GitHub - pytorch/FBGEMM: FB (Facebook) + GEMM (General Matrix-Matrix Multiplication) - https://code.fb.com/ml-applications/fbgemm/

New Features and Recent Improvements (January, 2020)
Examples
The tests (in test folder) and benchmarks (in bench folder) are some great examples of using FBGEMM. For instance, SpMDMTest test in test/PackedRequantizeAcc16Test.cc shows how to combine row offset calculations with packing of A (PackAWithRowOffset), how to pack B matrix (PackBMatrix) and construct output pipeline (sparse_matrix*dense_matrix --> requantization --> nop) fused with inner GEMM macro kernel.
Build Notes
FBGEMM uses the standard CMAKE-based build flow.
Dependencies
FBGEMM requires gcc 5+ and a CPU with support for avx2 instruction set or higher. It's been tested on Mac OS X and Linux.
asmjit
With inner kernels, FBGEMM takes a “one size doesn't fit all” approach, so the implementation dynamically generates efficient matrix-shape specific vectorized code using a third-party library called asmjit . asmjit is required to build FBGEMM.
cpuinfo
FBGEMM detects CPU instruction set support at runtime using cpuinfo library and dispatches optimized kernels for the detected instruction set. Therefore, cpuinfo is required to detect CPU type.
googletest
googletest is required to build and run FBGEMM's tests. googletest is not required if you don't want to run FBGEMM tests. By default, building of tests is on. Turn it off by setting FBGEMM_BUILD_TESTS to off.
You can download asmjit , cpuinfo , googletest and set ASMJIT_SRC_DIR, CPUINFO_SRC_DIR, GOOGLETEST_SOURCE_DIR respectively for cmake to find these libraries. If any of these variables is not set, cmake will build the git submodules found in the third_party directory.
FBGEMM, in general, does not have any dependency on Intel MKL. However, for performance comparison, some benchmarks use MKL functions. If MKL is found or MKL path is provided with INTEL_MKL_DIR benchmarks are built with MKL and performance numbers are reported for MKL functions as well. However, if MKL is not found, the benchmarks are not built.
General build instructions are as follows:
git clone --recursive https://github.com/pytorch/FBGEMM.git cd FBGEMM # if you are updating an existing checkout git submodule sync git submodule update --init --recursive cmake -B build make -C build
To run the tests after building FBGEMM (if tests are built), use the following command:
make test
make install
How FBGEMM works
For a high-level overview, design philosophy and brief descriptions of various parts of FBGEMM please see our blog .
Full documentation
We have extensively used comments in our source files. The best and up-do-date documentation is available in the source files.
You can also turn on the option to generate the documentation (using Doxygen and Sphinx by setting FBGEMM_BUILD_DOCS to ON, and then follow the above cmake build process.
Citation
For those looking for the appropriate article to cite regarding FBGEMM, we recommend citing our paper :
@article{fbgemm, title={FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference}, author={Khudia, Daya and Huang, Jianyu and Basu, Protonu and Deng, Summer and Liu, Haixin and Park, Jongsoo and Smelyanskiy, Mikhail}, journal={arXiv preprint arXiv:2101.05615}, year={2021} }
Join the FBGEMM community
See the CONTRIBUTING file for how to help out.
License