测试说明

tests (in the fbgemm_gpu/test/ directory) and benchmarks (in the fbgemm_gpu/bench/ directory) provide good examples on how to use FBGEMM_GPU operators.

设置 FBGEMM_GPU 测试环境

After an environment is available from building / installing the FBGEMM_GPU package, additional packages need to be installed for tests to run correctly

# !! Run inside the Conda environment !!

# From the /fbgemm_gpu/ directory
python -m pip install -r requirements.txt

运行 FBGEMM_GPU 测试

To run the tests after building / installing the FBGEMM_GPU package

# !! Run inside the Conda environment !!

# From the /fbgemm_gpu/test/ directory
cd test

python -m pytest -v -rsx -s -W ignore::pytest.PytestCollectionWarning split_table_batched_embeddings_test.py
python -m pytest -v -rsx -s -W ignore::pytest.PytestCollectionWarning quantize_ops_test.py
python -m pytest -v -rsx -s -W ignore::pytest.PytestCollectionWarning sparse_ops_test.py
python -m pytest -v -rsx -s -W ignore::pytest.PytestCollectionWarning split_embedding_inference_converter_test.py

使用 CUDA 变体进行测试

For the FBGEMM_GPU CUDA package, GPUs will be automatically detected and used for testing. To run the tests and benchmarks on a GPU-capable machine in CPU-only mode, CUDA_VISIBLE_DEVICES=-1 must be set in the environment

# !! Run inside the Conda environment !!

# Specify the specific CUDA devices to run the tests on
# Alternatively, set to -1 for running in CPU-only mode (when on a GPU-capable machine)
export CUDA_VISIBLE_DEVICES=-1

# Enable for debugging failed kernel executions
export CUDA_LAUNCH_BLOCKING=1

# For operators involving NCCL, if the rpath is not set up correctly for
# libnccl.so.2, LD_LIBRARY_PATH will need to be updated.
export LD_LIBRARY_PATH="/path/to/nccl/lib:${LD_LIBRARY_PATH}"

python -m pytest -v -rsx -s -W ignore::pytest.PytestCollectionWarning split_table_batched_embeddings_test.py

使用 ROCm 变体进行测试

For ROCm machines, testing against a ROCm GPU needs to be enabled with FBGEMM_TEST_WITH_ROCM=1 set in the environment

# !! Run inside the Conda environment !!

# From the fbgemm_gpu/test/ directory
cd test

export FBGEMM_TEST_WITH_ROCM=1

# Specify the specific HIP devices to run the tests on
#
# NOTE: This is necessary if PyTorch is unable to see the devices that
# `rocm-smi --showproductname` can see
export HIP_VISIBLE_DEVICES=0,1,2,3

# Enable for debugging kernel executions
export HIP_LAUNCH_BLOCKING=1

python -m pytest -v -rsx -s -W ignore::pytest.PytestCollectionWarning split_table_batched_embeddings_test.py

运行 FBGEMM_GPU 基准测试

To run the benchmarks

# !! Run inside the Conda environment !!

# From the fbgemm_gpu/bench/ directory
cd bench

python tbe_training_benchmark.py device