xiaobing.zhang b47e9b97a2 Add op bitwise_and (#31104) ... Summary: Refer to https://github.com/pytorch/pytorch/pull/25665, add `bitwise_and` operator. Benchmark script : ``` import timeit #for __and__ for n, t in [(10, 100000),(1000, 10000)]: print('__and__ (a.numel() == {}) for {} times'.format(n, t)) for device in ('cpu', 'cuda'): for dtype in ('torch.int8', 'torch.uint8', 'torch.int16', 'torch.int32', 'torch.int64'): print(f'device: {device}, dtype: {dtype}, {t} times', end='\t\t') print(timeit.timeit(f'a & b\nif "{device}" == "cuda": torch.cuda.synchronize()', setup=f'import torch; a = torch.randint(0, 10, ({n},), dtype = {dtype}, device="{device}"); b = torch.randint(0, 10, ({n},), dtype = {dtype}, device="{device}")', number=t)) #for __iand__ for n, t in [(10, 100000),(1000, 10000)]: print('__iand__ (a.numel() == {}) for {} times'.format(n, t)) for device in ('cpu', 'cuda'): for dtype in ('torch.int8', 'torch.uint8', 'torch.int16', 'torch.int32', 'torch.int64'): print(f'device: {device}, dtype: {dtype}, {t} times', end='\t\t') print(timeit.timeit(f'a & b\nif "{device}" == "cuda": torch.cuda.synchronize()', setup=f'import torch; a = torch.randint(0, 10, ({n},), dtype = {dtype}, device="{device}"); b = torch.tensor(5, dtype = {dtype}, device="{device}")', number=t)) ``` Device: **Tesla P100, skx-8180** Cuda verison: **9.0.176** Before: ``` __and__ (a.numel() == 10) for 100000 times device: cpu, dtype: torch.int8, 100000 times 0.1766007635742426 device: cpu, dtype: torch.uint8, 100000 times 0.17322628945112228 device: cpu, dtype: torch.int16, 100000 times 0.17650844901800156 device: cpu, dtype: torch.int32, 100000 times 0.17711848113685846 device: cpu, dtype: torch.int64, 100000 times 0.18240160401910543 device: cuda, dtype: torch.int8, 100000 times 1.273967768996954 device: cuda, dtype: torch.uint8, 100000 times 1.2778537990525365 device: cuda, dtype: torch.int16, 100000 times 1.2753686187788844 device: cuda, dtype: torch.int32, 100000 times 1.2797665279358625 device: cuda, dtype: torch.int64, 100000 times 1.2933144550770521 __and__ (a.numel() == 1000) for 10000 times device: cpu, dtype: torch.int8, 10000 times 0.031139614060521126 device: cpu, dtype: torch.uint8, 10000 times 0.03091452084481716 device: cpu, dtype: torch.int16, 10000 times 0.022756479680538177 device: cpu, dtype: torch.int32, 10000 times 0.025045674294233322 device: cpu, dtype: torch.int64, 10000 times 0.024164282716810703 device: cuda, dtype: torch.int8, 10000 times 0.12820732593536377 device: cuda, dtype: torch.uint8, 10000 times 0.12775669433176517 device: cuda, dtype: torch.int16, 10000 times 0.12697868794202805 device: cuda, dtype: torch.int32, 10000 times 0.12832533661276102 device: cuda, dtype: torch.int64, 10000 times 0.1280576130375266 __iand__ (a.numel() == 10) for 100000 times device: cpu, dtype: torch.int8, 100000 times 0.3687064303085208 device: cpu, dtype: torch.uint8, 100000 times 0.36253443732857704 device: cpu, dtype: torch.int16, 100000 times 0.362891579978168 device: cpu, dtype: torch.int32, 100000 times 0.37680106051266193 device: cpu, dtype: torch.int64, 100000 times 0.3689364707097411 device: cuda, dtype: torch.int8, 100000 times 1.419940729625523 device: cuda, dtype: torch.uint8, 100000 times 1.4247053815051913 device: cuda, dtype: torch.int16, 100000 times 1.4191444097086787 device: cuda, dtype: torch.int32, 100000 times 1.4305962566286325 device: cuda, dtype: torch.int64, 100000 times 1.4567416654899716 __iand__ (a.numel() == 1000) for 10000 times device: cpu, dtype: torch.int8, 10000 times 0.06224383972585201 device: cpu, dtype: torch.uint8, 10000 times 0.06205617543309927 device: cpu, dtype: torch.int16, 10000 times 0.05016433447599411 device: cpu, dtype: torch.int32, 10000 times 0.05216377507895231 device: cpu, dtype: torch.int64, 10000 times 0.06139362137764692 device: cuda, dtype: torch.int8, 10000 times 0.14827249851077795 device: cuda, dtype: torch.uint8, 10000 times 0.14801877550780773 device: cuda, dtype: torch.int16, 10000 times 0.14952312968671322 device: cuda, dtype: torch.int32, 10000 times 0.14999118447303772 device: cuda, dtype: torch.int64, 10000 times 0.14951884001493454 ``` After: ``` __and__ (a.numel() == 10) for 100000 times device: cpu, dtype: torch.int8, 100000 times 0.23157884553074837 device: cpu, dtype: torch.uint8, 100000 times 0.23063660878688097 device: cpu, dtype: torch.int16, 100000 times 0.23005440644919872 device: cpu, dtype: torch.int32, 100000 times 0.23748818412423134 device: cpu, dtype: torch.int64, 100000 times 0.24106105230748653 device: cuda, dtype: torch.int8, 100000 times 1.4394256137311459 device: cuda, dtype: torch.uint8, 100000 times 1.4436759827658534 device: cuda, dtype: torch.int16, 100000 times 1.4631587155163288 device: cuda, dtype: torch.int32, 100000 times 1.459101552143693 device: cuda, dtype: torch.int64, 100000 times 1.4784048134461045 __and__ (a.numel() == 1000) for 10000 times device: cpu, dtype: torch.int8, 10000 times 0.028442862443625927 device: cpu, dtype: torch.uint8, 10000 times 0.028130197897553444 device: cpu, dtype: torch.int16, 10000 times 0.025318274274468422 device: cpu, dtype: torch.int32, 10000 times 0.02519288007169962 device: cpu, dtype: torch.int64, 10000 times 0.028299466706812382 device: cuda, dtype: torch.int8, 10000 times 0.14342594426125288 device: cuda, dtype: torch.uint8, 10000 times 0.145280827768147 device: cuda, dtype: torch.int16, 10000 times 0.14673697855323553 device: cuda, dtype: torch.int32, 10000 times 0.14499565307050943 device: cuda, dtype: torch.int64, 10000 times 0.14582364354282618 __iand__ (a.numel() == 10) for 100000 times device: cpu, dtype: torch.int8, 100000 times 0.25548241566866636 device: cpu, dtype: torch.uint8, 100000 times 0.2552562616765499 device: cpu, dtype: torch.int16, 100000 times 0.25905191246420145 device: cpu, dtype: torch.int32, 100000 times 0.26635489892214537 device: cpu, dtype: torch.int64, 100000 times 0.26269810926169157 device: cuda, dtype: torch.int8, 100000 times 1.485458506271243 device: cuda, dtype: torch.uint8, 100000 times 1.4742380809038877 device: cuda, dtype: torch.int16, 100000 times 1.507783885113895 device: cuda, dtype: torch.int32, 100000 times 1.4926990242674947 device: cuda, dtype: torch.int64, 100000 times 1.519851053133607 __iand__ (a.numel() == 1000) for 10000 times device: cpu, dtype: torch.int8, 10000 times 0.03425929415971041 device: cpu, dtype: torch.uint8, 10000 times 0.03293587639927864 device: cpu, dtype: torch.int16, 10000 times 0.029559112153947353 device: cpu, dtype: torch.int32, 10000 times 0.030915481969714165 device: cpu, dtype: torch.int64, 10000 times 0.03292469773441553 device: cuda, dtype: torch.int8, 10000 times 0.15792148280888796 device: cuda, dtype: torch.uint8, 10000 times 0.16000914946198463 device: cuda, dtype: torch.int16, 10000 times 0.1600684942677617 device: cuda, dtype: torch.int32, 10000 times 0.16162546630948782 device: cuda, dtype: torch.int64, 10000 times 0.1629159888252616 ``` Fix https://github.com/pytorch/pytorch/issues/24508, https://github.com/pytorch/pytorch/issues/24509, https://github.com/pytorch/pytorch/issues/24655, https://github.com/pytorch/pytorch/issues/24656. Pull Request resolved: https://github.com/pytorch/pytorch/pull/31104 Differential Revision: D18938930 Pulled By: VitalyFedyunin fbshipit-source-id: a77e805a0b84e8ace16c6e648c2f67dad44f2e44		2020-01-03 10:32:36 -08:00
..
caffe2	Fix interpreter lines for files with python2-only syntax.	2019-07-09 10:51:43 -07:00
cpp	Docs: c++11 -> c++14 (#30530)	2019-12-17 14:09:02 -08:00
source	Add op bitwise_and (#31104)	2020-01-03 10:32:36 -08:00
.gitignore	.gitignore for the docs folder	2019-10-08 12:18:30 -07:00
libtorch.rst	Update libtorch docs (#21150)	2019-05-30 12:49:56 -07:00
make.bat	Convert all tabs to spaces, add CI. (#18959)	2019-04-09 08:12:26 -07:00
Makefile	Rewrite http://pytorch.org -> https://pytorch.org throughout project (#12636)	2018-10-15 13:03:27 -07:00
requirements.txt	Switch to official releases of katex and update doc for installing katex. (#27758)	2019-10-11 17:20:06 -07:00