Simplify code

Signed-off-by: DarkLight1337 <[email protected]>

vllm/model_executor/models/adapters.py

@@ -9,11 +9,6 @@
 _T = TypeVar("_T", bound=type[nn.Module])
 
 
-def _is_paramless(module: nn.Module):
-    # NOTE: all([]) returns True
-    return all(False for _ in module.parameters())
-
-
 def as_embedding_model(cls: _T) -> _T:
     """Subclass an existing vLLM model to support embeddings."""
     # Avoid modifying existing embedding models
@@ -40,10 +35,9 @@ def __init__(
             super().__init__(vllm_config=vllm_config, prefix=prefix, **kwargs)
 
             # These are not used in embedding models
-            if hasattr(self, "lm_head"):
-                del self.lm_head
-            if hasattr(self, "logits_processor"):
-                del self.logits_processor
+            for attr in ("lm_head", "logits_processor"):
+                if hasattr(self, attr):
+                    delattr(self, attr)
 
             pooler_config = vllm_config.model_config.pooler_config
             assert pooler_config is not None
@@ -77,7 +71,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
             if hasattr(self, "model") and hasattr(self.model, "load_weights"):
                 # Whether only `self.model` contains parameters
                 model_is_only_param = all(
-                    name == "model" or _is_paramless(child)
+                    name == "model" or next(child.parameters(), None) is None
                     for name, child in self.named_children())
 
                 if model_is_only_param: