## Summary
We are currently [updating](https://github.com/conda-forge/pytorch-cpu-feedstock/pull/277) the [`conda-forge::pytorch`](https://anaconda.org/conda-forge/pytorch) package to version 2.5.0. This update includes a new dependency, the third_party/NVTX submodule. However, like other package management frameworks (e.g., apt), conda-forge prefers using system-installed packages instead of vendor-provided third-party packages.
This pull request aims to add an option, `USE_SYSTEM_NVTX`, to select whether to use the vendored nvtx or the system-installed one, with the default being the vendored one (which is the current behavior).
## Test Plan
The `USE_SYSTEM_NVTX` option is tested by building the `conda-forge::pytorch` package with the change applied as a [patch](cd1d2464dd/recipe/patches/0005-Use-system-nvtx3.patch).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138287
Approved by: https://github.com/albanD
This PR switches to cuDSS library and has the same purpose of #127692, which is to add Sparse CSR tensor support to linalg.solve.
Fixes#69538
Minimum example of usage:
```
import torch
if __name__ == '__main__':
spd = torch.rand(4, 3)
A = spd.T @ spd
b = torch.rand(3).to(torch.float64).cuda()
A = A.to_sparse_csr().to(torch.float64).cuda()
x = torch.linalg.solve(A, b)
print((A @ x - b).norm())
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129856
Approved by: https://github.com/amjames, https://github.com/lezcano, https://github.com/huydhn
Co-authored-by: Zihang Fang <zhfang1108@gmail.com>
Co-authored-by: Huy Do <huydhn@gmail.com>
Another attempt to update NVTX to NVTX3. We now avoid changing NVTX header inclusion of existing code. The advantage of NVTX3 over NVTX is that it is a header-only library so that linking with NVTX3 can greatly simplify our CMake and other building scripts for finding libraries in user environments. In addition, NVTX are indeed still present in the latest CUDA versions, but they're no longer a compiled library: It's now a header-only library. That's why there isn't a .lib file anymore.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109843
Approved by: https://github.com/peterbell10, https://github.com/eqy
Co-authored-by: Ivan Zaitsev <108101595+izaitsevfb@users.noreply.github.com>
As FindPythonInterp and FindPythonLibs has been deprecated since cmake-3.12
Replace `PYTHON_EXECUTABLE` with `Python_EXECUTABLE` everywhere (CMake variable names are case-sensitive)
This makes PyTorch buildable with python3 binary shipped with XCode on MacOS
TODO: Get rid of `FindNumpy` as its part of Python package
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124613
Approved by: https://github.com/cyyever, https://github.com/Skylion007
Closes#108931, closes#108932, see also conda-forge/pytorch-cpu-feedstock#203
Currently we compare `CUDA_INCLUDE_DIRS` and expect exact equality
with `CUDAToolkit_INCLUDE_DIR` however this fails in the presense of
symbolic links or for split installs where there are multiple include paths.
Given that, it makes sense to loosen the requirement to just version
equality under the assumption that two installs of the same version
should still be compatible.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113174
Approved by: https://github.com/malfet
PR #90689 replaces NVTX with NVTX3. However, the torch::nvtoolsext is created only when the third party NVTX is used.
This is clear a logical error. We now move the creation code out of the branch to cover all cases. This should fix the issues reported in the comments of #90689.
It would be better to move configurations of the failed FRL jobs to CI tests so that we can find such issues early before merging.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97582
Approved by: https://github.com/peterbell10
Summary:
This stack of PR's integrates cuSPARSELt into PyTorch.
This PR adds support for cuSPARSELt into the build process.
It adds in a new flag, USE_CUSPARSELT that defaults to false.
When USE_CUSPASRELT=1 is specified, the user can also specify
CUSPASRELT_ROOT, which defines the path to the library.
Compiling pytorch with cusparselt support can be done as follows:
``
USE_CUSPARSELT=1
CUSPARSELT_ROOT=/path/to/cusparselt
python setup.py develop
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103700
Approved by: https://github.com/albanD
We greatly simplify the handing of OpenMP in CMake by using caffe2::openmp target thoroughly. We follow the old behavior by defaulting to MKL OMP library and detecting OMP flags otherwise.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91576
Approved by: https://github.com/malfet
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
Summary:
Fixes : https://github.com/pytorch/pytorch/issues/73377
We've migrated to CUDA-11.3 as default toolkit in 1.9, it's time to stop builds (especially considering forward-compatibility guarantee across CUDA-11.x drivers)
Hence we are removing CUDA 11.1 support. We should also cleanup old cuda related code from our builder and pytorch repo making scripts a little more clean.
We have code that references cuda 9.2 , 10.1 , 11.0, 11.1, 11.2 and none of these are currently use
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73514
Reviewed By: janeyx99
Differential Revision: D34551989
Pulled By: atalman
fbshipit-source-id: 9ceaaa9b25ad49689986f4b29a26d20370d9d011
(cherry picked from commit fe109c62daf429e9053c03f6e374568ba23cd041)
Summary:
Remove forcing CUDNN_STATIC when CAFFE2_STATIC_LINK_CUDA is set
Since we are transitioning to using dynamic loading for multiple pytorch dependecies and CUDNN is the first step in this transition, hence we want to remove forcing CUDNN to statically load, and instead load it dynamically.
Tested using following workflow:
https://github.com/pytorch/pytorch/actions/runs/1790666862
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72290
Reviewed By: albanD
Differential Revision: D34003793
Pulled By: atalman
fbshipit-source-id: 41bda7ac019a612ee53ceb18d1e372b1bb3cb68e
(cherry picked from commit 4a01940e68)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62445
PyTorch currently uses the old style of compiling CUDA in CMake which is just a
bunch of scripts in `FindCUDA.cmake`. Newer versions support CUDA natively as
a language just like C++ or C.
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D31503350
fbshipit-source-id: 2ee817edc9698531ae1b87eda3ad271ee459fd55
Summary:
This is a PR on build system that provides support for cross compiling on Jetson platforms.
The major change is:
1. Disable try runs for cross compiling in `COMPILER_WORKS`, `BLAS`, and `CUDA`. They will not be able to perform try run on a cross compile setup
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59764
Reviewed By: soulitzer
Differential Revision: D29524363
Pulled By: malfet
fbshipit-source-id: f06d1ad30b704c9a17d77db686c65c0754db07b8
Summary:
This is only important for builds where cuDNN is linked statically into libtorch_cpu.
Before this PR PyTorch wheels often accidentally contained several partial copies of cudnn_static library.
Splitting the interface into header only (cudnn-public) and library+headers(cudnn-private) prevents those from happening.
Preliminary step towards enabling optional linking whole cudnn_library to workaround issue reported in https://github.com/pytorch/pytorch/issues/50153
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59721
Reviewed By: ngimel
Differential Revision: D29000967
Pulled By: malfet
fbshipit-source-id: f054df92b265e9494076ab16c247427b39da9336
Summary:
Library linking order matters during static linking
Not sure whether its a bug or a feature, but if cublas is reference
before CuDNN, it will be partially statically linked into the library,
even if it is not used
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58287
Reviewed By: janeyx99
Differential Revision: D28433165
Pulled By: malfet
fbshipit-source-id: 8dffa0533075126dc383428f838f7d048074205c
Summary:
Fixes following error during static linking, by enforcing that cudart dependency is put after cublasLt
```
/usr/bin/ld: /usr/local/cuda/lib64/libcublasLt_static.a(libcublasLt_static.a.o): undefined reference to symbol 'cudaStreamWaitEvent@libcudart.so.11.0'
/usr/local/cuda/lib64/libcudart.so: error adding symbols: DSO missing from command line
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52509
Reviewed By: janeyx99
Differential Revision: D26547622
Pulled By: malfet
fbshipit-source-id: 4e17f18cf0ab5479a549299faf2583a79fbda4b9
