[Bugfix][MoE] Snapshot max_cudagraph_capture_size into FusedMoEConfig (#44613)

Signed-off-by: aoshen02 <aoshen@inferact.ai>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

vllm/model_executor/layers/fused_moe/config.py

@@ -1266,6 +1266,8 @@ class FusedMoEConfig:
     # cannot silently select one and drop the clamp.
     swiglu_limit: float | None = None
 
+    max_capture_size: int = 0
+
     def __post_init__(self):
         if self.dp_size > 1:
             logger.debug_once(

vllm/model_executor/layers/fused_moe/experts/flashinfer_cutlass_moe.py

@@ -4,7 +4,6 @@
 import torch
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
-from vllm.config import get_current_vllm_config
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
@@ -94,9 +93,7 @@ class FlashInferExperts(mk.FusedMoEExpertsModular):
         # - pass per-block weight scales to the kernel
         # - skip input activation quantization (kernel applies scaling)
         self.use_deepseek_fp8_block_scale = quant_config.is_block_quantized
-        self.max_capture_size = (
-            get_current_vllm_config().compilation_config.max_cudagraph_capture_size
-        )
+        self.max_capture_size = moe_config.max_capture_size
         self.gemm1_clamp_limit: torch.Tensor | None = None
         if quant_config.gemm1_clamp_limit is not None:
             self.gemm1_clamp_limit = torch.tensor(

vllm/model_executor/layers/fused_moe/experts/trtllm_mxfp4_moe.py

@@ -79,11 +79,7 @@ class TrtLlmMxfp4ExpertsBase:
         else:
             self.gemm1_clamp_limit = None
 
-        from vllm.config import get_current_vllm_config
-
-        self.max_capture_size = (
-            get_current_vllm_config().compilation_config.max_cudagraph_capture_size
-        )
+        self.max_capture_size = moe_config.max_capture_size
 
     @staticmethod
     def _supports_current_device() -> bool:

vllm/model_executor/layers/fused_moe/layer.py

@@ -326,6 +326,7 @@ class FusedMoE(PluggableLayer):
             moe_backend=vllm_config.kernel_config.moe_backend,
             router_logits_dtype=router_logits_dtype,
             max_num_tokens=max_num_batched_tokens,
+            max_capture_size=compilation_config.max_cudagraph_capture_size,
             has_bias=has_bias,
             is_act_and_mul=is_act_and_mul,
             is_lora_enabled=vllm_config.lora_config is not None,

vllm/model_executor/layers/quantization/mxfp4.py

@@ -3,7 +3,6 @@
 
 import torch
 
-from vllm.config import get_current_vllm_config
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
 from vllm.model_executor.layers.fused_moe import (
@@ -141,9 +140,7 @@ class GptOssMxfp4MoEMethod(FusedMoEMethodBase):
         self.weight_dtype = "gpt_oss_mxfp4"
         self.mxfp4_backend, self.experts_cls = select_mxfp4_moe_backend(moe)
 
-        self.max_capture_size = (
-            get_current_vllm_config().compilation_config.max_cudagraph_capture_size
-        )
+        self.max_capture_size = moe.max_capture_size
 
         self._cache_permute_indices: dict[torch.Size, torch.Tensor] = {}
         self.moe_kernel: mk.FusedMoEKernel | None = None
@@ -475,9 +472,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         self.weight_dtype = "mxfp4"
         self.mxfp4_backend, self.experts_cls = select_deepseek_v4_mxfp4_moe_backend(moe)
 
-        self.max_capture_size = (
-            get_current_vllm_config().compilation_config.max_cudagraph_capture_size
-        )
+        self.max_capture_size = moe.max_capture_size
 
         self._cache_permute_indices: dict[torch.Size, torch.Tensor] = {}
         self.moe_kernel: mk.FusedMoEKernel | None = None