| commit | cd86d4c5548c15e0bc9773565fa4fad73569f948 | [log] [tgz] |
|---|---|---|
| author | Jorghi12 <jorgsharp@gmail.com> | Tue May 15 18:38:01 2018 -0700 |
| committer | GitHub <noreply@github.com> | Tue May 15 18:38:01 2018 -0700 |
| tree | 7250faf6fcfc6a521ebe731f743e34614d2e28eb | |
| parent | 2de1b4488f14db5ed6caaa3a8edd09ab6039e7a1 [diff] |
PyTorch AMD Build Scripts (#6625) * PyTorch AMD Build Script. * Python invocation for hipify * Adding individual hip fles. * Updating CWD Use the actual path for the file instead of the current working directory, which depends on where the script is invoked. * Updating folder path for amd_build * Removing previous amd_build directory * Updated setup.py to support WITH_ROCM * Renaming the files for CuDNN BatchNorm & Conv since having two .cpp files with the same name results in a linking error in the HCC compiler used for ROCm/AMD. * Removing old BatchNorm & Conv files since they've been renamed. * Updating build path to handle ROCM * Cleaned up the build path and created a FindHIP cmake file for setting up relevant hip paths. * Seperated the individual patch files to make it easier to detect issues while building. * Removed CMakeLists hip files and fixed directory structure * Adding build pytorch amd script * Merged setup patch into PyTorch setup.py & cleaned a few issues * Added information on where to download the hipify-python script. * Resolved linting issues inside of build_pytorch_amd.py * Removing many unnecessary patch files. Removing unnecessary .hip files. Fixing up the build process. * Refactored the PR for supporting HIP * Minimizing the number of changes inside individual patches. * Cleaned up patch files. * Removed patch files. * Updating patches * Removing HIP change from file. * Cleaned up patches * Added AVX/SSE avoidance due to bug with ROCms stack. Just temporary for now. * Removing the other HIP file * Removed patch file + merged ROCm into Aten/test * Removed ATen tests patch file and updated disbale_features yaml to remove headers that don't exist on the HIP stack. * Reduced the number of patches down to 14 after Edward's suggestions. * Transferred deletion of certain functions from patch to yaml file. * Set default Thrust path * Fixed aten files so we now use the templated pow/abs instead of std:: directly. * Removed error from aten/src/THCUNN/Abs.cu * Updated the locations of the cmake build files. Moved THCTensorRandom from a hip to a patch file. Added executable/library commands that can successfully handle either CUDA or HIP. * Removed hip extraction from the build script and removed the old hip file. * Replaced MACRO with function in upper level cmake. * Added empty ELSE() block to prevent the loading of a command without CUDA or HIP. Also added IF guards around torch_cuda_based_add_executable in Aten tests. * Updated aten tests. * Removed the hip include from the ATen header. * Can't throw exceptions on C++ AMP, using abort * Missing IF guards for cuda/hip executables in aten tests. * Removed a series of patch files. * Added template keyword to help out the HCC compiler. * Rebased the specific files displayed in the PR * Fixing typo. * Change flag from "WITH_CUDA" to "NOT NO_CUDA" Replacing "WITH_CUDA" with "NOT NO_CUDA" after the rebase. * Fix LoadHIP path * Updating build files after rebasing. * Reorganization after cpu/gpu separation. * Removed HIPCC from setup.py & removed -shared extra linking args. * Updated CMake / Setup build to correctly link when under ROCm stack. * Removed the unnecessary argument from Extension constructor. * Adding another test to be included with ROCm building. * Updated the setup_helpers scripts in order to get around linter error * Fix syntax issue * Solving lint issue: line too long
PyTorch is a Python package that provides two high-level features:
You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.
We are in an early-release beta. Expect some adventures and rough edges.
| System | 2.7 | 3.5 |
|---|---|---|
| Linux CPU | ||
| Linux GPU | ||
| Windows GPU | — |
At a granular level, PyTorch is a library that consists of the following components:
Usually one uses PyTorch either as:
Elaborating further:
If you use NumPy, then you have used Tensors (a.k.a ndarray).
PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate compute by a huge amount.
We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, math operations, linear algebra, reductions. And they are fast!
PyTorch has a unique way of building neural networks: using and replaying a tape recorder.
Most frameworks such as TensorFlow, Theano, Caffe and CNTK have a static view of the world. One has to build a neural network, and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.
With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such as torch-autograd, autograd, Chainer, etc.
While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research.
PyTorch is not a Python binding into a monolithic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use NumPy / SciPy / scikit-learn etc. You can write your new neural network layers in Python itself, using your favorite libraries and use packages such as Cython and Numba. Our goal is to not reinvent the wheel where appropriate.
PyTorch is designed to be intuitive, linear in thought and easy to use. When you execute a line of code, it gets executed. There isn't an asynchronous view of the world. When you drop into a debugger, or receive error messages and stack traces, understanding them is straightforward. The stack trace points to exactly where your code was defined. We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.
PyTorch has minimal framework overhead. We integrate acceleration libraries such as Intel MKL and NVIDIA (cuDNN, NCCL) to maximize speed. At the core, its CPU and GPU Tensor and neural network backends (TH, THC, THNN, THCUNN) are written as independent libraries with a C99 API. They are mature and have been tested for years.
Hence, PyTorch is quite fast – whether you run small or large neural networks.
The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before.
Writing new neural network modules, or interfacing with PyTorch's Tensor API was designed to be straightforward and with minimal abstractions.
You can write new neural network layers in Python using the torch API or your favorite NumPy-based libraries such as SciPy.
If you want to write your layers in C/C++, we provide an extension API based on cffi that is efficient and with minimal boilerplate. There is no wrapper code that needs to be written. You can see a tutorial here and an example here.
Commands to install from binaries via Conda or pip wheels are on our website:
If you are installing from source, we highly recommend installing an Anaconda environment. You will get a high-quality BLAS library (MKL) and you get a controlled compiler version regardless of your Linux distro.
Once you have Anaconda installed, here are the instructions.
If you want to compile with CUDA support, install
If you want to disable CUDA support, export environment variable NO_CUDA=1. Other potentially useful environment variables may be found in setup.py.
If you want to build on Windows, Visual Studio 2017 and NVTX are also needed.
On Linux
export CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" # [anaconda root directory] # Install basic dependencies conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing # Add LAPACK support for the GPU conda install -c pytorch magma-cuda80 # or magma-cuda90 if CUDA 9
On macOS
export CMAKE_PREFIX_PATH=[anaconda root directory] conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing
On Windows
conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing
git clone --recursive https://github.com/pytorch/pytorch cd pytorch
On Linux
python setup.py install
On macOS
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install
On Windows
set "VS150COMNTOOLS=C:\Program Files (x86)\Microsoft Visual Studio\2017\Enterprise\VC\Auxiliary\Build" set CMAKE_GENERATOR=Visual Studio 15 2017 Win64 set DISTUTILS_USE_SDK=1 REM The following line is needed for Python 2.7, but the support for it is very experimental. set MSSdk=1 call "%VS150COMNTOOLS%\vcvarsall.bat" x64 -vcvars_ver=14.11 python setup.py install
Dockerfile is supplied to build images with cuda support and cudnn v7. Build as usual
docker build -t pytorch -f docker/pytorch/Dockerfile .
You can also pull a pre-built docker image from Docker Hub and run with nvidia-docker, but this is not currently maintained and will pull PyTorch 0.2.
nvidia-docker run --rm -ti --ipc=host pytorch/pytorch:latest
Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g. for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.
Installation instructions and binaries for previous PyTorch versions may be found on our website.
Three pointers to get you started:
PyTorch has a 90 day release cycle (major releases). Its current state is Beta, we expect no obvious bugs. Please let us know if you encounter a bug by filing an issue.
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.
If you plan to contribute new features, utility functions or extensions to the core, please first open an issue and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR, because we might be taking the core in a different direction than you might be aware of.
PyTorch is a community driven project with several skillful engineers and researchers contributing to it.
PyTorch is currently maintained by Adam Paszke, Sam Gross, Soumith Chintala and Gregory Chanan with major contributions coming from 10s of talented individuals in various forms and means. A non-exhaustive but growing list needs to mention: Trevor Killeen, Sasank Chilamkurthy, Sergey Zagoruyko, Adam Lerer, Francisco Massa, Alykhan Tejani, Luca Antiga, Alban Desmaison, Andreas Kopf, James Bradbury, Zeming Lin, Yuandong Tian, Guillaume Lample, Marat Dukhan, Natalia Gimelshein, Christian Sarofeen, Martin Raison, Edward Yang, Zachary Devito.
Note: this project is unrelated to hughperkins/pytorch with the same name. Hugh is a valuable contributor in the Torch community and has helped with many things Torch and PyTorch.