[Core] Support Lora lineage and base model metadata management

docs/source/models/lora.rst

@@ -159,3 +159,67 @@ Example request to unload a LoRA adapter:
     -d '{
         "lora_name": "sql_adapter"
     }'
+
+
+New format for `--lora-modules`
+-------------------------------
+
+In the previous version, users would provide LoRA modules via the following format, either as a key-value pair or in JSON format. For example:
+
+.. code-block:: bash
+
+    --lora-modules sql-lora=$HOME/.cache/huggingface/hub/models--yard1--llama-2-7b-sql-lora-test/snapshots/0dfa347e8877a4d4ed19ee56c140fa518470028c/
+
+This would only include the `name` and `path` for each LoRA module, but did not provide a way to specify a `base_model_name`.
+Now, you can specify a base_model_name alongside the name and path using JSON format. For example:
+
+.. code-block:: bash
+
+    --lora-modules '{"name": "sql-lora", "path": "/path/to/lora", "base_model_name": "meta-llama/Llama-2-7b"}'
+
+To provide the backward compatibility support, you can still use the old key-value format (name=path), but the `base_model_name` will remain unspecified in that case.
+
+
+Lora model lineage in model card
+--------------------------------
+
+The new format of `--lora-modules` is mainly to support the display of parent model information in the model card. Here's an explanation of how your current response supports this:
+
+- The `parent` field of LoRA model `sql-lora` now links to its base model `meta-llama/Llama-2-7b-hf`. This correctly reflects the hierarchical relationship between the base model and the LoRA adapter.
+- The `root` field points to the artifact location of the lora adapter.
+
+.. code-block:: bash
+
+    $ curl http://localhost:8000/v1/models
+
+    {
+        "object": "list",
+        "data": [
+            {
+            "id": "meta-llama/Llama-2-7b-hf",
+            "object": "model",
+            "created": 1715644056,
+            "owned_by": "vllm",
+            "root": "~/.cache/huggingface/hub/models--meta-llama--Llama-2-7b-hf/snapshots/01c7f73d771dfac7d292323805ebc428287df4f9/",
+            "parent": null,
+            "permission": [
+                {
+                .....
+                }
+            ]
+            },
+            {
+            "id": "sql-lora",
+            "object": "model",
+            "created": 1715644056,
+            "owned_by": "vllm",
+            "root": "~/.cache/huggingface/hub/models--yard1--llama-2-7b-sql-lora-test/snapshots/0dfa347e8877a4d4ed19ee56c140fa518470028c/",
+            "parent": meta-llama/Llama-2-7b-hf,
+            "permission": [
+                {
+                ....
+                }
+            ]
+            }
+        ]
+    }

tests/entrypoints/openai/test_cli_args.py

@@ -0,0 +1,91 @@
+import json
+import unittest
+
+from vllm.entrypoints.openai.cli_args import make_arg_parser
+from vllm.entrypoints.openai.serving_engine import LoRAModulePath
+from vllm.utils import FlexibleArgumentParser
+
+LORA_MODULE = {
+    "name": "module2",
+    "path": "/path/to/module2",
+    "base_model_name": "llama"
+}
+
+
+class TestLoraParserAction(unittest.TestCase):
+
+    def setUp(self):
+        # Setting up argparse parser for tests
+        parser = FlexibleArgumentParser(
+            description="vLLM's remote OpenAI server.")
+        self.parser = make_arg_parser(parser)
+
+    def test_valid_key_value_format(self):
+        # Test old format: name=path
+        args = self.parser.parse_args([
+            '--lora-modules',
+            'module1=/path/to/module1',
+        ])
+        expected = [LoRAModulePath(name='module1', path='/path/to/module1')]
+        self.assertEqual(args.lora_modules, expected)
+
+    def test_valid_json_format(self):
+        # Test valid JSON format input
+        args = self.parser.parse_args([
+            '--lora-modules',
+            json.dumps(LORA_MODULE),
+        ])
+        expected = [
+            LoRAModulePath(name='module2',
+                           path='/path/to/module2',
+                           base_model_name='llama')
+        ]
+        self.assertEqual(args.lora_modules, expected)
+
+    def test_invalid_json_format(self):
+        # Test invalid JSON format input, missing closing brace
+        with self.assertRaises(SystemExit):
+            self.parser.parse_args([
+                '--lora-modules',
+                '{"name": "module3", "path": "/path/to/module3"'
+            ])
+
+    def test_invalid_type_error(self):
+        # Test type error when values are not JSON or key=value
+        with self.assertRaises(SystemExit):
+            self.parser.parse_args([
+                '--lora-modules',
+                'invalid_format'  # This is not JSON or key=value format
+            ])
+
+    def test_invalid_json_field(self):
+        # Test valid JSON format but missing required fields
+        with self.assertRaises(SystemExit):
+            self.parser.parse_args([
+                '--lora-modules',
+                '{"name": "module4"}'  # Missing required 'path' field
+            ])
+
+    def test_empty_values(self):
+        # Test when no LoRA modules are provided
+        args = self.parser.parse_args(['--lora-modules', ''])
+        self.assertEqual(args.lora_modules, [])
+
+    def test_multiple_valid_inputs(self):
+        # Test multiple valid inputs (both old and JSON format)
+        args = self.parser.parse_args([
+            '--lora-modules',
+            'module1=/path/to/module1',
+            json.dumps(LORA_MODULE),
+        ])
+        expected = [
+            LoRAModulePath(name='module1', path='/path/to/module1'),
+            LoRAModulePath(name='module2',
+                           path='/path/to/module2',
+                           base_model_name='llama')
+        ]
+        self.assertEqual(args.lora_modules, expected)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/entrypoints/openai/test_lora_lineage.py

@@ -0,0 +1,83 @@
+import json
+
+import openai  # use the official client for correctness check
+import pytest
+import pytest_asyncio
+# downloading lora to test lora requests
+from huggingface_hub import snapshot_download
+
+from ...utils import RemoteOpenAIServer
+
+# any model with a chat template should work here
+MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
+# technically this needs Mistral-7B-v0.1 as base, but we're not testing
+# generation quality here
+LORA_NAME = "typeof/zephyr-7b-beta-lora"
+
+
+@pytest.fixture(scope="module")
+def zephyr_lora_files():
+    return snapshot_download(repo_id=LORA_NAME)
+
+
+@pytest.fixture(scope="module")
+def server_with_lora_modules_json(zephyr_lora_files):
+    # Define the json format LoRA module configurations
+    lora_module_1 = {
+        "name": "zephyr-lora",
+        "path": zephyr_lora_files,
+        "base_model_name": MODEL_NAME
+    }
+
+    lora_module_2 = {
+        "name": "zephyr-lora2",
+        "path": zephyr_lora_files,
+        "base_model_name": MODEL_NAME
+    }
+
+    args = [
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "8192",
+        "--enforce-eager",
+        # lora config below
+        "--enable-lora",
+        "--lora-modules",
+        json.dumps(lora_module_1),
+        json.dumps(lora_module_2),
+        "--max-lora-rank",
+        "64",
+        "--max-cpu-loras",
+        "2",
+        "--max-num-seqs",
+        "64",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client_for_lora_lineage(server_with_lora_modules_json):
+    async with server_with_lora_modules_json.get_async_client(
+    ) as async_client:
+        yield async_client
+
+
+@pytest.mark.asyncio
+async def test_check_lora_lineage(client_for_lora_lineage: openai.AsyncOpenAI,
+                                  zephyr_lora_files):
+    models = await client_for_lora_lineage.models.list()
+    models = models.data
+    served_model = models[0]
+    lora_models = models[1:]
+    assert served_model.id == MODEL_NAME
+    assert served_model.root == MODEL_NAME
+    assert served_model.parent is None
+    assert all(lora_model.root == zephyr_lora_files
+               for lora_model in lora_models)
+    assert all(lora_model.parent == MODEL_NAME for lora_model in lora_models)
+    assert lora_models[0].id == "zephyr-lora"
+    assert lora_models[1].id == "zephyr-lora2"

tests/entrypoints/openai/test_models.py

@@ -51,12 +51,14 @@ async def client(server):
 
 
 @pytest.mark.asyncio
-async def test_check_models(client: openai.AsyncOpenAI):
+async def test_check_models(client: openai.AsyncOpenAI, zephyr_lora_files):
     models = await client.models.list()
     models = models.data
     served_model = models[0]
     lora_models = models[1:]
     assert served_model.id == MODEL_NAME
-    assert all(model.root == MODEL_NAME for model in models)
+    assert served_model.root == MODEL_NAME
+    assert all(lora_model.root == zephyr_lora_files
+               for lora_model in lora_models)
     assert lora_models[0].id == "zephyr-lora"
     assert lora_models[1].id == "zephyr-lora2"

tests/entrypoints/openai/test_serving_chat.py

@@ -7,10 +7,12 @@
 from vllm.engine.multiprocessing.client import MQLLMEngineClient
 from vllm.entrypoints.openai.protocol import ChatCompletionRequest
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.entrypoints.openai.serving_engine import BaseModelPath
 from vllm.transformers_utils.tokenizer import get_tokenizer
 
 MODEL_NAME = "openai-community/gpt2"
 CHAT_TEMPLATE = "Dummy chat template for testing {}"
+BASE_MODEL_PATHS = [BaseModelPath(name=MODEL_NAME, model_path=MODEL_NAME)]
 
 
 @dataclass
@@ -37,7 +39,7 @@ async def _async_serving_chat_init():
 
     serving_completion = OpenAIServingChat(engine,
                                            model_config,
-                                           served_model_names=[MODEL_NAME],
+                                           BASE_MODEL_PATHS,
                                            response_role="assistant",
                                            chat_template=CHAT_TEMPLATE,
                                            lora_modules=None,
@@ -58,7 +60,7 @@ def test_serving_chat_should_set_correct_max_tokens():
 
     serving_chat = OpenAIServingChat(mock_engine,
                                      MockModelConfig(),
-                                     served_model_names=[MODEL_NAME],
+                                     BASE_MODEL_PATHS,
                                      response_role="assistant",
                                      chat_template=CHAT_TEMPLATE,
                                      lora_modules=None,

tests/entrypoints/openai/test_serving_engine.py

@@ -8,9 +8,10 @@
 from vllm.entrypoints.openai.protocol import (ErrorResponse,
                                               LoadLoraAdapterRequest,
                                               UnloadLoraAdapterRequest)
-from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
 
 MODEL_NAME = "meta-llama/Llama-2-7b"
+BASE_MODEL_PATHS = [BaseModelPath(name=MODEL_NAME, model_path=MODEL_NAME)]
 LORA_LOADING_SUCCESS_MESSAGE = (
     "Success: LoRA adapter '{lora_name}' added successfully.")
 LORA_UNLOADING_SUCCESS_MESSAGE = (
@@ -25,7 +26,7 @@ async def _async_serving_engine_init():
 
     serving_engine = OpenAIServing(mock_engine_client,
                                    mock_model_config,
-                                   served_model_names=[MODEL_NAME],
+                                   BASE_MODEL_PATHS,
                                    lora_modules=None,
                                    prompt_adapters=None,
                                    request_logger=None)

vllm/entrypoints/openai/api_server.py

@@ -50,6 +50,7 @@
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
 from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
+from vllm.entrypoints.openai.serving_engine import BaseModelPath
 from vllm.entrypoints.openai.serving_tokenization import (
     OpenAIServingTokenization)
 from vllm.logger import init_logger
@@ -476,13 +477,18 @@ def init_app_state(
     else:
         request_logger = RequestLogger(max_log_len=args.max_log_len)
 
+    base_model_paths = [
+        BaseModelPath(name=name, model_path=args.model)
+        for name in served_model_names
+    ]
+
     state.engine_client = engine_client
     state.log_stats = not args.disable_log_stats
 
     state.openai_serving_chat = OpenAIServingChat(
         engine_client,
         model_config,
-        served_model_names,
+        base_model_paths,
         args.response_role,
         lora_modules=args.lora_modules,
         prompt_adapters=args.prompt_adapters,
@@ -494,7 +500,7 @@ def init_app_state(
     state.openai_serving_completion = OpenAIServingCompletion(
         engine_client,
         model_config,
-        served_model_names,
+        base_model_paths,
         lora_modules=args.lora_modules,
         prompt_adapters=args.prompt_adapters,
         request_logger=request_logger,
@@ -503,13 +509,13 @@ def init_app_state(
     state.openai_serving_embedding = OpenAIServingEmbedding(
         engine_client,
         model_config,
-        served_model_names,
+        base_model_paths,
         request_logger=request_logger,
     )
     state.openai_serving_tokenization = OpenAIServingTokenization(
         engine_client,
         model_config,
-        served_model_names,
+        base_model_paths,
         lora_modules=args.lora_modules,
         request_logger=request_logger,
         chat_template=args.chat_template,