llama-stack-mirror/llama_stack/providers/tests
Fred Reiss 8b2376bfb3
Add inline vLLM inference provider to regression tests and fix regressions (#662)
# What does this PR do?

This PR adds the inline vLLM inference provider to the regression tests
for inference providers. The PR also fixes some regressions in that
inference provider in order to make the tests pass.


## Test Plan

Command to run the new tests (from root of project):
```
pytest \
    -vvv \
    llama_stack/providers/tests/inference/test_text_inference.py \
    --providers inference=vllm \
    --inference-model meta-llama/Llama-3.2-3B-Instruct \
```

Output of the above command after these changes:
```
/mnt/datadisk1/freiss/llama/env/lib/python3.12/site-packages/pytest_asyncio/plugin.py:207: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset.
The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session"

  warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET))
=================================================================== test session starts ===================================================================
platform linux -- Python 3.12.7, pytest-8.3.4, pluggy-1.5.0 -- /mnt/datadisk1/freiss/llama/env/bin/python3.12
cachedir: .pytest_cache
rootdir: /mnt/datadisk1/freiss/llama/llama-stack
configfile: pyproject.toml
plugins: asyncio-0.25.0, anyio-4.6.2.post1
asyncio: mode=Mode.STRICT, asyncio_default_fixture_loop_scope=None
collected 9 items                                                                                                                                         

llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_model_list[-vllm] PASSED                                          [ 11%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion[-vllm] SKIPPED (Other inference providers don't
support completion() yet)                                                                                                                           [ 22%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_logprobs[-vllm] SKIPPED (Other inference providers
don't support completion() yet)                                                                                                                     [ 33%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_structured_output[-vllm] SKIPPED (This test is not
quite robust)                                                                                                                                       [ 44%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_non_streaming[-vllm] PASSED                       [ 55%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output[-vllm] SKIPPED (Other inference providers don't
support structured output yet)                                                                                                                      [ 66%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_streaming[-vllm] PASSED                           [ 77%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling[-vllm] PASSED                   [ 88%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling_streaming[-vllm] PASSED         [100%]

======================================================== 5 passed, 4 skipped, 2 warnings in 25.56s ========================================================
Task was destroyed but it is pending!
task: <Task pending name='Task-6' coro=<AsyncLLMEngine.run_engine_loop() running at /mnt/datadisk1/freiss/llama/env/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py:848> cb=[_log_task_completion(error_callback=<bound method...7cfc479440b0>>)() at /mnt/datadisk1/freiss/llama/env/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py:45, shield.<locals>._inner_done_callback() at /mnt/datadisk1/freiss/llama/env/lib/python3.12/asyncio/tasks.py:905]>
[rank0]:[W1219 11:38:34.689424319 ProcessGroupNCCL.cpp:1250] Warning: WARNING: process group has NOT been destroyed before we destruct ProcessGroupNCCL. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL operations have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present,  but this warning has only been added since PyTorch 2.4 (function operator())
```

The warning about "asyncio_default_fixture_loop_scope" appears to be due
to my environment having a newer version of pytest-asyncio.

The warning about a pending task appears to be due to a bug in
`vllm.AsyncLLMEngine.shutdown_background_loop()`. It looks like that
method returns without stopping a pending task. I will look into that
issue separately.

## Sources


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [X] Ran pre-commit to handle lint / formatting issues.
- [X] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [X] Wrote necessary unit or integration tests.
2025-01-10 16:35:16 -08:00
..
agents agents to use tools api (#673) 2025-01-08 19:01:00 -08:00
datasetio [rag evals] refactor & add ability to eval retrieval + generation in agentic eval pipeline (#664) 2025-01-02 11:21:33 -08:00
eval [remove import *] clean up import *'s (#689) 2024-12-27 15:45:44 -08:00
inference Add inline vLLM inference provider to regression tests and fix regressions (#662) 2025-01-10 16:35:16 -08:00
memory agents to use tools api (#673) 2025-01-08 19:01:00 -08:00
post_training [remove import *] clean up import *'s (#689) 2024-12-27 15:45:44 -08:00
safety [remove import *] clean up import *'s (#689) 2024-12-27 15:45:44 -08:00
scoring [rag evals] refactor & add ability to eval retrieval + generation in agentic eval pipeline (#664) 2025-01-02 11:21:33 -08:00
tools agents to use tools api (#673) 2025-01-08 19:01:00 -08:00
__init__.py Remove "routing_table" and "routing_key" concepts for the user (#201) 2024-10-10 10:24:13 -07:00
conftest.py agents to use tools api (#673) 2025-01-08 19:01:00 -08:00
env.py Significantly simpler and malleable test setup (#360) 2024-11-04 17:36:43 -08:00
README.md update tests --inference-model to hf id 2024-11-18 17:36:58 -08:00
resolver.py Add X-LlamaStack-Client-Version, rename ProviderData -> Provider-Data (#735) 2025-01-09 11:51:36 -08:00

Testing Llama Stack Providers

The Llama Stack is designed as a collection of Lego blocks -- various APIs -- which are composable and can be used to quickly and reliably build an app. We need a testing setup which is relatively flexible to enable easy combinations of these providers.

We use pytest and all of its dynamism to enable the features needed. Specifically:

  • We use pytest_addoption to add CLI options allowing you to override providers, models, etc.

  • We use pytest_generate_tests to dynamically parametrize our tests. This allows us to support a default set of (providers, models, etc.) combinations but retain the flexibility to override them via the CLI if needed.

  • We use pytest_configure to make sure we dynamically add appropriate marks based on the fixtures we make.

Common options

All tests support a --providers option which can be a string of the form api1=provider_fixture1,api2=provider_fixture2. So, when testing safety (which need inference and safety APIs) you can use --providers inference=together,safety=meta_reference to use these fixtures in concert.

Depending on the API, there are custom options enabled. For example, inference tests allow for an --inference-model override, etc.

By default, we disable warnings and enable short tracebacks. You can override them using pytest's flags as appropriate.

Some providers need special API keys or other configuration options to work. You can check out the individual fixtures (located in tests/<api>/fixtures.py) for what these keys are. These can be specified using the --env CLI option. You can also have it be present in the environment (exporting in your shell) or put it in the .env file in the directory from which you run the test. For example, to use the Together fixture you can use --env TOGETHER_API_KEY=<...>

Inference

We have the following orthogonal parametrizations (pytest "marks") for inference tests:

  • providers: (meta_reference, together, fireworks, ollama)
  • models: (llama_8b, llama_3b)

If you want to run a test with the llama_8b model with fireworks, you can use:

pytest -s -v llama_stack/providers/tests/inference/test_text_inference.py \
  -m "fireworks and llama_8b" \
  --env FIREWORKS_API_KEY=<...>

You can make it more complex to run both llama_8b and llama_3b on Fireworks, but only llama_3b with Ollama:

pytest -s -v llama_stack/providers/tests/inference/test_text_inference.py \
  -m "fireworks or (ollama and llama_3b)" \
  --env FIREWORKS_API_KEY=<...>

Finally, you can override the model completely by doing:

pytest -s -v llama_stack/providers/tests/inference/test_text_inference.py \
  -m fireworks \
  --inference-model "meta-llama/Llama3.1-70B-Instruct" \
  --env FIREWORKS_API_KEY=<...>

Agents

The Agents API composes three other APIs underneath:

  • Inference
  • Safety
  • Memory

Given that each of these has several fixtures each, the set of combinations is large. We provide a default set of combinations (see tests/agents/conftest.py) with easy to use "marks":

  • meta_reference -- uses all the meta_reference fixtures for the dependent APIs
  • together -- uses Together for inference, and meta_reference for the rest
  • ollama -- uses Ollama for inference, and meta_reference for the rest

An example test with Together:

pytest -s -m together llama_stack/providers/tests/agents/test_agents.py  \
 --env TOGETHER_API_KEY=<...>

If you want to override the inference model or safety model used, you can use the --inference-model or --safety-shield CLI options as appropriate.

If you wanted to test a remotely hosted stack, you can use -m remote as follows:

pytest -s -m remote llama_stack/providers/tests/agents/test_agents.py \
  --env REMOTE_STACK_URL=<...>