[Fix] flashinfer_trtllm intermediate_size assertion with Qwen3 + TP=8 (#16824)

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

@@ -206,6 +206,16 @@ class FusedMoE(torch.nn.Module):
         self.use_triton_kernels = get_moe_runner_backend().is_triton_kernels()
         self.use_flashinfer_trtllm_moe = get_moe_runner_backend().is_flashinfer_trtllm()
 
+        # flashinfer_trtllm kernel requires intermediate_size to be a multiple of 128
+        # Pad the intermediate_size_per_partition if necessary
+        if (
+            self.use_flashinfer_trtllm_moe
+            and self.intermediate_size_per_partition % 128 != 0
+        ):
+            self.intermediate_size_per_partition = round_up(
+                self.intermediate_size_per_partition, 128
+            )
+
         self.quant_config = quant_config
         self.use_flashinfer_mxfp4_moe = get_moe_runner_backend().is_flashinfer_mxfp4()
         # TODO maybe we should remove this `if`, since `Mxfp4MoEMethod` does another round-up logic
@@ -395,7 +405,12 @@ class FusedMoE(torch.nn.Module):
         else:
             start = 0
 
-        if _is_cpu:
+        # Use narrow_padded_param_and_loaded_weight for:
+        # 1. CPU (always)
+        # 2. GPU with flashinfer_trtllm padding (when intermediate_size is padded to 128)
+        # This handles the case where the loaded weights are smaller than the padded expert_data
+        use_padded_loading = _is_cpu or self.use_flashinfer_trtllm_moe
+        if use_padded_loading:
             expert_data, loaded_weight = narrow_padded_param_and_loaded_weight(
                 expert_data,
                 loaded_weight,
@@ -464,7 +479,12 @@ class FusedMoE(torch.nn.Module):
             # for w2 in TP, it shards the input_features, i.e., shard_dim=2
             shard_size = expert_data.shape[shard_dim]
 
-        if _is_cpu:
+        # Use narrow_padded_param_and_loaded_weight for:
+        # 1. CPU (always)
+        # 2. GPU with flashinfer_trtllm padding (when intermediate_size is padded to 128)
+        # This handles the case where the loaded weights are smaller than the padded expert_data
+        use_padded_loading = _is_cpu or self.use_flashinfer_trtllm_moe
+        if use_padded_loading:
             expert_data, loaded_weight = narrow_padded_param_and_loaded_weight(
                 expert_data,
                 loaded_weight,

python/sglang/srt/server_args.py

@@ -42,6 +42,7 @@ from sglang.srt.utils.common import (
     get_device_memory_capacity,
     get_device_name,
     get_device_sm,
+    get_quantization_config,
     is_blackwell_supported,
     is_cuda,
     is_fa3_default_architecture,
@@ -1196,12 +1197,7 @@ class ServerArgs:
 
             # Set moe backend for DeepSeek
             if is_sm100_supported():
-                quantization_config = getattr(hf_config, "quantization_config", None)
-                quant_method = (
-                    quantization_config.get("quant_method")
-                    if quantization_config is not None
-                    else None
-                )
+                quant_method = get_quantization_config(hf_config)
                 if self.quantization is None:
                     # Default DeepSeek V3/R1 native FP8 when not explicitly set,
                     # Because we need this condition for an assertion in
@@ -1265,11 +1261,8 @@ class ServerArgs:
                 f"- Decode: {decode_attn_backend}\n"
             )
 
-            quantization_config = getattr(hf_config, "quantization_config", None)
-            is_mxfp4_quant_format = (
-                quantization_config is not None
-                and quantization_config.get("quant_method") == "mxfp4"
-            )
+            quant_method = get_quantization_config(hf_config)
+            is_mxfp4_quant_format = quant_method == "mxfp4"
             if is_mxfp4_quant_format:
                 # use bf16 for mxfp4 triton kernels
                 self.dtype = "bfloat16"
@@ -1445,16 +1438,14 @@ class ServerArgs:
             "Qwen3VLMoeForConditionalGeneration",
         ]:
             if is_sm100_supported():
-                quantization_config = getattr(hf_config, "quantization_config", None)
-                quant_method = (
-                    quantization_config.get("quant_method")
-                    if quantization_config is not None
-                    else None
-                )
+                quant_method = get_quantization_config(hf_config)
                 if self.quantization is None and quant_method is not None:
                     self.quantization = quant_method
                 if (
-                    self.quantization in ("fp8", "modelopt_fp4")
+                    (
+                        self.quantization in ("fp8", "modelopt_fp4")
+                        or self.quantization is None
+                    )
                     and self.moe_a2a_backend == "none"
                     and self.moe_runner_backend == "auto"
                 ):
@@ -1465,16 +1456,14 @@ class ServerArgs:
                     )
         elif model_arch in ["Qwen3NextForCausalLM"]:
             if is_sm100_supported():
-                quantization_config = getattr(hf_config, "quantization_config", None)
-                quant_method = (
-                    quantization_config.get("quant_method")
-                    if quantization_config is not None
-                    else None
-                )
+                quant_method = get_quantization_config(hf_config)
                 if self.quantization is None and quant_method is not None:
                     self.quantization = quant_method
                 if (
-                    (self.quantization == "fp8" or self.quantization == "modelopt_fp4")
+                    (
+                        self.quantization in ("fp8", "modelopt_fp4")
+                        or self.quantization is None
+                    )
                     and self.moe_a2a_backend == "none"
                     and self.moe_runner_backend == "auto"
                 ):

python/sglang/srt/utils/common.py

@@ -2708,6 +2708,14 @@ def has_hf_quant_config(model_path: str) -> bool:
         return False
 
 
+def get_quantization_config(hf_config) -> str | None:
+    """Extract quantization method from HuggingFace config."""
+    quantization_config = getattr(hf_config, "quantization_config", None)
+    if quantization_config is not None:
+        return quantization_config.get("quant_method")
+    return None
+
+
 def flatten_nested_list(nested_list):
     if isinstance(nested_list, list):
         return [