TensorRT-LLMs/cpp/tests
2025-04-02 17:01:16 +08:00
..
common TensorRT-LLM Release 0.15.0 (#2529) 2024-12-04 13:44:56 +08:00
kernels open source 09df54c0cc99354a60bbc0303e3e8ea33a96bef0 (#2725) 2025-02-11 02:21:51 +00:00
layers TensorRT-LLM v0.16 Release 2024-12-24 15:58:43 +08:00
resources TensorRT-LLM v0.18 release (#3231) 2025-04-02 17:01:16 +08:00
runtime open source 09df54c0cc99354a60bbc0303e3e8ea33a96bef0 (#2725) 2025-02-11 02:21:51 +00:00
thop TensorRT-LLM v0.12 Update (#2164) 2024-08-29 17:25:07 +08:00
utils open source 09df54c0cc99354a60bbc0303e3e8ea33a96bef0 (#2725) 2025-02-11 02:21:51 +00:00
CMakeLists.txt open source 09df54c0cc99354a60bbc0303e3e8ea33a96bef0 (#2725) 2025-02-11 02:21:51 +00:00
README.md TensorRT-LLM Release 0.15.0 (#2529) 2024-12-04 13:44:56 +08:00

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.

All-in-one script

The script test_cpp.py can be executed to build TRT-LLM, build engines, generate expected outputs and run C++ tests all in one go. To get an overview of the parameters call:

python3 cpp/tests/resources/scripts/test_cpp.py -h

It is possible to choose a single model for end-to-end tests or skip models that should not be tested. An example call may look like this:

CPP_BUILD_DIR=cpp/build
MODEL_CACHE=/path/to/model_cache
python3 cpp/tests/resources/scripts/test_cpp.py -a "80-real;86-real" --build_dir ${CPP_BUILD_DIR} --trt_root /usr/local/tensorrt --model_cache ${MODEL_CACHE} --run_gptj --skip_unit_tests

Manual steps

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} --trt_root /usr/local/tensorrt
pip install -r requirements-dev.txt
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:$PYTHONPATH python3 cpp/tests/resources/scripts/build_llama_engines.py
PYTHONPATH=examples/chatglm:$PYTHONPATH python3 cpp/tests/resources/scripts/build_chatglm_engines.py
PYTHONPATH=examples/medusa:$PYTHONPATH python3 cpp/tests/resources/scripts/build_medusa_engines.py
PYTHONPATH=examples/eagle:$PYTHONPATH python3 cpp/tests/resources/scripts/build_eagle_engines.py
PYTHONPATH=examples/redrafter:$PYTHONPATH python3 cpp/tests/resources/scripts/build_redrafter_engines.py --has_tllm_checkpoint

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

If there is an issue finding model_spec.so in engine building, manually build model_spec.so by

make -C cpp/build/ modelSpec

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:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_gpt_output.py
PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_gptj_output.py
PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_llama_output.py
PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_chatglm_output.py
PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_medusa_output.py
PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_eagle_output.py
PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_redrafter_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 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"