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I don't see a synchronization call ( |
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@ngimel Thanks for your remind. I already changed the code and result. |
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ROCm build OOMed: |
ezyang
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Nice! Inverse index algorithm is not "great" but it seems to be good enough.
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@pytorchbot retest this please |
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@yueyericardo If you want to unblock this for merge, I'd advise preprocessoring out the implementation when ROCm is building (so we'll have unique for CUDA but not ROCm). Use |
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@ezyang I already implemented your advise.Thank you. |
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Great, thanks! |
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zdevito
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Jul 9, 2018
Summary:
Add cuda support for unique.
There is a simple test below for a tensor including 1M <int> data.
And the performance is faster.
```python
Performance
cpu: 0.05040597915649414 s
x: tensor([1, 3, 1, ..., 4, 9, 4])
x output: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
x inverse: tensor([0, 2, 0, ..., 3, 8, 3])
gpu: 0.015192985534667969 s
y: tensor([1, 3, 1, ..., 4, 9, 4], device='cuda:0')
y output: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9], device='cuda:0')
y inverse: tensor([0, 2, 0, ..., 3, 8, 3], device='cuda:0')
```
```python
Code
import torch
import time
x=torch.randint(1,10,(1000000,),dtype=torch.long)
device = torch.device("cuda")
y=x.to(device)
start = time.time();
output,inverse = x.unique(sorted=True,return_inverse=True)
stop = time.time();
print('cpu:',stop-start,'s')
print('x:',x)
print('x output:',output)
print('x inverse:',inverse)
start = time.time();
output1,inverse1 = y.unique(sorted=True,return_inverse=True)
torch.cuda.synchronize();
stop = time.time();
print('gpu:',stop-start,'s')
print('y:',y)
print('y output:',output1)
print('y inverse:',inverse1)
```
Closes pytorch/pytorch#8899
Reviewed By: SsnL
Differential Revision: D8677655
Pulled By: ezyang
fbshipit-source-id: 09df3f0602f235c5d36c7a6e7e1d89dbf82570bb
zdevito
pushed a commit
to zdevito/ATen
that referenced
this pull request
Jul 13, 2018
Summary:
Add cuda support for unique.
There is a simple test below for a tensor including 1M <int> data.
And the performance is faster.
```python
Performance
cpu: 0.05040597915649414 s
x: tensor([1, 3, 1, ..., 4, 9, 4])
x output: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
x inverse: tensor([0, 2, 0, ..., 3, 8, 3])
gpu: 0.015192985534667969 s
y: tensor([1, 3, 1, ..., 4, 9, 4], device='cuda:0')
y output: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9], device='cuda:0')
y inverse: tensor([0, 2, 0, ..., 3, 8, 3], device='cuda:0')
```
```python
Code
import torch
import time
x=torch.randint(1,10,(1000000,),dtype=torch.long)
device = torch.device("cuda")
y=x.to(device)
start = time.time();
output,inverse = x.unique(sorted=True,return_inverse=True)
stop = time.time();
print('cpu:',stop-start,'s')
print('x:',x)
print('x output:',output)
print('x inverse:',inverse)
start = time.time();
output1,inverse1 = y.unique(sorted=True,return_inverse=True)
torch.cuda.synchronize();
stop = time.time();
print('gpu:',stop-start,'s')
print('y:',y)
print('y output:',output1)
print('y inverse:',inverse1)
```
Closes pytorch/pytorch#8899
Reviewed By: SsnL
Differential Revision: D8677655
Pulled By: ezyang
fbshipit-source-id: 09df3f0602f235c5d36c7a6e7e1d89dbf82570bb
goodlux
pushed a commit
to goodlux/pytorch
that referenced
this pull request
Aug 15, 2018
Summary: Add cuda support for unique. There is a simple test below for a tensor including 1M <int> data. And the performance is faster. ```python Performance cpu: 0.05040597915649414 s x: tensor([1, 3, 1, ..., 4, 9, 4]) x output: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9]) x inverse: tensor([0, 2, 0, ..., 3, 8, 3]) gpu: 0.015192985534667969 s y: tensor([1, 3, 1, ..., 4, 9, 4], device='cuda:0') y output: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9], device='cuda:0') y inverse: tensor([0, 2, 0, ..., 3, 8, 3], device='cuda:0') ``` ```python Code import torch import time x=torch.randint(1,10,(1000000,),dtype=torch.long) device = torch.device("cuda") y=x.to(device) start = time.time(); output,inverse = x.unique(sorted=True,return_inverse=True) stop = time.time(); print('cpu:',stop-start,'s') print('x:',x) print('x output:',output) print('x inverse:',inverse) start = time.time(); output1,inverse1 = y.unique(sorted=True,return_inverse=True) torch.cuda.synchronize(); stop = time.time(); print('gpu:',stop-start,'s') print('y:',y) print('y output:',output1) print('y inverse:',inverse1) ``` Closes pytorch#8899 Reviewed By: SsnL Differential Revision: D8677655 Pulled By: ezyang fbshipit-source-id: 09df3f0602f235c5d36c7a6e7e1d89dbf82570bb
theweiho
added a commit
to theweiho/translate
that referenced
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May 16, 2019
Summary: pytorch/pytorch#8899 had added CUDA support for `torch.unique()` pytorch/pytorch#16145 has some timing stats that could be relevant --- Experiment results: https://fb.quip.com/olQOA853j0mb Words per second (`gpu-unique_wps_avg_vs_base`): 1.046x Total train time (`gpu-unique_total_train_time_vs_base`; excl ar_AR-fr_XX): 0.987x Even though train time reduction is pretty minimal (probably overshadowed by random variance, scheduling delay, etc), WPS does seem to be ~5% faster - so might as well land this. Training time for ar_AR-fr_XX increased significantly - but that's b/c it trained for many more updates (`gpu-unique_num_updates_avg_vs_base`) - and also ended up w/ +1.43 BLEU. I think this is probably just an anomaly? Differential Revision: D15073468 fbshipit-source-id: a9710738c827013afb35a67bd3a9be259b0e2d5f
theweiho
added a commit
to theweiho/translate
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May 16, 2019
…torch#537) Summary: Pull Request resolved: pytorch#537 pytorch/pytorch#8899 had added CUDA support for `torch.unique()` pytorch/pytorch#16145 has some timing stats that could be relevant --- Experiment results: https://fb.quip.com/olQOA853j0mb Words per second (`gpu-unique_wps_avg_vs_base`): 1.046x Total train time (`gpu-unique_total_train_time_vs_base`; excl ar_AR-fr_XX): 0.987x Even though train time reduction is pretty minimal (probably overshadowed by random variance, scheduling delay, etc), WPS does seem to be ~5% faster - so might as well land this. Training time for ar_AR-fr_XX increased significantly - but that's b/c it trained for many more updates (`gpu-unique_num_updates_avg_vs_base`) - and also ended up w/ +1.43 BLEU. I think this is probably just an anomaly? Differential Revision: D15073468 fbshipit-source-id: 713288fc7c77f582840f270dd2e343a3b63f8fe5
theweiho
added a commit
to theweiho/translate
that referenced
this pull request
May 16, 2019
…torch#537) Summary: Pull Request resolved: pytorch#537 pytorch/pytorch#8899 had added CUDA support for `torch.unique()` pytorch/pytorch#16145 has some timing stats that could be relevant --- Experiment results: https://fb.quip.com/olQOA853j0mb Words per second (`gpu-unique_wps_avg_vs_base`): 1.046x Total train time (`gpu-unique_total_train_time_vs_base`; excl ar_AR-fr_XX): 0.987x Even though train time reduction is pretty minimal (probably overshadowed by random variance, scheduling delay, etc), WPS does seem to be ~5% faster - so might as well land this. Training time for ar_AR-fr_XX increased significantly - but that's b/c it trained for many more updates (`gpu-unique_num_updates_avg_vs_base`) - and also ended up w/ +1.43 BLEU. I think this is probably just an anomaly? Differential Revision: D15073468 fbshipit-source-id: 29c7eaaddd63d629866c7314920fe27b22690603
facebook-github-bot
pushed a commit
to pytorch/translate
that referenced
this pull request
May 17, 2019
Summary: Pull Request resolved: #537 pytorch/pytorch#8899 had added CUDA support for `torch.unique()` pytorch/pytorch#16145 has some timing stats that could be relevant --- Experiment results: https://fb.quip.com/olQOA853j0mb Words per second (`gpu-unique_wps_avg_vs_base`): 1.046x Total train time (`gpu-unique_total_train_time_vs_base`; excl ar_AR-fr_XX): 0.987x Even though train time reduction is pretty minimal (probably overshadowed by random variance, scheduling delay, etc), WPS does seem to be ~5% faster - so might as well land this. Training time for ar_AR-fr_XX increased significantly - but that's b/c it trained for many more updates (`gpu-unique_num_updates_avg_vs_base`) - and also ended up w/ +1.43 BLEU. I think this is probably just an anomaly? Reviewed By: akinh, jmp84 Differential Revision: D15073468 fbshipit-source-id: c2dba562b6d4fb4d15d2a56d03ce6a6e3ddff07d
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Add cuda support for unique.
There is a simple test below for a tensor including 1M data.
And the performance is faster.