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| author | Daya S Khudia <dskhudia@fb.com> | 2019-05-17 01:49:03 +0300 |
|---|---|---|
| committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | 2019-05-17 01:53:00 +0300 |
| commit | 9ae8912fc9d09cd22f333c226188cc161d9509a6 (patch) | |
| tree | a8d577abb0bdeb3f68ebca13577133c946d31073 | |
| parent | d8f740de7689a449d58a8c9cb13b49c7998fde63 (diff) | |
update readme
Summary: Readme update for submodules
Reviewed By: protonu
Differential Revision: D15345910
fbshipit-source-id: 1dfe4f9ae602f4b3801064a1bb68a506c4d954cf
| -rw-r--r-- | README.md | 9 |
1 files changed, 5 insertions, 4 deletions
@@ -4,7 +4,7 @@ FBGEMM (Facebook GEneral Matrix Multiplication) is a low-precision, high-performance matrix-matrix multiplications and convolution library for -server-side inference. +server-side inference. The library provides efficient low-precision general matrix multiplication for small batch sizes and support for accuracy-loss minimizing techniques such as @@ -36,7 +36,7 @@ higher. It's been tested on Mac OS X and Linux. 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][1]. **asmjit is required** to -build FBGEMM. +build FBGEMM. + ###### cpuinfo FBGEMM detects CPU instruction set support at runtime using cpuinfo library and @@ -51,8 +51,7 @@ is **on**. Turn it off by setting FBGEMM\_BUILD\_TESTS to off. You can download [asmjit][1], [cpuinfo][2], [googletest][3] 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 -try to download that missing library in a folder called third\_party in the -build directory and build it using the downloaded source code. +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 @@ -63,6 +62,8 @@ not found, the benchmarks are not built. General build instructions are as follows: ``` +git clone --recursive https://github.com/pytorch/FBGEMM.git +cd FBGEMM mkdir build && cd build cmake .. make |