feat: NVIDIA allow non-llama model registration (#1859)

# What does this PR do?
Adds custom model registration functionality to NVIDIAInferenceAdapter
which let's the inference happen on:
- post-training model
- non-llama models in API Catalogue(behind
https://integrate.api.nvidia.com and endpoints compatible with
AyncOpenAI)

## Example Usage:
```python
from llama_stack.apis.models import Model, ModelType
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
client = LlamaStackAsLibraryClient("nvidia")
_ = client.initialize()

client.models.register(
        model_id=model_name,
        model_type=ModelType.llm,
        provider_id="nvidia"
)

response = client.inference.chat_completion(
    model_id=model_name,
    messages=[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Write a limerick about the wonders of GPU computing."}],
)
```

## Test Plan
```bash
pytest tests/unit/providers/nvidia/test_supervised_fine_tuning.py 
========================================================== test session starts ===========================================================
platform linux -- Python 3.10.0, pytest-8.3.5, pluggy-1.5.0
rootdir: /home/ubuntu/llama-stack
configfile: pyproject.toml
plugins: anyio-4.9.0
collected 6 items                                                                                                                        

tests/unit/providers/nvidia/test_supervised_fine_tuning.py ......                                                                  [100%]

============================================================ warnings summary ============================================================
../miniconda/envs/nvidia-1/lib/python3.10/site-packages/pydantic/fields.py:1076
  /home/ubuntu/miniconda/envs/nvidia-1/lib/python3.10/site-packages/pydantic/fields.py:1076: PydanticDeprecatedSince20: Using extra keyword arguments on `Field` is deprecated and will be removed. Use `json_schema_extra` instead. (Extra keys: 'contentEncoding'). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.11/migration/
    warn(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
====================================================== 6 passed, 1 warning in 1.51s ======================================================
```

[//]: # (## Documentation)
Updated Readme.md

cc: @dglogo, @sumitb, @mattf

docs/source/distributions/self_hosted_distro/nvidia.md

@@ -22,9 +22,8 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
 The following environment variables can be configured:
 
 - `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
-- `NVIDIA_USER_ID`: NVIDIA User ID (default: `llama-stack-user`)
+- `NVIDIA_APPEND_API_VERSION`: Whether to append the API version to the base_url (default: `True`)
 - `NVIDIA_DATASET_NAMESPACE`: NVIDIA Dataset Namespace (default: `default`)
-- `NVIDIA_ACCESS_POLICIES`: NVIDIA Access Policies (default: `{}`)
 - `NVIDIA_PROJECT_ID`: NVIDIA Project ID (default: `test-project`)
 - `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `https://customizer.api.nvidia.com`)
 - `NVIDIA_OUTPUT_MODEL_DIR`: NVIDIA Output Model Directory (default: `test-example-model@v1`)

llama_stack/providers/remote/inference/nvidia/config.py

@@ -47,10 +47,15 @@ class NVIDIAConfig(BaseModel):
         default=60,
         description="Timeout for the HTTP requests",
     )
+    append_api_version: bool = Field(
+        default_factory=lambda: os.getenv("NVIDIA_APPEND_API_VERSION", "True").lower() != "false",
+        description="When set to false, the API version will not be appended to the base_url. By default, it is true.",
+    )
 
     @classmethod
     def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
         return {
             "url": "${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com}",
             "api_key": "${env.NVIDIA_API_KEY:}",
+            "append_api_version": "${env.NVIDIA_APPEND_API_VERSION:True}",
         }

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -33,7 +33,6 @@ from llama_stack.apis.inference import (
     TextTruncation,
     ToolChoice,
     ToolConfig,
-    ToolDefinition,
 )
 from llama_stack.apis.inference.inference import (
     OpenAIChatCompletion,
@@ -42,7 +41,11 @@ from llama_stack.apis.inference.inference import (
     OpenAIMessageParam,
     OpenAIResponseFormatParam,
 )
-from llama_stack.models.llama.datatypes import ToolPromptFormat
+from llama_stack.apis.models import Model, ModelType
+from llama_stack.models.llama.datatypes import ToolDefinition, ToolPromptFormat
+from llama_stack.providers.utils.inference import (
+    ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR,
+)
 from llama_stack.providers.utils.inference.model_registry import (
     ModelRegistryHelper,
 )
@@ -120,10 +123,10 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
             "meta/llama-3.2-90b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct",
         }
 
-        base_url = f"{self._config.url}/v1"
+        base_url = f"{self._config.url}/v1" if self._config.append_api_version else self._config.url
+
         if _is_nvidia_hosted(self._config) and provider_model_id in special_model_urls:
             base_url = special_model_urls[provider_model_id]
-
         return _get_client_for_base_url(base_url)
 
     async def _get_provider_model_id(self, model_id: str) -> str:
@@ -387,3 +390,44 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
             return await self._get_client(provider_model_id).chat.completions.create(**params)
         except APIConnectionError as e:
             raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
+
+    async def register_model(self, model: Model) -> Model:
+        """
+        Allow non-llama model registration.
+
+        Non-llama model registration: API Catalogue models, post-training models, etc.
+            client = LlamaStackAsLibraryClient("nvidia")
+            client.models.register(
+                    model_id="mistralai/mixtral-8x7b-instruct-v0.1",
+                    model_type=ModelType.llm,
+                    provider_id="nvidia",
+                    provider_model_id="mistralai/mixtral-8x7b-instruct-v0.1"
+            )
+
+            NOTE: Only supports models endpoints compatible with AsyncOpenAI base_url format.
+        """
+        if model.model_type == ModelType.embedding:
+            # embedding models are always registered by their provider model id and does not need to be mapped to a llama model
+            provider_resource_id = model.provider_resource_id
+        else:
+            provider_resource_id = self.get_provider_model_id(model.provider_resource_id)
+
+        if provider_resource_id:
+            model.provider_resource_id = provider_resource_id
+        else:
+            llama_model = model.metadata.get("llama_model")
+            existing_llama_model = self.get_llama_model(model.provider_resource_id)
+            if existing_llama_model:
+                if existing_llama_model != llama_model:
+                    raise ValueError(
+                        f"Provider model id '{model.provider_resource_id}' is already registered to a different llama model: '{existing_llama_model}'"
+                    )
+            else:
+                # not llama model
+                if llama_model in ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR:
+                    self.provider_id_to_llama_model_map[model.provider_resource_id] = (
+                        ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[llama_model]
+                    )
+                else:
+                    self.alias_to_provider_id_map[model.provider_model_id] = model.provider_model_id
+        return model

llama_stack/providers/remote/post_training/nvidia/README.md

@@ -36,7 +36,6 @@ import os
 
 os.environ["NVIDIA_API_KEY"] = "your-api-key"
 os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test"
-os.environ["NVIDIA_USER_ID"] = "llama-stack-user"
 os.environ["NVIDIA_DATASET_NAMESPACE"] = "default"
 os.environ["NVIDIA_PROJECT_ID"] = "test-project"
 os.environ["NVIDIA_OUTPUT_MODEL_DIR"] = "test-example-model@v1"
@@ -125,6 +124,21 @@ client.post_training.job.cancel(job_uuid="your-job-id")
 
 ### Inference with the fine-tuned model
 
+#### 1. Register the model
+
+```python
+from llama_stack.apis.models import Model, ModelType
+
+client.models.register(
+    model_id="test-example-model@v1",
+    provider_id="nvidia",
+    provider_model_id="test-example-model@v1",
+    model_type=ModelType.llm,
+)
+```
+
+#### 2. Inference with the fine-tuned model
+
 ```python
 response = client.inference.completion(
     content="Complete the sentence using one word: Roses are red, violets are ",

llama_stack/templates/nvidia/nvidia.py

@@ -98,19 +98,15 @@ def get_distribution_template() -> DistributionTemplate:
                 "",
                 "NVIDIA API Key",
             ),
-            ## Nemo Customizer related variables
-            "NVIDIA_USER_ID": (
-                "llama-stack-user",
-                "NVIDIA User ID",
+            "NVIDIA_APPEND_API_VERSION": (
+                "True",
+                "Whether to append the API version to the base_url",
             ),
+            ## Nemo Customizer related variables
             "NVIDIA_DATASET_NAMESPACE": (
                 "default",
                 "NVIDIA Dataset Namespace",
             ),
-            "NVIDIA_ACCESS_POLICIES": (
-                "{}",
-                "NVIDIA Access Policies",
-            ),
             "NVIDIA_PROJECT_ID": (
                 "test-project",
                 "NVIDIA Project ID",

llama_stack/templates/nvidia/run-with-safety.yaml

@@ -18,6 +18,7 @@ providers:
     config:
       url: ${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com}
       api_key: ${env.NVIDIA_API_KEY:}
+      append_api_version: ${env.NVIDIA_APPEND_API_VERSION:True}
   - provider_id: nvidia
     provider_type: remote::nvidia
     config:

llama_stack/templates/nvidia/run.yaml

@@ -18,6 +18,7 @@ providers:
     config:
       url: ${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com}
       api_key: ${env.NVIDIA_API_KEY:}
+      append_api_version: ${env.NVIDIA_APPEND_API_VERSION:True}
   vector_io:
   - provider_id: faiss
     provider_type: inline::faiss

tests/unit/providers/nvidia/test_supervised_fine_tuning.py

@@ -17,6 +17,8 @@ from llama_stack_client.types.post_training_supervised_fine_tune_params import (
     TrainingConfigOptimizerConfig,
 )
 
+from llama_stack.apis.models import Model, ModelType
+from llama_stack.providers.remote.inference.nvidia.nvidia import NVIDIAConfig, NVIDIAInferenceAdapter
 from llama_stack.providers.remote.post_training.nvidia.post_training import (
     ListNvidiaPostTrainingJobs,
     NvidiaPostTrainingAdapter,
@@ -40,8 +42,22 @@ class TestNvidiaPostTraining(unittest.TestCase):
         )
         self.mock_make_request = self.make_request_patcher.start()
 
+        # Mock the inference client
+        inference_config = NVIDIAConfig(base_url=os.environ["NVIDIA_BASE_URL"], api_key=None)
+        self.inference_adapter = NVIDIAInferenceAdapter(inference_config)
+
+        self.mock_client = unittest.mock.MagicMock()
+        self.mock_client.chat.completions.create = unittest.mock.AsyncMock()
+        self.inference_mock_make_request = self.mock_client.chat.completions.create
+        self.inference_make_request_patcher = patch(
+            "llama_stack.providers.remote.inference.nvidia.nvidia.NVIDIAInferenceAdapter._get_client",
+            return_value=self.mock_client,
+        )
+        self.inference_make_request_patcher.start()
+
     def tearDown(self):
         self.make_request_patcher.stop()
+        self.inference_make_request_patcher.stop()
 
     @pytest.fixture(autouse=True)
     def inject_fixtures(self, run_async):
@@ -303,6 +319,31 @@ class TestNvidiaPostTraining(unittest.TestCase):
             expected_params={"job_id": job_id},
         )
 
+    def test_inference_register_model(self):
+        model_id = "default/job-1234"
+        model_type = ModelType.llm
+        model = Model(
+            identifier=model_id,
+            provider_id="nvidia",
+            provider_model_id=model_id,
+            provider_resource_id=model_id,
+            model_type=model_type,
+        )
+        result = self.run_async(self.inference_adapter.register_model(model))
+        assert result == model
+        assert len(self.inference_adapter.alias_to_provider_id_map) > 1
+        assert self.inference_adapter.get_provider_model_id(model.provider_model_id) == model_id
+
+        with patch.object(self.inference_adapter, "chat_completion") as mock_chat_completion:
+            self.run_async(
+                self.inference_adapter.chat_completion(
+                    model_id=model_id,
+                    messages=[{"role": "user", "content": "Hello, model"}],
+                )
+            )
+
+            mock_chat_completion.assert_called()
+
 
 if __name__ == "__main__":
     unittest.main()