pytorch

3a3e2002 - [Quant] Add unified x86 quant backend (#84329)

[Quant] Add unified x86 quant backend (#84329) ## Description Implement unified quantization backend 'X86' for x86 platforms. It combines the advantages of FBGEMM and ONEDNN. It selects kernels during weight prepacking and hide the details from end users. It will be the default backend in place of FBGEMM. For details, please refer to this RFC: [[RFC] Unified quantization backend for x86 CPU platforms](https://github.com/pytorch/pytorch/issues/83888) ## Validation **Correctness** Covered by UT **Accuracy** By running torchvision models on imagenet, no accuracy difference is found between FBGEMM and the unified X86 backend: [torchvision_accuracy_comparison_fbgemm_vs_x86.xlsx](https://github.com/pytorch/pytorch/files/9598114/torchvision_accuracy_comparison_fbgemm_vs_x86.xlsx) **Performance** Depends on https://github.com/pytorch/pytorch/pull/84470 which improves performance. For early PoC results, please refer to https://github.com/pytorch/pytorch/files/9399202/unified_qengine_poc_performance_bechmark.xlsx With the two PRs combined, we collected some data on Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz Method: Run multi-instances with 4 cores per instance on whole socket. Using JeMalloc and Intel OMP. Models/throughput | fbgemm | x86 | improvement -- | -- | -- | -- wide_resnet101_2 | 173.5675 | 241.815 | 39.32% resnext101_32x8d | 174.365 | 339.8175 | 94.89% resnet50 | 573.155 | 1174.14 | 104.86% vgg19_bn | 260.335 | 337.92 | 29.80% vgg19 | 257.935 | 333.265 | 29.21% inception_v3 | 601.1175 | 1309.33 | 117.82% densenet161 | 296.645 | 435.5625 | 46.83% mnasnet1_0 | 1216.7 | 4057.515 | 233.49% squeezenet1_0 | 1220.085 | 5153.3875 | 322.38% alexnet | 2294.91 | 2624.6375 | 14.37% fbnetc_100 | 976.2825 | 3110.1825 | 218.57% shufflenet_v2_x0_5 | 1555.76 | 3026.125 | 94.51% spnasnet_100 | 1059.065 | 3502.0975 | 230.68% pytorch-unet | 192.76 | 246.77 | 28.02% acgan | 257.32 | 333.7325 | 29.70% cgan | 7790.6925 | 7803.1025 | 0.16% sgan | 257.565 | 338.8875 | 31.57% se_resnet50 | 492.3725 | 916.5175 | 86.14% vggm | 300.2875 | 316.2075 | 5.30% Environment: - PyTorch version: 1.13.0a0+gitcdd625b - Is debug build: False - CUDA used to build PyTorch: None - ROCM used to build PyTorch: N/A - OS: Ubuntu 20.04.3 LTS (x86_64) - GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 - Clang version: Could not collect - CMake version: version 3.22.5 - Libc version: glibc-2.31 - Python version: 3.9.12 (main, Jun 1 2022, 11:38:51) [GCC 7.5.0] (64-bit runtime) - Python platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31 - Is CUDA available: False - CUDA runtime version: No CUDA - GPU models and configuration: No CUDA - Nvidia driver version: No CUDA - cuDNN version: No CUDA - HIP runtime version: N/A - MIOpen runtime version: N/A - Is XNNPACK available: True Versions of relevant libraries: - [pip3] intel-extension-for-pytorch==1.13.0+cpu - [pip3] numpy==1.23.3 - [pip3] pytorch-widedeep==0.3.7 - [pip3] torch==1.13.0a0+git48b423b - [pip3] torchvision==0.14.0a0+ebb68f3 - [conda] blas 1.0 mkl - [conda] intel-extension-for-pytorch 1.13.0+cpu pypi_0 pypi - [conda] mkl 2021.4.0 h06a4308_640 - [conda] mkl-include 2022.1.0 pypi_0 pypi - [conda] mkl-service 2.4.0 py39h7f8727e_0 - [conda] mkl-static 2022.1.0 pypi_0 pypi - [conda] mkl_fft 1.3.1 py39hd3c417c_0 - [conda] mkl_random 1.2.2 py39h51133e4_0 - [conda] numpy 1.23.3 pypi_0 pypi - [conda] numpy-base 1.22.3 py39hf524024_0 - [conda] torch 1.13.0a0+git48b423b pypi_0 pypi - [conda] torchvision 0.14.0a0+ebb68f3 pypi_0 pypi Pull Request resolved: https://github.com/pytorch/pytorch/pull/84329 Approved by: https://github.com/jerryzh168