Migrate sinh and sinh_ from the TH to Aten (CUDA)
#28527
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Benchmark (Debian Buster, CUDA 9.2, Quadro P400, turbo off, Release):
```python
import timeit
for n, t in [(10_000, 20000),
(100_000, 20000)]:
for dtype in ('torch.half', 'torch.float', 'torch.double'):
print(f'torch.sinh(a) a.numel() == {n} for {t} times {dtype}')
print(timeit.timeit(f'torch.sinh(a); torch.cuda.synchronize()', setup=f'import torch; a=torch.arange({n}, dtype={dtype}, device="cuda")', number=t))
```
Before:
```
torch.sinh(a) a.numel() == 10000 for 20000 times torch.half
0.3807680979998622
torch.sinh(a) a.numel() == 10000 for 20000 times torch.float
0.37430476099962107
torch.sinh(a) a.numel() == 10000 for 20000 times torch.double
1.0580407639999976
torch.sinh(a) a.numel() == 100000 for 20000 times torch.half
0.7996397469996737
torch.sinh(a) a.numel() == 100000 for 20000 times torch.float
1.010930432999885
torch.sinh(a) a.numel() == 100000 for 20000 times torch.double
7.310400856999877
```
After:
```
torch.sinh(a) a.numel() == 10000 for 20000 times torch.half
0.3720399889998589
torch.sinh(a) a.numel() == 10000 for 20000 times torch.float
0.3694016069994177
torch.sinh(a) a.numel() == 10000 for 20000 times torch.double
1.0551542660004998
torch.sinh(a) a.numel() == 100000 for 20000 times torch.half
0.7431191599998783
torch.sinh(a) a.numel() == 100000 for 20000 times torch.float
0.9953043630002867
torch.sinh(a) a.numel() == 100000 for 20000 times torch.double
7.3146168890007175
```
Close #24628
[ghstack-poisoned]
This was referenced Oct 23, 2019
xuhdev
added a commit
that referenced
this pull request
Oct 23, 2019
Benchmark (Debian Buster, CUDA 9.2, Quadro P400, turbo off, Release):
```python
import timeit
for n, t in [(10_000, 20000),
(100_000, 20000)]:
for dtype in ('torch.half', 'torch.float', 'torch.double'):
print(f'torch.sinh(a) a.numel() == {n} for {t} times {dtype}')
print(timeit.timeit(f'torch.sinh(a); torch.cuda.synchronize()', setup=f'import torch; a=torch.arange({n}, dtype={dtype}, device="cuda")', number=t))
```
Before:
```
torch.sinh(a) a.numel() == 10000 for 20000 times torch.half
0.3807680979998622
torch.sinh(a) a.numel() == 10000 for 20000 times torch.float
0.37430476099962107
torch.sinh(a) a.numel() == 10000 for 20000 times torch.double
1.0580407639999976
torch.sinh(a) a.numel() == 100000 for 20000 times torch.half
0.7996397469996737
torch.sinh(a) a.numel() == 100000 for 20000 times torch.float
1.010930432999885
torch.sinh(a) a.numel() == 100000 for 20000 times torch.double
7.310400856999877
```
After:
```
torch.sinh(a) a.numel() == 10000 for 20000 times torch.half
0.3720399889998589
torch.sinh(a) a.numel() == 10000 for 20000 times torch.float
0.3694016069994177
torch.sinh(a) a.numel() == 10000 for 20000 times torch.double
1.0551542660004998
torch.sinh(a) a.numel() == 100000 for 20000 times torch.half
0.7431191599998783
torch.sinh(a) a.numel() == 100000 for 20000 times torch.float
0.9953043630002867
torch.sinh(a) a.numel() == 100000 for 20000 times torch.double
7.3146168890007175
```
Close #24628
ghstack-source-id: 501674a
Pull Request resolved: #28527
ifedan
approved these changes
Oct 24, 2019
VitalyFedyunin
approved these changes
Oct 24, 2019
zdevito
pushed a commit
to zdevito/ATen
that referenced
this pull request
Oct 30, 2019
Summary: Pull Request resolved: pytorch/pytorch#28527 Benchmark (Debian Buster, CUDA 9.2, Quadro P400, turbo off, Release, gcc 7.4): ```python import timeit for n, t in [(10_000, 20000), (100_000, 20000)]: for dtype in ('torch.half', 'torch.float', 'torch.double'): print(f'torch.sinh(a) a.numel() == {n} for {t} times {dtype}') print(timeit.timeit(f'torch.sinh(a); torch.cuda.synchronize()', setup=f'import torch; a=torch.arange({n}, dtype={dtype}, device="cuda")', number=t)) ``` Before: ``` torch.sinh(a) a.numel() == 10000 for 20000 times torch.half 0.3807680979998622 torch.sinh(a) a.numel() == 10000 for 20000 times torch.float 0.37430476099962107 torch.sinh(a) a.numel() == 10000 for 20000 times torch.double 1.0580407639999976 torch.sinh(a) a.numel() == 100000 for 20000 times torch.half 0.7996397469996737 torch.sinh(a) a.numel() == 100000 for 20000 times torch.float 1.010930432999885 torch.sinh(a) a.numel() == 100000 for 20000 times torch.double 7.310400856999877 ``` After: ``` torch.sinh(a) a.numel() == 10000 for 20000 times torch.half 0.3720399889998589 torch.sinh(a) a.numel() == 10000 for 20000 times torch.float 0.3694016069994177 torch.sinh(a) a.numel() == 10000 for 20000 times torch.double 1.0551542660004998 torch.sinh(a) a.numel() == 100000 for 20000 times torch.half 0.7431191599998783 torch.sinh(a) a.numel() == 100000 for 20000 times torch.float 0.9953043630002867 torch.sinh(a) a.numel() == 100000 for 20000 times torch.double 7.3146168890007175 ``` Close #24628 Test Plan: Imported from OSS Differential Revision: D18124732 Pulled By: VitalyFedyunin fbshipit-source-id: 054b0c0884ac12de2dd1a92c5de916aaf047f9e9
|
@VitalyFedyunin merged this pull request in e0009fd. |
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Stack from ghstack:
sinhandsinh_from the TH to Aten (CUDA) #28527 Migratesinhandsinh_from the TH to Aten (CUDA)asinandasin_from the TH to Aten (CUDA) #28482 Migrateasinandasin_from the TH to Aten (CUDA)sinandsin_from the TH to Aten (CUDA) #28237 Migratesinandsin_from the TH to Aten (CUDA)Benchmark (Debian Buster, CUDA 9.2, Quadro P400, turbo off, Release, gcc 7.4):
Before:
After:
Close #24628
Differential Revision: D18124732