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FBGEMM_GPU

FBGEMM_GPU CI FBGEMM_GPU-CPU Nightly Build FBGEMM_GPU-CUDA Nightly Build

FBGEMM_GPU (FBGEMM GPU Kernels Library) is a collection of high-performance PyTorch GPU operator libraries for training and inference. The library provides efficient table batched embedding bag, data layout transformation, and quantization supports.

FBGEMM_GPU is currently tested with CUDA 11.7.1 and 11.8 in CI, and with PyTorch packages that are built against those CUDA versions.

Only Intel/AMD CPUs with AVX2 extensions are currently supported.

Build Instructions

This section is intended for FBGEMM_GPU developers. The full build instructions for the CUDA, ROCm, and CPU-only variants of FBGEMM_GPU can be found here.

Installation Install through PIP

Currently only built with sm70/80 (V100/A100 GPU) wheel supports:

# Release GPU conda install pytorch cuda -c pytorch -c "nvidia/label/cuda-11.7.1" pip install fbgemm-gpu # Release CPU-only conda install pytorch cuda -c pytorch -c "nvidia/label/cuda-11.7.1" pip install fbgemm-gpu-cpu # Nightly GPU conda install pytorch cuda -c pytorch-nightly -c "nvidia/label/cuda-11.7.1" pip install fbgemm-gpu-nightly # Nightly CPU-only pip install --pre torch -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html pip install fbgemm-gpu-nightly-cpu Running FBGEMM_GPU

The tests (in test folder) and benchmarks (in bench folder) are some great examples of using FBGEMM_GPU. To run the tests or benchmarks after building FBGEMM_GPU (if tests or benchmarks are built), use the following command:

# run the tests and benchmarks of table batched embedding bag op, # data layout transform op, quantized ops, etc. cd test python split_table_batched_embeddings_test.py python quantize_ops_test.py python sparse_ops_test.py python split_embedding_inference_converter_test.py cd ../bench python split_table_batched_embeddings_benchmark.py

To run the tests and benchmarks on a GPU-capable device in CPU-only mode use CUDA_VISIBLE_DEVICES=-1

CUDA_VISIBLE_DEVICES=-1 python split_table_batched_embeddings_test.py Run the tests on ROCm

Please add FBGEMM_TEST_WITH_ROCM=1 flag when running tests on ROCm.

cd test FBGEMM_TEST_WITH_ROCM=1 python split_table_batched_embeddings_test.py Benchmark Example cd bench python split_table_batched_embeddings_benchmark.py uvm Documentation How FBGEMM_GPU works

For a high-level overview, design philosophy and brief descriptions of various parts of FBGEMM_GPU please see our Wiki (work in progress).

We have extensively used comments in our source files. The best and up-to-date documentation is available in the source files.

Building the API Documentation

See docs/README.md.

Join the FBGEMM_GPU Community

For questions or feature requests, please file a ticket over on GitHub Issues or reach out to us on the #fbgemm channel in PyTorch Slack.

For contributions, please see the CONTRIBUTING file for ways to help out.

License

FBGEMM_GPU is BSD licensed, as found in the LICENSE file.

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