[Misc] Avoid loading incorrect LoRA config (#3777)

tests/lora/test_lora_checkpoints.py

+import pytest
+
+from vllm.lora.models import LoRAModel
+from vllm.model_executor.models.baichuan import BaiChuanBaseForCausalLM
+
+
+@pytest.mark.parametrize("lora_name", ["baichuan7B", "chatglm3-6b"])
+def test_load_checkpoints(lora_name, chatglm3_lora_files, baichuan_lora_files):
+    supported_lora_modules = BaiChuanBaseForCausalLM.supported_lora_modules
+    packed_modules_mapping = BaiChuanBaseForCausalLM.packed_modules_mapping
+    embedding_modules = BaiChuanBaseForCausalLM.embedding_modules
+    embed_padding_modules = BaiChuanBaseForCausalLM.embedding_padding_modules
+    expected_lora_modules = []
+    for module in supported_lora_modules:
+        if module in packed_modules_mapping:
+            expected_lora_modules.extend(packed_modules_mapping[module])
+        else:
+            expected_lora_modules.append(module)
+    if lora_name == "baichuan7B":
+        # For the baichuan7B model, load it's LoRA,
+        # and the test should pass.
+        LoRAModel.from_local_checkpoint(
+            baichuan_lora_files,
+            expected_lora_modules,
+            lora_model_id=1,
+            device="cpu",
+            embedding_modules=embedding_modules,
+            embedding_padding_modules=embed_padding_modules)
+    else:
+        # For the baichuan7B model, load chatglm3-6b's LoRA,
+        # and the test should raise the following error.
+        expected_error = "Please verify that the loaded LoRA module is correct"  # noqa: E501
+        with pytest.raises(ValueError, match=expected_error):
+            LoRAModel.from_local_checkpoint(
+                chatglm3_lora_files,
+                expected_lora_modules,
+                lora_model_id=1,
+                device="cpu",
+                embedding_modules=embedding_modules,
+                embedding_padding_modules=embed_padding_modules)

vllm/lora/models.py

@@ -191,6 +191,7 @@ class LoRAModel:
     def from_local_checkpoint(
         cls,
         lora_dir: str,
+        expected_lora_modules: List[str],
         lora_model_id: Optional[int] = None,
         device: str = "cuda",
         dtype: Optional[torch.dtype] = None,
@@ -206,6 +207,20 @@ class LoRAModel:
             lora_dir, "new_embeddings.safetensors")
         new_embeddings_bin_file_path = os.path.join(lora_dir,
                                                     "new_embeddings.bin")
+        with open(lora_config_path) as f:
+            config = json.load(f)
+        target_modules = config["target_modules"]
+        unexpected_modules = []
+        for module in target_modules:
+            if module not in expected_lora_modules:
+                unexpected_modules.append(module)
+        # loaded lora's target modules must be a subset of expected_lora_modules
+        if unexpected_modules:
+            raise ValueError(
+                f"While loading {lora_dir}, expected"
+                f" target modules in {expected_lora_modules}"
+                f" but received {unexpected_modules}."
+                f" Please verify that the loaded LoRA module is correct")
         if os.path.isfile(lora_tensor_path):
             tensors = safetensors.torch.load_file(lora_tensor_path)
         elif os.path.isfile(lora_bin_file_path):
@@ -220,8 +235,6 @@ class LoRAModel:
         elif os.path.isfile(new_embeddings_bin_file_path):
             embeddings = torch.load(new_embeddings_bin_file_path)
 
-        with open(lora_config_path) as f:
-            config = json.load(f)
         rank = config["r"]
         lora_alpha = config["lora_alpha"]
         return cls.from_lora_tensors(

vllm/lora/worker_manager.py

@@ -136,8 +136,19 @@ class WorkerLoRAManager(AbstractWorkerLoRAManager):
 
     def _load_lora(self, lora_request: LoRARequest) -> LoRAModel:
         try:
+            model = self._lora_manager.model
+            supported_lora_modules = model.supported_lora_modules
+            packed_modules_mapping = model.packed_modules_mapping
+            expected_lora_modules = []
+            for module in supported_lora_modules:
+                if module in packed_modules_mapping:
+                    expected_lora_modules.extend(
+                        packed_modules_mapping[module])
+                else:
+                    expected_lora_modules.append(module)
             lora = self._lora_model_cls.from_local_checkpoint(
                 lora_request.lora_local_path,
+                expected_lora_modules,
                 lora_model_id=lora_request.lora_int_id,
                 device="cpu",
                 dtype=self.lora_config.lora_dtype,