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* chore: Remove unused tensors from DecoderBuffers Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com> * fix: Remove unused argument from readme Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com> * chore: remove unused tensor Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com> * refactor: Remove unnecessary newOutputTokens Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com> * refactor: Remove unnecessary event in getDecoderSlotHostOutputs Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com> --------- Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
120 lines
4.6 KiB
Markdown
120 lines
4.6 KiB
Markdown
# C++ Tests
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This document explains how to build and run the C++ tests, and the included [resources](resources).
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## All-in-one script
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The Pytest script [test_cpp.py](../../tests/integration/defs/test_cpp.py) builds TRT-LLM, builds engines, and generates expected outputs and executes the C++ tests all in one go.
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To get an overview of the tests and their parameterization, call:
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```bash
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pytest tests/integration/defs/test_cpp.py --collect-only
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```
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All tests take the number of the CUDA architecture of the GPU you wish to use as a parameter e.g. 90 for Hopper.
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It is possible to choose unit tests or a single model for end-to-end tests.
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Example calls could look like this:
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```bash
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export LLM_MODELS_ROOT="/path/to/model_cache"
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pytest tests/integration/defs/test_cpp.py::test_unit_tests[90]
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pytest tests/integration/defs/test_cpp.py::test_model[llama-90]
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pytest tests/integration/defs/test_cpp.py::test_benchmarks[gpt-90]
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pytest tests/integration/defs/test_cpp.py::test_multi_gpu[90]
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```
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## Manual steps
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### Compile
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From the top-level directory call:
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```bash
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CPP_BUILD_DIR=cpp/build
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python3 scripts/build_wheel.py -a "80-real;86-real" --build_dir ${CPP_BUILD_DIR}
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pip install -r requirements-dev.txt
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pip install build/tensorrt_llm*.whl
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cd $CPP_BUILD_DIR && make -j$(nproc) google-tests
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```
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Single tests can be executed from `CPP_BUILD_DIR/tests`, e.g.
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```bash
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./$CPP_BUILD_DIR/tests/allocatorTest
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```
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### End-to-end tests
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`gptSessionTest`,`trtGptModelRealDecoderTest` and `executorTest` require pre-built TensorRT engines, which are loaded in the tests. They also require data files which are stored in [cpp/tests/resources/data](resources/data).
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#### Build engines
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[Scripts](resources/scripts) are provided that download the GPT2 and GPT-J models from Huggingface and convert them to TensorRT engines.
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The weights and built engines are stored under [cpp/tests/resources/models](resources/models).
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To build the engines from the top-level directory:
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```bash
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PYTHONPATH=examples/gpt:$PYTHONPATH python3 cpp/tests/resources/scripts/build_gpt_engines.py
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PYTHONPATH=examples/gptj:$PYTHONPATH python3 cpp/tests/resources/scripts/build_gptj_engines.py
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PYTHONPATH=examples/llama:$PYTHONPATH python3 cpp/tests/resources/scripts/build_llama_engines.py
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PYTHONPATH=examples/chatglm:$PYTHONPATH python3 cpp/tests/resources/scripts/build_chatglm_engines.py
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PYTHONPATH=examples/medusa:$PYTHONPATH python3 cpp/tests/resources/scripts/build_medusa_engines.py
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PYTHONPATH=examples/eagle:$PYTHONPATH python3 cpp/tests/resources/scripts/build_eagle_engines.py
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PYTHONPATH=examples/redrafter:$PYTHONPATH python3 cpp/tests/resources/scripts/build_redrafter_engines.py
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```
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It is possible to build engines with tensor and pipeline parallelism for LLaMA using 4 GPUs.
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```bash
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PYTHONPATH=examples/llama python3 cpp/tests/resources/scripts/build_llama_engines.py --only_multi_gpu
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```
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If there is an issue finding model_spec.so in engine building, manually build model_spec.so by
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```bash
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make -C cpp/build/ modelSpec
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```
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#### Generate expected output
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End-to-end tests read inputs and expected outputs from Numpy files located at [cpp/tests/resources/data](resources/data). The expected outputs can be generated using [scripts](resources/scripts) which employ the Python runtime to run the built engines:
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```bash
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PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_gpt_output.py
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PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_gptj_output.py
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PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_llama_output.py
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PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_chatglm_output.py
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PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_medusa_output.py
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PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_eagle_output.py
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PYTHONPATH=examples:$PYTHONPATH python3 cpp/tests/resources/scripts/generate_expected_redrafter_output.py
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```
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#### Generate data with tensor and pipeline parallelism
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It is possible to generate tensor and pipeline parallelism data for LLaMA using 4 GPUs. To generate results from the top-level directory:
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```bash
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PYTHONPATH=examples mpirun -n 4 python3 cpp/tests/resources/scripts/generate_expected_llama_output.py --only_multi_gpu
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```
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#### Run test
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After building the engines and generating the expected output execute the tests
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```bash
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./$CPP_BUILD_DIR/tests/gptSessionTest
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```
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### Run all tests with ctest
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To run all tests and produce an xml report, call
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```bash
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./$CPP_BUILD_DIR/ctest --output-on-failure --output-junit "cpp-test-report.xml"
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```
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