Replace (more) exact calculation with hardware approximation.
Benefits:
Reduced code size.
Improved performance for certain scenarios.
Experiments show low reduction in precision.
Experiments show no significant performance regressions. bfloat16 as well as float16 related calculations may benefit largely from this change.
Co-author: @mhalk @amd-hhashemi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165589
Approved by: https://github.com/jeffdaily
Shared memory is allocated by creating a file in /dev/shm (by default) that can run out of space. Pytorch reserves the file size by calling ftruncate() that creates a sparse file, so it succeeds even if sufficient disk space is not available.
This could lead to a situation when a shared memory region is successfully created but a subsequent access to a shared memory page results in SIGBUS due to the disk being full.
Using posix_fallocate() instead of ftruncate() eliminates this problem because the former syscall always allocates space and it returns an error if the disk is full.
Related to https://github.com/pytorch/pytorch/issues/5040
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161910
Approved by: https://github.com/mikaylagawarecki
This pull request adds the following ops for sparse matrices using Eigen library:
```python
add(a_csr, b_csr)
add(a_csc, b_csc)
addmm(c_csr, a_csr, b_csr)
addmm(c_csr, a_csr, b_csc)
addmm(c_csr, a_csc, b_csc)
addmm(c_csr, a_csc, b_csr)
addmm(c_csc, a_csr, b_csr)
addmm(c_csc, a_csr, b_csc)
addmm(c_csc, a_csc, b_csc)
addmm(c_csc, a_csc, b_csr)
```
Currently, the operations for sparse matrices on CPU are available through MKL only. The non-existence of MKL on `aarch64` causes the unavailability of these ops on any machines with ARM based CPUs, including Apple Silicon, AWS Graviton and NVIDIA Grace. This PR addresses this issue by using Eigen as a backend for the above ops.
This is a re-factored version of my previous PR #101814. The main difference with the old one, this does not enable Eigen by default.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155357
Approved by: https://github.com/pearu, https://github.com/eqy
Co-authored-by: Eli Uriegas <eliuriegas@meta.com>
This pull request adds the following ops for sparse matrices using Eigen library:
```python
add(a_csr, b_csr)
add(a_csc, b_csc)
addmm(c_csr, a_csr, b_csr)
addmm(c_csr, a_csr, b_csc)
addmm(c_csr, a_csc, b_csc)
addmm(c_csr, a_csc, b_csr)
addmm(c_csc, a_csr, b_csr)
addmm(c_csc, a_csr, b_csc)
addmm(c_csc, a_csc, b_csc)
addmm(c_csc, a_csc, b_csr)
```
Currently, the operations for sparse matrices on CPU are available through MKL only. The non-existence of MKL on `aarch64` causes the unavailability of these ops on any machines with ARM based CPUs, including Apple Silicon, AWS Graviton and NVIDIA Grace. This PR addresses this issue by using Eigen as a backend for the above ops.
This is a re-factored version of my previous PR #101814. The main difference with the old one, this does not enable Eigen by default.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155357
Approved by: https://github.com/pearu, https://github.com/eqy
Merge the recent commits of FBGEMM and remove unnecessary CMake code.
Specifically, we
1. enable `fbgemm_autovec` since the target is now correctly handled.
2. remove option `USE_FAKELOWP` which is not used.
3. remove `CAFFE2_COMPILER_SUPPORTS_AVX512_EXTENSIONS` check.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158210
Approved by: https://github.com/q10
Refactors how the enablement/disablement of CK Gemms and SDPA works.
- Adds USE_ROCM_CK_GEMM compile flag for enabling CK gemms.
- USE_ROCM_CK_GEMM is set to True by default on Linux
- Updates USE_CK_FLASH_ATTENTION to USE_ROCM_CK_SDPA.
- USE_ROCM_CK_SDPA is set to False by default
- (USE_CK_FLASH_ATTENTION still works for now, but will be deprecated in a future release)
- Prevents these CK libraries from being used unless pytorch has been built specifically with the functionality AND is running on a system architecture that supports it.
- the getters for these library backends will also do some validity checking in case the user used an environment variable to change the backend. If invalid, (i.e. one of the cases mentioned above is false) the backend will be set as the current non-CK default
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152951
Approved by: https://github.com/eqy, https://github.com/jeffdaily, https://github.com/m-gallus
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
Co-authored-by: Jithun Nair <jithun.nair@amd.com>
Co-authored-by: Jane (Yuan) Xu <31798555+janeyx99@users.noreply.github.com>
Fix incorrect linking of Gloo's libraries when building with system Gloo. Previously, either Gloo's native library or Gloo's CUDA library were linked. However, Gloo had changed such that all users of Gloo must link the native library, and can optionally link the CUDA or HIP library for Gloo + CUDA/HIP support.
This had been updated when building/linking with vendored Gloo, but not when using system Gloo.
Fixes: #146239
Reported-by: Adam J Stewart <ajstewart426@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146637
Approved by: https://github.com/malfet
No functional changes, just:
- Update C++ standard to C++17
- Update `cmake` min version to 3.18
- Update `libuv` dependency to 1.51 (to move its cmake min version to 3.10)
- Replace boost optional implementation with `std::optional` wrapper
- Make it compilable with gcc-14.x plus by including `cstddef` in few headers
- Avoid using deprecated enums for MacOS builds
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159834
Approved by: https://github.com/Skylion007
This PR fixes `cmake/Dependencies.cmake` to work when compiling with `USE_SYSTEM_XNNPACK=ON` by changing a lowercase `or` to an uppercase `OR`.
---
For a personal project, I was building pytorch with a customized build of XNNPACK. When trying to do so I encountered the following error:
```
CMake Error at cmake/Dependencies.cmake:566 (if):
if given arguments:
"NOT" "XNNPACK_LIBRARY" "or" "NOT" "microkernels-prod_LIBRARY"
Unknown arguments specified
Call Stack (most recent call first):
CMakeLists.txt:868 (include)
```
Upon making the change in this PR (changing `or` to `OR`), the process continued as expected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159527
Approved by: https://github.com/janeyx99
Whose version is controlled by `eigen_pin.txt`, but which will be installed only if BLAS providers could not be found.
Why this is good for CI: we don't really build with Eigen ever and gitlab can be down when github is up, which causes spurious CI failures in the past, for example.
Remove eigen submodule and replace it with eigen_pin.txt
Fixes https://github.com/pytorch/pytorch/issues/108773
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155955
Approved by: https://github.com/atalman
Whose version is controlled by `eigen_pin.txt`, but which will be installed only if BLAS providers could not be found.
Why this is good for CI: we don't really build with Eigen ever and gitlab can be down when github is up, which causes spurious CI failures in the past, for example.
Remove eigen submodule and replace it with eigen_pin.txt
Fixes https://github.com/pytorch/pytorch/issues/108773
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155955
Approved by: https://github.com/atalman
ghstack dependencies: #155947, #155954
* The `rocm-core` CMake package only started appearing in ROCm 6.4, so rework the version probing to work if it is not present. Also collapses the unneeded operating system conditioning in favor of feature probing.
* Make `hipsparselt` optional: it only started appearing in ROCm 6.4 and it is not in all recent distribution channels yet.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155305
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
