[Bugfix][Model] Fix inference for Hunyuan dense models (vllm-project#25354)

Signed-off-by: anion <1005128408@qq.com>
Signed-off-by: Anion <123177548+Anionex@users.noreply.github.com>

Author: Anionex

vllm/model_executor/models/hunyuan_v1.py

@@ -888,7 +888,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
         return loaded_params
 
 
-class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts):
+class HunyuanV1ModelBase(nn.Module, SupportsLoRA, SupportsPP):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -930,6 +930,56 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         else:
             self.lm_head = PPMissingLayer()
 
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        model_output = self.model(input_ids, positions, intermediate_tensors,
+                                  inputs_embeds)
+        return model_output
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+
+class HunYuanMoEV1Base(HunyuanV1ModelBase, MixtureOfExperts):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
         # Set MoE hyperparameters
         self.expert_weights = []
         self.num_expert_groups = 1
@@ -988,57 +1038,19 @@ def update_physical_experts_metadata(
                 moe.n_redundant_experts = self.num_redundant_experts
                 moe.experts.update_expert_map()
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
 
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        model_output = self.model(input_ids, positions, intermediate_tensors,
-                                  inputs_embeds)
-        return model_output
 
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-    ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.lm_head, hidden_states)
-        return logits
+class HunYuanDenseV1Base(HunyuanV1ModelBase):
 
-    def make_empty_intermediate_tensors(
-            self, batch_size: int, dtype: torch.dtype,
-            device: torch.device) -> IntermediateTensors:
-        return IntermediateTensors({
-            "hidden_states":
-            torch.zeros((batch_size, self.config.hidden_size),
-                        dtype=dtype,
-                        device=device),
-            "residual":
-            torch.zeros((batch_size, self.config.hidden_size),
-                        dtype=dtype,
-                        device=device),
-        })
-
-    def load_weights(self, weights: Iterable[tuple[str,
-                                                   torch.Tensor]]) -> set[str]:
-        loader = AutoWeightsLoader(
-            self,
-            skip_prefixes=(["lm_head."]
-                           if self.config.tie_word_embeddings else None),
-        )
-        return loader.load_weights(weights)
-
-    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return self.model.get_expert_mapping()
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
 
 
-class HunYuanDenseV1ForCausalLM(HunYuanV1Base):
+class HunYuanDenseV1ForCausalLM(HunYuanDenseV1Base):
     pass
 
 
-class HunYuanMoEV1ForCausalLM(HunYuanV1Base):
-    pass
+class HunYuanMoEV1ForCausalLM(HunYuanMoEV1Base):
+    pass