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++` ;))
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> _`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".
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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
As we are linking with cuDNN and cuBLAS dynamically for all configs anyway, as statically linked cuDNN is different library than dynamically linked one, increases default memory footprint, etc, and libtorch_cuda even if compiled for all GPU architectures is no longer approaching 2Gb binary size limit, so BUILD_SPLIT_CUDA can go away.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87502
Approved by: https://github.com/atalman
The function checks the output of e.g. `c++ -v` for "gcc version". But on another locale than English it might be "gcc-Version" which makes the check fail.
This causes the function to wrongly return false on systems where `c++` is a hardlink to `g++` and the current locale returns another output format.
Fix this by setting `LC_ALL=C`.
I found this as `test_utils.py` was failing in `test_cpp_compiler_is_ok`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85891
Approved by: https://github.com/ezyang
This is a new version of #15648 based on the latest master branch.
Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR.
In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.)
Fixes https://github.com/pytorch/pytorch/issues/71105
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797
Approved by: https://github.com/ezyang
### Description
<!-- What did you change and why was it needed? -->
Listed in the commit message:
> The user may want to use `python3 -c "..."` to get the torch library
> path and the include path. Printing messages to stdout will mess up
> the output.
I'm using the command:
```bash
LIBTORCH_PATH="$(
python3 -c 'print(":".join(__import__("torch.utils.cpp_extension", fromlist=[None]).library_paths()))'
)"
export LD_LIBRARY_PATH="${LIBTORCH_PATH}${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}"
```
To let the command line tools find the torch shared libraries. I think this would be a common use case for users who writing C/C++ extensions.
I got:
```console
$ LIBTORCH_PATH="$(python3 -c 'print(":".join(__import__("torch.utils.cpp_extension", fromlist=[None]).library_paths()))')"
$ export LD_LIBRARY_PATH="${LIBTORCH_PATH}${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}"
$ echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}"
LD_LIBRARY_PATH=No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda-11.6'
/opt/hostedtoolcache/Python/3.7.13/x64/lib/python3.7/site-packages/torch/lib:/usr/local/cuda-11.6/lib64:
$ ls -alh "${LIBTORCH_PATH}"
ls: cannot access 'No CUDA runtime is found, using CUDA_HOME='\''/usr/local/cuda-11.6'\'''$'\n''/opt/hostedtoolcache/Python/3.7.13/x64/lib/python3.7/site-packages/torch/lib': No such file or directory
```
This PR prints messages in `torch.utils.cpp_extension` to `stderr`, which allows users to get correct result using `VAR="$(python3 -c '...')"`
### Issue
<!-- Link to Issue ticket or RFP -->
N/A
### Testing
<!-- How did you test your change? -->
N/A
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82097
Approved by: https://github.com/ezyang
*even if no GPUs are available*
When building PyTorch extensions for ROCm Pytorch, if the user doesn't specify a list of archs using PYTORCH_ROCM_ARCH env var, we would like to use the list of gfx archs that PyTorch was built for as the default value. To do this successfully even in an environment where no GPUs are available eg. a build-only CPU node, we need to be able to get the list of archs. `torch.cuda.get_arch_list()` doesn't work here because it calls `torch.cuda.available()` first: 0922cc024e/torch/cuda/__init__.py (L463), which will return `False` if no GPUs are available, resulting in an empty list being returned by `torch.cuda.get_arch_list()`. To get around this issue, we call the underlying API `torch._C._cuda_getArchFlags()`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80498
Approved by: https://github.com/ezyang, https://github.com/malfet
