# Motivation
This PR aims to maintain backward compatibility when building PyTorch XPU with the old and new compilers.
# Additional Context
The details are described here. The new compiler (2025.0.0) has some breaking changes compared with the old compiler(2024.1), for examples:
1. On Windows, sycl library is named `sycl7.lib` in the old compiler but is named `sycl.lib` in the new compiler.
2. On Linux, in order to support ABI=0, we have to link `libsycl-preview.so` in the old compiler but we could link `libsycl.so` in the new compiler to have the same ABI compatibility.
3. We added a macro `SYCL_COMPILER_VERSION` to support our new code has good backward compatibility with the old compiler. Now the new feature(Event elapsed_time, memory summary, and device architecture property) introduced by the new compiler will be controlled within the macro `SYCL_COMPILER_VERSION`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139258
Approved by: https://github.com/EikanWang, https://github.com/atalman, https://github.com/gujinghui
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>
**Motivation:**
In Pytorch, Aten vectorization supports multiple platforms, including x86 and Arm, as well as multiple data types. It provides a generic implementation of Vector (Vec) type that allows the programmer to write code packing various primitives (such as floats) within 256bit & 512bits registers. It can be extended to support other ISAs easily by adding more VecISA sub-classes.
**Reference Link:** https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/cpu/vec
**This PR:**
* Our goal with this contribution is to add support for SVE backend for Vec in the Aten vectorization for CPU backend which can be benefitted by any ARM architecture supported CPU's that supports SVE.
* More about SVE ISA for ARM: [https://developer.arm.com/Architectures/Scalable Vector Extensions](https://developer.arm.com/Architectures/Scalable%20Vector%20Extensions)
* We are using the ARM C Language Extensions for SVE (https://developer.arm.com/documentation/102699/0100/Optimizing-with-intrinsics ) to accelerate performance for various operators in the SVE backend for Vec.
* Currently we are adding support only for SVE ISA with the vector length of 256 bits (SVE 256). In future, we plan to extend this SVE support for other vector lengths as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119571
Approved by: https://github.com/malfet, https://github.com/snadampal
Co-authored-by: Divya Kotadiya <divya.kotadiya@fujitsu.com>
# Motivation
If build XPU via oneAPI 2024.2, it will fail because `sycl-preview.lib` exists in windows. And linking the unexpected lib results in `error LNK2019: unresolved external symbol`.
# Solution
Use explicitly `sycl-preview` in linux build only.
# Additional Context
For `find_library`, please note that the variable will not be updated if it has been stored.
```
If the library is found the result is stored in the variable and the search will not be repeated unless the variable is cleared.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133845
Approved by: https://github.com/min-jean-cho, https://github.com/EikanWang, https://github.com/atalman, https://github.com/malfet
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>
# Motivation
This PR intends to support ABI=0 build for XPU backend.
# Additional Context
The major change is adding a compilation option `-D__INTEL_PREVIEW_BREAKING_CHANGES` for the host compiler(gcc) and `-fpreview-breaking-changes` for XPU device kernel code compiler(icpx), why?
Because we use
- gcc to compile host code and link SYCL runtime. So we need to pass `-D__INTEL_PREVIEW_BREAKING_CHANGES` to tell the host compiler invoking the ABI-neutral API included in SYCL. And
- use icpx to compile device kernel code and link SYCL runtime. So we need to pass `-fpreview-breaking-changes` to tell the device kernel compiler building ABI-neutral code. Besides,
- `libsycl-preview.so` is an ABI-neutral library but `libsycl.so` is not.
This PR depends on https://github.com/pytorch/pytorch/pull/131643.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130110
Approved by: https://github.com/EikanWang, https://github.com/gujinghui, https://github.com/albanD
It was deprecated since CMake-3.0 in favor of `execute_process`, see https://cmake.org/cmake/help/v3.18/command/exec_program.html
This makes the following warning disappear:
```
CMake Warning (dev) at cmake/Modules/FindARM.cmake:5 (EXEC_PROGRAM):
Policy CMP0153 is not set: The exec_program command should not be called.
Run "cmake --help-policy CMP0153" for policy details. Use the cmake_policy
command to set the policy and suppress this warning.
Use execute_process() instead.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129714
Approved by: https://github.com/kit1980
Current logic to set the HAS_SBGEMM flag is ignored in case the BLAS libraries are found already, ie, if set from environment variable BLAS=OpenBLAS . If BLAS_LIBRARIES are already set the code to find if BLAS_LIBRARY has sbgemm is never executed. The following commit brings out this logic outside unconditionally.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125227
Approved by: https://github.com/malfet
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
# Motivation
This PR is a part of RFC #114848, and it is a successor PR of #116249 and #116019. This PR would depend on oneDNN compilation in #116249. Some runtime support is needed in #116019.
Aten operators like `addmm`, `baddmm` is defined in `Blas.cpp` in `aten/src/ATen/native/mkldnn/xpu/`.
Accompanied with these files provide core functionaliy, `BlasImpl.h`, `Utils.h` and other file provide basic utilities for them. For instance, `Utils.h` provide common memory descriptor query utils for `Matmul.h` and these utility function will also be used in other primitive, like `convolution`. `BlasImpl.h` is a header file that provide helper for handling shape info processing in matmul related operators. It would not only help basic GEMM operator like `addmm, baddmm` but also help fusion operators used in `torch.compile` like `linear_pointwise` in #117824.
In next stage, we would continually complete the oneDNN support through enabling `matmul fusion` and `convolution` related code.
Co-authored-by: xiaolil1 <xiaoli.liu@intel.com>
Co-authored-by: lei,zhenyuan <zhenyuan.lei@intel.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117202
Approved by: https://github.com/EikanWang, https://github.com/jgong5, https://github.com/malfet
ghstack dependencies: #117098, #117112
# Motivation
As proposed in https://github.com/pytorch/pytorch/issues/114848 and https://github.com/pytorch/pytorch/issues/114723, oneDNN library is an important component for Intel GPU software ecosystem.
This PR is intended to enable oneDNN compilation for Intel GPU. It is the first step for we enabling any operators like `at::baddmm`.
With this PR, a static library `libdnnl.a` for GPU would be compiled in directory `/build/xpumkldnn_proj-prefix`. It can be further linked to `libtorch_xpu.so` in future. The compilation would depend on `USE_XPU` bool variables and runtime check like SYCL, which is defined in https://github.com/pytorch/pytorch/pull/116019 for runtime support. Once the #116019 merged, the compilation should be able to be triggered.
The modification is independent to oneDNN CPU compilation, hence no modification would be introduced for CPU Cmakefiles(e.g. FindMKLDNN.cmake)
Co-authored-by: xiaolil1 <xiaoli.liu@intel.com>
Co-authored-by: lei,zhenyuan <zhenyuan.lei@intel.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117098
Approved by: https://github.com/EikanWang, https://github.com/jgong5, https://github.com/atalman
`CXX_AVX[2|512]_FOUND` flags should indicate whether compiler supports generating code for given instruction set, rather than whether host machine can run the generated code.
This fixes a weird problem that surfaced after https://github.com/pytorch/pytorch/pull/122503 when builder can sometimes be dispatched to an old CPU architecture, that can not run AVX512 instructions, but can compile for those just fine
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122708
Approved by: https://github.com/jeanschmidt
Fix https://github.com/pytorch/pytorch/issues/97352.
This PR changes the way the linking to intel MKL is done and updating MKL on Windows to mkl-2021.4.0 .
There are for both conda and pip packages MKL version with which you can link dynamically. mkl-devel contains the static versions of the dlls and MKL contains the needed dlls for the runtime. MKL dlls and static libs starting with 2021.4.0 have the version in their names( for MKL 2023 we have mkl_core.2.dll and for 2021.4.0 we have mkl_core.1.dll) so its possible to have multiple versions installed and it will work properly.
For the wheel build, I added dependency for whell MKL and on conda a dependecy for the conda MKL and on libtorch I copied the MKL binaries in libtorch.
In order to test this PR I have to use custom builder https://github.com/pytorch/builder/pull/1467
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102604
Approved by: https://github.com/IvanYashchuk, https://github.com/malfet
# Motivation
As mentioned in [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842), The first runtime component we would like to upstream is `Device` which contains the device management functions of Intel GPU's runtime. To facilitate the code review, we split the code changes into 4 PRs. This is one of the 4 PRs and covers the changes under `c10`.
# Design
Intel GPU device is a wrapper of sycl device on which kernels can be executed. In our design, we will maintain a sycl device pool containing all the GPU devices of the current machine, and manage the status of the device pool by PyTorch. The thread local safe is considered in this design. The corresponding C++ files related to `Device` will be placed in c10/xpu folder. And we provide the c10 device runtime APIs, like
- `c10::xpu::device_count`
- `c10::xpu::set_device`
- ...
# Additional Context
In our plan, 4 PRs should be submitted to PyTorch for `Device`:
1. for c10
2. for aten
3. for python frontend
4. for lazy initialization shared with CUDA
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116019
Approved by: https://github.com/gujinghui, https://github.com/jgong5, https://github.com/EikanWang, https://github.com/malfet
# Motivation
As mentioned in [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842), The first runtime component we would like to upstream is `Device` which contains the device management functions of Intel GPU's runtime. To facilitate the code review, we split the code changes into 4 PRs. This is one of the 4 PRs and covers the changes under `c10`.
# Design
Intel GPU device is a wrapper of sycl device on which kernels can be executed. In our design, we will maintain a sycl device pool containing all the GPU devices of the current machine, and manage the status of the device pool by PyTorch. The thread local safe is considered in this design. The corresponding C++ files related to `Device` will be placed in c10/xpu folder. And we provide the c10 device runtime APIs, like
- `c10::xpu::device_count`
- `c10::xpu::set_device`
- ...
# Additional Context
In our plan, 4 PRs should be submitted to PyTorch for `Device`:
1. for c10
2. for aten
3. for python frontend
4. for lazy initialization shared with CUDA
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116019
Approved by: https://github.com/gujinghui, https://github.com/jgong5, https://github.com/EikanWang, https://github.com/malfet
