[Perf] Optimize memory peak during EAGLE model loading.

vllm/model_executor/models/deepseek_eagle.py

@@ -229,14 +229,15 @@ def compute_logits(
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+
+        def transform(inputs):
+            name, loaded_weight = inputs
+            if "lm_head" not in name:
+                name = "model." + name
+            return name, loaded_weight
+
         loader = AutoWeightsLoader(
             self,
             skip_prefixes=None,
         )
-
-        model_weights = {}
-        for name, loaded_weight in weights:
-            if "lm_head" not in name:
-                name = "model." + name
-            model_weights[name] = loaded_weight
-        loader.load_weights(model_weights.items())
+        loader.load_weights(map(transform, weights))

vllm/model_executor/models/llama4_eagle.py

@@ -205,23 +205,21 @@ def forward(
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> None:
+
+        def transform(inputs):
+            name, loaded_weight = inputs
+            name, weight = self.permute_qk_weight_for_rotary(
+                name, loaded_weight)
+            if "lm_head" not in name:
+                name = "model." + name
+            return name, weight
+
         loader = AutoWeightsLoader(
             self,
             # lm_head is tied with target model (Llama4ForCausalLM)
             skip_prefixes=(["lm_head."]),
         )
-
-        model_weights = {}
-        weights = [
-            self.permute_qk_weight_for_rotary(name, loaded_weight)
-            for name, loaded_weight in weights
-        ]
-        for name, loaded_weight in weights:
-            if "lm_head" not in name:
-                name = "model." + name
-            model_weights[name] = loaded_weight
-
-        loader.load_weights(model_weights.items())
+        loader.load_weights(map(transform, weights))
 
     def get_input_embeddings(
         self,

vllm/model_executor/models/llama_eagle.py

@@ -158,14 +158,15 @@ def forward(
         return self.model(input_ids, positions, hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+
+        def transform(inputs):
+            name, loaded_weight = inputs
+            if "lm_head" not in name:
+                name = "model." + name
+            return name, loaded_weight
+
         loader = AutoWeightsLoader(
             self,
             skip_prefixes=None,
         )
-
-        model_weights = {}
-        for name, loaded_weight in weights:
-            if "lm_head" not in name:
-                name = "model." + name
-            model_weights[name] = loaded_weight
-        loader.load_weights(model_weights.items())
+        loader.load_weights(map(transform, weights))