Replace [silu_and_mul_]scaled_fp4_group_quant by Flashinfer equivalent

benchmark/kernels/quantization/bench_fp4_quant.py

@@ -3,7 +3,10 @@
 
 import torch
 import triton
-from sgl_kernel import scaled_fp4_grouped_quant, silu_and_mul_scaled_fp4_grouped_quant
+from flashinfer import (
+    scaled_fp4_grouped_quantize,
+    silu_and_mul_scaled_nvfp4_experts_quantize,
+)
 from sgl_kernel.elementwise import silu_and_mul
 
 from sglang.srt.layers import deep_gemm_wrapper
@@ -14,11 +17,11 @@ def _test_accuracy_once(E, M, K, input_dtype, device):
     x = torch.randn(E, M, K, device=device, dtype=input_dtype)
     glb_scales = torch.ones((E,), dtype=torch.float32, device=device)
     masks = torch.full((E,), M, dtype=torch.int32, device=device)
-    out, blk_scales = silu_and_mul_scaled_fp4_grouped_quant(x, glb_scales, masks)
-    out1, blk_scales1 = scaled_fp4_grouped_quant(
+    out, blk_scales = silu_and_mul_scaled_nvfp4_experts_quantize(x, masks, glb_scales)
+    out1, blk_scales1 = scaled_fp4_grouped_quantize(
         silu_and_mul(x),
-        glb_scales,
         masks,
+        glb_scales,
     )
 
     torch.testing.assert_close(out, out1)
@@ -87,19 +90,19 @@ def benchmark(M, K, provider):
         )
     if provider == "cuda_unfused_fp4":
         ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: scaled_fp4_grouped_quant(
+            lambda: scaled_fp4_grouped_quantize(
                 silu_and_mul(x),
-                glb_scales,
                 masks,
+                glb_scales,
             ),
             quantiles=quantiles,
         )
     if provider == "cuda_fused_fp4":
         ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: silu_and_mul_scaled_fp4_grouped_quant(
+            lambda: silu_and_mul_scaled_nvfp4_experts_quantize(
                 x,
-                glb_scales,
                 masks,
+                glb_scales,
             ),
             quantiles=quantiles,
         )

docs/references/environment_variables.md

@@ -48,6 +48,7 @@ SGLang supports various environment variables that can be used to configure its
 
 | Environment Variable | Description | Default Value |
 | `SGLANG_DEEPEP_BF16_DISPATCH` | Use Bfloat16 for dispatch | `"false"` |
+| `SGLANG_CUTEDSL_MOE_NVFP4_DISPATCH` | Use nvfp4 for dispatch | `"false"` |
 
 ## Memory Management
 

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

@@ -1,11 +1,11 @@
 from typing import 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,
+from flashinfer import (
+    scaled_fp4_grouped_quantize,
+    silu_and_mul_scaled_nvfp4_experts_quantize,
 )
+from flashinfer.cute_dsl.blockscaled_gemm import grouped_gemm_nt_masked
 
 
 def get_cute_dtype(input: torch.Tensor) -> str:
@@ -97,10 +97,10 @@ def flashinfer_cutedsl_moe_masked(
             num_experts,
         ), f"input_global_scale must be (l,), got {input_global_scale.shape}"
 
-        a_q, a_q_sf = scaled_fp4_grouped_quant(
+        a_q, a_q_sf = scaled_fp4_grouped_quantize(
             hidden_states[0],
-            input_global_scale,
             masked_m,
+            input_global_scale,
         )
 
     assert w1.shape[-2] == 2 * n, f"w1 last-2 dim must be 2*n, got {w1.shape}"
@@ -148,10 +148,10 @@ def flashinfer_cutedsl_moe_masked(
     )  # in logical [m, n, l]
 
     # SILU and quantization
-    diq, diq_sf = silu_and_mul_scaled_fp4_grouped_quant(
+    diq, diq_sf = silu_and_mul_scaled_nvfp4_experts_quantize(
         gateup_output.permute(2, 0, 1),
-        a2_global_scale,
         masked_m,
+        a2_global_scale,
     )
 
     if down_start_event is not None:

sgl-kernel/tests/test_fp4_quantize.py

@@ -1,11 +1,10 @@
 import pytest
 import torch
-from sgl_kernel import (
-    scaled_fp4_grouped_quant,
-    scaled_fp4_quant,
-    silu_and_mul,
-    silu_and_mul_scaled_fp4_grouped_quant,
+from flashinfer import (
+    scaled_fp4_grouped_quantize,
+    silu_and_mul_scaled_nvfp4_experts_quantize,
 )
+from sgl_kernel import scaled_fp4_quant, silu_and_mul
 
 skip_condition = torch.cuda.get_device_capability() < (10, 0)
 
@@ -186,10 +185,10 @@ def test_quantize_to_fp4_grouped(shape):
     mask = torch.randint(1, max_m, (l,), dtype=torch.int32)
     tensor_amax = x.abs().amax(dim=(1, 2)).to(torch.float32)
     x_sf_global = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / tensor_amax
-    output, output_scales = scaled_fp4_grouped_quant(
+    output, output_scales = scaled_fp4_grouped_quantize(
         x,
-        x_sf_global,
         mask,
+        x_sf_global,
     )
     # output in logical (m, k, l), but its physical layout is (l, m, k).
     # So permute first to (l, m, k).
@@ -225,15 +224,15 @@ def test_silu_and_mul_quantize_to_fp4_grouped(shape):
     ref_y = silu_and_mul(x)
     tensor_amax = ref_y.abs().amax(dim=(1, 2)).to(torch.float32)
     y_sf_global = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / tensor_amax
-    ref_output, ref_output_scales = scaled_fp4_grouped_quant(
+    ref_output, ref_output_scales = scaled_fp4_grouped_quantize(
         ref_y,
-        y_sf_global,
         mask,
+        y_sf_global,
     )
-    output, output_scales = silu_and_mul_scaled_fp4_grouped_quant(
+    output, output_scales = silu_and_mul_scaled_nvfp4_experts_quantize(
         x,
-        y_sf_global,
         mask,
+        y_sf_global,
     )
 
     # output in logical (m, k, l), but its physical layout is (l, m, k).

test/srt/test_cutedsl_moe.py

@@ -3,8 +3,8 @@
 from typing import Callable
 
 import torch
-from flashinfer import fp4_quantize
-from sgl_kernel import scaled_fp4_grouped_quant, scaled_fp4_quant
+from flashinfer import fp4_quantize, scaled_fp4_grouped_quantize
+from sgl_kernel import scaled_fp4_quant
 from torch.nn import functional as F
 
 from sglang.srt.layers.activation import SiluAndMul
@@ -370,18 +370,18 @@ def test_flashinfer_cutedsl_moe_masked(self):
                         (num_experts,), dtype=torch.float32, device=hidden_states.device
                     )  # assume intermediate scale is 1.0
 
-                    w1_fp4, w1_blockscale = scaled_fp4_grouped_quant(
+                    w1_fp4, w1_blockscale = scaled_fp4_grouped_quantize(
                         w1,
-                        w1_global_scale,
                         torch.ones(num_experts, dtype=torch.int32, device=w1.device)
                         * 2
                         * inter_dim,
+                        w1_global_scale,
                     )
-                    w2_fp4, w2_blockscale = scaled_fp4_grouped_quant(
+                    w2_fp4, w2_blockscale = scaled_fp4_grouped_quantize(
                         w2,
-                        w2_global_scale,
                         torch.ones(num_experts, dtype=torch.int32, device=w2.device)
                         * hidden_dim,
+                        w2_global_scale,
                     )
 
                     w1_alpha = 1.0 / (input_global_scale * w1_global_scale)

test/srt/test_fp4_moe.py

@@ -3,9 +3,9 @@
 
 import pytest
 import torch
-from flashinfer import fp4_quantize
+from flashinfer import fp4_quantize, scaled_fp4_grouped_quantize
 from flashinfer.fused_moe import cutlass_fused_moe as flashinfer_cutlass_fused_moe
-from sgl_kernel import scaled_fp4_grouped_quant, scaled_fp4_quant, silu_and_mul
+from sgl_kernel import scaled_fp4_quant
 from torch.nn import functional as F
 
 from sglang.srt.layers.moe.cutlass_moe import cutlass_moe_fp4
@@ -190,16 +190,16 @@ def flashinfer_cutedsl_grouped_gemm_nt_masked(
 
     # hidden_states: [l, m, k]
     # weights: [l, n, k]
-    aq, aq_sf = scaled_fp4_grouped_quant(
+    aq, aq_sf = scaled_fp4_grouped_quantize(
         hidden_states,
-        input_global_scale,
         masked_m.to(hidden_states.device),
+        input_global_scale,
     )
     num_experts, n, k = weights.shape
-    bq, bq_sf = scaled_fp4_grouped_quant(
+    bq, bq_sf = scaled_fp4_grouped_quantize(
         weights,
-        w_global_scale,
         torch.ones(num_experts, device=weights.device, dtype=torch.int32) * n,
+        w_global_scale,
     )
 
     out = torch.zeros(