Update flashinfer to 0.6.1 (#15551)

python/pyproject.toml

@@ -28,8 +28,8 @@ dependencies = [
   "datasets",
   "einops",
   "fastapi",
-  "flashinfer_python==0.5.3", # keep it aligned with jit-cache version in Dockerfile
-  "flashinfer_cubin==0.5.3",
+  "flashinfer_python==0.6.1", # keep it aligned with jit-cache version in Dockerfile
+  "flashinfer_cubin==0.6.1",
   "gguf",
   "hf_transfer",
   "huggingface_hub",

python/sglang/srt/entrypoints/engine.py

@@ -800,7 +800,7 @@ def _set_envs_and_config(server_args: ServerArgs):
         if server_args.attention_backend == "flashinfer":
             assert_pkg_version(
                 "flashinfer_python",
-                "0.5.3",
+                "0.6.1",
                 "Please uninstall the old version and "
                 "reinstall the latest version by following the instructions "
                 "at https://docs.flashinfer.ai/installation.html.",

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

@@ -1299,7 +1299,6 @@ class FlashInferFP4MoE(FusedMoE):
             local_expert_offset=self.moe_ep_rank * self.num_local_experts,
             local_num_experts=self.num_local_experts,
             routed_scaling_factor=self.moe_runner_config.routed_scaling_factor,
-            tile_tokens_dim=None,
             # Respect the routing method configured for this layer (e.g., Renormalize for Qwen3),
             # instead of always assuming DeepSeekV3.
             routing_method_type=(

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

@@ -190,7 +190,6 @@ def fused_experts_none_to_flashinfer_trtllm_fp8(
                     if runner_config.routed_scaling_factor is not None
                     else 1.0
                 ),
-                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]),

python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -537,7 +537,6 @@ class CompressedTensorsW4A4Nvfp4MoEMethod(CompressedTensorsMoEMethod):
             local_expert_offset=layer.moe_ep_rank * layer.num_local_experts,
             local_num_experts=layer.num_local_experts,
             routed_scaling_factor=routed_scaling_factor,
-            tile_tokens_dim=None,
             routing_method_type=layer.routing_method_type,
             do_finalize=True,
             tune_max_num_tokens=next_power_of_2(hs_fp4.shape[0]),

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

@@ -783,7 +783,6 @@ class ModelOptFp8MoEMethod(FusedMoEMethodBase):
                         else 1.0
                     ),
                     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]),
                 )

python/sglang/srt/layers/quantization/mxfp4.py

@@ -674,7 +674,6 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                 layer.moe_ep_rank * layer.num_local_experts,  # local_expert_offset
                 layer.num_local_experts,  # local num experts
                 None,
-                None,  # tile_tokens_dim
                 1,  # routing_method_type, renormalize
                 True,  # do finalize
                 tune_max_num_tokens=next_power_of_2(x_quant.shape[0]),

python/sglang/srt/utils/common.py

@@ -2862,6 +2862,7 @@ def is_fa3_default_architecture(hf_config):
         "Olmo2ForCausalLM",
         "Gemma2ForCausalLM",
         "Gemma3ForConditionalGeneration",
+        "MixtralForCausalLM",
         "Qwen2ForCausalLM",
         "Qwen3ForCausalLM",
         "Qwen3MoeForCausalLM",