| commit | 2f7cfecd86009a9d396fdbdcdfb4ba7a005db16b | [log] [tgz] |
|---|---|---|
| author | Edward Z. Yang <ezyang@meta.com> | Wed Jun 05 17:22:11 2024 -0700 |
| committer | PyTorch MergeBot <pytorchmergebot@users.noreply.github.com> | Thu Jun 06 02:29:45 2024 +0000 |
| tree | aea2f863c756b65526189d413712339db72998ee | |
| parent | c1a43a69e422780dcfe5bc0171d921dc3a5b6836 [diff] |
Complete revamp of float/promotion sympy handling (#126905) At a high level, the idea behind this PR is: * Make it clearer what the promotion and int/float rules for various Sympy operations are. Operators that previously were polymorphic over int/float are now split into separate operators for clarity. We never do mixed int/float addition/multiplication etc in sympy, instead, we always promote to the appropriate operator. (However, equality is currently not done correctly.) * Enforce strict typing on ValueRanges: if you have a ValueRange for a float, the lower and upper MUST be floats, and so forth for integers. The story begins in **torch/utils/_sympy/functions.py**. Here, I make some changes to how we represent certain operations in sympy expressions: * FloorDiv now only supports integer inputs; to do float floor division, do a truediv and then a trunc. Additionally, we remove the divide out addition by gcd optimization, because sympy gcd is over fields and is willing to generate rationals (but rationals are bad for ValueRange strict typing). * ModularIndexing, LShift, RShift now assert they are given integer inputs. * Mod only supports integer inputs; eventually we will support FloatMod (left for later work, when we build out Sympy support for floating operations). Unfortunately, I couldn't assert integer inputs here, because of a bad interaction with sympy's inequality solver that is used by the offline solver * TrueDiv is split into FloatTrueDiv and IntTrueDiv. This allows for us to eventually generate accurate code for Python semantics IntTrueDiv, which is written in a special way to preserve precision when the inputs are >= 2**53 beyond what first coercing the integer to floats and then doing true division. * Trunc is split to TruncToFloat and TruncToInt. * Round is updated to return a float, not an int, making it consistent with the round op handler in Inductor. To get Python-style conversion to int, we call TruncToInt on the result. * RoundDecimal updated to consistently only ever return a float * Add ToFloat for explicit coercion to float (required so we can enforce strict ValueRanges typing) In **torch/__init__.py**, we modify SymInt and SymFloat to appropriately call into new bindings that route to these refined sympy operations. Also, we modify `torch.sym_min` and `torch.sym_max` to have promotion semantics (if one argument is a float, the return result is always a float), making them inconsistent with builtins.min/max, but possible to do type analysis without runtime information. We also need to introduce some new op handlers in **torch/_inductor/ops_handler.py**: * `to_int` for truncation to int64, directly corresponding to TruncToInt; this can be implemented by trunc and dtype, but with a dedicated handler it is more convenient for roundtripping in Sympy * `int_truediv` for Python-style integer true division, which has higher precision than casting to floats and then running `truediv` These changes have consequences. First, we need to make some administrative changes: * Actually wire up these Sympy functions from SymInt/SymFloat in **torch/fx/experimental/sym_node.py**, including the new promotion rules (promote2) * Add support for new Sympy functions in **torch/utils/_sympy/interp.py**, **torch/utils/_sympy/reference.py** * In particular, in torch.utils._sympy.reference, we have a strong preference to NOT do nontrivial compute, instead, everything in ops handler should map to a singular sympy function * TODO: I chose to roundtrip mod back to our Mod function, but I think I'm going to have to deal with the C/Python inconsistency this to fix tests here * Add printer support for the Sympy functions in **torch/_inductor/codegen/common.py**, **torch/_inductor/codegen/cpp_utils.py**, **torch/_inductor/codegen/triton.py**. `int_truediv` and mixed precision equality is currently not implemented soundly, so we will lose precision in codegen for large values. TODO: The additions here are not exhaustive yet * Update ValueRanges logic to use new sympy functions in **torch/utils/_sympy/value_ranges.py**. In general, we prefer to use the new Sympy function rather than try to roll things by hand, which is what was done previously for many VR analysis functions. In **torch/fx/experimental/symbolic_shapes.py** we need to make some symbolic reasoning adjustments: * Avoid generation of rational subexpressions by removing simplification of `x // y` into `floor(x / y)`. This simplification then triggers an addition simplification rule `(x + y) / c --> x / c + y / c` which is bad because x / c is a rational number now * `_assert_bound_is_rational` is no more, we no longer generate rational bounds * Don't intersect non-int value ranges with the `int_range` * Support more sympy Functions for guard SYMPY_INTERP * Assert the type of value range is consistent with the variable type The new asserts uncovered necessary bug fixes: * **torch/_inductor/codegen/cpp.py**, **torch/_inductor/select_algorithm.py**, **torch/_inductor/sizevars.py** - Ensure Wild/Symbol manually allocated in Inductor is marked `is_integer` so it's accepted to build expressions * **torch/_inductor/utils.py** - make sure you actually pass in sympy.Expr to these functions * **torch/_inductor/ir.py** - make_contiguous_strides_for takes int/SymInt, not sympy.Expr! * **torch/export/dynamic_shapes.py** - don't use infinity to represent int ranges, instead use sys.maxsize - 1 Because of the removal of some symbolic reasoning that produced rationals, some of our symbolic reasoning has gotten worse and we are unable to simplify some guards. Check the TODO at **test/test_proxy_tensor.py** Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/126905 Approved by: https://github.com/xadupre, https://github.com/lezcano
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.
Our trunk health (Continuous Integration signals) can be found at hud.pytorch.org.
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:
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 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!
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 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 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.
Commands to install binaries via Conda or pip wheels are on our website: https://pytorch.org/get-started/locally/
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.
If you are installing from source, you will need:
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.
If you want to compile with CUDA support, select a supported version of CUDA from our support matrix, then install the following:
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
If you want to compile with ROCm support, install
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.
If you want to compile with Intel GPU support, follow these
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.
Common
conda install cmake ninja # 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
conda install intel::mkl-static intel::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 make triton
On MacOS
# Add this package on intel x86 processor machines only conda install intel::mkl-static intel::mkl-include # Add these packages if torch.distributed is needed conda install pkg-config libuv
On Windows
conda install intel::mkl-static intel::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
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
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
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))/../"} python setup.py develop
Aside: If you are using Anaconda, you may experience an error caused by the linker:
build/temp.linux-x86_64-3.7/torch/csrc/stub.o: file not recognized: file format not recognized collect2: error: ld returned 1 exit status error: command 'g++' failed with exit status 1This is caused by
ldfrom the Conda environment shadowing the systemld. You should use a newer version of Python that fixes this issue. The recommended Python version is 3.8.1+.
On macOS
python3 setup.py develop
On Windows
Choose Correct Visual Studio Version.
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.
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
conda activate 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
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 $(which conda))/../"} python setup.py build --cmake-only ccmake build # or cmake-gui build
On macOS
export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"} MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only ccmake build # or cmake-gui build
You can also pull a pre-built docker image from Docker Hub and run with docker v19.03+
docker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest
Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g. for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.
NOTE: Must be built with a docker version > 18.06
The Dockerfile is supplied to build images with CUDA 11.1 support and cuDNN v8. You can pass PYTHON_VERSION=x.y make variable to specify which Python version is to be used by Miniconda, or leave it unset to use the default.
make -f docker.Makefile # images are tagged as docker.io/${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
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
Installation instructions and binaries for previous PyTorch versions may be found on our website.
Three-pointers to get you started:
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.
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.
PyTorch has a BSD-style license, as found in the LICENSE file.