Expert Parallelism (EP) Support for DeepSeek V2 (#12583)

vllm/config.py

@@ -677,6 +677,23 @@ class ModelConfig:
                 "fallback to the eager mode.")
             self.enforce_eager = True
 
+    def _verify_with_expert_parallelism(self) -> None:
+        num_expert_names = [
+            "moe_num_experts",  # Dbrx
+            "num_experts",  # Jamba
+            "n_routed_experts",  # DeepSeek
+            "num_local_experts",  # Mixtral
+        ]
+        num_experts = 0
+        for name in num_expert_names:
+            num_experts = getattr(self.hf_text_config, name, 0)
+            if num_experts > 0:
+                break
+        if num_experts < 1:
+            raise ValueError(
+                "Number of experts in the model must be greater than 0 "
+                "when expert parallelism is enabled.")
+
     def verify_async_output_proc(self, parallel_config, speculative_config,
                                  device_config) -> None:
         if not self.use_async_output_proc:
@@ -730,6 +747,9 @@ class ModelConfig:
                 " must be divisible by tensor parallel size "
                 f"({tensor_parallel_size}).")
 
+        if envs.VLLM_TEST_ENABLE_EP:
+            self._verify_with_expert_parallelism()
+
         pipeline_parallel_size = parallel_config.pipeline_parallel_size
         if pipeline_parallel_size > 1:
             architectures = getattr(self.hf_config, "architectures", [])