[Optimization] Avoid repeated model architecture conversion for pooling models (#25261)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/model_loader/utils.py

@@ -165,7 +165,11 @@ def device_loading_context(module: torch.nn.Module,
         # New parameters or parameters already on target device are untouched
 
 
-def get_model_architecture(
+_MODEL_ARCH_BY_HASH = dict[str, tuple[type[nn.Module], str]]()
+"""Caches the outputs of `_get_model_architecture`."""
+
+
+def _get_model_architecture(
         model_config: ModelConfig) -> tuple[type[nn.Module], str]:
     architectures = getattr(model_config.hf_config, "architectures", [])
 
@@ -209,6 +213,17 @@ def get_model_architecture(
     return model_cls, arch
 
 
+def get_model_architecture(
+        model_config: ModelConfig) -> tuple[type[nn.Module], str]:
+    key = model_config.compute_hash()
+    if key in _MODEL_ARCH_BY_HASH:
+        return _MODEL_ARCH_BY_HASH[key]
+
+    model_arch = _get_model_architecture(model_config)
+    _MODEL_ARCH_BY_HASH[key] = model_arch
+    return model_arch
+
+
 def get_model_cls(model_config: ModelConfig) -> type[nn.Module]:
     return get_model_architecture(model_config)[0]