7e65060410
Adds x86-simd-sort as a submodule to accelerate sorting for 32-bit and 64-bit datatypes when AVX2 or AVX512 are available. For contiguous data, this can be over a 10x speedup for large arrays. For discontiguous data, it can give over a 4x speedup with larger arrays. These benchmarks were gathered on a Skylake system (7900x), limited to 8 threads. <details> <summary><b>Contiguous Benchmarks</b></summary> ``` float32, normally distributed (in microseconds) size Default AVX2 AVX512 Default/AVX2 Default/AVX512 16 7.150844336 6.886271477 7.132277489 1.038420335 1.002603214 128 9.208030939 8.478154898 7.846915245 1.086089019 1.173458697 1024 37.79037627 23.60707456 16.44122627 1.600807257 2.298513241 10000 714.7355628 203.9921844 105.5683001 3.503739934 6.770361577 100000 8383.074408 721.6333354 465.3709247 11.61680593 18.01374766 1000000 97124.31945 5632.054572 3920.148401 17.24491803 24.77567416 10000000 1161974.907 86070.48988 71533.82301 13.50027063 16.24371323 int32_t, uniformly distributed (in microseconds) size Default AVX2 AVX512 Default/AVX2 Default/AVX512 16 7.203208685 6.92212224 7.014458179 1.040606975 1.026908779 128 8.972388983 8.195516348 7.592543125 1.094792396 1.18173698 1024 32.77489477 23.6874548 15.36617105 1.383639359 2.132925285 10000 607.8824128 193.3402024 99.25090471 3.144107667 6.124703997 100000 523.9384684 608.1836536 442.3166784 0.861480682 1.184532472 1000000 5211.348627 5271.598405 3518.861883 0.988570871 1.480975611 10000000 133853.6263 81463.05084 67852.97394 1.643120714 1.972700952 ``` </details> Note that the int32_t sort is accelerated by FBGEMM's radix sort for larger arrays, but this only handles contiguous data and in one sorting direction. <details> <summary><b>Discontiguous Benchmarks</b></summary> ``` float, normal distributed, discontiguous in sorted dimension (in microseconds) size Default AVX2 AVX512 Default/AVX2 Default/AVX512 16 3.836543679 4.011214256 3.84376061 0.956454439 0.99812243 128 5.755310194 5.755723127 4.820394962 0.999928257 1.193949923 1024 49.46946019 24.78790785 15.47874362 1.995709379 3.195960952 10000 665.2505291 236.6165959 143.9490662 2.811512551 4.621429974 100000 4328.002203 1329.001212 818.3516414 3.256582586 5.288682743 1000000 47651.5018 16693.72045 11827.39551 2.854456677 4.028909133 10000000 556655.1288 236252.6258 184215.9828 2.356185998 3.021752621 int32_t, uniformly distributed, discontiguous in sorted dimension (in microseconds) size Default AVX2 AVX512 Default/AVX2 Default/AVX512 16 3.817994356 3.878117442 3.770039797 0.984496837 1.012719908 128 5.578731397 5.577152082 4.716770534 1.000283176 1.182743862 1024 43.3412619 23.61275801 14.55446819 1.835501887 2.977866408 10000 634.3997478 224.4322851 133.9518324 2.826686667 4.736028889 100000 4084.358152 1292.363303 781.7867576 3.16037924 5.22438902 1000000 46262.20465 16608.35284 11367.51817 2.785478192 4.06968381 10000000 541231.9104 235185.1861 180249.9294 2.301301028 3.002674742 ``` </details> Pull Request resolved: https://github.com/pytorch/pytorch/pull/127936 Approved by: https://github.com/jgong5, https://github.com/peterbell10, https://github.com/sanchitintel
PyTorch is a Python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system
You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
Our trunk health (Continuous Integration signals) can be found at hud.pytorch.org.
- More About PyTorch
- Installation
- Getting Started
- Resources
- Communication
- Releases and Contributing
- The Team
- License
More About PyTorch
At a granular level, PyTorch is a library that consists of the following components:
| Component | Description |
|---|---|
| torch | A Tensor library like NumPy, with strong GPU support |
| torch.autograd | A tape-based automatic differentiation library that supports all differentiable Tensor operations in torch |
| torch.jit | A compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code |
| torch.nn | A neural networks library deeply integrated with autograd designed for maximum flexibility |
| torch.multiprocessing | Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training |
| torch.utils | DataLoader and other utility functions for convenience |
Usually, PyTorch is used either as:
- A replacement for NumPy to use the power of GPUs.
- A deep learning research platform that provides maximum flexibility and speed.
Elaborating Further:
A GPU-Ready Tensor Library
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 accelerates the computation by a huge amount.
We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, mathematical operations, linear algebra, reductions. And they are fast!
Dynamic Neural Networks: Tape-Based Autograd
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.
Python First
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.
Imperative Experiences
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.
Fast and Lean
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 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.
Extensions Without Pain
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 a convenient extension API that is efficient and with minimal boilerplate. No wrapper code needs to be written. You can see a tutorial here and an example here.
Installation
Binaries
Commands to install binaries via Conda or pip wheels are on our website: https://pytorch.org/get-started/locally/
NVIDIA Jetson Platforms
Python wheels for NVIDIA's Jetson Nano, Jetson TX1/TX2, Jetson Xavier NX/AGX, and Jetson AGX Orin are provided here and the L4T container is published here
They require JetPack 4.2 and above, and @dusty-nv and @ptrblck are maintaining them.
From Source
Prerequisites
If you are installing from source, you will need:
- Python 3.9 or later
- A compiler that fully supports C++17, such as clang or gcc (gcc 9.4.0 or newer is required, on Linux)
- Visual Studio or Visual Studio Build Tool on Windows
* PyTorch CI uses Visual C++ BuildTools, which come with Visual Studio Enterprise, Professional, or Community Editions. You can also install the build tools from https://visualstudio.microsoft.com/visual-cpp-build-tools/. The build tools do not come with Visual Studio Code by default.
* We highly recommend installing an Anaconda environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.
An example of environment setup is shown below:
- Linux:
$ source <CONDA_INSTALL_DIR>/bin/activate
$ conda create -y -n <CONDA_NAME>
$ conda activate <CONDA_NAME>
- Windows:
$ source <CONDA_INSTALL_DIR>\Scripts\activate.bat
$ conda create -y -n <CONDA_NAME>
$ conda activate <CONDA_NAME>
$ call "C:\Program Files\Microsoft Visual Studio\<VERSION>\Community\VC\Auxiliary\Build\vcvarsall.bat" x64
NVIDIA CUDA Support
If you want to compile with CUDA support, select a supported version of CUDA from our support matrix, then install the following:
- NVIDIA CUDA
- NVIDIA cuDNN v8.5 or above
- Compiler compatible with CUDA
Note: You could refer to the cuDNN Support Matrix for cuDNN versions with the various supported CUDA, CUDA driver and NVIDIA hardware
If you want to disable CUDA support, export the environment variable USE_CUDA=0.
Other potentially useful environment variables may be found in setup.py.
If you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano are available here
AMD ROCm Support
If you want to compile with ROCm support, install
- AMD ROCm 4.0 and above installation
- ROCm is currently supported only for Linux systems.
By default the build system expects ROCm to be installed in /opt/rocm. If ROCm is installed in a different directory, the ROCM_PATH environment variable must be set to the ROCm installation directory. The build system automatically detects the AMD GPU architecture. Optionally, the AMD GPU architecture can be explicitly set with the PYTORCH_ROCM_ARCH environment variable AMD GPU architecture
If you want to disable ROCm support, export the environment variable USE_ROCM=0.
Other potentially useful environment variables may be found in setup.py.
Intel GPU Support
If you want to compile with Intel GPU support, follow these
- PyTorch Prerequisites for Intel GPUs instructions.
- Intel GPU is supported for Linux and Windows.
If you want to disable Intel GPU support, export the environment variable USE_XPU=0.
Other potentially useful environment variables may be found in setup.py.
Get the PyTorch Source
git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
# if you are updating an existing checkout
git submodule sync
git submodule update --init --recursive
Install Dependencies
Common
conda install cmake ninja
# Run this command on native Windows
conda install rust
# Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section below
pip install -r requirements.txt
On Linux
pip install mkl-static mkl-include
# CUDA only: Add LAPACK support for the GPU if needed
conda install -c pytorch magma-cuda121 # or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo
# (optional) If using torch.compile with inductor/triton, install the matching version of triton
# Run from the pytorch directory after cloning
# For Intel GPU support, please explicitly `export USE_XPU=1` before running command.
make triton
On MacOS
# Add this package on intel x86 processor machines only
pip install mkl-static mkl-include
# Add these packages if torch.distributed is needed
conda install pkg-config libuv
On Windows
pip install mkl-static mkl-include
# Add these packages if torch.distributed is needed.
# Distributed package support on Windows is a prototype feature and is subject to changes.
conda install -c conda-forge libuv=1.39
Install PyTorch
On Linux
If you would like to compile PyTorch with new C++ ABI enabled, then first run this command:
export _GLIBCXX_USE_CXX11_ABI=1
Please note that starting from PyTorch 2.5, the PyTorch build with XPU supports both new and old C++ ABIs. Previously, XPU only supported the new C++ ABI. If you want to compile with Intel GPU support, please follow Intel GPU Support.
If you're compiling for AMD ROCm then first run this command:
# Only run this if you're compiling for ROCm
python tools/amd_build/build_amd.py
Install PyTorch
export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}"
python setup.py develop
On macOS
python3 setup.py develop
On Windows
If you want to build legacy python code, please refer to Building on legacy code and CUDA
CPU-only builds
In this mode PyTorch computations will run on your CPU, not your GPU
python setup.py develop
Note on OpenMP: The desired OpenMP implementation is Intel OpenMP (iomp). In order to link against iomp, you'll need to manually download the library and set up the building environment by tweaking CMAKE_INCLUDE_PATH and LIB. The instruction here is an example for setting up both MKL and Intel OpenMP. Without these configurations for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.
CUDA based build
In this mode PyTorch computations will leverage your GPU via CUDA for faster number crunching
NVTX is needed to build Pytorch with CUDA. NVTX is a part of CUDA distributive, where it is called "Nsight Compute". To install it onto an already installed CUDA run CUDA installation once again and check the corresponding checkbox. Make sure that CUDA with Nsight Compute is installed after Visual Studio.
Currently, VS 2017 / 2019, and Ninja are supported as the generator of CMake. If ninja.exe is detected in PATH, then Ninja will be used as the default generator, otherwise, it will use VS 2017 / 2019.
If Ninja is selected as the generator, the latest MSVC will get selected as the underlying toolchain.
Additional libraries such as Magma, oneDNN, a.k.a. MKLDNN or DNNL, and Sccache are often needed. Please refer to the installation-helper to install them.
You can refer to the build_pytorch.bat script for some other environment variables configurations
cmd
:: Set the environment variables after you have downloaded and unzipped the mkl package,
:: else CMake would throw an error as `Could NOT find OpenMP`.
set CMAKE_INCLUDE_PATH={Your directory}\mkl\include
set LIB={Your directory}\mkl\lib;%LIB%
:: Read the content in the previous section carefully before you proceed.
:: [Optional] If you want to override the underlying toolset used by Ninja and Visual Studio with CUDA, please run the following script block.
:: "Visual Studio 2019 Developer Command Prompt" will be run automatically.
:: Make sure you have CMake >= 3.12 before you do this when you use the Visual Studio generator.
set CMAKE_GENERATOR_TOOLSET_VERSION=14.27
set DISTUTILS_USE_SDK=1
for /f "usebackq tokens=*" %i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -version [15^,17^) -products * -latest -property installationPath`) do call "%i\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=%CMAKE_GENERATOR_TOOLSET_VERSION%
:: [Optional] If you want to override the CUDA host compiler
set CUDAHOSTCXX=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\HostX64\x64\cl.exe
python setup.py develop
Adjust Build Options (Optional)
You can adjust the configuration of cmake variables optionally (without building first), by doing the following. For example, adjusting the pre-detected directories for CuDNN or BLAS can be done with such a step.
On Linux
export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}"
python setup.py build --cmake-only
ccmake build # or cmake-gui build
On macOS
export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}"
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only
ccmake build # or cmake-gui build
Docker Image
Using pre-built images
You can also pull a pre-built docker image from Docker Hub and run with docker v19.03+
docker run --gpus all --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.
Building the image yourself
NOTE: Must be built with a docker version > 18.06
The Dockerfile is supplied to build images with CUDA 11.1 support and cuDNN v8.
You can pass PYTHON_VERSION=x.y make variable to specify which Python version is to be used by Miniconda, or leave it
unset to use the default.
make -f docker.Makefile
# images are tagged as docker.io/${your_docker_username}/pytorch
You can also pass the CMAKE_VARS="..." environment variable to specify additional CMake variables to be passed to CMake during the build.
See setup.py for the list of available variables.
make -f docker.Makefile
Building the Documentation
To build documentation in various formats, you will need Sphinx and the readthedocs theme.
cd docs/
pip install -r requirements.txt
You can then build the documentation by running make <format> from the
docs/ folder. Run make to get a list of all available output formats.
If you get a katex error run npm install katex. If it persists, try
npm install -g katex
Note: if you installed
nodejswith a different package manager (e.g.,conda) thennpmwill probably install a version ofkatexthat is not compatible with your version ofnodejsand doc builds will fail. A combination of versions that is known to work isnode@6.13.1andkatex@0.13.18. To install the latter withnpmyou can runnpm install -g katex@0.13.18
Previous Versions
Installation instructions and binaries for previous PyTorch versions may be found on our website.
Getting Started
Three-pointers to get you started:
- Tutorials: get you started with understanding and using PyTorch
- Examples: easy to understand PyTorch code across all domains
- The API Reference
- Glossary
Resources
- PyTorch.org
- PyTorch Tutorials
- PyTorch Examples
- PyTorch Models
- Intro to Deep Learning with PyTorch from Udacity
- Intro to Machine Learning with PyTorch from Udacity
- Deep Neural Networks with PyTorch from Coursera
- PyTorch Twitter
- PyTorch Blog
- PyTorch YouTube
Communication
- Forums: Discuss implementations, research, etc. https://discuss.pytorch.org
- GitHub Issues: Bug reports, feature requests, install issues, RFCs, thoughts, etc.
- Slack: The PyTorch Slack hosts a primary audience of moderate to experienced PyTorch users and developers for general chat, online discussions, collaboration, etc. If you are a beginner looking for help, the primary medium is PyTorch Forums. If you need a slack invite, please fill this form: https://goo.gl/forms/PP1AGvNHpSaJP8to1
- Newsletter: No-noise, a one-way email newsletter with important announcements about PyTorch. You can sign-up here: https://eepurl.com/cbG0rv
- Facebook Page: Important announcements about PyTorch. https://www.facebook.com/pytorch
- For brand guidelines, please visit our website at pytorch.org
Releases and Contributing
Typically, PyTorch has three minor releases a year. 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.
To learn more about making a contribution to Pytorch, please see our Contribution page. For more information about PyTorch releases, see Release page.
The Team
PyTorch is a community-driven project with several skillful engineers and researchers contributing to it.
PyTorch is currently maintained by Soumith Chintala, Gregory Chanan, Dmytro Dzhulgakov, Edward Yang, and Nikita Shulga with major contributions coming from hundreds 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 Koepf, 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 to the Torch community and has helped with many things Torch and PyTorch.
License
PyTorch has a BSD-style license, as found in the LICENSE file.