torch.utils.cpp_extension

torch.utils.cpp_extension.CppExtension(name, sources, *args, **kwargs)

Create a setuptools.Extension for C++.

Convenience method that creates a setuptools.Extension with the bare minimum (but often sufficient) arguments to build a C++ extension.

All arguments are forwarded to the setuptools.Extension constructor. Full list arguments can be found at https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference

Example

>>> from setuptools import setup
>>> from torch.utils.cpp_extension import BuildExtension, CppExtension
>>> setup(
...     name='extension',
...     ext_modules=[
...         CppExtension(
...             name='extension',
...             sources=['extension.cpp'],
...             extra_compile_args=['-g'],
...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
...     ],
...     cmdclass={
...         'build_ext': BuildExtension
...     })

torch.utils.cpp_extension.CUDAExtension(name, sources, *args, **kwargs)

Create a setuptools.Extension for CUDA/C++.

Convenience method that creates a setuptools.Extension with the bare minimum (but often sufficient) arguments to build a CUDA/C++ extension. This includes the CUDA include path, library path and runtime library.

All arguments are forwarded to the setuptools.Extension constructor. Full list arguments can be found at https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference

Example

>>> from setuptools import setup
>>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
>>> setup(
...     name='cuda_extension',
...     ext_modules=[
...         CUDAExtension(
...                 name='cuda_extension',
...                 sources=['extension.cpp', 'extension_kernel.cu'],
...                 extra_compile_args={'cxx': ['-g'],
...                                     'nvcc': ['-O2']},
...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
...     ],
...     cmdclass={
...         'build_ext': BuildExtension
...     })

Compute capabilities:

By default the extension will be compiled to run on all archs of the cards visible during the building process of the extension, plus PTX. If down the road a new card is installed the extension may need to be recompiled. If a visible card has a compute capability (CC) that’s newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch will make nvcc fall back to building kernels with the newest version of PTX your nvcc does support (see below for details on PTX).

You can override the default behavior using TORCH_CUDA_ARCH_LIST to explicitly specify which CCs you want the extension to support:

TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py

The +PTX option causes extension kernel binaries to include PTX instructions for the specified CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with CC >= 8.6). This improves your binary’s forward compatibility. However, relying on older PTX to provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on those newer CCs. If you know exact CC(s) of the GPUs you want to target, you’re always better off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, “8.0+PTX” would work functionally because it includes PTX that can runtime-compile for 8.6, but “8.0 8.6” would be better.

Note that while it’s possible to include all supported archs, the more archs get included the slower the building process will be, as it will build a separate kernel image for each arch.

Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. To workaround the issue, move python binding logic to pure C++ file.

Example use:

#include <ATen/ATen.h> at::Tensor SigmoidAlphaBlendForwardCuda(….)

Instead of:

#include <torch/extension.h> torch::Tensor SigmoidAlphaBlendForwardCuda(…)

Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48

Relocatable device code linking:

If you want to reference device symbols across compilation units (across object files), the object files need to be built with relocatable device code (-rdc=true or -dc). An exception to this rule is “dynamic parallelism” (nested kernel launches) which is not used a lot anymore. Relocatable device code is less optimized so it needs to be used only on object files that need it. Using -dlto (Device Link Time Optimization) at the device code compilation step and dlink step help reduce the protentional perf degradation of -rdc. Note that it needs to be used at both steps to be useful.

If you have rdc objects you need to have an extra -dlink (device linking) step before the CPU symbol linking step. There is also a case where -dlink is used without -rdc: when an extension is linked against a static lib containing rdc-compiled objects like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).

Note: Ninja is required to build a CUDA Extension with RDC linking.

Example

>>> CUDAExtension(
...        name='cuda_extension',
...        sources=['extension.cpp', 'extension_kernel.cu'],
...        dlink=True,
...        dlink_libraries=["dlink_lib"],
...        extra_compile_args={'cxx': ['-g'],
...                            'nvcc': ['-O2', '-rdc=true']})

torch.utils.cpp_extension.BuildExtension(*args, **kwargs)

A custom setuptools build extension .

This setuptools.build_ext subclass takes care of passing the minimum required compiler flags (e.g. -std=c++17) as well as mixed C++/CUDA compilation (and support for CUDA files in general).

When using BuildExtension, it is allowed to supply a dictionary for extra_compile_args (rather than the usual list) that maps from languages (cxx or nvcc) to a list of additional compiler flags to supply to the compiler. This makes it possible to supply different flags to the C++ and CUDA compiler during mixed compilation.

use_ninja (bool): If use_ninja is True (default), then we attempt to build using the Ninja backend. Ninja greatly speeds up compilation compared to the standard setuptools.build_ext. Fallbacks to the standard distutils backend if Ninja is not available.

Note

By default, the Ninja backend uses #CPUS + 2 workers to build the extension. This may use up too many resources on some systems. One can control the number of workers by setting the MAX_JOBS environment variable to a non-negative number.

torch.utils.cpp_extension.load(name, sources, extra_cflags=None, extra_cuda_cflags=None, extra_ldflags=None, extra_include_paths=None, build_directory=None, verbose=False, with_cuda=None, is_python_module=True, is_standalone=False, keep_intermediates=True)

Load a PyTorch C++ extension just-in-time (JIT).

To load an extension, a Ninja build file is emitted, which is used to compile the given sources into a dynamic library. This library is subsequently loaded into the current Python process as a module and returned from this function, ready for use.

By default, the directory to which the build file is emitted and the resulting library compiled to is <tmp>/torch_extensions/<name>, where <tmp> is the temporary folder on the current platform and <name> the name of the extension. This location can be overridden in two ways. First, if the TORCH_EXTENSIONS_DIR environment variable is set, it replaces <tmp>/torch_extensions and all extensions will be compiled into subfolders of this directory. Second, if the build_directory argument to this function is supplied, it overrides the entire path, i.e. the library will be compiled into that folder directly.

To compile the sources, the default system compiler (c++) is used, which can be overridden by setting the CXX environment variable. To pass additional arguments to the compilation process, extra_cflags or extra_ldflags can be provided. For example, to compile your extension with optimizations, pass extra_cflags=['-O3']. You can also use extra_cflags to pass further include directories.

CUDA support with mixed compilation is provided. Simply pass CUDA source files (.cu or .cuh) along with other sources. Such files will be detected and compiled with nvcc rather than the C++ compiler. This includes passing the CUDA lib64 directory as a library directory, and linking cudart. You can pass additional flags to nvcc via extra_cuda_cflags, just like with extra_cflags for C++. Various heuristics for finding the CUDA install directory are used, which usually work fine. If not, setting the CUDA_HOME environment variable is the safest option.

Parameters

• name – The name of the extension to build. This MUST be the same as the name of the pybind11 module!

• sources (Union[str, List[str]]) – A list of relative or absolute paths to C++ source files.

• extra_cflags – optional list of compiler flags to forward to the build.

• extra_cuda_cflags – optional list of compiler flags to forward to nvcc when building CUDA sources.

• extra_ldflags – optional list of linker flags to forward to the build.

• extra_include_paths – optional list of include directories to forward to the build.

• build_directory – optional path to use as build workspace.

• verbose – If True, turns on verbose logging of load steps.

• with_cuda (Optional[bool]) – Determines whether CUDA headers and libraries are added to the build. If set to None (default), this value is automatically determined based on the existence of .cu or .cuh in sources. Set it to True` to force CUDA headers and libraries to be included.

• is_python_module – If True (default), imports the produced shared library as a Python module. If False, behavior depends on is_standalone.

• is_standalone – If False (default) loads the constructed extension into the process as a plain dynamic library. If True, build a standalone executable.

Returns

Returns the loaded PyTorch extension as a Python module.

If is_python_module is False and is_standalone is False:

Returns nothing. (The shared library is loaded into the process as a side effect.)

If is_standalone is True.

Return the path to the executable. (On Windows, TORCH_LIB_PATH is added to the PATH environment variable as a side effect.)

Return type

If is_python_module is True

Example

>>> from torch.utils.cpp_extension import load
>>> module = load(
...     name='extension',
...     sources=['extension.cpp', 'extension_kernel.cu'],
...     extra_cflags=['-O2'],
...     verbose=True)

torch.utils.cpp_extension.load_inline(name, cpp_sources, cuda_sources=None, functions=None, extra_cflags=None, extra_cuda_cflags=None, extra_ldflags=None, extra_include_paths=None, build_directory=None, verbose=False, with_cuda=None, is_python_module=True, with_pytorch_error_handling=True, keep_intermediates=True, use_pch=False)

Load a PyTorch C++ extension just-in-time (JIT) from string sources.

This function behaves exactly like load(), but takes its sources as strings rather than filenames. These strings are stored to files in the build directory, after which the behavior of load_inline() is identical to load().

See the tests for good examples of using this function.

Sources may omit two required parts of a typical non-inline C++ extension: the necessary header includes, as well as the (pybind11) binding code. More precisely, strings passed to cpp_sources are first concatenated into a single .cpp file. This file is then prepended with #include <torch/extension.h>.

Furthermore, if the functions argument is supplied, bindings will be automatically generated for each function specified. functions can either be a list of function names, or a dictionary mapping from function names to docstrings. If a list is given, the name of each function is used as its docstring.

The sources in cuda_sources are concatenated into a separate .cu file and prepended with torch/types.h, cuda.h and cuda_runtime.h includes. The .cpp and .cu files are compiled separately, but ultimately linked into a single library. Note that no bindings are generated for functions in cuda_sources per se. To bind to a CUDA kernel, you must create a C++ function that calls it, and either declare or define this C++ function in one of the cpp_sources (and include its name in functions).

See load() for a description of arguments omitted below.

Parameters

• cpp_sources – A string, or list of strings, containing C++ source code.

• cuda_sources – A string, or list of strings, containing CUDA source code.

• functions – A list of function names for which to generate function bindings. If a dictionary is given, it should map function names to docstrings (which are otherwise just the function names).

• with_cuda – Determines whether CUDA headers and libraries are added to the build. If set to None (default), this value is automatically determined based on whether cuda_sources is provided. Set it to True to force CUDA headers and libraries to be included.

• with_pytorch_error_handling – Determines whether pytorch error and warning macros are handled by pytorch instead of pybind. To do this, each function foo is called via an intermediary _safe_foo function. This redirection might cause issues in obscure cases of cpp. This flag should be set to False when this redirect causes issues.

Example

>>> from torch.utils.cpp_extension import load_inline
>>> source = """
at::Tensor sin_add(at::Tensor x, at::Tensor y) {
  return x.sin() + y.sin();
}
"""
>>> module = load_inline(name='inline_extension',
...                      cpp_sources=[source],
...                      functions=['sin_add'])

Note

Since load_inline will just-in-time compile the source code, please ensure that you have the right toolchains installed in the runtime. For example, when loading C++, make sure a C++ compiler is available. If you’re loading a CUDA extension, you will need to additionally install the corresponding CUDA toolkit (nvcc and any other dependencies your code has). Compiling toolchains are not included when you install torch and must be additionally installed.

During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build the extension. This may use up too many resources on some systems. One can control the number of workers by setting the MAX_JOBS environment variable to a non-negative number.

torch.utils.cpp_extension.include_paths(device_type='cpu')

Get the include paths required to build a C++ or CUDA or SYCL extension.

Parameters

device_type (str) – Defaults to “cpu”.

Returns

A list of include path strings.

Return type

List[str]

torch.utils.cpp_extension.get_compiler_abi_compatibility_and_version(compiler)

Determine if the given compiler is ABI-compatible with PyTorch alongside its version.

Parameters

compiler (str) – The compiler executable name to check (e.g. g++). Must be executable in a shell process.

Returns

A tuple that contains a boolean that defines if the compiler is (likely) ABI-incompatible with PyTorch, followed by a TorchVersion string that contains the compiler version separated by dots.

Return type

Tuple[bool, TorchVersion]

torch.utils.cpp_extension.verify_ninja_availability()

Raise RuntimeError if ninja build system is not available on the system, does nothing otherwise.

torch.utils.cpp_extension.is_ninja_available()

Return True if the ninja build system is available on the system, False otherwise.