[Perf] Enable Flashinfer autotune by default (#14357)

docs/advanced_features/server_arguments.md

@@ -246,6 +246,7 @@ Please consult the documentation below and [server_args.py](https://github.com/s
 | `--nsa-prefill` | Choose the NSA backend for the prefill stage (overrides `--attention-backend` when running DeepSeek NSA-style attention). | `flashmla_sparse` | `flashmla_sparse`, `flashmla_decode`, `fa3`, `tilelang`, `aiter` |
 | `--nsa-decode` | Choose the NSA backend for the decode stage when running DeepSeek NSA-style attention. Overrides `--attention-backend` for decoding. | `flashmla_kv` | `flashmla_prefill`, `flashmla_kv`, `fa3`, `tilelang`, `aiter` |
 | `--fp8-gemm-backend` | Choose the runner backend for Blockwise FP8 GEMM operations. Options: 'auto' (default, auto-selects based on hardware), 'deep_gemm' (JIT-compiled; enabled by default on NVIDIA Hopper (SM90) and Blackwell (SM100) when DeepGEMM is installed), 'flashinfer_trtllm' (optimal for Blackwell and low-latency), 'cutlass' (optimal for Hopper/Blackwell GPUs and high-throughput), 'triton' (fallback, widely compatible), 'aiter' (ROCm only). **NOTE**: This replaces the deprecated environment variables SGLANG_ENABLE_FLASHINFER_FP8_GEMM and SGLANG_SUPPORT_CUTLASS_BLOCK_FP8. | `auto` | `auto`, `deep_gemm`, `flashinfer_trtllm`, `cutlass`, `triton`, `aiter` |
+| `--disable-flashinfer-autotune` | Flashinfer autotune is enabled by default. Set this flag to disable the autotune. | `False` | bool flag (set to enable) |
 
 ## Speculative decoding
 | Argument | Description | Defaults | Options |

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

@@ -54,6 +54,7 @@ from sglang.srt.utils import (
     is_cpu,
     is_flashinfer_available,
     is_hip,
+    next_power_of_2,
     round_up,
 )
 
@@ -1087,6 +1088,7 @@ class FlashInferFusedMoE(FusedMoE):
                     local_expert_offset=self.moe_ep_rank * self.num_local_experts,
                     local_num_experts=self.num_local_experts,
                     routing_method_type=self.routing_method_type,
+                    tune_max_num_tokens=next_power_of_2(hidden_states.shape[0]),
                 )
 
         else:
@@ -1228,6 +1230,7 @@ class FlashInferFP4MoE(FusedMoE):
             tile_tokens_dim=None,
             routing_method_type=routing_method_type,
             do_finalize=True,
+            tune_max_num_tokens=next_power_of_2(hs_fp4.shape[0]),
             output=symm_output,
         )[0]
 

python/sglang/srt/layers/quantization/fp8.py

@@ -69,6 +69,7 @@ from sglang.srt.utils import (
     is_sm90_supported,
     is_sm100_supported,
     log_info_on_rank0,
+    next_power_of_2,
     print_warning_once,
     set_weight_attrs,
     use_intel_amx_backend,
@@ -1384,6 +1385,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                     tile_tokens_dim=None,
                     routing_method_type=routing_method_type,
                     use_shuffled_weight=False,
+                    tune_max_num_tokens=next_power_of_2(a_q.shape[0]),
                 )
             else:
                 routing_bias_cast = (
@@ -1415,6 +1417,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                     ),
                     use_routing_scales_on_input=False,
                     routing_method_type=routing_method_type,
+                    tune_max_num_tokens=next_power_of_2(a_q.shape[0]),
                 )
 
     def maybe_apply_hip_fused_experts(

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

@@ -793,6 +793,7 @@ class ModelOptFp8MoEMethod(FusedMoEMethodBase):
                     use_routing_scales_on_input=use_routing_scales_on_input,
                     tile_tokens_dim=None,
                     routing_method_type=routing_method_type,
+                    tune_max_num_tokens=next_power_of_2(x.shape[0]),
                 )
 
             from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput

python/sglang/srt/model_executor/model_runner.py

@@ -2219,11 +2219,15 @@ class ModelRunner:
 
     def _should_run_flashinfer_autotune(self) -> bool:
         """Check if flashinfer autotune should be run."""
-        if not self.server_args.enable_flashinfer_autotune:
+        if self.server_args.disable_flashinfer_autotune:
             return False
 
-        backend_str = self.server_args.attention_backend
+        backend_str = self.server_args.moe_runner_backend
-        if backend_str not in ["flashinfer", "trtllm_mla", "trtllm_mha"]:
+        if backend_str not in [
+            "flashinfer_trtllm",
+            "flashinfer_cutlass",
+            "flashinfer_mxfp4",
+        ]:
             return False
 
         major, _ = torch.cuda.get_device_capability()

python/sglang/srt/server_args.py

@@ -418,7 +418,7 @@ class ServerArgs:
     fp8_gemm_runner_backend: str = "auto"
     nsa_prefill_backend: str = "flashmla_sparse"
     nsa_decode_backend: str = "fa3"
-    enable_flashinfer_autotune: bool = False
+    disable_flashinfer_autotune: bool = False
 
     # Speculative decoding
     speculative_algorithm: Optional[str] = None
@@ -3313,10 +3313,10 @@ class ServerArgs:
             "SGLANG_ENABLE_FLASHINFER_FP8_GEMM and SGLANG_SUPPORT_CUTLASS_BLOCK_FP8.",
         )
         parser.add_argument(
-            "--enable-flashinfer-autotune",
+            "--disable-flashinfer-autotune",
-            default=ServerArgs.enable_flashinfer_autotune,
+            default=ServerArgs.disable_flashinfer_autotune,
             action="store_true",
-            help="Enable FlashInfer autotuning for optimal kernel selection.",
+            help="Disable FlashInfer autotuning.",
         )
 
         # Speculative decoding

test/srt/test_deepseek_v3_fp4_4gpu.py

@@ -16,7 +16,7 @@ from sglang.test.test_utils import (
 )
 
 FULL_DEEPSEEK_V3_FP4_MODEL_PATH = "nvidia/DeepSeek-V3-0324-FP4"
-SERVER_LAUNCH_TIMEOUT = 1000
+SERVER_LAUNCH_TIMEOUT = 1200
 
 
 class TestDeepseekV3FP4(CustomTestCase):