chore: support pytorch format in lora (#22790)

Signed-off-by: jaeeun.kil <rha3122@naver.com>
Signed-off-by: 길재은 <rha3122@naver.com>

vllm/lora/models.py

@@ -207,6 +207,7 @@ class LoRAModel(AdapterModel):
         """
         lora_tensor_path = os.path.join(lora_dir, "adapter_model.safetensors")
         lora_bin_file_path = os.path.join(lora_dir, "adapter_model.bin")
+        lora_pt_file_path = os.path.join(lora_dir, "adapter_model.pt")
         new_embeddings_tensor_path = os.path.join(
             lora_dir, "new_embeddings.safetensors")
         new_embeddings_bin_file_path = os.path.join(lora_dir,
@@ -255,9 +256,10 @@ class LoRAModel(AdapterModel):
                 check_unexpected_modules(f)
                 for module in f.keys():  # noqa
                     tensors[module] = f.get_tensor(module)
-        elif os.path.isfile(lora_bin_file_path):
-            # When a bin file is provided, we rely on config to find unexpected
-            # modules.
+        elif os.path.isfile(lora_bin_file_path) or os.path.isfile(
+                lora_pt_file_path):
+            # When a bin/pt file is provided, we rely on config to find
+            # unexpected modules.
             unexpected_modules = []
             target_modules = peft_helper.target_modules
             if not isinstance(target_modules, list):
@@ -279,7 +281,10 @@ class LoRAModel(AdapterModel):
                     f" target modules in {expected_lora_modules}"
                     f" but received {unexpected_modules}."
                     f" Please verify that the loaded LoRA module is correct")
-            tensors = torch.load(lora_bin_file_path,
+            lora_file_path = (lora_bin_file_path
+                              if os.path.isfile(lora_bin_file_path) else
+                              lora_pt_file_path)
+            tensors = torch.load(lora_file_path,
                                  map_location=device,
                                  weights_only=True)
         else: