cavis/libnd4j
raver119 3e2dbc65dd
MatMul for gemm/gemv calls (#365)
* libnd4j added optional alpha and beta support to matmul

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j typos fixes

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j add optional alpha and beta to matmul_bp

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j one more typo fix

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j added optional alpha and beta to mkl implementation

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* MatMul alpha/beta on java side

Signed-off-by: raver119 <raver119@gmail.com>

* alpha/beta fix in libnd4j

Signed-off-by: raver119 <raver119@gmail.com>

* alpha/beta fix in matmul_bp

Signed-off-by: raver119 <raver119@gmail.com>

* restored view validation

Signed-off-by: raver119 <raver119@gmail.com>

* gemv/gemm now use MatMul op

Signed-off-by: raver119 <raver119@gmail.com>

* few tests fixed

Signed-off-by: raver119 <raver119@gmail.com>

* additional INDArray.mmul signature

Signed-off-by: raver119 <raver119@gmail.com>

* make C order default for INDArray.mmul, unless both A/B have F order

Signed-off-by: raver119 <raver119@gmail.com>

* Nd4j.gemm validation fix

Signed-off-by: raver119 <raver119@gmail.com>

* disable mkldnn matmul for xxf with beta != 0 case

Signed-off-by: raver119 <raver119@gmail.com>

* SimpleRnn workspace fix + timeouts

Signed-off-by: Alex Black <blacka101@gmail.com>

* two more tests + minor fix in matmul platform check

Signed-off-by: raver119 <raver119@gmail.com>

* Flaky test fixes

Signed-off-by: Alex Black <blacka101@gmail.com>

* propagate testresources profile

Signed-off-by: raver119 <raver119@gmail.com>

* Resources fix + flaky test fix

Signed-off-by: Alex Black <blacka101@gmail.com>

Co-authored-by: Oleg <oleg.semeniv@gmail.com>
Co-authored-by: Alex Black <blacka101@gmail.com>
2020-04-10 17:57:02 +03:00
..
auto_vectorization auto-vectorization check for gcc (#172) 2020-01-28 19:00:12 +03:00
blas Fixes #8763 (#310) 2020-03-19 14:53:21 +09:00
cmake Bugfix failing builds (#341) 2020-03-24 12:55:47 +11:00
include MatMul for gemm/gemv calls (#365) 2020-04-10 17:57:02 +03:00
minifier libnd4j polishing (#273) 2020-03-02 12:49:41 +03:00
msi Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
packages Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
profile Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
server libnd4j polishing (#273) 2020-03-02 12:49:41 +03:00
tests_cpu MatMul for gemm/gemv calls (#365) 2020-04-10 17:57:02 +03:00
.gitignore Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
AddingNewOps.md Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
CMakeLists.txt Fixes #8763 (#310) 2020-03-19 14:53:21 +09:00
CMakeLists.txt.cpu_features.in Platform helpers (#8216) 2019-09-11 21:50:28 +03:00
CMakeLists.txt.in roll back flatbuffers version 2020-01-31 15:57:55 +03:00
CMakeLists.txt.mkldnn.in mkldnn version bump 2020-04-07 13:14:43 +03:00
CMakeSettings.json libnd4j polishing (#273) 2020-03-02 12:49:41 +03:00
LICENSE Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
README.md auto-vectorization check for gcc (#172) 2020-01-28 19:00:12 +03:00
RaspberryPi.md Update links to eclipse repos (#252) 2019-09-10 19:09:46 +10:00
UnderstandingGraph.md Update links to eclipse repos (#252) 2019-09-10 19:09:46 +10:00
assembly-cuda.xml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
assembly.xml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
buildnativeoperations.sh Bugfix failing builds (#341) 2020-03-24 12:55:47 +11:00
cibuild.sh Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
development.md Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
flatproto.txt Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
iOS.md Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
linuxOnPower.md Update links to eclipse repos (#252) 2019-09-10 19:09:46 +10:00
macOSx10 (CPU only).md Update links to eclipse repos (#252) 2019-09-10 19:09:46 +10:00
pom.xml Bugfix failing builds (#341) 2020-03-24 12:55:47 +11:00
proto.sh Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
setuposx.sh Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
windows.md Lu wrapper and tests fixes (#144) 2019-12-30 15:06:12 +03:00

README.md

LibND4J

Native operations for nd4j. Build using cmake

Prerequisites

  • GCC 4.9+
  • CUDA 8.0 or 9.0 (if desired)
  • CMake 3.8 (as of Nov 2017, in near future will require 3.9)

Additional build arguments

There's few additional arguments for buildnativeoperations.sh script you could use:

 -a XXXXXXXX// shortcut for -march/-mtune, i.e. -a native
 -b release OR -b debug // enables/desables debug builds. release is considered by default
 -j XX // this argument defines how many threads will be used to binaries on your box. i.e. -j 8 
 -cc XX// CUDA-only argument, builds only binaries for target GPU architecture. use this for fast builds
 --check-vectorization  auto-vectorization report for developers. (Currently, only GCC is supported)

More about AutoVectorization report

You can find the compute capability for your card on the NVIDIA website here.

For example, a GTX 1080 has compute capability 6.1, for which you would use -cc 61 (note no decimal point).

OS Specific Requirements

Android

Download the NDK, extract it somewhere, and execute the following commands, replacing android-xxx with either android-arm or android-x86:

git clone https://github.com/deeplearning4j/libnd4j
git clone https://github.com/deeplearning4j/nd4j
export ANDROID_NDK=/path/to/android-ndk/
cd libnd4j
bash buildnativeoperations.sh -platform android-xxx
cd ../nd4j
mvn clean install -Djavacpp.platform=android-xxx -DskipTests -pl '!:nd4j-cuda-9.0,!:nd4j-cuda-9.0-platform,!:nd4j-tests'

OSX

Run ./setuposx.sh (Please ensure you have brew installed)

See macOSx10 CPU only.md

Linux

Depends on the distro - ask in the earlyadopters channel for specifics on distro

Ubuntu Linux 15.10

wget http://developer.download.nvidia.com/compute/cuda/7.5/Prod/local_installers/cuda-repo-ubuntu1504-7-5-local_7.5-18_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1504-7-5-local_7.5-18_amd64.deb
sudo apt-get update
sudo apt-get install cuda
sudo apt-get install cmake
sudo apt-get install gcc-4.9
sudo apt-get install g++-4.9
sudo apt-get install git
git clone https://github.com/deeplearning4j/libnd4j
cd libnd4j/
export LIBND4J_HOME=~/libnd4j/
sudo rm /usr/bin/gcc
sudo rm /usr/bin/g++
sudo ln -s /usr/bin/gcc-4.9 /usr/bin/gcc
sudo ln -s /usr/bin/g++-4.9 /usr/bin/g++
./buildnativeoperations.sh
./buildnativeoperations.sh -c cuda -сс YOUR_DEVICE_ARCH

Ubuntu Linux 16.04

sudo apt install cmake
sudo apt install nvidia-cuda-dev nvidia-cuda-toolkit nvidia-361
export TRICK_NVCC=YES
./buildnativeoperations.sh
./buildnativeoperations.sh -c cuda -сс YOUR_DEVICE_ARCH

The standard development headers are needed.

CentOS 6

yum install centos-release-scl-rh epel-release
yum install devtoolset-3-toolchain maven30 cmake3 git
scl enable devtoolset-3 maven30 bash
./buildnativeoperations.sh
./buildnativeoperations.sh -c cuda -сс YOUR_DEVICE_ARCH

Windows

See Windows.md

Setup for All OS

  1. Set a LIBND4J_HOME as an environment variable to the libnd4j folder you've obtained from GIT

    • Note: this is required for building nd4j as well.
  2. Setup cpu followed by gpu, run the following on the command line:

    • For standard builds:

      ./buildnativeoperations.sh
      ./buildnativeoperations.sh -c cuda -сс YOUR_DEVICE_ARCH
      
    • For Debug builds:

      ./buildnativeoperations.sh blas -b debug
      ./buildnativeoperations.sh blas -c cuda -сс YOUR_DEVICE_ARCH -b debug
      
    • For release builds (default):

      ./buildnativeoperations.sh
      ./buildnativeoperations.sh -c cuda -сс YOUR_DEVICE_ARCH
      

OpenMP support

OpenMP 4.0+ should be used to compile libnd4j. However, this shouldn't be any trouble, since OpenMP 4 was released in 2015 and should be available on all major platforms.

Linking with MKL

We can link with MKL either at build time, or at runtime with binaries initially linked with another BLAS implementation such as OpenBLAS. In either case, simply add the path containing libmkl_rt.so (or mkl_rt.dll on Windows), say /path/to/intel64/lib/, to the LD_LIBRARY_PATH environment variable on Linux (or PATH on Windows), and build or run your Java application as usual. If you get an error message like undefined symbol: omp_get_num_procs, it probably means that libiomp5.so, libiomp5.dylib, or libiomp5md.dll is not present on your system. In that case though, it is still possible to use the GNU version of OpenMP by setting these environment variables on Linux, for example:

export MKL_THREADING_LAYER=GNU
export LD_PRELOAD=/usr/lib64/libgomp.so.1

##Troubleshooting MKL

Sometimes the above steps might not be all you need to do. Another additional step might be the need to add:

export LD_LIBRARY_PATH=/opt/intel/lib/intel64/:/opt/intel/mkl/lib/intel64

This ensures that mkl will be found first and liked to.

Packaging

If on Ubuntu (14.04 or above) or CentOS (6 or above), this repository is also set to create packages for your distribution. Let's assume you have built:

  • for the cpu, your command-line was ./buildnativeoperations.sh ...:
cd blasbuild/cpu
make package
  • for the gpu, your command-line was ./buildnativeoperations.sh -c cuda ...:
cd blasbuild/cuda
make package

Uploading package to Bintray

The package upload script is in packaging. The upload command for an rpm built for cpu is:

./packages/push_to_bintray.sh myAPIUser myAPIKey deeplearning4j blasbuild/cpu/libnd4j-0.8.0.fc7.3.1611.x86_64.rpm https://github.com/deeplearning4j

The upload command for a deb package built for cuda is:

./packages/push_to_bintray.sh myAPIUser myAPIKey deeplearning4j blasbuild/cuda/libnd4j-0.8.0.fc7.3.1611.x86_64.deb https://github.com/deeplearning4j

Running tests

Tests are written with gtest, run using cmake. Tests are currently under tests_cpu/

There are 2 directories for running tests:

1. libnd4j_tests: These are older legacy ops tests.
2. layers_tests: This covers the newer graph operations and ops associated with samediff.

For running the tests, we currently use cmake or CLion to run the tests.

To run tests using CUDA backend it's pretty much similar process:

1. ./buildnativeoperations.h -c cuda -cc <YOUR_ARCH> -b debug -t -j <NUMBER_OF_CORES>
2. ./blasbuild/cuda/tests_cpu/layers_tests/runtests (.exe on Windows)