This patch adds support for sycl kernels build via `torch.utils.cpp_extension.load`, `torch.utils.cpp_extension.load_inline` and (new) `class SyclExtension` APIs. Files having `.sycl` extension are considered to have sycl kernels and are compiled with `icpx` (dpc++ sycl compiler from Intel). Files with other extensions, `.cpp`, `.cu`, are handled as before. API supports building sycl along with other file types into single extension.
Note that `.sycl` file extension is a PyTorch convention for files containing sycl code which I propose to adopt. We did follow up with compiler team to introduce such file extension in the compiler, but they are opposed to this. At the same time discussion around sycl file extension and adding sycl language support into such tools as cmake is ongoing. Eventually cmake also considers to introduce some file extension convention for sycl. I hope we can further influence cmake and compiler communities to broader adopt `.sycl` file extension.
By default SYCL kernels are compiled for all Intel GPU devices for which pytorch native aten SYCL kernels are compiled. At the moment `pvc,xe-lpg`. This behavior can be overridden by setting `TORCH_XPU_ARCH_LIST` environment variables to the comma separated list of desired devices to compile for.
Fixes: #132944
CC: @gujinghui @EikanWang @fengyuan14 @guangyey @jgong5
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132945
Approved by: https://github.com/albanD, https://github.com/guangyey
This could be BC breaking, because there was a period of time when we use py_limited_api=True but don't enforce the flag, and now that we will start enforcing the flag, people's custom extensions may fail to build.
This is strictly still better behavior, as it is sketchy to claim CPython agnosticism without the flag, but calling this out as potential people yelling at us. Ways to mitigate this risk + reasons this may not be too big a deal:
- People haven't known about py_limited_api for extensions much due to lack of docs from python so usage is low right now
- My current tutorial is in store to make new users of py_limited_api pass this flag, so it'd be a noop for them.
Test plan:
* Locally i'm confident as I tried rebuilding ao with this change and it reliably failed (cuz importing torch/extension.h is a nono)
* Unit test wise, the normal python_agnostic one I added should work
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145764
Approved by: https://github.com/ezyang, https://github.com/zou3519, https://github.com/albanD
While working on conda-forge integration, I needed to look at the way the include paths are calculated, and noticed an avoidable duplication between `torch/utils/cpp_extension.py` and `torch/_inductor/cpp_builder.py`. The latter already imports the former anyway, so simply reuse the same function.
Furthermore, remove long-obsolete include-paths. AFAICT, the `/TH` headers have not existed since pytorch 1.11.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145480
Approved by: https://github.com/ezyang
Getting tested with ao, but now there is a real test i added.
## What does this PR do?
We want to allow custom PyTorch extensions to be able to build one wheel for multiple Python versions, in other words, achieve python agnosticism. It turns out that there is such a way that setuptools/Python provides already! Namely, if the user promises to use only the Python limited API in their extension, they can pass in `py_limited_api` to their Extension class and to the bdist_wheel command (with a min python version) in order to build 1 wheel that will suffice across multiple Python versions.
Sounds lovely! Why don't people do that already with PyTorch? Well 2 things. This workflow is hardly documented (even searching for python agnostic specifically does not reveal many answers) so I'd expect that people simply don't know about it. But even if they did, _PyTorch_ custom Extensions would still not work because we always link torch_python, which does not abide by py_limited_api rules.
So this is where this PR comes in! We respect when the user specifies py_limited_api and skip linking torch_python under that condition, allowing users to enroll in the provided functionality I just described.
## How do I know this PR works?
I manually tested my silly little ultra_norm locally (with `import python_agnostic`) and wrote a test case for the extension showing that
- torch_python doesn't show up in the ldd tree
- no Py- symbols show up
It may be a little confusing that our test case is actually python-free (more clean than python-agnostic) but it is sufficient (and not necessary) towards showing that this change works.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138088
Approved by: https://github.com/ezyang, https://github.com/albanD
Over time, a large number of the existing type ignores have become irrelevant/unused/dead as a result of improvements in annotations and type checking.
Having these `# type: ignore` linger around is not ideal for two reasons:
- They are verbose/ugly syntatically.
- They could hide genuine bugs in the future, if a refactoring would actually introduce a bug but it gets hidden by the ignore.
I'm counting over 1500 unused ignores already. This is a first PR that removes some of them. Note that I haven't touched type ignores that looked "conditional" like the import challenge mentioned in https://github.com/pytorch/pytorch/pull/60006#issuecomment-2480604728. I will address these at a later point, and eventually would enable `warn_unused_ignores = True` in the mypy configuration as discussed in that comment to prevent accumulating more dead ignores going forward.
This PR should have no effect on runtime at all.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142325
Approved by: https://github.com/Skylion007, https://github.com/janeyx99
Newer versions of distutils no longer import `_msvccompiler` upon init(on Windows platform, that was not the case on other platforms even before 74), but it's still accessible if one chooses to import it directly.
Test plan:
```
% python -c 'from setuptools import distutils; print(distutils.__version__, hasattr(distutils, "_msvccompiler")); from distutils import _msvccompiler; import setuptools; print(setuptools.__version__, _msvccompiler.__file__)'
3.10.9 False
65.5.0 /usr/local/fbcode/platform010/Python3.10.framework/Versions/3.10/lib/python3.10/site-packages/setuptools/_distutils/_msvccompiler.py
```
and
```
% python -c 'from setuptools import distutils; print(distutils.__version__, hasattr(distutils, "_msvccompiler")); from distutils import _msvccompiler; import setuptools; print(setuptools.__version__, _msvccompiler.__file__)'
3.13.0 False
75.6.0 /Users/malfet/py312-venv/lib/python3.13/site-packages/setuptools/_distutils/_msvccompiler.py
```
Gave up trying to appease the linker, so rewrote it as following function:
```python
def _get_vc_env(vc_arch: str) -> dict[str, str]:
try:
from setuptools import distutils # type: ignore[import]
return distutils._msvccompiler._get_vc_env(vc_arch) # type: ignore[no-any-return]
except AttributeError:
from setuptools._distutils import _msvccompiler #type: ignore[import]
return _msvccompiler._get_vc_env(vc_arch) # type: ignore[no-any-return]
```
This PR also undoes setuptools version restriction introduced by https://github.com/pytorch/pytorch/pull/136489 as premise for restriction is incorrect
Fixes https://github.com/pytorch/pytorch/issues/141319
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141363
Approved by: https://github.com/huydhn, https://github.com/atalman
This PR enables Pytorch for Windows on Arm64 - CPU only.
Currently, there aren't any checks in place to build and test for Windows on Arm64, but we're working to implement those as soon as possible.
We recommend using [Arm Performance Libraries (APL)](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Libraries) as a BLAS option, which is introduced in this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133088
Approved by: https://github.com/malfet
Co-authored-by: cristian panaite <panaite.cristian2000@gmail.com>
Co-authored-by: Stefan-Alin Pahontu <56953855+alinpahontu2912@users.noreply.github.com>
Co-authored-by: Ozan Aydin <148207261+ozanMSFT@users.noreply.github.com>
This is to avoid cache confusion between normal vs pydebug vs nogil builds in cpp extensions which can lead to catastrophic ABI issues.
This is rare today for people to run both normal and pydebug on the same machine, but we expect quite a few people will run normal and nogil on the same machine going forward.
This is tested locally by running each version alternatively.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136890
Approved by: https://github.com/colesbury
Greetings!
Fixes#125403
Please assist me with the testing as it is possible for my reproducer to miss the error in the code. Several (at least two) threads should enter the same part of the code at the same time to check file lock is actually working
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125404
Approved by: https://github.com/ezyang
nvcc flag `--generate-dependencies-with-compile` doesn't seem to be supported by `sccache` for now. Builds with this flag enabled will not benefit from sccache.
This PR adds an environment variable that allows users to set this flag and skip those nvcc dependencies to speed up their build with compiler caches. If everything is "fresh build" in CI, we don't care if there are unnecessary recompile during incremental builds.
related: https://github.com/pytorch/pytorch/pull/49344
- [ ] todo: raise an issue to sccache
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119936
Approved by: https://github.com/ezyang
