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chore: support pytorch format in lora (#22790)

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Signed-off-by: jaeeun.kil <rha3122@naver.com>
Signed-off-by: 길재은 <rha3122@naver.com>
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길재은 2025-08-20 13:02:50 +09:00 committed by GitHub
parent 941f56858a
commit 1a3079a15e
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1 changed files with 9 additions and 4 deletions
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13
vllm/lora/models.py
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@ -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: