pytorch
1c017f0c - Migrate max and min (binary) from TH to ATen. (#30851)
Commit
4 years agoMigrate max and min (binary) from TH to ATen. (#30851)
Summary:
TH implementation will be removed after the unary max and min are
migrated.
Benchmark: (Debian 10, Release build, gcc 7.4, no turbo)
```python
import timeit
for device in ('cpu', 'cuda'):
print(f'device: {device}')
for op in ('max', 'min'):
for dtype in ('torch.double', 'torch.float', 'torch.int16',
'torch.int32', 'torch.int64'):
for n, t in [(10_000, 200000),
(100_000, 20000)]:
print(f'torch.{op}(a, b), numel() == {n} for {t} times,
dtype={dtype}')
print(timeit.timeit(f'torch.{op}(a)' +
(';torch.cuda.synchronize()' if device == 'cuda' else ''),
setup=f'import torch; a =
torch.arange({n}, dtype={dtype}); b = torch.ones({n}, 0, dtype={dtype})
* ({n} / 2)', number=t))
print()
```
Before:
```
device: cpu
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.double
2.241763713000182
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.double
1.7138833169992722
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.float
2.2183356810000987
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.float
1.7031846980007685
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int16
1.7704679510006827
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int16
1.289198366999699
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int32
1.7937613740014058
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int32
1.2930124340000475
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int64
1.8032857640009752
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int64
1.2908709189996443
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.double
1.8829010000008566
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.double
1.2994690759987861
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.float
1.8037853410005482
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.float
1.2929310759991495
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int16
1.8075240359994496
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int16
1.2932477679987642
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int32
1.7868400779989315
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int32
1.2885970789993735
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int64
1.8389664830010588
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int64
1.29402057399966
device: cuda
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.double
4.787109836999662
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.double
1.842438002999188
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.float
3.429616614999759
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.float
1.835390076999829
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int16
2.940423873000327
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int16
1.4108991760003846
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int32
2.9318018840003788
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int32
1.4168134739993548
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int64
2.9610764919998473
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int64
1.4189234130008117
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.double
2.960172712999338
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.double
1.4162539499993727
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.float
2.8985912560001452
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.float
1.4113489299998037
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int16
2.9160250799995993
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int16
1.4128787690005993
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int32
2.8806865219994506
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int32
1.4086357010000938
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int64
2.9362181240012433
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int64
1.4151225870009512
```
After:
```
device: cpu
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.double
2.2685823729998447
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.double
1.72004808300062
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.float
2.212242640000113
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.float
1.7089235590001408
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int16
1.7767087259999244
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int16
1.2916517639996528
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int32
1.8265984959998605
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int32
1.3002885240002797
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int64
1.8084679720004715
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int64
1.3012119999993956
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.double
1.8800218449996464
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.double
1.3060645710002063
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.float
2.4905043950002437
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.float
1.9126290209997023
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int16
1.7972335520007618
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int16
1.2918074379995232
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int32
1.8047651860006226
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int32
1.2992197730000044
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int64
1.8526509560006161
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int64
1.3030709570002728
device: cuda
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.double
4.700986622000528
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.double
1.8415469050005413
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.float
3.3051693249999516
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.float
1.8321999460004008
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int16
2.8086475109994353
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int16
1.405110773999695
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int32
2.913458047999484
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int32
1.4236377289998927
torch.max(a, b), numel() == 10000 for 200000 times, dtype=torch.int64
2.9386842409994642
torch.max(a, b), numel() == 100000 for 20000 times, dtype=torch.int64
1.4230227469997772
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.double
3.0341797270002644
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.double
1.4289592409995748
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.float
3.6091147850002017
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.float
2.036691903999781
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int16
2.8256167649997224
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int16
1.4078955400000268
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int32
2.8631781489993955
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int32
1.4210130069996012
torch.min(a, b), numel() == 10000 for 200000 times, dtype=torch.int64
3.0112479260005784
torch.min(a, b), numel() == 100000 for 20000 times, dtype=torch.int64
1.4297719679998409
```
Solve partly https://github.com/pytorch/pytorch/issues/24594 #24595
Close https://github.com/pytorch/pytorch/issues/25016
Continuing https://github.com/pytorch/pytorch/issues/27185
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30851
Differential Revision: D19515694
Pulled By: ezyang
fbshipit-source-id: 1764897f912d6ae24b0c361f19a1aacf96e0826e
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