Adjust reward model's score module and pooler module order for reducing computation

python/sglang/srt/models/gemma2_reward.py

@@ -58,43 +58,10 @@ def forward(
         ), "Gemma2ForSequenceClassification is only used for embedding"
 
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
-        scores = self.score(hidden_states)
+        last_token_hidden = self.pooler(hidden_states, forward_batch).embeddings
+        scores = self.score(last_token_hidden)
 
-        return self.pooler(scores, forward_batch)
-
-    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
-        ]
-        params_dict = dict(self.named_parameters())
-        for name, loaded_weight in weights:
-            for param_name, shard_name, shard_id in stacked_params_mapping:
-                if shard_name not in name:
-                    continue
-                name = name.replace(shard_name, param_name)
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                param = params_dict[name]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
-                break
-            else:
-                # lm_head is not used in vllm as it is tied with embed_token.
-                # To prevent errors, skip loading lm_head.weight.
-                if "lm_head.weight" in name:
-                    continue
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                weight_loader(param, loaded_weight)
+        return EmbeddingPoolerOutput(scores)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         Gemma2ForCausalLM.load_weights(self, weights)

python/sglang/srt/models/llama_reward.py

@@ -59,22 +59,13 @@ def forward(
         ), "LlamaForSequenceClassification is only used for embedding"
 
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
-        scores = self.score(hidden_states)
+        last_token_hidden = self.pooler(hidden_states, forward_batch).embeddings
+        scores = self.score(last_token_hidden)
 
-        return self.pooler(scores, forward_batch)
+        return EmbeddingPoolerOutput(scores)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        params_dict = dict(self.named_parameters())
-
-        for name, loaded_weight in weights:
-            if "classification_head" in name:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                weight_loader(param, loaded_weight)
-            elif "lm_head" in name:
-                continue
-            else:
-                LlamaForCausalLM.load_weights(self, [(name, loaded_weight)])
+        return LlamaForCausalLM.load_weights(self, weights)
 
 
 class LlamaForSequenceClassificationWithNormal_Weights(LlamaForSequenceClassification):
@@ -127,17 +118,7 @@ def forward(
         return EmbeddingPoolerOutput(scores)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        params_dict = dict(self.named_parameters())
-
-        for name, loaded_weight in weights:
-            if "classification_head" in name:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                weight_loader(param, loaded_weight)
-            elif "lm_head" in name:
-                continue
-            else:
-                LlamaForCausalLM.load_weights(self, [(name, loaded_weight)])
+        return super().load_weights(weights)
 
 
 EntryClass = [