_weighted[r] += out[i, : len(r), :] * routing_weights[r, c].to(
+                out.device
+            ).unsqueeze(-1)
+    torch.testing.assert_close(
+        out_weighted.cpu(), ref_output.cpu(), atol=5e-2, rtol=5e-2
+    )
+
+
+@pytest.mark.parametrize(
+    "bs, hidden_dim, inter_dim, topk", [(2, 128, 256, 2), (16, 128, 512, 5)]
+)
+@torch.inference_mode()
+def test_grouped_gemm_nt_masked(
+    bs: int, hidden_dim: int, inter_dim: int, topk: int
+) -> None:
+    torch.manual_seed(42)
+    B = bs
+    D = hidden_dim
+    N = inter_dim
+    num_experts = 8
+    hidden_states = torch.randn(B, D, dtype=torch.bfloat16, device="cuda")
+    weights = torch.randn(num_experts, N, D, dtype=torch.bfloat16, device="cuda")
+    router_logits = torch.randn(B, num_experts, dtype=torch.float32)
+
+    hidden_states_expanded = (
+        hidden_states.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
+    )
+    hidden_states_3d, masked_m, topk_idx, _ = prepare_inputs(
+        hidden_states_expanded, router_logits, num_experts, topk
+    )
+
+    # reference
+    out = torch.zeros(
+        (B * topk, weights.shape[1]), dtype=weights.dtype, device=weights.device
+    )
+    for i in range(num_experts):
+        mask = topk_idx.view(-1) == i
+        if mask.sum():
+            lhs = hidden_states_expanded[mask]
+            rhs = weights[i]
+            a_amax = lhs.abs().max().to(torch.float32).to(hidden_states.device)
+            b_amax = rhs.abs().amax().to(torch.float32).to(weights.device)
+            a_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / a_amax
+            b_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / b_amax
+
+            lhsq, lhsq_sf = fp4_quantize(
+                lhs,
+                a_gs,
+            )
+            rhsq, rhsq_sf = fp4_quantize(
+                rhs,
+                b_gs,
+            )
+
+            lhs_in_dtype = dequantize_nvfp4_to_dtype(
+                lhsq,
+                lhsq_sf,
+                a_gs,
+                dtype=hidden_states.dtype,
+                device=hidden_states.device,
+                block_size=16,
+            )
+
+            rhs_in_dtype = dequantize_nvfp4_to_dtype(
+                rhsq,
+                rhsq_sf,
+                b_gs,
+                dtype=hidden_states.dtype,
+                device=hidden_states.device,
+                block_size=16,
+            )
+            out[mask] = lhs_in_dtype @ rhs_in_dtype.t()
+
+    a_amax = (
+        hidden_states_3d.abs()
+        .amax(dim=(1, 2))
+        .to(torch.float32)
+        .to(hidden_states.device)
+    )
+    b_amax = weights.abs().amax(dim=(1, 2)).to(torch.float32).to(weights.device)
+    a_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / a_amax
+    b_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / b_amax
+    out_flashinfer = flashinfer_cutedsl_grouped_gemm_nt_masked(
+        hidden_states_3d.to(hidden_states.device), a_gs, weights, b_gs, masked_m
+    )
+
+    # re-pack out into [num_experts, max_m, n]
+    out_ref = torch.zeros(
+        (num_experts, max(masked_m), weights.shape[1]), dtype=out.dtype
+    )
+    expert_slot = [0] * num_experts
+    for i, expert_id in enumerate(topk_idx.view(-1).tolist()):
+        out_ref[expert_id, expert_slot[expert_id], :] = out[i]
+        expert_slot[expert_id] += 1
+
+    # Note: just to compare the masked position due to cutedsl may write nan
+    # into unmasked position.
+    for i in range(num_experts):
+        torch.testing.assert_close(
+            out_flashinfer.permute(2, 0, 1)[i, : masked_m[i]],
+            out_ref.to(out_flashinfer.device)[i, : masked_m[i]],
+            atol=1e-1,
+            rtol=5e-2,
+        )
+
+
 if __name__ == "__main__":
     test_cutlass_fp4_moe_no_graph(224, 1024, 1024, 256, 8, torch.half)
     test_flashinfer_fp4_moe_no_graph(224, 1024, 1024, 256, 8, torch.half)
+    test_flashinfer_cutedsl_moe_masked(16, 128, 512, 4)
+    test_grouped_gemm_nt_masked(16, 128, 512, 4)

python/sglang/test/test_utils.py

@@ -53,6 +53,9 @@ DEFAULT_MLA_FP8_MODEL_NAME_FOR_TEST = "neuralmagic/DeepSeek-Coder-V2-Lite-Instru
 DEFAULT_MODEL_NAME_FOR_TEST_MLA = "lmsys/sglang-ci-dsv3-test"
 DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN = "lmsys/sglang-ci-dsv3-test-NextN"
 
+# NVFP4 models
+DEFAULT_DEEPSEEK_NVFP4_MODEL_FOR_TEST = "nvidia/DeepSeek-R1-0528-FP4"
+
 # FP8 models
 DEFAULT_MODEL_NAME_FOR_TEST_FP8 = "neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8"
 DEFAULT_MODEL_NAME_FOR_ACCURACY_TEST_FP8 = "neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8"

test/srt/test_cutedsl_flashinfer_8gpu.py

+import os
+import unittest
+from types import SimpleNamespace
+
+import requests
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
+from sglang.test.test_utils import (
+    DEFAULT_DEEPSEEK_NVFP4_MODEL_FOR_TEST,
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    popen_launch_server,
+    try_cached_model,
+)
+
+
+class TestDeepseekR1Nvfp4CuteDSLDeepEP(CustomTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = try_cached_model(DEFAULT_DEEPSEEK_NVFP4_MODEL_FOR_TEST)
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        other_args = [
+            "--trust-remote-code",
+            "--disable-radix-cache",
+            "--max-running-requests",
+            "256",
+            "--chunked-prefill-size",
+            "2048",
+            "--tp",
+            "8",
+            "--dp",
+            "8",
+            "--enable-dp-attention",
+            "--enable-ep-moe",
+            "--quantization",
+            "modelopt_fp4",
+            "--enable-flashinfer-cutedsl-moe",
+            "--enable-deepep-moe",
+            "--deepep-mode",
+            "low_latency",
+        ]
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=other_args,
+            env={
+                **os.environ,
+                "SGLANG_DEEPEP_BF16_DISPATCH": "1",
+                "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
+            },
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(self):
+        args = SimpleNamespace(
+            num_shots=5,
+            data_path=None,
+            num_questions=512,
+            parallel=512,
+            max_new_tokens=512,
+            host="http://127.0.0.1",
+            port=int(self.base_url.split(":")[-1]),
+        )
+        metrics = run_eval_few_shot_gsm8k(args)
+        print(f"Eval accuracy of GSM8K: {metrics=}")
+
+        self.assertGreater(metrics["accuracy"], 0.92)
+
+
+if __name__ == "__main__":
+    unittest.main()