`libshm.so` depends on the torch library exclusively for `at::RefcountedMapAllocator`,
so it makes sense to move it to c10 along with the other memory allocators.
This means `libshm.so` only depends on `c10` and we don't need to relink
`libshm.so` for every ATen change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109881
Approved by: https://github.com/albanD
Reland of PR #94924. The purpose of this PR is to deal with the complicated interactions between MKL and OpenMP.
There are two improvements:
1. It uses a flag to avoid infinite mutual recursion in calling find_package(MKL) and find_package(OpenMP) in some cases.
2. The logic of finding iomp5 is improved and now we can test MKLDNN under ASAN.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104224
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
Hello!
Recently i was playing with LibTorch libs, but i noticed that currently there is only one LR Scheduler implementation available. I needed 'Reduce on plateau scheduler', so implemented it by myself. Used it a lot of times, and it seem work as it should, so decided to share my implementation here.
If u will decide that this is something worth to merge, or it needs tweaking/tests let me know!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100311
Approved by: https://github.com/albanD
This is reland of PR #94402 that tries to solve the additional link issues.
The PR #94402 failed because caffe2::mkl had been converted to private dependency while libtorch_cuda_linalg hadn't linked to it explicitly. This is fixed in commit 4373bf0ae3dee32afc178f9d51a4154d6c5904c6
We also replace more references of MKL_LIBRARIES by caffe2::mkl in this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94924
Approved by: https://github.com/malfet
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This pull request refactors the CMake configuration to enable the `USE_FLASH_ATTENTION` feature for the `torch_cuda` target only, using a target-specific macro. This avoids conflicts with other libraries that also use this feature, such as fairseq.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97579
Approved by: https://github.com/kit1980
Number of OSS PR were reverted, because new signed-unsigned comparison warnings, which are treated as errors in some internal builds.
Not sure how those selective rules are applied, but this PR removes `-Wno-sign-compare` from PyTorch codebase.
The only tricky part in this PR, as making sure that non-ASCII character detection works for both signed and unsigned chars here:
6e3d51b08a/torch/csrc/jit/serialization/python_print.cpp (L926)
Exclude several files from sign-compare if flash attention is used, due to the violation in cutlass, to be fixed by https://github.com/NVIDIA/cutlass/pull/869
Do not try to fix sign compare violations in caffe2 codebase
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96723
Approved by: https://github.com/albanD
This PR do two things:
1. It moves some Windows warning suppression from various CMake files into the main CMakeList.txt, following the conventions of gcc and clang.
2. It fixes some Windows warnings in the source code. Most importantly, it fixes lots of dll warnings by adjusting C10_API to TORCH_API or TORCH_PYTHON_API. There are still some dll warnings because some TORCH_API functions are actually built as part of libtorch_python
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94927
Approved by: https://github.com/malfet
Currently there is a potential conflict for `GLIBCXX_USE_CXX11_ABI` configuration if users don't explicitly set this variable.
In `caffe2/CMakeLists.txt`, if the variable is not set, an `abi checker` will be used to retrieve the ABI configuration from compiler.
https://github.com/pytorch/pytorch/blob/master/caffe2/CMakeLists.txt#L1165-L1183
However, in 'torch/csrc/Module.cpp`, if the variable is not set, it will be set to `0`. The conflict happens when the default ABI of the compiler is `1`.
https://github.com/pytorch/pytorch/blob/master/torch/csrc/Module.cpp#L1612
This PR eliminate this uncertainty and potential conflict.
The ABI will be checked and set in `CMakeLists.txt`, and pass the value to `caffe2/CMakeLists.txt`. Meanwhile, in case the `caffe2/CMakeLists.txt` is directly invoked from a `cmake` command, The original GLIBC check logic is kept in this file.
If users doesn't explicitly assign a value to `GLIBCXX_USE_CXX11_ABI`, the `abi checker` will be executed and set the value accordingly. If the `abi checker` failed to compile or execute, the value will be set to `0`. If users explicitly assigned a value, then the provided value will be used.
Moreover, if `GLIBCXX_USE_CXX11_ABI` is set to `0`, the '-DGLIBCXX_USE_CXX11_ABI=0' flag won't be appended to `CMAKE_CXX_FLAGS`. Thus, whether to use ABI=0 or ABI=1 fully depends on compiler's default configuration. It could cause an issue that even users explicitly set `GLIBCXX_USE_CXX11_ABI` to `0`, the compiler still builds the binaries with ABI=1.
https://github.com/pytorch/pytorch/blob/master/CMakeLists.txt#L44-L51
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94306
Approved by: https://github.com/malfet
