Adds dim argument to torch.unique
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added 11 commits
August 10, 2018 23:35
ptrblck
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Aug 14, 2018
aten/src/ATen/native/cuda/Unique.cu
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| Tensor output = input_sorted.index_select(0, input_sorted_indices); | ||
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| // // reshape back | ||
| std::vector<int64_t> new_sizes(orig_sizes.begin(), orig_sizes.end()); |
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aten/src/ATen/native/Unique.cpp
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| int mask_idx = 1; | ||
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| std::vector<Tensor> input_unbind = at::unbind(input_sorted, 0); | ||
| auto last = std::unique(input_unbind.begin(), input_unbind.end(), [&](Tensor a, Tensor b) { |
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ptrblck
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Aug 15, 2018
aten/src/ATen/native/cuda/Unique.cu
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| @@ -7,6 +7,7 @@ | |||
| #include <tuple> | |||
| #include <thrust/unique.h> | |||
| #include <thrust/sort.h> | |||
| #include <thrust/device_vector.h> | |||
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As there is no switch between |
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Aug 30, 2018
Summary: Initial version of `unique` supporting a `dim` argument. As discussed in [this issue](pytorch/pytorch#9997) I added the `dim` argument to `torch.unique` with the same behavior like [numpy](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.unique.html). Since the implementation is based on `std/thrust::unique`, the `tensor` always needs to be sorted. The `sorted` argument in `torch.unique` does not have any function, as in the CUDA version of the plain `torch.unique`. To check the performance and equal behavior between `torch.unique` and `np.unique`, I've used [this gist](https://gist.github.com/ptrblck/ac0dc862f4e1766f0e1036c252cdb105). Currently we achieve the following timings for an input of `x = torch.randint(2, (1000, 1000))`: (The values are calculated by taking the average of the times for both dimension) | Device | PyTorch (return_inverse=False) | Numpy (return_inverse=False) | PyTorch (return_inverse=True) | Numpy (return_inverse=True) | | --- | --- | --- | --- | --- | | CPU | ~0.007331s | ~0.022452s | ~0.011139s | ~0.044800s | | GPU | ~0.006154s | - | ~0.105373s | - | Many thanks to colesbury for the awesome mentoring and the valuable advices on the general implementation and performance issues! Pull Request resolved: pytorch/pytorch#10423 Differential Revision: D9517289 Pulled By: soumith fbshipit-source-id: a4754f805223589c2847c98b8e4e39d8c3ddb7b5
PenghuiCheng
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Sep 11, 2018
Summary: Initial version of `unique` supporting a `dim` argument. As discussed in [this issue](pytorch#9997) I added the `dim` argument to `torch.unique` with the same behavior like [numpy](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.unique.html). Since the implementation is based on `std/thrust::unique`, the `tensor` always needs to be sorted. The `sorted` argument in `torch.unique` does not have any function, as in the CUDA version of the plain `torch.unique`. To check the performance and equal behavior between `torch.unique` and `np.unique`, I've used [this gist](https://gist.github.com/ptrblck/ac0dc862f4e1766f0e1036c252cdb105). Currently we achieve the following timings for an input of `x = torch.randint(2, (1000, 1000))`: (The values are calculated by taking the average of the times for both dimension) | Device | PyTorch (return_inverse=False) | Numpy (return_inverse=False) | PyTorch (return_inverse=True) | Numpy (return_inverse=True) | | --- | --- | --- | --- | --- | | CPU | ~0.007331s | ~0.022452s | ~0.011139s | ~0.044800s | | GPU | ~0.006154s | - | ~0.105373s | - | Many thanks to colesbury for the awesome mentoring and the valuable advices on the general implementation and performance issues! Pull Request resolved: pytorch#10423 Differential Revision: D9517289 Pulled By: soumith fbshipit-source-id: a4754f805223589c2847c98b8e4e39d8c3ddb7b5
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Initial version of
uniquesupporting adimargument.As discussed in this issue I added the
dimargument totorch.uniquewith the same behavior like numpy.Since the implementation is based on
std/thrust::unique, thetensoralways needs to be sorted. Thesortedargument intorch.uniquedoes not have any function, as in the CUDA version of the plaintorch.unique.To check the performance and equal behavior between
torch.uniqueandnp.unique, I've used this gist.Currently we achieve the following timings for an input of
x = torch.randint(2, (1000, 1000)):(The values are calculated by taking the average of the times for both dimension)
Many thanks to @colesbury for the awesome mentoring and the valuable advices on the general implementation and performance issues!