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
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Co-authored-by: Catherine Lee <csl@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Related to #118494, it is not clear to users that the default behavior is to include **all** feasible archs (if the 'TORCH_CUDA_ARCH_LIST' is not set).
In these scenarios, a user may experience a long build time. Adding a print statement to reflect this behavior. [`verbose` arg is not available and not feeling necessary to add `verbose` arg to this function and all its parent functions...]
Co-authored-by: Edward Z. Yang <ezyang@mit.edu>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118503
Approved by: https://github.com/ezyang
It doesn't make sense to set this (on import!) as CUDA cannot be used with PyTorch in this case but leads to messages like
> No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
when CUDA happens to be installed which is at least confusing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106310
Approved by: https://github.com/ezyang
The default rendering of these code snippets renders the `TORCH_CUDA_ARCH_LIST` values with typographic quotes which prevent the examples from being directly copyable. Use code style for the two extension examples.
Fixes#112763
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112764
Approved by: https://github.com/malfet
- rename `__HIP_PLATFORM_HCC__` to `__HIP_PLATFORM_AMD__`
- rename `HIP_HCC_FLAGS` to `HIP_CLANG_FLAGS`
- rename `PYTORCH_HIP_HCC_LIBRARIES` to `PYTORCH_HIP_LIBRARIES`
- workaround in tools/amd_build/build_amd.py until submodules are updated
These symbols have had a long deprecation cycle and will finally be removed in ROCm 6.0.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111975
Approved by: https://github.com/ezyang, https://github.com/hongxiayang
Did some easy fixes from enabling TRY200. Most of these seem like oversights instead of intentional. The proper way to silence intentional errors is with `from None` to note that you thought about whether it should contain the cause and decided against it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111496
Approved by: https://github.com/malfet
The CUDA architecture flags from TORCH_CUDA_ARCH_LIST will be skipped if the TORCH_EXTENSION_NAME includes the substring "arch". A C++ Extension should be allowed to have any name. I just manually skip the TORCH_EXTENSION_NAME flag when checking if one of the flags is "arch". There is probably a better fix, but I'll leave this to experts.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111211
Approved by: https://github.com/ezyang
On Linux, CUDA header dependencies are not correctly tracked. After you modify a CUDA header, affected CUDA files won't be rebuilt. This PR will fix this problem.
```console
$ ninja -t deps
rep_penalty.o: #deps 2, deps mtime 1693956351892493247 (VALID)
/home/qc/Workspace/NotMe/exllama/exllama_ext/cpu_func/rep_penalty.cpp
/home/qc/Workspace/NotMe/exllama/exllama_ext/cpu_func/rep_penalty.h
rms_norm.cuda.o: #deps 0, deps mtime 1693961188871054130 (VALID)
rope.cuda.o: #deps 0, deps mtime 1693961188954388632 (VALID)
cuda_buffers.cuda.o: #deps 0, deps mtime 1693961188797719768 (VALID)
...
```
Historically, this line of code has been changed twice. It was first implemented in #49344 and there's no `if IS_WINDOWS`, just like now. Then in #56015 someone added `if IS_WINDOWS` for unknown reason. That PR has no description so I don't know what bug he encountered. I don't think there's any bug with these flags on Linux, at least for today. CMake generates exactly the same flags for CUDA.
```ninja
#############################################
# Rule for compiling CUDA files.
rule CUDA_COMPILER__cpp_cuda_unscanned_Debug
depfile = $DEP_FILE
deps = gcc
command = ${LAUNCHER}${CODE_CHECK}/opt/cuda/bin/nvcc -forward-unknown-to-host-compiler $DEFINES $INCLUDES $FLAGS -MD -MT $out -MF $DEP_FILE -x cu -c $in -o $out
description = Building CUDA object $out
```
where `-MD` is short for `--generate-dependencies-with-compile` and `-MF` is short for `--dependency-output`. My words can be verified by `nvcc --help`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108613
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This PR fixes the circular issue during hipification process by introducing current_state to track whether a file is processed for hipification. (Iterative DFS)
The issue arises when two header files try to include themselves, which leads to a circular recursion or an infinite loop.
Fixes the related issues such as :
https://github.com/pytorch/pytorch/issues/93827https://github.com/ROCmSoftwarePlatform/hipify_torch/issues/39
Error log:
```
File "/opt/conda/lib/python3.8/posixpath.py", line 471, in relpath
start_list = [x for x in abspath(start).split(sep) if x]
File "/opt/conda/lib/python3.8/posixpath.py", line 375, in abspath
if not isabs(path):
File "/opt/conda/lib/python3.8/posixpath.py", line 63, in isabs
sep = _get_sep(s)
File "/opt/conda/lib/python3.8/posixpath.py", line 42, in _get_sep
if isinstance(path, bytes):
RecursionError: maximum recursion depth exceeded while calling a Python object
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104085
Approved by: https://github.com/jithunnair-amd, https://github.com/malfet
Not sure, why was it excluded previous (oversight I guess).
Also, please note, that `clang++` is already considered acceptable compiler (as it ends with `g++` ;))
<!--
copilot:poem
-->
### <samp>🤖 Generated by Copilot at 55aa7db</samp>
> _`clang` or `gcc`, we don't care what you use_
> _We'll build our extensions with the tools we choose_
> _Don't try to stop us with your version string_
> _We'll update our logic and make our code sing_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103349
Approved by: https://github.com/seemethere
When we need to link extra libs, we should notice that 64-bit CUDA may be installed in "lib", not in "lib64".
<!--
copilot:summary
-->
### <samp>🤖 Generated by Copilot at 05c1ca6</samp>
Improve CUDA compatibility in `torch.utils.cpp_extension` by checking for `lib64` or `lib` directory.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101285
Approved by: https://github.com/ezyang, https://github.com/malfet
Currently if `setuptools<49.4.0` and there is a minor version mismatch `_check_cuda_version` fails with a misleading non-actionable error:
```
2023-03-24T20:21:35.0625644Z RuntimeError:
2023-03-24T20:21:35.0628441Z The detected CUDA version (11.2) mismatches the version that was used to compile
2023-03-24T20:21:35.0630681Z PyTorch (11.3). Please make sure to use the same CUDA versions.
```
This condition shouldn't be failing since minor version match isn't required.
It fails because the other condition to have a certain version of `setuptools` isn't met. But that condition is written in a comment (!!!). So this PR changes it to actually tell the user how to fix the problem.
While at it, I adjusted the version number as a lower `setuptools>=49.4.0` is sufficient for this to work.
Thanks.
p.s. this problem manifests on `nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04` docker image.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97602
Approved by: https://github.com/ezyang
Merges startswith, endswith calls to into a single call that feeds in a tuple. Not only are these calls more readable, but it will be more efficient as it iterates through each string only once.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96754
Approved by: https://github.com/ezyang
These warnings are disabled to avoid long log on Windows tests. They are also disabled on CMake buildings currently.
'/wd4624': MSVC complains "destructor was implicitly defined as delete" on c10::optional and other templates
'/wd4076': "unexpected tokens following preprocessor directive - expected a newline" on some header
'/wd4068': "The compiler ignored an unrecognized [pragma]"
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95933
Approved by: https://github.com/ezyang
Applies the remaining flake8-comprehension fixes and checks. This changes replace all remaining unnecessary generator expressions with list/dict/set comprehensions which are more succinct, performant, and better supported by our torch.jit compiler. It also removes useless generators such as 'set(a for a in b)`, resolving it into just the set call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94676
Approved by: https://github.com/ezyang
Optimize unnecessary collection cast calls, unnecessary calls to list, tuple, and dict, and simplify calls to the sorted builtin. This should strictly improve speed and improve readability.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94323
Approved by: https://github.com/albanD
The main changes are:
1. Remove outdated checks for old compiler versions because they can't support C++17.
2. Remove outdated CMake checks because it now requires 3.18.
3. Remove outdated CUDA checks because we are moving to CUDA 11.
Almost all changes are in CMake files for easy audition.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90599
Approved by: https://github.com/soumith
Switch GCC/Clang max versions to be exclusive as the `include/crt/host_config.h` checks the major version only for the upper bound. This allows to be less restrictive and match the checks in the aforementioned header.
Also update the versions using that header in the CUDA SDKs.
Follow up to #82860
I noticed this as PyTorch 1.12.1 with CUDA 11.3.1 and GCC 10.3 was failing in the `test_cpp_extensions*` tests.
Example for CUDA 11.3.1 from the SDK header:
```
#if __GNUC__ > 11
// Error out
...
#if (__clang_major__ >= 12) || (__clang_major__ < 3) || ((__clang_major__ == 3) && (__clang_minor__ < 3))
// Error out
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86360
Approved by: https://github.com/ezyang
