When clang-cl parses its command line arguments, it expects MSVC-style arguments (beggining with `/` such as `/WX`, `/MD`, etc.) to be provided, and clang-style arguments to be preceded by `-Xclang`, otherwise, the clang-style parameters are ignored as they are interpreted unrecognized compiler options.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148097
Approved by: https://github.com/jeffdaily
ACL is already built with PyTorch as a shared library when USE_MKLDNN_ACL is set.
Currently, it is only used indirectly in ATen via oneDNN for AArch64 targets. However there are cases where it makes sense to utilize ACL directly without oneDNN as an intermediary - e.g. quantization. See #145942, #147337, #146620.
This patch enables such use cases by exposing ACL to ATen
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148584
Approved by: https://github.com/malfet
# Motivation
Currently, Intel GPU is moving forward rapidly with the development of feature. We(Intel GPU) want an independent version control over oneDNN component so as to quickly adopt the optimization or bug fixing provided by oneDNN team.
This PR does not change the behaviors of other backends like Intel CPU, ARM. They can keep using the stable version contained in `third_party/ideep`.
# Detail
At compilation time, we will `git clone` oneDNN via URL `https://github.com/oneapi-src/oneDNN` and checkout to the tag/commit that Intel GPU backend prefers. This feature is supported by CMake `Externalproject_add` command.
Following is a build log example:
```bash
[11/60] Performing download step (git clone) for 'xpu_mkldnn_proj'
Cloning into 'xpu_mkldnn_proj'...
HEAD is now at 5e92240360 meta: updated citation file
[12/60] Performing update step for 'xpu_mkldnn_proj'
-- Already at requested tag: v3.7
[13/60] No patch step for 'xpu_mkldnn_proj'
```
The log demonstates that, we explicitly download the source files and checkout to a specific tag. The source file of oneDNN is located at `build/xpu_mkldnn_proj-prefix/src/xpu_mkldnn_proj`
# Runtime verification
Running UT for CPU
```bash
onednn_verbose,v1,info,oneDNN v3.7.0 (commit fc3f17ad469b8a6da7192ae12d32625faa509f1e)
onednn_verbose,v1,info,cpu,runtime:OpenMP,nthr:24
onednn_verbose,v1,info,cpu,isa:Intel AVX-512 with Intel DL Boost
onednn_verbose,v1,info,gpu,runtime:none
onednn_verbose,v1,info,graph,backend,0:dnnl_backend
onednn_verbose,v1,primitive,info,template:operation,engine
```
Runnint UT for Intel GPU
```bash
onednn_verbose,v1,info,oneDNN v3.7.0 (commit 5e9224036021433d2577548ed0539fe9a53256bc)
onednn_verbose,v1,info,cpu,runtime:threadpool,nthr:24
onednn_verbose,v1,info,cpu,isa:Intel AVX-512 with Intel DL Boost
onednn_verbose,v1,info,gpu,runtime:DPC++
onednn_verbose,v1,info,gpu,engine,sycl gpu device count:2
```
We can see that, Intel GPU would uses commit `5e922` (tag v3.7), while CPU uses `fc3f17`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147926
Approved by: https://github.com/EikanWang
Co-authored-by: leizhenyuan <zhenyuan.lei@intel.com>
According to the [APL documentation](https://developer.arm.com/documentation/101004/2404/General-information/Arm-Performance-Libraries-example-programs), libraries ending with _mp are OpenMP multi-threaded libraries.
When a project is compiled with MSVC and the -openmp flag, the vcomp library (Visual C++ implementation of OpenMP) is used for runtime calls.
However, the current APL implementation uses the libomp.dll (LLVM) variant.
As a result, there are unexpected behaviors at runtime.
---
For Example:
```python
import torch
# Create a sparse tensor
# Input (Sparse Tensor):
# [[0, 1],
# [1, 0]]
indices = torch.tensor([[0, 1], [1, 0]])
values = torch.tensor([1, 1], dtype=torch.float32)
size = torch.Size([2, 2])
sparse_tensor = torch.sparse_coo_tensor(indices, values, size)
# Convert sparse tensor to dense tensor
dense_tensor = sparse_tensor.to_dense()
# Expected Output (Dense Tensor):
# [[0, 1],
# [1, 0]]
print("\nDense Tensor:")
print(dense_tensor)
```
However, it prints unexpected outputs such as:
```python
# [[0, 11],
# [10, 0]]
```
The issue arises because the following code does not function as expected at runtime:
https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/ParallelOpenMP.h#L30
```c++
// returns 1 , however since OpenMP is enabled it should return total number of threads
int64_t num_threads = omp_get_num_threads();
```
---
In the runtime, loading multiple OpenMP libraries (in this case `libomp` and `vcomp`) is causing unexpected behaviours.
So, we've changed libraries from `_mp` to non `_mp` versions and we used `vcomp` for OpenMP calls.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145215
Approved by: https://github.com/ozanMSFT, https://github.com/malfet
Co-authored-by: Ozan Aydin <148207261+ozanMSFT@users.noreply.github.com>
# 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
