[FlashInfer v0.6.13] Use CuTe DSL backend for FlashInfer per-token NVFP4 quantization (#28220)

python/sglang/srt/layers/moe/moe_runner/flashinfer_trtllm.py

@@ -964,6 +964,7 @@ def fused_experts_none_to_flashinfer_trtllm_fp4(
             1.0 / (e4m3_max * 6.0),
             sfLayout=SfLayout.layout_linear,
             per_token_activation=True,
+            backend="cute-dsl",
         )
 
         seq_len, hidden_size = hidden_states.shape

python/sglang/srt/layers/quantization/nvfp4_online.py

@@ -225,7 +225,7 @@ class ModelOptNvFp4OnlineFusedMoEMethod(ModelOptNvFp4FusedMoEMethod):
             weight.contiguous(),
             1.0 / weight_scale_2,
             sfLayout=SfLayout.layout_linear,
-            backend="cuda",
+            backend="cute-dsl",
         )
         rows, cols = weight.shape
         weight_sf = weight_sf.view(torch.float8_e4m3fn).reshape(rows, cols // 16)

python/sglang/srt/model_loader/loader.py

@@ -817,10 +817,7 @@ class DefaultModelLoader(BaseModelLoader):
         if is_nvfp4_online:
             # Scope exact FP4 quantization math to load-time conversion only;
             # restore the original environment before serving starts.
-            with temp_set_env(
-                TRTLLM_DISABLE_FP4_QUANT_FAST_MATH="1",
-                FLASHINFER_DISABLE_FP4_QUANT_FAST_MATH="1",
-            ):
+            with temp_set_env(FLASHINFER_DISABLE_FP4_QUANT_FAST_MATH="1"):
                 model.load_weights(weights)
             if target_device.type == "cuda":
                 torch.cuda.synchronize()