We did a lot of optimization for PyTorch Windows, and we got good progress of it. But still some models have performance gap between PyTorch Windows and PyTorch Linux. Ref: https://pytorch.org/blog/performance-boost-windows/#conclusion
From the blog conclusion, we found the `ResNet50` is typical case of it.
Let's focus on the `ResNet50`, and collect the profiling log:
```cmd
(nightly) D:\xu_git\dnnl_cb>python test_script_resnet50.py
--------------------------------- ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls
--------------------------------- ------------ ------------ ------------ ------------ ------------ ------------
model_inference 3.91% 682.427ms 100.00% 17.448s 17.448s 1
aten::conv2d 0.18% 30.906ms 64.79% 11.305s 2.133ms 5300
aten::convolution 0.45% 78.031ms 64.62% 11.275s 2.127ms 5300
aten::_convolution 0.30% 51.670ms 64.17% 11.196s 2.113ms 5300
aten::mkldnn_convolution 63.58% 11.093s 63.87% 11.145s 2.103ms 5300
aten::batch_norm 0.13% 23.536ms 20.10% 3.506s 661.580us 5300
aten::_batch_norm_impl_index 0.28% 49.486ms 19.96% 3.483s 657.139us 5300
aten::native_batch_norm 19.26% 3.360s 19.64% 3.427s 646.615us 5300
aten::max_pool2d 0.01% 1.038ms 5.84% 1.018s 10.181ms 100
aten::max_pool2d_with_indices 5.83% 1.017s 5.83% 1.017s 10.171ms 100
aten::add_ 3.38% 588.907ms 3.38% 588.907ms 85.349us 6900
aten::relu_ 0.35% 60.358ms 1.67% 292.155ms 59.624us 4900
aten::clamp_min_ 1.33% 231.797ms 1.33% 231.797ms 47.306us 4900
aten::empty 0.46% 80.195ms 0.46% 80.195ms 1.513us 53000
aten::linear 0.01% 927.300us 0.23% 39.353ms 393.532us 100
aten::addmm 0.20% 35.379ms 0.21% 37.016ms 370.155us 100
aten::empty_like 0.12% 20.455ms 0.17% 29.976ms 5.656us 5300
aten::as_strided_ 0.11% 18.830ms 0.11% 18.830ms 3.553us 5300
aten::adaptive_avg_pool2d 0.00% 419.900us 0.08% 14.265ms 142.647us 100
aten::mean 0.01% 1.737ms 0.08% 13.845ms 138.448us 100
aten::sum 0.05% 8.113ms 0.05% 8.648ms 86.479us 100
aten::resize_ 0.03% 5.182ms 0.03% 5.182ms 0.978us 5300
aten::div_ 0.01% 1.445ms 0.02% 3.460ms 34.600us 100
aten::to 0.00% 337.000us 0.01% 2.015ms 20.154us 100
aten::_to_copy 0.01% 977.500us 0.01% 1.678ms 16.784us 100
aten::copy_ 0.01% 1.474ms 0.01% 1.474ms 7.371us 200
aten::t 0.00% 775.900us 0.01% 1.410ms 14.104us 100
aten::flatten 0.00% 420.900us 0.01% 1.311ms 13.106us 100
aten::view 0.01% 889.700us 0.01% 889.700us 8.897us 100
aten::transpose 0.00% 410.700us 0.00% 634.500us 6.345us 100
aten::expand 0.00% 496.800us 0.00% 566.800us 5.668us 100
aten::fill_ 0.00% 534.800us 0.00% 534.800us 5.348us 100
aten::as_strided 0.00% 293.800us 0.00% 293.800us 1.469us 200
aten::empty_strided 0.00% 241.700us 0.00% 241.700us 2.417us 100
aten::resolve_conj 0.00% 54.800us 0.00% 54.800us 0.274us 200
--------------------------------- ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 17.448s
Execution time: 20.02380895614624
```
We found the major kernel consume CPU resource is `aten::mkldnn_convolution`. It was dispatched to `MKLDNN`.
Acturally, we had optimized memory allocation via integrated mimalloc to pytorch C10 module. It helps PyTorch Windows boost a lot, but it does not cover `MKL` and `MKLDNN`'s intermediary temporary memory.
We still have potential to improve PyTorch Windows performance via optimize `MKL` and `MKLDNN`'s intermediary temporary memory.
So, I discussed with Intel MKL team, and get a method to register high performance memory allocation API to MKL, and it would help MKL to boost memory performance. Please check the online document: https://www.intel.com/content/www/us/en/docs/onemkl/developer-guide-windows/2023-0/redefining-memory-functions.html
This PR is optimize MKL memory alloction performance on Windows, via register mi_malloc to MKL. PR Changes:
1. Add cmake option: `USE_MIMALLOC_ON_MKL`, It is sub-option of `USE_MIMALLOC`.
2. Wrap and export mi_malloc APIs in C10, when `USE_MIMALLOC_ON_MKL` is `ON`.
3. Add MklAllocationHelp.cpp to register allocation APIs to MKL, when `USE_MIMALLOC_ON_MKL` is `ON`.
For `oneDNN`, it is still tracking in this proposal: https://github.com/oneapi-src/oneDNN/issues/1898
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138419
Approved by: https://github.com/jgong5, https://github.com/ezyang
## 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
Enable -Werror on s390x
Example of original issue on s390x:
https://github.com/pytorch/pytorch/actions/runs/11014606340/job/30585632704
Most of warnings are not specific to s390x, but specific to gcc-13 or gcc-14. To test it on s390x an image with gcc-13 is needed. For s390x it's tested for new regressions on every merge due to trunk workflow.
`-Wdangling-reference` produces either obviously false warnings or suspicious warnings, which on closer inspection look plausibly safe.
`-Wredundant-move` with new gcc complains about `std::move(...)` disabling copy elision. But removing `std::move(...)` makes used clang versions complain about copying objects when they could be moved. For now also disable it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136527
Approved by: https://github.com/malfet
Fixes#136440
**Issue:**
When building PyTorch in debug mode on aarch64 architecture using GCC, we encounter relocation errors due to the R_AARCH64_CALL26 relocation limit. This occurs because debug builds with -O0 optimization generate larger code sizes, potentially exceeding the range limit for these relocations.
**Fix:**
Apply -Og optimization instead of -O0 for aarch64 GCC debug builds. This slightly reduces code size while maintaining debuggability, bringing function calls back within the range of R_AARCH64_CALL26 relocations.
The fix is implemented by conditionally setting compiler and linker flags in CMakeLists.txt:
- For aarch64 GCC debug builds: use -Og
- For all other debug builds: retain -O0
This change affects only debug builds on aarch64 with GCC, leaving other configurations unchanged.
**Testing:**
Verified that the build succeeds without relocation errors on aarch64 systems with GCC in debug mode. Ensured that debugging information is still available and useful for debugging purposes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136990
Approved by: https://github.com/malfet
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.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>
# Motivation
Structured codegen is beneficial for easier decoupling tensor meta setting and kernel implementation. At present, XPU operators need to handle tensor metas in hand-written way.
We plan to leverage the codegen system for auto generate structured operators. This PR facilitate the `DispatchStub` support for Intel GPUs. Based on that, XPU operators would have possibility to register kernel functor to operator stubs.
This is a prerequisite of PR #130082, where we will modify the codegen system to generate XPU needed source files and headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130019
Approved by: https://github.com/EikanWang, https://github.com/gujinghui, https://github.com/albanD
This should prevent regressions like the ones fixed by https://github.com/pytorch/pytorch/pull/131204
- Remove global `-Wno-error=inconsistent-missing-override`
- Wrap offending includes (protobuf and asmjit) with `C10_DIAGNOSTIC_PUSH_AND_IGNORE` and `C10_DIAGNOSTIC_POP_AND_IGNORED`
- Add `override` keyword to `at::namespace::tunable::StreamTimer` and `LLVMCodeGenImpl`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131524
Approved by: https://github.com/atalman
This patch implements `with sdpa_kernel(SDPBackend.EFFICIENT_ATTENTION):` by reusing AOTriton's accelerated SDPA implementation
Known limitations:
- Only supports MI200/MI300X GPUs
- Does not support varlen
- Does not support `CausalVariant`
- Optional arguments `causal_diagonal` and `seqlen_k` in `_efficient_attention_forward/backward` must be null
- Does not work well with inductor's SDPA rewriter. The rewriter has been updated to only use math and flash attention on ROCM.
This PR also uses a different approach of installing AOTriton binary instead of building it from source in the base docker image. More details on motivation: https://github.com/pytorch/pytorch/pull/124885#issuecomment-2153229129
`PYTORCH_TEST_WITH_ROCM=1 PYTORCH_TESTING_DEVICE_ONLY_FOR="cuda" python test/test_transformers.py` yields "55028 passed, 20784 skipped" results with this change. [Previous result](https://hud.pytorch.org/pr/127528) of `test_transformers.py` was 0 error, 0 failure, 55229 skipped out of 75517 tests in total (the XML report does not contain total number of passed tests).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124885
Approved by: https://github.com/malfet
Summary:
Added USE_LITE_AOTI cmake flag, which is turned OFF by default.
When it is turned on, the AOTI sources (inductor_core_resources) are included when building lite interpreter
Test Plan:
```
ANDROID_ABI=arm64-v8a ./scripts/build_android.sh -DUSE_LITE_AOTI=ON
```
Differential Revision: D57394078
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126572
Approved by: https://github.com/malfet
