Support loading transformers models with named parameters

vllm/model_executor/models/transformers.py

@@ -166,6 +166,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         # Initialize buffers (e.g. rotary embedding inverse frequency)
         self.init_buffers(self.model)
 
+        # Initialize parameters
+        self.init_parameters(self.model)
+
         # Move remaining meta tensors to device (should happen last)
         self.meta_to_empty(self.model)
 
@@ -298,6 +301,25 @@ def init_buffers(self, module: nn.Module):
         for child in module.children():
             self.init_buffers(child)
 
+    def init_parameters(self, module: nn.Module):
+        """
+        If a `parameter` is on the `meta` device, then its parent
+        `module` is the original module created by:
+
+        ```python
+        with torch.device("meta"):
+            self.model: PreTrainedModel = AutoModel.from_config(...)
+        ```
+        """
+        for name, param in module.named_parameters(recurse=False):
+            if param.device == torch.device("meta"):
+                new_param = nn.Parameter(
+                    torch.empty_like(param.data,
+                                     device=self.device_config.device))
+                setattr(module, name, new_param)
+        for child in module.children():
+            self.init_parameters(child)
+
     def meta_to_empty(self, module: nn.Module):
         tensors = list(chain(module.buffers(), module.parameters()))
         if tensors and all(t.device == torch.device("meta") for t in tensors):
@@ -342,6 +364,7 @@ def forward(
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
         params_dict = dict(self.named_parameters())
+
         loaded_params = set[str]()
         for name, loaded_weight in weights:
             # Use "model" instead of base_model_prefix because