TensorRT-LLMs/cpp/tests

C++ Tests

This document explains how to build and run the C++ tests, and the included resources.

Windows users: Be sure to set DLL paths as specified in Extra Steps for C++ Runtime Usage.

Compile

From the top-level directory call:

CPP_BUILD_DIR=cpp/build
python3 scripts/build_wheel.py -a "80-real;86-real" --build_dir ${CPP_BUILD_DIR}
pip install -r requirements-dev.txt --extra-index-url https://pypi.ngc.nvidia.com
pip install build/tensorrt_llm*.whl
cd $CPP_BUILD_DIR && make -j$(nproc) google-tests

Single tests can be executed from CPP_BUILD_DIR/tests, e.g.

./$CPP_BUILD_DIR/tests/allocatorTest

End-to-end tests

gptSessionTest, gptManagerTest and trtGptModelRealDecoderTest require pre-built TensorRT engines, which are loaded in the tests. They also require data files which are stored in cpp/tests/resources/data.

Build engines

Scripts are provided that download the GPT2 and GPT-J models from Huggingface and convert them to TensorRT engines. The weights and built engines are stored under cpp/tests/resources/models. To build the engines from the top-level directory:

PYTHONPATH=examples/gpt:$PYTHONPATH python3 cpp/tests/resources/scripts/build_gpt_engines.py
PYTHONPATH=examples/gptj:$PYTHONPATH python3 cpp/tests/resources/scripts/build_gptj_engines.py
PYTHONPATH=examples/llama:$PYTHONPATH python3 cpp/tests/resources/scripts/build_llama_engines.py
PYTHONPATH=examples/CHATGLM6B:$PYTHONPATH python3 cpp/tests/resources/scripts/build_chatglm6b_engines.py

It is possible to build engines with tensor and pipeline parallelism for LLaMA using 4 GPUs.

PYTHONPATH=examples/llama python3 cpp/tests/resources/scripts/build_llama_engines.py --only_multi_gpu

Generate expected output

End-to-end tests read inputs and expected outputs from Numpy files located at cpp/tests/resources/data. The expected outputs can be generated using scripts which employ the Python runtime to run the built engines:

PYTHONPATH=examples/gpt:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_gpt_output.py
PYTHONPATH=examples/gptj:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_gptj_output.py
PYTHONPATH=examples/llama:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_llama_output.py
PYTHONPATH=examples/chatglm6b:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_chatglm6b_output.py
PYTHONPATH=examples/chatglm2-6b:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_chatglm2-6b_output.py

Generate data with tensor and pipeline parallelism

It is possible to generate tensor and pipeline parallelism data for LLaMA using 4 GPUs. To generate results from the top-level directory:

PYTHONPATH=examples/llama mpirun -n 4 python3 cpp/tests/resources/scripts/generate_expected_llama_output.py --only_multi_gpu

Run test

After building the engines and generating the expected output execute the tests

./$CPP_BUILD_DIR/tests/gptSessionTest

Run all tests with ctest

To run all tests and produce an xml report, call

./$CPP_BUILD_DIR/ctest --output-on-failure --output-junit "cpp-test-report.xml"