[Compilation][WideEP] Enable Piecewise CUDAGraph for DeepEPHT (#24123)

vllm/config/compilation.py

@@ -546,7 +546,8 @@ class CompilationConfig:
             # full cudagraph outside the fx graph. This reduces some cpu
             # overhead when the runtime batch_size is not cudagraph captured.
             # see https://github.com/vllm-project/vllm/pull/20059 for details.
-            self.splitting_ops = self._attention_ops
+            # make a copy to avoid mutating the class-level list via reference.
+            self.splitting_ops = list(self._attention_ops)
         elif len(self.splitting_ops) == 0:
             logger.warning_once("Using piecewise compilation with empty "
                                 "splitting_ops.")
@@ -561,6 +562,18 @@ class CompilationConfig:
                 self.cudagraph_mode = CUDAGraphMode.FULL
             self.splitting_ops = []
 
+        if envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput":
+            # exclude MoE dispatch/combine from capture by ensuring
+            # piecewise splitting includes them, so communication remains
+            # outside CUDA graphs while compute can still be graphed.
+            moe_ops = [
+                "vllm.moe_forward",
+                "vllm.moe_forward_shared",
+            ]
+            for op in moe_ops:
+                if op not in self.splitting_ops:
+                    self.splitting_ops.append(op)
+
     def splitting_ops_contain_attention(self) -> bool:
         return self.splitting_ops is not None and all(
             op in self.splitting_ops for op in self._attention_ops)

vllm/platforms/cuda.py

@@ -183,16 +183,14 @@ class CudaPlatformBase(Platform):
         compilation_config = vllm_config.compilation_config
         if (envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput"
                 and parallel_config.data_parallel_size > 1
-                and compilation_config.cudagraph_mode != CUDAGraphMode.NONE):
+                and compilation_config.cudagraph_mode
+                not in [CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE]):
             logger.info(
-                "Data Parallel: disabling cudagraphs since DP "
-                "with DeepEP high-throughput kernels are not CUDA Graph "
-                "compatible. The DeepEP low-latency kernels are CUDA Graph "
-                "compatible. Set the all_to_all backend to deepep_low_latency "
-                "to use those kernels instead.")
-            compilation_config.cudagraph_mode = CUDAGraphMode.NONE
-            if model_config is not None:
-                model_config.enforce_eager = True
+                "Data Parallel with DeepEP high-throughput: using PIECEWISE "
+                "CUDA graphs and excluding MoE ops from capture. Set "
+                "VLLM_ALL2ALL_BACKEND=deepep_low_latency if you need MoE "
+                "graphs captured as well.")
+            compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
 
     @classmethod
     def get_current_memory_usage(cls,