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10b1056dea
# What does this PR do? This fixes an issue in how we used the tool_call_buf from streaming tool calls in the remote-vllm provider where it would end up concatenating parameters from multiple different tool call results instead of aggregating the results from each tool call separately. It also fixes an issue found while digging into that where we were accidentally mixing the json string form of tool call parameters with the string representation of the python form, which mean we'd end up with single quotes in what should be double-quoted json strings. Closes #1120 ## Test Plan The following tests are now passing 100% for the remote-vllm provider, where some of the test_text_inference were failing before this change: ``` VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/inference/test_text_inference.py --text-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/inference/test_vision_inference.py --vision-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" ``` All but one of the agent tests are passing (including the multi-tool one). See the PR at https://github.com/vllm-project/vllm/pull/17917 and a gist at https://gist.github.com/bbrowning/4734240ce96b4264340caa9584e47c9e for changes needed there, which will have to get made upstream in vLLM. Agent tests: ``` VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/agents/test_agents.py --text-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" ```` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com> |
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| .. | ||
| agents | ||
| datasets | ||
| eval | ||
| fixtures | ||
| inference | ||
| inspect | ||
| post_training | ||
| providers | ||
| safety | ||
| scoring | ||
| telemetry | ||
| test_cases | ||
| tool_runtime | ||
| tools | ||
| vector_io | ||
| __init__.py | ||
| conftest.py | ||
| README.md | ||
Llama Stack Integration Tests
We use pytest for parameterizing and running tests. You can see all options with:
cd tests/integration
# this will show a long list of options, look for "Custom options:"
pytest --help
Here are the most important options:
--stack-config: specify the stack config to use. You have three ways to point to a stack:- a URL which points to a Llama Stack distribution server
- a template (e.g.,
fireworks,together) or a path to arun.yamlfile - a comma-separated list of api=provider pairs, e.g.
inference=fireworks,safety=llama-guard,agents=meta-reference. This is most useful for testing a single API surface.
--env: set environment variables, e.g. --env KEY=value. this is a utility option to set environment variables required by various providers.
Model parameters can be influenced by the following options:
--text-model: comma-separated list of text models.--vision-model: comma-separated list of vision models.--embedding-model: comma-separated list of embedding models.--safety-shield: comma-separated list of safety shields.--judge-model: comma-separated list of judge models.--embedding-dimension: output dimensionality of the embedding model to use for testing. Default: 384
Each of these are comma-separated lists and can be used to generate multiple parameter combinations. Note that tests will be skipped if no model is specified.
Experimental, under development, options:
--record-responses: record new API responses instead of using cached ones
Examples
Run all text inference tests with the together distribution:
pytest -s -v tests/integration/inference/test_text_inference.py \
--stack-config=together \
--text-model=meta-llama/Llama-3.1-8B-Instruct
Run all text inference tests with the together distribution and meta-llama/Llama-3.1-8B-Instruct:
pytest -s -v tests/integration/inference/test_text_inference.py \
--stack-config=together \
--text-model=meta-llama/Llama-3.1-8B-Instruct
Running all inference tests for a number of models:
TEXT_MODELS=meta-llama/Llama-3.1-8B-Instruct,meta-llama/Llama-3.1-70B-Instruct
VISION_MODELS=meta-llama/Llama-3.2-11B-Vision-Instruct
EMBEDDING_MODELS=all-MiniLM-L6-v2
export TOGETHER_API_KEY=<together_api_key>
pytest -s -v tests/integration/inference/ \
--stack-config=together \
--text-model=$TEXT_MODELS \
--vision-model=$VISION_MODELS \
--embedding-model=$EMBEDDING_MODELS
Same thing but instead of using the distribution, use an adhoc stack with just one provider (fireworks for inference):
export FIREWORKS_API_KEY=<fireworks_api_key>
pytest -s -v tests/integration/inference/ \
--stack-config=inference=fireworks \
--text-model=$TEXT_MODELS \
--vision-model=$VISION_MODELS \
--embedding-model=$EMBEDDING_MODELS
Running Vector IO tests for a number of embedding models:
EMBEDDING_MODELS=all-MiniLM-L6-v2
pytest -s -v tests/integration/vector_io/ \
--stack-config=inference=sentence-transformers,vector_io=sqlite-vec \
--embedding-model=$EMBEDDING_MODELS