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Merge pull request #48 from facebookresearch/update-c2-docs
Bump c2, add c2-python example and c2 conda package
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‎CMakeLists.txt

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@@ -214,7 +214,7 @@ include(cmake/GetGitRevisionDescription.cmake)
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################################################################################
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# gflag
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# gflags
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message("find_library(GFLAGS_LIBRARIES gflags")
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find_library(GFLAGS_LIBRARIES gflags PATHS ${CMAKE_INSTALL_PREFIX} PATH_SUFFIXES lib lib64)
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message(STATUS "Found gflags: ${GFLAGS_LIBRARIES}")

‎build.sh

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@@ -416,9 +416,14 @@ if ! test -z $aten || ! test -z $all; then
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fi
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if ! test -z $caffe2 || ! test -z $all ; then
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if [ "$WITH_CAFFE2" == "ON" ]; then
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install_caffe2
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fi
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if [ "$WITH_CAFFE2" == "ON" ]; then
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if [[ $(find $CONDA_PREFIX -name libcaffe2_gpu.so) ]]; then
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echo "caffe2 found"
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else
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echo "no files found"
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install_caffe2
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fi
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fi
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fi
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if ! test -z $isl || ! test -z $all; then

‎docs/source/index.rst

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@@ -38,6 +38,7 @@ Tensor Comprehensions provides framework-Agnostic Abstractions for High-Performa
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installation_conda_dep
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installation_conda
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installation_non_conda
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installation_caffe2_integration
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.. toctree::
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:maxdepth: 1

‎docs/source/installation_caffe2_integration.rst

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Using Tensor Comprehensions with Caffe2
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=======================================
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We provide very basic integration of Tensor Comprehensions with Caffe2. Follow
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the instructions below on how to build Tensor Comprehensions so that Caffe2 works
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fine with it. We only support :code:`conda` environment build for this right now.
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Step 1: Install system dependencies
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. code-block:: bash
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$ apt-get update
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$ apt-get install -y wget cmake git build-essential unzip curl automake build-essential realpath libtool software-properties-common
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Step 2: Setup gcc / g++
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^^^^^^^^^^^^^^^^^^^^^^^
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First, check your gcc/g++ and make sure they are in system path, somewhere like :code:`/usr/bin`. Also, check your gcc/g++ version. Currently, TC officially supports :code:`gcc 4.8` when installing using conda packages.
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.. code-block:: bash
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$ which gcc
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$ which g++
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If you don't have correct gcc, g++, follow the instructions below, otherwise skip:
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.. code-block:: bash
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$ add-apt-repository ppa:ubuntu-toolchain-r/test
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$ apt-get update
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$ apt-get install -y --no-install-recommends libcilkrts5 gcc-4.8 g++-4.8
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*Optionally*, you can configure the gcc versions available on your machine and set up higher priority for the version you want.
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.. code-block:: bash
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$ update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 50
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$ update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8 50
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Step 3: Install Anaconda3
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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In order to contribute to TC python/C++ API, you need to install TC from source. For this,
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:code:`anaconda3` is required. Install :code:`anaconda3` by following the instructions below:
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.. code-block:: bash
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$ cd $HOME
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$ wget https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh -O anaconda3.sh
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$ chmod +x anaconda3.sh
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$ ./anaconda3.sh -b -p $HOME/anaconda3 && rm anaconda3.sh
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Now add :code:`anaconda3` to your :code:`PATH` so that you can use it. For that run the following command:
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.. code-block:: bash
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$ export PATH=$HOME/anaconda3/bin:$PATH
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Now, verify your conda installation and check the version:
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.. code-block:: bash
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$ which conda
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This command should print the path of your conda bin. If it doesn't, make sure conda is in your :code:`PATH`.
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Step 4: Get CUDA and CUDNN
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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In order to build TC, you also need to have :code:`CUDA` and :code:`CUDNN`. If you already have it
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you can just export the :code:`PATH`, :code:`LD_LIBRARY_PATH` (see the end of this step). If you don't have CUDA/CUDNN, then follow the instructions below:
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First, install :code:`CUDA` Toolkit v8.0 (skip if you have it):
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.. code-block:: bash
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$ CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
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$ wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
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$ dpkg -i ${CUDA_REPO_PKG}
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$ rm -f ${CUDA_REPO_PKG}
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$ apt-get update
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$ apt-get -y install cuda
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Now, Install cuDNN v6.0 (skip if you have it already):
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.. code-block:: bash
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$ CUDNN_TAR_FILE="cudnn-8.0-linux-x64-v6.0.tgz"
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$ wget http://developer.download.nvidia.com/compute/redist/cudnn/v6.0/${CUDNN_TAR_FILE}
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$ tar -xzvf ${CUDNN_TAR_FILE}
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$ cp -P cuda/include/cudnn.h /usr/local/cuda/include
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$ cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64/
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$ chmod a+r /usr/local/cuda/lib64/libcudnn*
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.. note::
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Please use :code:`sudo` to run the command that might fail with permission issues. Otherwise, run
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the commands as is.
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Set environment variables:
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.. code-block:: bash
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$ export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/targets/x86_64-linux/lib/stubs/:$LD_LIBRARY_PATH
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$ export PATH=/usr/local/bin:/usr/local/cuda/bin:$PATH
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.. _conda_dep_install_tc:
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Step 5: Install TC with Caffe2
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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We ship conda packages for most of TC dependencies like :code:`clang+llvm`, :code:`glog`,
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:code:`gflags`, :code:`protobuf3`, :code:`halide`. We will directly install the
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conda packages of TC dependencies and then build TC.
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.. code-block:: bash
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$ conda create -y --name tc-build-conda python=3.6
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$ source activate tc-build-conda
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$ conda install -y -c prigoyal llvm-tapir50 isl-tc gflags glog caffe2 protobuf
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$ conda install -y -c pytorch pytorch
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$ cd $HOME && git clone https://github.com/facebookresearch/TensorComprehensions.git --recursive
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$ cd TensorComprehensions && git submodule update --init --recursive
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$ BUILD_TYPE=Release INSTALL_PREFIX=$CONDA_PREFIX CLANG_PREFIX=$(llvm-config --prefix) ./build.sh --all
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.. note::
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Please also make sure that you don't have gflags or glog in your system path. Those might conflict with the TC gflags/glog.
129+
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Step 6: Run TC Caffe2 Python test
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. code-block:: bash
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$ cd $HOME/TensorComprehensions
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$ python test_python/test_c2.py # if you have GPU

‎docs/source/installation_conda.rst

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@@ -40,6 +40,7 @@ If you don't have correct gcc, g++, follow the instructions below, otherwise ski
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$ add-apt-repository ppa:ubuntu-toolchain-r/test
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$ apt-get update
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$ apt-get install -y --no-install-recommends libcilkrts5
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$ apt-get install -y --no-install-recommends gcc-5 g++-5
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*Optionally*, you can configure the gcc versions available on your machine and set up higher priority for the version you want.
@@ -190,8 +191,7 @@ Now, you need to install TC from source. For installing TC from source, checkout
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.. code-block:: bash
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$ cd $HOME && git clone https://github.com/facebookresearch/TensorComprehensions.git --recursive
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$ cd TensorComprehensions
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$ git submodule update --init --recursive
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$ cd TensorComprehensions && git submodule update --init --recursive
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$ conda install -y pyyaml
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$ export TC_DIR=$(pwd)
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$ BUILD_TYPE=Release PYTHON=$(which python3) WITH_CAFFE2=OFF CLANG_PREFIX=$HOME/clang+llvm-tapir5.0 ./build.sh --all
@@ -212,25 +212,12 @@ Step 8: Verify TC installation
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213213
Build with Basic Caffe2 Integration
214214
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
215-
1. If you want to install TC with Caffe2 as well, run the following:
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217-
.. code-block:: bash
218-
219-
$ BUILD_TYPE=Release PYTHON=$(which python3) WITH_PYTHON_C2=OFF CLANG_PREFIX=$HOME/clang+llvm-tapir5.0 ./build.sh --all
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.. note::
223-
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This turns off the Caffe2 python build. If you want to turn on the Caffe2 python build, see next step:
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2. For installing python binaries as well of Caffe2 with TC:
216+
If you want to install TC with Caffe2 as well, run the following:
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228218
.. code-block:: bash
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230-
$ BUILD_TYPE=Release PYTHON=$(which python3) WITH_PYTHON_C2=ON CLANG_PREFIX=$HOME/clang+llvm-tapir5.0 ./build.sh --all
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.. note::
233-
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Caffe2 doesn't provide support for pip/conda at the moment and this means in order to use the caffe2 python, you might need to set $PYTHONPATH. Normally, it could be :code:`${TC_DIR}/third-party-install/`
220+
$ conda install -y -c prigoyal caffe2
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$ BUILD_TYPE=Release PYTHON=$(which python3) CLANG_PREFIX=$HOME/clang+llvm-tapir5.0 ./build.sh --all
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However, please check caffe2 official instructions `here <https://caffe2.ai/docs/getting-started.html?platform=mac&configuration=compile#test-the-caffe2-installation>`_ . TC doesn't yet provide support for caffe2 python usage.
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Now, you have the TC bindings with Caffe2 built as well and and you write python examples for TC in caffe2.

‎docs/source/installation_conda_dep.rst

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@@ -13,7 +13,7 @@ Step 1: Install system dependencies
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.. code-block:: bash
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$ apt-get update
16-
$ apt-get install -y wget cmake git build-essential unzip curl automake build-essential realpath libtool
16+
$ apt-get install -y wget cmake git build-essential unzip curl automake build-essential realpath libtool software-properties-common
1717
1818
.. note::
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@@ -38,7 +38,7 @@ If you don't have correct gcc, g++, follow the instructions below, otherwise ski
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$ add-apt-repository ppa:ubuntu-toolchain-r/test
4040
$ apt-get update
41-
$ apt-get install -y --no-install-recommends gcc-4.8 g++-4.8
41+
$ apt-get install -y --no-install-recommends libcilkrts5 gcc-4.8 g++-4.8
4242
4343
*Optionally*, you can configure the gcc versions available on your machine and set up higher priority for the version you want.
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@@ -47,37 +47,8 @@ If you don't have correct gcc, g++, follow the instructions below, otherwise ski
4747
$ update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 50
4848
$ update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8 50
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50-
Step 3: Get Protobuf3.4
51-
^^^^^^^^^^^^^^^^^^^^^^^
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TC officially support protobuf3.4 at the moment. Please follow the below instructions
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to install the protobuf.
55-
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.. note::
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Anaconda3 also has a protobuf3 available but that might not be compatible with TC. So we recommend following the below instructions to install Protobuf3.4
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.. code-block:: bash
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$ export CC=$(which gcc)
63-
$ export CXX=$(which g++)
64-
$ mkdir -p /tmp/proto-install && cd /tmp/proto-install
65-
$ wget --quiet https://github.com/google/protobuf/archive/v3.4.0.zip -O proto.zip && unzip -qq proto.zip -d .
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$ cd protobuf-3.4.0 && ./autogen.sh && ./configure && make -j 8 && make install && ldconfig
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.. note::
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Please use :code:`sudo` to run the command that might fail with permission issues. Otherwise, run
71-
the commands as is.
72-
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Now check your proto version by running:
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.. code-block:: bash
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$ protoc --version
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Step 4: Install Anaconda3
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Step 3: Install Anaconda3
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^^^^^^^^^^^^^^^^^^^^^^^^^^
8253
In order to contribute to TC python/C++ API, you need to install TC from source. For this,
8354
:code:`anaconda3` is required. Install :code:`anaconda3` by following the instructions below:
@@ -103,7 +74,7 @@ Now, verify your conda installation and check the version:
10374
10475
This command should print the path of your conda bin. If it doesn't, make sure conda is in your :code:`PATH`.
10576

106-
Step 5: Get CUDA and CUDNN
77+
Step 4: Get CUDA and CUDNN
10778
^^^^^^^^^^^^^^^^^^^^^^^^^^
10879
In order to build TC, you also need to have :code:`CUDA` and :code:`CUDNN`. If you already have it
10980
you can just export the :code:`PATH`, :code:`LD_LIBRARY_PATH` (see the end of this step). If you don't have CUDA/CUDNN, then follow the instructions below:
@@ -144,7 +115,7 @@ Set environment variables:
144115
145116
.. _conda_dep_install_tc:
146117

147-
Step 6: Install TC
118+
Step 5: Install TC
148119
^^^^^^^^^^^^^^^^^^
149120

150121
We ship conda packages for most of TC dependencies like :code:`clang+llvm`, :code:`glog`,
@@ -153,23 +124,35 @@ conda packages of TC dependencies and then build TC.
153124

154125
.. code-block:: bash
155126
156-
$ conda create -y --name tc-build-conda python=3.6
157-
$ source activate tc-build-conda
158-
$ conda install -y -c prigoyal llvm-tapir50 isl-tc gflags glog
127+
$ conda create -y --name tc-build-conda python=3.6 && source activate tc-build-conda
128+
$ conda install -y -c prigoyal llvm-tapir50 isl-tc gflags glog protobuf
159129
$ conda install -y -c pytorch pytorch
160130
$ cd $HOME && git clone https://github.com/facebookresearch/TensorComprehensions.git --recursive
161-
$ cd TensorComprehensions
162-
$ git submodule update --init --recursive
131+
$ cd TensorComprehensions && git submodule update --init --recursive
163132
$ BUILD_TYPE=Release INSTALL_PREFIX=$CONDA_PREFIX WITH_CAFFE2=OFF CLANG_PREFIX=$(llvm-config --prefix) ./build.sh --all
164133
165134
.. note::
166-
Please also make sure that you don't have gflags or glog in your system path. Those might conflict with the TC gflags/glog.
135+
Please also make sure that you don't have :code:`gflags` or :code:`glog` in your system path. Those might conflict with the TC gflags/glog.
167136

168-
Step 7: Verify TC installation
137+
138+
Step 6: Verify TC installation
169139
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
170140

171141
.. code-block:: bash
172142
173143
$ cd $HOME/TensorComprehensions
174144
$ ./test.sh # if you have GPU
175145
$ ./test_cpu.sh # if you have only CPU
146+
147+
148+
Build with Basic Caffe2 Integration
149+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
150+
151+
If you want to install TC with Caffe2 as well, run the following:
152+
153+
.. code-block:: bash
154+
155+
$ conda install -y -c prigoyal caffe2
156+
$ BUILD_TYPE=Release INSTALL_PREFIX=$CONDA_PREFIX CLANG_PREFIX=$(llvm-config --prefix) ./build.sh --all
157+
158+
Now, you have the TC bindings with Caffe2 built as well and and you write python examples for TC in caffe2.

‎include/tc/c2/tc_op.h

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public:
3838
TcOp(const OperatorDef& operator_def, Workspace* ws)
3939
: caffe2::Operator<Context>(operator_def, ws),
40+
tc_(OperatorBase::GetSingleArgument<std::string>("tcDef", "ERROR")),
41+
tcName_(
42+
OperatorBase::GetSingleArgument<std::string>("tcName", "ERROR")),
4043
mappingOptions_(tc::MappingOptions::makeNaiveMappingOptions()),
4144
gradMappingOptions_(tc::MappingOptions::makeNaiveMappingOptions()) {
42-
tc_ = OperatorBase::GetSingleArgument<std::string>("def", "ERROR");
43-
gradTc_ = OperatorBase::GetSingleArgument<std::string>("grad_def", "ERROR");
44-
tcName_ = OperatorBase::GetSingleArgument<std::string>("name", "ERROR");
45+
gradTc_ =
46+
OperatorBase::GetSingleArgument<std::string>("tcGradDef", "ERROR");
4547
gradTcName_ =
46-
OperatorBase::GetSingleArgument<std::string>("grad_name", "ERROR");
48+
OperatorBase::GetSingleArgument<std::string>("tcGradName", "ERROR");
4749
profile_ = OperatorBase::GetSingleArgument<bool>("profile", false);
4850
ArgumentHelper args(operator_def);
4951
if (args.HasArgument("mappingOptions")) {

‎test/test_caffe2.cc

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638638
Workspace w_test;
639639
init_ws(w_test);
640-
Argument tcArg = MakeArgument<string>("def", tc);
641-
Argument tcNameArg = MakeArgument<string>("name", "fun");
640+
Argument tcArg = MakeArgument<string>("tcDef", tc);
641+
Argument tcNameArg = MakeArgument<string>("tcName", "fun");
642642
MappingOptions options = tc::makeBaseCliStrategy()
643643
.tile({1})
644644
.mapToThreads({128})
@@ -686,8 +686,8 @@ def fun(float(N) X, int(A,B) I) -> (Z) {
686686
}
687687
)TC";
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689-
Argument tcArg = MakeArgument<string>("def", tc);
690-
Argument tcNameArg = MakeArgument<string>("name", "fun");
689+
Argument tcArg = MakeArgument<string>("tcDef", tc);
690+
Argument tcNameArg = MakeArgument<string>("tcName", "fun");
691691
MappingOptions options = tc::makeBaseCliStrategy()
692692
.tile({1})
693693
.mapToThreads({128})
@@ -726,8 +726,8 @@ def fun(float(B, N, M) X) -> (Z) {
726726

727727
Workspace w_test;
728728
init_ws(w_test);
729-
Argument tcArg = MakeArgument<string>("def", tc);
730-
Argument tcNameArg = MakeArgument<string>("name", "fun");
729+
Argument tcArg = MakeArgument<string>("tcDef", tc);
730+
Argument tcNameArg = MakeArgument<string>("tcName", "fun");
731731
MappingOptions options = tc::makeBaseCliStrategy()
732732
.tile({1})
733733
.mapToThreads({128})

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