- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

… unique_dim for performance"

Step 1: Secretly add return_counts to unique, and refactor unique_dim for performance

`unique` is fragile, previously I tried to change it in #18391 and #17097, they all pass OSS tests but finally get reverted due to internal failure. My previous work of refactoring unique #18459 is based on #18391, and after #18391 get reverted, I could not work on #18459. To continue working on #18459, #18391, and #17097 without worrying about internal failures, I am suggesting the following steps for the improvements of `unique` and `unique_dim`. @soumith Please take this and there is no need to put #18391 back.

The motivation is basically to move forward as much as possible without causing any internal failures. So I will try to divide it into steps and sort from low probability of internal failure to high probability. (I don't know what the internal failure is, so I have to guess). Let's merge these PR stack one by one until we enounter internal failure.

Step 1: Create two new ATen operators, `_unique2_temporary_will_remove_soon` and `_unique_dim2_temporary_will_remove_soon` and keep `_unique` and `_unique_dim` unchanged. The backend of these two functions and `_unique` and `_unique_dim` are all the same, the only difference is the temporary ones support `return_counts` but not the `_unique` and `_unique_dim`. Step one is mostly #18391 + #18459. The cuda8 errors has been fixed. At this point, there is no user visible API change, so no docs are updated. `torch.unique` does not support `return_counts` yet, and `return_counts` is tested through the newly added temporary operators. This step just added two new ATen operators, so there shouldn't be any internal failure.

Step 2: Rename `_unique_dim2_temporary_will_remove_soon` to `unique_dim`. This should cause no internal failure either, because no change to existing operators. The only thing to worry about is to delete `unique_dim` from python side because we don't want users to use it. At this point, C++ users now have `return_counts` support for `unique_dim`.

Step 3: Update the docs of `torch.unique` and use `unique_dim` inside `torch.unique` to support `return_counts` In the docs, we should say `torch.unique` with None dim support does not support `return_counts` yet. This might cause internal failure.

Step 4: Rename `_unique2_temporary_will_remove_soon` to `_unique2` and use `_unique2` inside `torch.unique` to support `return_counts`. Update the docs saying that `torch.unique` with None dim now support `return_counts`. This might cause internal failure.

Step 5: Remove `_unique_dim`. This might cause internal failure.

Step 6: Rename `_unique2` to `unique`, add optional `dim` argument to make it looks like the signature of Python's `torch.unique`. Inside `torch.unique`, use `unique` and get rid of `unique_dim`. Unbind `unique_dim` totally from Python at codegen. This is likely to cause internal fail.

Step 7: Remove `_unique`. This is very likely to cause internal failure.

This PR is for step 1. This create two new ATen operators, `_unique2_temporary_will_remove_soon` and `_unique_dim2_temporary_will_remove_soon` and implement `return_counts` inside them and do refactor for performance improvements.

Please review @ngimel @VitalyFedyunin. They are mostly copied from #18391 and #18459, so the review should be easy.

Below is a benchmark on a tensor of shape `torch.Size([15320, 2])`:

```python
print(torch.__version__)
%timeit a.unique(dim=0, sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(dim=0, sorted=True, return_inverse=True); torch.cuda.synchronize()
```

```
1.0.1
192 µs ± 1.61 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
548 ms ± 3.39 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```

```python
print(torch.__version__)
%timeit a.unique(sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(sorted=True, return_inverse=True); torch.cuda.synchronize()
```

```
1.0.1
226 µs ± 929 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
302 µs ± 7.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

```python
print(torch.__version__)
%timeit a.unique(dim=0, sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(dim=0, sorted=True, return_inverse=True); torch.cuda.synchronize()
%timeit torch._unique_dim2_temporary_will_remove_soon(a, dim=0, sorted=True, return_inverse=False, return_counts=True); torch.cuda.synchronize()
%timeit torch._unique_dim2_temporary_will_remove_soon(a, dim=0, sorted=True, return_inverse=True, return_counts=True); torch.cuda.synchronize()
```

```
1.1.0a0+83ab8ac
190 µs ± 2.14 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
237 µs ± 1.23 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
219 µs ± 2.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
263 µs ± 1.15 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

```python
print(torch.__version__)
%timeit a.unique(sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(sorted=True, return_inverse=True); torch.cuda.synchronize()
%timeit torch._unique2_temporary_will_remove_soon(a, sorted=True, return_inverse=False, return_counts=True); torch.cuda.synchronize()
%timeit torch._unique2_temporary_will_remove_soon(a, sorted=True, return_inverse=True, return_counts=True); torch.cuda.synchronize()
```

```
1.1.0a0+83ab8ac
232 µs ± 2.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
301 µs ± 1.65 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
264 µs ± 7.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
339 µs ± 9.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

gh-metadata: pytorch pytorch 18648 gh/zasdfgbnm/1/head

… for performance

`unique` is fragile, previously I tried to change it in #18391 and #17097, they all pass OSS tests but finally get reverted due to internal failure. My previous work of refactoring unique #18459 is based on #18391, and after #18391 get reverted, I could not work on #18459. To continue working on #18459, #18391, and #17097 without worrying about internal failures, I am suggesting the following steps for the improvements of `unique` and `unique_dim`. @soumith Please take this and there is no need to put #18391 back.

The motivation is basically to move forward as much as possible without causing any internal failures. So I will try to divide it into steps and sort from low probability of internal failure to high probability. (I don't know what the internal failure is, so I have to guess). Let's merge these PR stack one by one until we enounter internal failure.

Step 1: Create two new ATen operators, `_unique2_temporary_will_remove_soon` and `_unique_dim2_temporary_will_remove_soon` and keep `_unique` and `_unique_dim` unchanged. The backend of these two functions and `_unique` and `_unique_dim` are all the same, the only difference is the temporary ones support `return_counts` but not the `_unique` and `_unique_dim`. Step one is mostly #18391 + #18459. The cuda8 errors has been fixed. At this point, there is no user visible API change, so no docs are updated. `torch.unique` does not support `return_counts` yet, and `return_counts` is tested through the newly added temporary operators. This step just added two new ATen operators, so there shouldn't be any internal failure.

Step 2: Rename `_unique_dim2_temporary_will_remove_soon` to `unique_dim`. This should cause no internal failure either, because no change to existing operators. The only thing to worry about is to delete `unique_dim` from python side because we don't want users to use it. At this point, C++ users now have `return_counts` support for `unique_dim`.

Step 3: Update the docs of `torch.unique` and use `unique_dim` inside `torch.unique` to support `return_counts` In the docs, we should say `torch.unique` with None dim support does not support `return_counts` yet. This might cause internal failure.

Step 4: Rename `_unique2_temporary_will_remove_soon` to `_unique2` and use `_unique2` inside `torch.unique` to support `return_counts`. Update the docs saying that `torch.unique` with None dim now support `return_counts`. This might cause internal failure.

Step 5: Remove `_unique_dim`. This might cause internal failure.

Step 6: Rename `_unique2` to `unique`, add optional `dim` argument to make it looks like the signature of Python's `torch.unique`. Inside `torch.unique`, use `unique` and get rid of `unique_dim`. Unbind `unique_dim` totally from Python at codegen. This is likely to cause internal fail.

Step 7: Remove `_unique`. This is very likely to cause internal failure.

This PR is for step 1. This create two new ATen operators, `_unique2_temporary_will_remove_soon` and `_unique_dim2_temporary_will_remove_soon` and implement `return_counts` inside them and do refactor for performance improvements.

Please review @ngimel @VitalyFedyunin. They are mostly copied from #18391 and #18459, so the review should be easy.

Below is a benchmark on a tensor of shape `torch.Size([15320, 2])`:

```python
print(torch.__version__)
%timeit a.unique(dim=0, sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(dim=0, sorted=True, return_inverse=True); torch.cuda.synchronize()
```

```
1.0.1
192 µs ± 1.61 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
548 ms ± 3.39 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```

```python
print(torch.__version__)
%timeit a.unique(sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(sorted=True, return_inverse=True); torch.cuda.synchronize()
```

```
1.0.1
226 µs ± 929 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
302 µs ± 7.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

```python
print(torch.__version__)
%timeit a.unique(dim=0, sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(dim=0, sorted=True, return_inverse=True); torch.cuda.synchronize()
%timeit torch._unique_dim2_temporary_will_remove_soon(a, dim=0, sorted=True, return_inverse=False, return_counts=True); torch.cuda.synchronize()
%timeit torch._unique_dim2_temporary_will_remove_soon(a, dim=0, sorted=True, return_inverse=True, return_counts=True); torch.cuda.synchronize()
```

```
1.1.0a0+83ab8ac
190 µs ± 2.14 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
237 µs ± 1.23 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
219 µs ± 2.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
263 µs ± 1.15 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

```python
print(torch.__version__)
%timeit a.unique(sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(sorted=True, return_inverse=True); torch.cuda.synchronize()
%timeit torch._unique2_temporary_will_remove_soon(a, sorted=True, return_inverse=False, return_counts=True); torch.cuda.synchronize()
%timeit torch._unique2_temporary_will_remove_soon(a, sorted=True, return_inverse=True, return_counts=True); torch.cuda.synchronize()
```

```
1.1.0a0+83ab8ac
232 µs ± 2.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
301 µs ± 1.65 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
264 µs ± 7.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
339 µs ± 9.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

gh-metadata: pytorch pytorch 18648 gh/zasdfgbnm/1/head

… for performance (#18648)

Summary:
Pull Request resolved: #18648
ghimport-source-id: 1cf4a8f

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18661 Step 7: remove _unique
* #18655 Step 6: Rename _unique2 to unique and add int? dim
* #18654 Step 5: remove _unque_dim in favor of unique_dim
* #18651 Step 4: add support for unique with dim=None
* #18650 Step 3: Add support for return_counts to torch.unique for dim not None
* #18649 Step 2: Rename _unique_dim2_temporary_will_remove_soon to unique_dim
* **#18648 Step 1: Secretly add return_counts to unique, and refactor unique_dim for performance**

`unique` is fragile, previously I tried to change it in #18391 and #17097, they all pass OSS tests but finally get reverted due to internal failure. My previous work of refactoring unique #18459 is based on #18391, and after #18391 get reverted, I could not work on #18459. To continue working on #18459, #18391, and #17097 without worrying about internal failures, I am suggesting the following steps for the improvements of `unique` and `unique_dim`. soumith Please take this and there is no need to put #18391 back.

The motivation is basically to move forward as much as possible without causing any internal failures. So I will try to divide it into steps and sort from low probability of internal failure to high probability. (I don't know what the internal failure is, so I have to guess). Let's merge these PR stack one by one until we enounter internal failure.

Step 1: Create two new ATen operators, `_unique2_temporary_will_remove_soon` and `_unique_dim2_temporary_will_remove_soon` and keep `_unique` and `_unique_dim` unchanged. The backend of these two functions and `_unique` and `_unique_dim` are all the same, the only difference is the temporary ones support `return_counts` but not the `_unique` and `_unique_dim`. Step one is mostly #18391 + #18459. The cuda8 errors has been fixed. At this point, there is no user visible API change, so no docs are updated. `torch.unique` does not support `return_counts` yet, and `return_counts` is tested through the newly added temporary operators. This step just added two new ATen operators, so there shouldn't be any internal failure.

Step 2: Rename `_unique_dim2_temporary_will_remove_soon` to `unique_dim`. This should cause no internal failure either, because no change to existing operators. The only thing to worry about is to delete `unique_dim` from python side because we don't want users to use it. At this point, C++ users now have `return_counts` support for `unique_dim`.

Step 3: Update the docs of `torch.unique` and use `unique_dim` inside `torch.unique` to support `return_counts` In the docs, we should say `torch.unique` with None dim support does not support `return_counts` yet. This might cause internal failure.

Step 4: Rename `_unique2_temporary_will_remove_soon` to `_unique2` and use `_unique2` inside `torch.unique` to support `return_counts`. Update the docs saying that `torch.unique` with None dim now support `return_counts`. This might cause internal failure.

Step 5: Remove `_unique_dim`. This might cause internal failure.

Step 6: Rename `_unique2` to `unique`, add optional `dim` argument to make it looks like the signature of Python's `torch.unique`. Inside `torch.unique`, use `unique` and get rid of `unique_dim`. Unbind `unique_dim` totally from Python at codegen. This is likely to cause internal fail.

Step 7: Remove `_unique`. This is very likely to cause internal failure.

This PR
======

This PR is for step 1. This create two new ATen operators, `_unique2_temporary_will_remove_soon` and `_unique_dim2_temporary_will_remove_soon` and implement `return_counts` inside them and do refactor for performance improvements.

Please review ngimel VitalyFedyunin. They are mostly copied from #18391 and #18459, so the review should be easy.

Below is a benchmark on a tensor of shape `torch.Size([15320, 2])`:

Before
---------

```python
print(torch.__version__)
%timeit a.unique(dim=0, sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(dim=0, sorted=True, return_inverse=True); torch.cuda.synchronize()
```

```
1.0.1
192 µs ± 1.61 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
548 ms ± 3.39 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```

```python
print(torch.__version__)
%timeit a.unique(sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(sorted=True, return_inverse=True); torch.cuda.synchronize()
```

```
1.0.1
226 µs ± 929 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
302 µs ± 7.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

After
-------

```python
print(torch.__version__)
%timeit a.unique(dim=0, sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(dim=0, sorted=True, return_inverse=True); torch.cuda.synchronize()
%timeit torch._unique_dim2_temporary_will_remove_soon(a, dim=0, sorted=True, return_inverse=False, return_counts=True); torch.cuda.synchronize()
%timeit torch._unique_dim2_temporary_will_remove_soon(a, dim=0, sorted=True, return_inverse=True, return_counts=True); torch.cuda.synchronize()
```

```
1.1.0a0+83ab8ac
190 µs ± 2.14 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
237 µs ± 1.23 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
219 µs ± 2.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
263 µs ± 1.15 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

```python
print(torch.__version__)
%timeit a.unique(sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(sorted=True, return_inverse=True); torch.cuda.synchronize()
%timeit torch._unique2_temporary_will_remove_soon(a, sorted=True, return_inverse=False, return_counts=True); torch.cuda.synchronize()
%timeit torch._unique2_temporary_will_remove_soon(a, sorted=True, return_inverse=True, return_counts=True); torch.cuda.synchronize()
```

```
1.1.0a0+83ab8ac
232 µs ± 2.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
301 µs ± 1.65 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
264 µs ± 7.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
339 µs ± 9.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

Differential Revision: D14730905

fbshipit-source-id: 10026b4b98628a8565cc28a13317d29adf1225cc

… unique_dim for performance"

Step 1: Secretly add return_counts to unique, and refactor unique_dim for performance

`unique` is fragile, previously I tried to change it in #18391 and #17097, they all pass OSS tests but finally get reverted due to internal failure. My previous work of refactoring unique #18459 is based on #18391, and after #18391 get reverted, I could not work on #18459. To continue working on #18459, #18391, and #17097 without worrying about internal failures, I am suggesting the following steps for the improvements of `unique` and `unique_dim`. @soumith Please take this and there is no need to put #18391 back.

The motivation is basically to move forward as much as possible without causing any internal failures. So I will try to divide it into steps and sort from low probability of internal failure to high probability. (I don't know what the internal failure is, so I have to guess). Let's merge these PR stack one by one until we enounter internal failure.

Step 1: Create two new ATen operators, `_unique2_temporary_will_remove_soon` and `_unique_dim2_temporary_will_remove_soon` and keep `_unique` and `_unique_dim` unchanged. The backend of these two functions and `_unique` and `_unique_dim` are all the same, the only difference is the temporary ones support `return_counts` but not the `_unique` and `_unique_dim`. Step one is mostly #18391 + #18459. The cuda8 errors has been fixed. At this point, there is no user visible API change, so no docs are updated. `torch.unique` does not support `return_counts` yet, and `return_counts` is tested through the newly added temporary operators. This step just added two new ATen operators, so there shouldn't be any internal failure.

Step 2: Rename `_unique_dim2_temporary_will_remove_soon` to `unique_dim`. This should cause no internal failure either, because no change to existing operators. The only thing to worry about is to delete `unique_dim` from python side because we don't want users to use it. At this point, C++ users now have `return_counts` support for `unique_dim`.

Step 3: Update the docs of `torch.unique` and use `unique_dim` inside `torch.unique` to support `return_counts` In the docs, we should say `torch.unique` with None dim support does not support `return_counts` yet. This might cause internal failure.

Step 4: Rename `_unique2_temporary_will_remove_soon` to `_unique2` and use `_unique2` inside `torch.unique` to support `return_counts`. Update the docs saying that `torch.unique` with None dim now support `return_counts`. This might cause internal failure.

Step 5: Remove `_unique_dim`. This might cause internal failure.

Step 6: Rename `_unique2` to `unique`, add optional `dim` argument to make it looks like the signature of Python's `torch.unique`. Inside `torch.unique`, use `unique` and get rid of `unique_dim`. Unbind `unique_dim` totally from Python at codegen. This is likely to cause internal fail.

Step 7: Remove `_unique`. This is very likely to cause internal failure.

This PR is for step 1. This create two new ATen operators, `_unique2_temporary_will_remove_soon` and `_unique_dim2_temporary_will_remove_soon` and implement `return_counts` inside them and do refactor for performance improvements.

Please review @ngimel @VitalyFedyunin. They are mostly copied from #18391 and #18459, so the review should be easy.

Below is a benchmark on a tensor of shape `torch.Size([15320, 2])`:

```python
print(torch.__version__)
%timeit a.unique(dim=0, sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(dim=0, sorted=True, return_inverse=True); torch.cuda.synchronize()
```

```
1.0.1
192 µs ± 1.61 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
548 ms ± 3.39 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```

```python
print(torch.__version__)
%timeit a.unique(sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(sorted=True, return_inverse=True); torch.cuda.synchronize()
```

```
1.0.1
226 µs ± 929 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
302 µs ± 7.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

```python
print(torch.__version__)
%timeit a.unique(dim=0, sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(dim=0, sorted=True, return_inverse=True); torch.cuda.synchronize()
%timeit torch._unique_dim2_temporary_will_remove_soon(a, dim=0, sorted=True, return_inverse=False, return_counts=True); torch.cuda.synchronize()
%timeit torch._unique_dim2_temporary_will_remove_soon(a, dim=0, sorted=True, return_inverse=True, return_counts=True); torch.cuda.synchronize()
```

```
1.1.0a0+83ab8ac
190 µs ± 2.14 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
237 µs ± 1.23 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
219 µs ± 2.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
263 µs ± 1.15 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

```python
print(torch.__version__)
%timeit a.unique(sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(sorted=True, return_inverse=True); torch.cuda.synchronize()
%timeit torch._unique2_temporary_will_remove_soon(a, sorted=True, return_inverse=False, return_counts=True); torch.cuda.synchronize()
%timeit torch._unique2_temporary_will_remove_soon(a, sorted=True, return_inverse=True, return_counts=True); torch.cuda.synchronize()
```

```
1.1.0a0+83ab8ac
232 µs ± 2.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
301 µs ± 1.65 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
264 µs ± 7.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
339 µs ± 9.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

gh-metadata: pytorch pytorch 18648 gh/zasdfgbnm/1/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

… unique_dim for performance"

Step 1: Secretly add return_counts to unique, and refactor unique_dim for performance

`unique` is fragile, previously I tried to change it in #18391 and #17097, they all pass OSS tests but finally get reverted due to internal failure. My previous work of refactoring unique #18459 is based on #18391, and after #18391 get reverted, I could not work on #18459. To continue working on #18459, #18391, and #17097 without worrying about internal failures, I am suggesting the following steps for the improvements of `unique` and `unique_dim`. @soumith Please take this and there is no need to put #18391 back.

The motivation is basically to move forward as much as possible without causing any internal failures. So I will try to divide it into steps and sort from low probability of internal failure to high probability. (I don't know what the internal failure is, so I have to guess). Let's merge these PR stack one by one until we enounter internal failure.

Step 1: Create two new ATen operators, `_unique2_temporary_will_remove_soon` and `_unique_dim2_temporary_will_remove_soon` and keep `_unique` and `_unique_dim` unchanged. The backend of these two functions and `_unique` and `_unique_dim` are all the same, the only difference is the temporary ones support `return_counts` but not the `_unique` and `_unique_dim`. Step one is mostly #18391 + #18459. The cuda8 errors has been fixed. At this point, there is no user visible API change, so no docs are updated. `torch.unique` does not support `return_counts` yet, and `return_counts` is tested through the newly added temporary operators. This step just added two new ATen operators, so there shouldn't be any internal failure.

Step 2: Rename `_unique_dim2_temporary_will_remove_soon` to `unique_dim`. This should cause no internal failure either, because no change to existing operators. The only thing to worry about is to delete `unique_dim` from python side because we don't want users to use it. At this point, C++ users now have `return_counts` support for `unique_dim`.

Step 3: Update the docs of `torch.unique` and use `unique_dim` inside `torch.unique` to support `return_counts` In the docs, we should say `torch.unique` with None dim support does not support `return_counts` yet. This might cause internal failure.

Step 4: Rename `_unique2_temporary_will_remove_soon` to `_unique2` and use `_unique2` inside `torch.unique` to support `return_counts`. Update the docs saying that `torch.unique` with None dim now support `return_counts`. This might cause internal failure.

Step 5: Remove `_unique_dim`. This might cause internal failure.

Step 6: Rename `_unique2` to `unique`, add optional `dim` argument to make it looks like the signature of Python's `torch.unique`. Inside `torch.unique`, use `unique` and get rid of `unique_dim`. Unbind `unique_dim` totally from Python at codegen. This is likely to cause internal fail.

Step 7: Remove `_unique`. This is very likely to cause internal failure.

This PR is for step 1. This create two new ATen operators, `_unique2_temporary_will_remove_soon` and `_unique_dim2_temporary_will_remove_soon` and implement `return_counts` inside them and do refactor for performance improvements.

Please review @ngimel @VitalyFedyunin. They are mostly copied from #18391 and #18459, so the review should be easy.

Below is a benchmark on a tensor of shape `torch.Size([15320, 2])`:

```python
print(torch.__version__)
%timeit a.unique(dim=0, sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(dim=0, sorted=True, return_inverse=True); torch.cuda.synchronize()
```

```
1.0.1
192 µs ± 1.61 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
548 ms ± 3.39 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```

```python
print(torch.__version__)
%timeit a.unique(sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(sorted=True, return_inverse=True); torch.cuda.synchronize()
```

```
1.0.1
226 µs ± 929 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
302 µs ± 7.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

```python
print(torch.__version__)
%timeit a.unique(dim=0, sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(dim=0, sorted=True, return_inverse=True); torch.cuda.synchronize()
%timeit torch._unique_dim2_temporary_will_remove_soon(a, dim=0, sorted=True, return_inverse=False, return_counts=True); torch.cuda.synchronize()
%timeit torch._unique_dim2_temporary_will_remove_soon(a, dim=0, sorted=True, return_inverse=True, return_counts=True); torch.cuda.synchronize()
```

```
1.1.0a0+83ab8ac
190 µs ± 2.14 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
237 µs ± 1.23 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
219 µs ± 2.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
263 µs ± 1.15 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

```python
print(torch.__version__)
%timeit a.unique(sorted=True, return_inverse=False); torch.cuda.synchronize()
%timeit a.unique(sorted=True, return_inverse=True); torch.cuda.synchronize()
%timeit torch._unique2_temporary_will_remove_soon(a, sorted=True, return_inverse=False, return_counts=True); torch.cuda.synchronize()
%timeit torch._unique2_temporary_will_remove_soon(a, sorted=True, return_inverse=True, return_counts=True); torch.cuda.synchronize()
```

```
1.1.0a0+83ab8ac
232 µs ± 2.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
301 µs ± 1.65 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
264 µs ± 7.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
339 µs ± 9.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

gh-metadata: pytorch pytorch 18648 gh/zasdfgbnm/1/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

…int? dim"

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

…int? dim"

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head

Step 6: Rename _unique2 to unique and add int? dim

- Rename `_unique2` to `unique`
- Add optional `dim` argument to make it looks like the signature of Python's `torch.unique`.
- Inside `torch.unique`, use `unique` and get rid of `unique_dim`.
- Unbind `unique_dim` totally from Python at codegen.
- Add OSS ONNX test for unique
- Add jit test for unique

Previously tried in #17097 and cause internal error, not sure about this
time.

cc: @wanchaol

gh-metadata: pytorch pytorch 18655 gh/zasdfgbnm/6/head