[Lora] Use safetensor keys instead of adapter_config.json to find unexpected modules. (#5909)

Co-authored-by: sang <sangcho@anyscale.com>

.buildkite/test-pipeline.yaml

@@ -226,3 +226,4 @@ steps:
   - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.5/flashinfer-0.0.5+cu121torch2.3-cp310-cp310-linux_x86_64.whl
   - VLLM_ATTENTION_BACKEND=FLASHINFER TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
   - VLLM_ATTENTION_BACKEND=FLASHINFER TEST_DIST_MODEL=meta-llama/Meta-Llama-3-8B DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
+  - pytest -v -s -x lora/test_mixtral.py

tests/lora/conftest.py

@@ -165,7 +165,9 @@ def sql_lora_files():
 
 @pytest.fixture(scope="session")
 def mixtral_lora_files():
-    return snapshot_download(repo_id="terrysun/mixtral-lora-adapter")
+    # Note: this module has incorrect adapter_config.json to test
+    # https://github.com/vllm-project/vllm/pull/5909/files.
+    return snapshot_download(repo_id="SangBinCho/mixtral-lora")
 
 
 @pytest.fixture(scope="session")

tests/lora/test_mixtral.py

@@ -40,14 +40,14 @@ def test_mixtral_lora(mixtral_lora_files, tp_size):
                    enable_lora=True,
                    max_num_seqs=16,
                    max_loras=4,
+                   distributed_executor_backend="ray",
                    tensor_parallel_size=tp_size)
 
     expected_lora_output = [
         "give_opinion(name[SpellForce 3], release_year[2017], developer[Grimlore Games], rating[poor])",  # noqa: E501
-        "give_opinion(name[SpellForce 3], release_year[2017], developer[Grimlore Games], rating[poor])",  # noqa: E501
+        "give_opinion(name[SpellForce 3], developer[Grimlore Games], release_year[2017], rating[poor])",  # noqa: E501
         "inform(name[BioShock], release_year[2007], rating[good], genres[action-adventure, role-playing, shooter], platforms[PlayStation, Xbox, PC], available_on_steam[yes], has_linux_release[no], has_mac_release[yes])",  # noqa: E501
     ]
-
     assert do_sample(llm, mixtral_lora_files,
                      lora_id=1) == expected_lora_output
     assert do_sample(llm, mixtral_lora_files,

vllm/lora/models.py

@@ -303,15 +303,47 @@ class LoRAModel:
                                                     "new_embeddings.bin")
         with open(lora_config_path) as f:
             config = json.load(f)
-        target_modules = config["target_modules"]
+        if os.path.isfile(lora_tensor_path):
+            tensors: Dict[str, torch.Tensor] = {}
+            # Find unexpected modules.
+            # Use safetensor key as a source of truth to find expected modules.
+            # in peft if you have target_modules A, B, C and C does not exist
+            # in the model it won’t error and model will be trained with A, B
+            # loraified. C won’t exist in the safetensor but it will exist in
+            # the target_modules of the adapter_config.json.
             unexpected_modules = []
+            with safetensors.safe_open(lora_tensor_path,
+                                       framework="pt") as f:  # type: ignore
+                for lora_module in f.keys():  # noqa
+                    module_name, _ = parse_fine_tuned_lora_name(lora_module)
+                    part_name = module_name.split(".")[-1]
+                    if part_name not in expected_lora_modules:
+                        unexpected_modules.append(module_name)
+                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"
+                    )
+                # Load tensors if there are only expected modules.
+                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.
+            unexpected_modules = []
+            target_modules = config["target_modules"]
             for module in target_modules:
-            # Compatible with more modules, such as:layers.11.self_attn.k_proj
+                # Compatible with more modules,
+                # such as:layers.11.self_attn.k_proj
                 part_name = module.split(".")[-1]
                 if part_name not in expected_lora_modules:
                     unexpected_modules.append(module)
-        # loaded lora's target modules must be a subset of expected_lora_modules
-
+            # loaded lora's target modules must be a subset of
+            # expected_lora_modules. It is not reliable. See
+            # https://github.com/vllm-project/vllm/pull/5909. But there's no
+            # other better mechanism.
             if unexpected_modules:
                 print(unexpected_modules, "modules")
                 raise ValueError(
@@ -319,9 +351,6 @@ class LoRAModel:
                     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):
             tensors = torch.load(lora_bin_file_path)
         else:
             raise ValueError(f"{lora_dir} doesn't contain tensors")