[Misc] Add uninitialized params tracking for AutoWeightsLoader (#10327)

Signed-off-by: Isotr0py <2037008807@qq.com>

vllm/model_executor/models/clip.py

@@ -1,6 +1,6 @@
 """Minimal implementation of CLIPVisionModel intended to be only used
 within a vision language model."""
-from typing import Iterable, List, Optional, Tuple, Union
+from typing import Iterable, List, Optional, Set, Tuple, Union
 
 import numpy as np
 import torch
@@ -483,7 +483,8 @@ class CLIPVisionModel(nn.Module):
 
     # (TODO) Add prefix argument for filtering out weights to be loaded
     #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
-    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
             ("qkv_proj", "q_proj", "q"),
@@ -491,6 +492,7 @@ class CLIPVisionModel(nn.Module):
             ("qkv_proj", "v_proj", "v"),
         ] if self.shard_weight else []
         params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
         layer_count = len(self.vision_model.encoder.layers)
 
         for name, loaded_weight in weights:
@@ -508,8 +510,9 @@ class CLIPVisionModel(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
+                name = name.replace(weight_name, param_name)
 
-                param = params_dict[name.replace(weight_name, param_name)]
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
@@ -518,3 +521,5 @@ class CLIPVisionModel(nn.Module):
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)
                 weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params