TensorRT-LLMs/tests/integration/README.md
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Signed-off-by: EmmaQiaoCh <qqiao@nvidia.com>
Signed-off-by: Yanchao Lu <yanchaol@nvidia.com>
Co-authored-by: Yanchao Lu <yanchaol@nvidia.com>
2025-04-10 17:29:30 +08:00

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# TensorRT LLM test definitions
The following subfolder contains test definitions for Tensorrt LLM.
## Directory structure
~~~
.
└── integration # Root directory for integration tests
├── defs # Test definitions
├── perf_configs # Configs for perf tests
└── test_lists # Test lists
├── test-db # Test-DB that is the test list convention adopted by CI
├── dev # Other test lists used by TRT LLM developers
├── qa # Test lists used by QA
└── waives.txt # Test waive list
~~~
- To run perf tests, you also need to first build the cpp benchmark by calling `build_wheel.py` with `--benchmarks` flag.
## Run perf tests
All the perf test names are in the form of `perf/test_perf.py::test_perf[...]` where the `...` part is the test parameters.
Below are some specific pytest options used for perf tests
```bash
# execute these in the tensorrt-llm source repo root dir.
# install dependencies, do not need to do it every time if already installed.
pip install -r requirements-dev.txt
# example 1: run a test case
# For example, if QA reports a perf bug for `perf/test_perf.py::test_perf[llama_7b-cppmanager-exe-plugin_ifb-float16-input_output_len:128,128,+512,32]`, then you can repro it by running:
cd LLM_ROOT/tests/integration/defs
echo "perf/test_perf.py::test_perf[llama_7b-cppmanager-exe-plugin_ifb-float16-input_output_len:128,128,+512,32]" > perf.txt
pytest --perf --test-list=perf.txt --output-dir=/workspace/test-log --perf-log-formats csv --perf-log-formats yaml
```
The captured perf metrics will be saved in `/workspace/test-log/perf_scripts_test_results.csv` or `/workspace/test-log/perf_scripts_test_results.yaml` depends on the option `--perf-log-formats`, and the test logs are saved in `/workspace/test-log/result.xmk`. Currently, we capture these perf metrics:
1. `test_perf_metric_build_time`: The engine building time in seconds.
2. `test_perf_metric_build_peak_cpu_memory`: The build-phase peak CPU mem usage in MB.
3. `test_perf_metric_build_peak_gpu_memory`: The build-phase peak GPU mem usage in MB.
4. `test_perf_metric_inference_time`: The inference latency in ms.
5. `test_perf_metric_inference_peak_gpu_memory`: The inference-phase peak GPU mem usage in GB.
6. `test_perf_metric_context_gpu_memory`: The context GPU mem usage in MB.
## Common Issues and solutions
1. No package 'libffi' found
Install libffi by `sudo apt-get install libffi-dev` and rerun.