add NVIDIA NIM inference adapter (#355)

# What does this PR do?

this PR adds a basic inference adapter to NVIDIA NIMs

what it does -
 - chat completion api
   - tool calls
   - streaming
   - structured output
   - logprobs
 - support hosted NIM on integrate.api.nvidia.com
 - support downloaded NIM containers

what it does not do -
 - completion api
 - embedding api
 - vision models
 - builtin tools
 - have certainty that sampling strategies are correct

## Feature/Issue validation/testing/test plan

`pytest -s -v --providers inference=nvidia
llama_stack/providers/tests/inference/ --env NVIDIA_API_KEY=...`

all tests should pass. there are pydantic v1 warnings.


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Did you read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?

Thanks for contributing 🎉!

llama_stack/providers/tests/inference/conftest.py

@@ -6,6 +6,8 @@
 
 import pytest
 
+from ..conftest import get_provider_fixture_overrides
+
 from .fixtures import INFERENCE_FIXTURES
 
 
@@ -67,11 +69,12 @@ def pytest_generate_tests(metafunc):
             indirect=True,
         )
     if "inference_stack" in metafunc.fixturenames:
-        metafunc.parametrize(
-            "inference_stack",
-            [
-                pytest.param(fixture_name, marks=getattr(pytest.mark, fixture_name))
-                for fixture_name in INFERENCE_FIXTURES
-            ],
-            indirect=True,
-        )
+        fixtures = INFERENCE_FIXTURES
+        if filtered_stacks := get_provider_fixture_overrides(
+            metafunc.config,
+            {
+                "inference": INFERENCE_FIXTURES,
+            },
+        ):
+            fixtures = [stack.values[0]["inference"] for stack in filtered_stacks]
+        metafunc.parametrize("inference_stack", fixtures, indirect=True)

llama_stack/providers/tests/inference/fixtures.py

@@ -18,6 +18,7 @@ from llama_stack.providers.inline.inference.meta_reference import (
 from llama_stack.providers.remote.inference.bedrock import BedrockConfig
 
 from llama_stack.providers.remote.inference.fireworks import FireworksImplConfig
+from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
 from llama_stack.providers.remote.inference.ollama import OllamaImplConfig
 from llama_stack.providers.remote.inference.together import TogetherImplConfig
 from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig
@@ -142,6 +143,19 @@ def inference_bedrock() -> ProviderFixture:
     )
 
 
+@pytest.fixture(scope="session")
+def inference_nvidia() -> ProviderFixture:
+    return ProviderFixture(
+        providers=[
+            Provider(
+                provider_id="nvidia",
+                provider_type="remote::nvidia",
+                config=NVIDIAConfig().model_dump(),
+            )
+        ],
+    )
+
+
 def get_model_short_name(model_name: str) -> str:
     """Convert model name to a short test identifier.
 
@@ -175,6 +189,7 @@ INFERENCE_FIXTURES = [
     "vllm_remote",
     "remote",
     "bedrock",
+    "nvidia",
 ]
 
 

llama_stack/providers/tests/inference/test_text_inference.py

@@ -198,6 +198,7 @@ class TestInference:
             "remote::fireworks",
             "remote::tgi",
             "remote::together",
+            "remote::nvidia",
         ):
             pytest.skip("Other inference providers don't support structured output yet")
 
@@ -361,7 +362,10 @@ class TestInference:
                 for chunk in grouped[ChatCompletionResponseEventType.progress]
             )
             first = grouped[ChatCompletionResponseEventType.progress][0]
-            assert first.event.delta.parse_status == ToolCallParseStatus.started
+            if not isinstance(
+                first.event.delta.content, ToolCall
+            ):  # first chunk may contain entire call
+                assert first.event.delta.parse_status == ToolCallParseStatus.started
 
         last = grouped[ChatCompletionResponseEventType.progress][-1]
         # assert last.event.stop_reason == expected_stop_reason