pytorch

19162083 - Improved perfs for vectorized bilinear interpolate cpu uint8 RGB-case (channels last) (#96848)

Improved perfs for vectorized bilinear interpolate cpu uint8 RGB-case (channels last) (#96848) ## Description - Based on https://github.com/pytorch/pytorch/pull/96651 - Improved perfs for vectorized **bilinear** interpolate uint8 RGB-case, **channels last** - unified RGB and RGBA processing code such that RGB input is not copied into RGBA - Performances are more close to Pillow-SIMD (labeled as `Pillow (9.0.0.post1)` in the results) - RGBA case perfs are the same after refactoring (see Source link below) - Fixed mem pointer alignment, added more comments (reviews from #96651) ## Results - `Pillow (9.0.0.post1)` == Pillow-SIMD ``` [-------------------------------------------------------------------------------------------------- Resize -------------------------------------------------------------------------------------------------] | Pillow (9.0.0.post1) | torch (2.1.0a0+gitd6e220c) PR | torch (2.1.0a0+git2b75955) nightly | Speed-up: PR vs nightly 1 threads: -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 3 torch.uint8 channels_last bilinear (256, 256) -> (32, 32) aa=True | 38.674 (+-0.323) | 57.591 (+-0.244) | 131.033 (+-1.448) | 2.275 (+-0.000) 3 torch.uint8 channels_last bilinear (256, 256) -> (32, 32) aa=False | | 39.471 (+-0.166) | 113.911 (+-1.736) | 2.886 (+-0.000) 3 torch.uint8 channels_last bilinear (256, 256) -> (224, 224) aa=True | 128.512 (+-1.916) | 161.592 (+-1.242) | 299.679 (+-2.099) | 1.855 (+-0.000) 3 torch.uint8 channels_last bilinear (256, 256) -> (224, 224) aa=False | | 150.994 (+-1.180) | 285.331 (+-1.919) | 1.890 (+-0.000) 3 torch.uint8 channels_last bilinear (256, 256) -> (320, 320) aa=True | 180.045 (+-2.223) | 220.581 (+-1.363) | 431.057 (+-3.536) | 1.954 (+-0.000) 3 torch.uint8 channels_last bilinear (256, 256) -> (320, 320) aa=False | | 219.391 (+-1.409) | 429.410 (+-3.620) | 1.957 (+-0.000) 3 torch.uint8 channels_last bilinear (520, 520) -> (32, 32) aa=True | 113.911 (+-1.024) | 129.457 (+-1.295) | 459.610 (+-13.322) | 3.550 (+-0.000) 3 torch.uint8 channels_last bilinear (520, 520) -> (32, 32) aa=False | | 59.800 (+-0.199) | 400.015 (+-11.815) | 6.689 (+-0.000) 3 torch.uint8 channels_last bilinear (520, 520) -> (224, 224) aa=True | 283.050 (+-2.664) | 339.143 (+-1.209) | 683.555 (+-4.466) | 2.016 (+-0.000) 3 torch.uint8 channels_last bilinear (520, 520) -> (224, 224) aa=False | | 250.601 (+-1.236) | 603.545 (+-2.644) | 2.408 (+-0.000) 3 torch.uint8 channels_last bilinear (712, 712) -> (32, 32) aa=True | 186.723 (+-2.213) | 199.960 (+-1.343) | 860.867 (+-21.763) | 4.305 (+-0.000) 3 torch.uint8 channels_last bilinear (712, 712) -> (32, 32) aa=False | | 79.188 (+-0.261) | 703.019 (+-25.805) | 8.878 (+-0.000) 3 torch.uint8 channels_last bilinear (712, 712) -> (224, 224) aa=True | 412.353 (+-4.476) | 462.230 (+-1.983) | 1101.673 (+-49.299) | 2.383 (+-0.000) 3 torch.uint8 channels_last bilinear (712, 712) -> (224, 224) aa=False | | 327.973 (+-1.852) | 941.062 (+-5.549) | 2.869 (+-0.000) 3 torch.uint8 channels_last bilinear (64, 64) -> (224, 224) aa=True | 61.191 (+-0.926) | 80.795 (+-0.518) | 160.853 (+-1.506) | 1.991 (+-0.000) 3 torch.uint8 channels_last bilinear (224, 224) -> (270, 268) aa=True | 134.488 (+-2.129) | 169.147 (+-1.324) | 327.343 (+-2.846) | 1.935 (+-0.000) 3 torch.uint8 channels_last bilinear (256, 256) -> (1024, 1024) aa=True | 1037.045 (+-24.982) | 938.623 (+-9.010) | 2603.360 (+-20.530) | 2.774 (+-0.000) 3 torch.uint8 channels_last bilinear (224, 224) -> (64, 64) aa=True | 52.792 (+-0.613) | 73.692 (+-0.264) | 131.829 (+-1.333) | 1.789 (+-0.000) 3 torch.uint8 channels_last bilinear (270, 268) -> (224, 224) aa=True | 139.596 (+-1.944) | 173.778 (+-1.039) | 320.063 (+-2.562) | 1.842 (+-0.000) 3 torch.uint8 channels_last bilinear (1024, 1024) -> (256, 256) aa=True | 690.132 (+-10.946) | 772.758 (+-2.864) | 2036.860 (+-36.109) | 2.636 (+-0.000) 3 torch.uint8 channels_last bilinear (64, 64) -> (224, 224) aa=False | | 78.747 (+-0.799) | 158.479 (+-1.702) | 2.013 (+-0.000) 3 torch.uint8 channels_last bilinear (224, 224) -> (270, 268) aa=False | | 167.046 (+-1.077) | 322.104 (+-2.764) | 1.928 (+-0.000) 3 torch.uint8 channels_last bilinear (256, 256) -> (1024, 1024) aa=False | | 918.967 (+-5.251) | 2611.388 (+-29.917) | 2.842 (+-0.000) 3 torch.uint8 channels_last bilinear (224, 224) -> (64, 64) aa=False | | 55.336 (+-0.251) | 113.869 (+-1.243) | 2.058 (+-0.000) 3 torch.uint8 channels_last bilinear (270, 268) -> (224, 224) aa=False | | 156.505 (+-1.095) | 299.861 (+-2.710) | 1.916 (+-0.000) 3 torch.uint8 channels_last bilinear (1024, 1024) -> (256, 256) aa=False | | 514.344 (+-1.905) | 1776.796 (+-19.660) | 3.454 (+-0.000) ``` Note: There is no perf regression for other case. There some cases (see Source below) with small speed-ups, for the rest it is roughly around 1.0 +/- 0.1 which may be attributed to noisy measurements ... [Source](https://gist.github.com/vfdev-5/1c0778904a07ce40401306548b9525e8#file-20230329-181023-pr_vs_nightly-speedup-md) ## Context - https://github.com/pytorch/pytorch/pull/90771 Pull Request resolved: https://github.com/pytorch/pytorch/pull/96848 Approved by: https://github.com/NicolasHug, https://github.com/peterbell10