OSSForks/pytorch
1
0
Fork 0
mirror of https://github.com/zebrajr/pytorch.git synced 2025-12-06 12:20:52 +01:00
Code Issues Actions 38 Packages Projects Releases Wiki Activity
35,690 Commits 8,304 Branches 150 Tags 1.5 GiB
59b61f912a
Commit Graph

144 Commits

Author SHA1 Message Date
Ailing Zhang
59b61f912a Switch assertWarnsOnceRegex logic to check any instead of all. (#56434) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56434

If we hit multiple TORCH_WARN from different sources when running the
statement, it makes more sense to me that we want to check the regex is
met in any one of the warning messages instead of all messages.

Test Plan: Imported from OSS

Reviewed By: mruberry

Differential Revision: D27871946

Pulled By: ailzhang

fbshipit-source-id: 5940a8e43e4cc91aef213ef01e48d506fd9a1132
 2021-04-20 10:37:36 -07:00
Sam Estep
e3900d2ba5 Add lint for unqualified noqa (#56272) 
Summary:
As this diff shows, currently there are a couple hundred instances of raw `noqa` in the codebase, which just ignore all errors on a given line. That isn't great, so this PR changes all existing instances of that antipattern to qualify the `noqa` with respect to a specific error code, and adds a lint to prevent more of this from happening in the future.

Interestingly, some of the examples the `noqa` lint catches are genuine attempts to qualify the `noqa` with a specific error code, such as these two:
```
test/jit/test_misc.py:27:            print(f"{hello + ' ' + test}, I'm a {test}") # noqa E999
test/jit/test_misc.py:28:            print(f"format blank") # noqa F541
```
However, those are still wrong because they are [missing a colon](https://flake8.pycqa.org/en/3.9.1/user/violations.html#in-line-ignoring-errors), which actually causes the error code to be completely ignored:

- If you change them to anything else, the warnings will still be suppressed.
- If you add the necessary colons then it is revealed that `E261` was also being suppressed, unintentionally:
  ```
  test/jit/test_misc.py:27:57: E261 at least two spaces before inline comment
  test/jit/test_misc.py:28:35: E261 at least two spaces before inline comment
  ```

I did try using [flake8-noqa](https://pypi.org/project/flake8-noqa/) instead of a custom `git grep` lint, but it didn't seem to work. This PR is definitely missing some of the functionality that flake8-noqa is supposed to provide, though, so if someone can figure out how to use it, we should do that instead.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/56272

Test Plan:
CI should pass on the tip of this PR, and we know that the lint works because the following CI run (before this PR was finished) failed:

- https://github.com/pytorch/pytorch/runs/2365189927

Reviewed By: janeyx99

Differential Revision: D27830127

Pulled By: samestep

fbshipit-source-id: d6dcf4f945ebd18cd76c46a07f3b408296864fcb
 2021-04-19 13:16:18 -07:00
Can Balioglu
42f5d66080 [DDP] Fixes flaky tests caused by incorrect floating-point comparison (#56192) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/50699.

The root cause was that some floating-point assertions had a "greater than or **equal to**" condition. The "equal to" part was causing flakiness due to strict equality check (`==`) in `TestCase.assertGreaterEqual()`. This PR introduces a new assertion method called `assertGreaterAlmostEqual()` in `common_utils.py` that mitigates the problem by behaving similar to `TestCase.assertAlmostEqual()`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/56192

Reviewed By: zhaojuanmao

Differential Revision: D27804724

Pulled By: cbalioglu

fbshipit-source-id: bc44a41ca4ce45dfee62fb3769fb47bfd9028831
 2021-04-15 17:15:42 -07:00
David Riazati
3c6b52ae62 Cache slow/disabled test files (#55682) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55682

Fixes #55648

For now it downloads and writes the relevant files to the system's temp dir and marks it as valid for 3 hours.

Test Plan: Imported from OSS

Reviewed By: malfet, nikithamalgifb

Differential Revision: D27685616

Pulled By: driazati

fbshipit-source-id: 27469b85fe4b6b4addde6b22bf795bca3d4990ef
 2021-04-12 09:17:07 -07:00
Mike Ruberry
399b66c813 Ports logdet from method_tests() to op_db (#55743) 
Summary:
Per title. Also updates some tensor construction helpers.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/55743

Reviewed By: ngimel

Differential Revision: D27702060

Pulled By: mruberry

fbshipit-source-id: f64b7bee855733ad1f4fd182819ceec5831d9878
 2021-04-11 20:39:16 -07:00
Alexander
6ee333cdb5 modernize test_sparse (#54572) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54572

Adding device generic tests to `test_sparse`.
Follow-up PR: #54153

I think is ready to review.
Looking forward your comments cc mruberry.

Thanks

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D27562663

Pulled By: mruberry

fbshipit-source-id: c48973e707f779b529bc7f61b75103194b428987
 2021-04-09 12:19:29 -07:00
Jane Xu
2a24a2418a common_utils.py use new file names for disabled/slow tests (#55620) 
Summary:
Following these changes in renaming the files:
https://github.com/pytorch/pytorch/pull/55618
https://github.com/pytorch/test-infra/pull/3

We should update the use sites in common_utils.py

Pull Request resolved: https://github.com/pytorch/pytorch/pull/55620

Reviewed By: samestep

Differential Revision: D27651884

Pulled By: janeyx99

fbshipit-source-id: 298a981e55e0b7c95202294d9bc4b3fcce359590
 2021-04-09 09:25:20 -07:00
Philip Meier
f4967d68f5 make torch.testing asserts importable (#54769) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54769

Follow-up to #53820. This

- makes the `asserts.py` module private as per suggestion from rgommers in https://github.com/pytorch/pytorch/pull/53820#issuecomment-802661387. With this the functions should only be accessible through `torch.testing`, giving us the option the change the underlying structure later.
- moves the code from `torch/testing/__init__.py` to `torch/testing/_core.py` (happy to accept other name suggestions). Otherwise we can't import the new `_asserts.py` in `torch/testing/__init__.py` due to circular imports.

Test Plan: Imported from OSS

Reviewed By: mrshenli

Differential Revision: D27438451

Pulled By: mruberry

fbshipit-source-id: c7292b4d5709185b42b4aac8016648562688040e
 2021-04-07 23:53:02 -07:00
Jane Xu
2e9eb5afa2 Use slow tests stats in common_utils (#55190) 
Summary:
This is a step in adding automatic slowTest detection to our testing infrastructure. This uses stats (updated daily) in https://github.com/pytorch/test-infra/blob/master/stats/.pytorch-slow-tests to determine whether more tests need to be marked as slow as they are run.

More details in previous PR draft/proposal [here](https://github.com/pytorch/pytorch/pull/54456#issue-598388491), though I no longer think we need the third step as using a raw git file does not require much processing.

Upon looking at [logs](https://circleci.com/api/v1.1/project/github/pytorch/pytorch/12060292/output/107/0?file=true&allocation-id=606660dbd8e5857bcc2b2e0f-0-build%2F60DCA8CD) for the coverage tests as of the first commit [when I had not skipped the tests so we could see their actual times], here are some slow tests that weren't marked as slow before:
```
test_fn_gradgrad_unfold_cpu_complex128 (__main__.TestGradientsCPU) (172.554s)
test_matmul_4d_4d_complex_cpu (__main__.TestAutogradDeviceTypeCPU) (180.057s)
test_conv1d_basic (__main__.TestXNNPACKConv1dTransformPass) (94.737s)
```

And here is a test that wasn't actually slow but was still marked as slow based on stats:
```
test_trunc_normal (__main__.TestNNInit) ... ok (1.208s)
```

The new logs show the above tests as skipped (as they should be):
[Coverage Test 1](https://app.circleci.com/pipelines/github/pytorch/pytorch/296224/workflows/ba6c2917-51f8-4fb8-be57-90151c2e5c25/jobs/12126156) and [Coverage Test 2](https://app.circleci.com/pipelines/github/pytorch/pytorch/296224/workflows/ba6c2917-51f8-4fb8-be57-90151c2e5c25/jobs/12126155)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/55190

Reviewed By: samestep

Differential Revision: D27566663

Pulled By: janeyx99

fbshipit-source-id: c13f8c676bb8eb15d9d697d224dbaef7df98aef3
 2021-04-07 08:04:39 -07:00
lezcano
fd02fc5d71 Port put_ and take from TH to ATen (#53356) 
Summary:
The two ports were don together, as they can be implemented with the same kernel. In TH, they were already implemented with the same kernel.

Resolves https://github.com/pytorch/pytorch/issues/24751
Resolves https://github.com/pytorch/pytorch/issues/24614
Resolves https://github.com/pytorch/pytorch/issues/24640
Resolves https://github.com/pytorch/pytorch/issues/24772

This port makes sure that it interacts correctly with the "deterministic algorithms" flag, as done in https://github.com/pytorch/pytorch/pull/51388

This PR also makes these two functions correct in the following aspects (all of them added to the tests as well):
- Support for complex numbers
- Correct handling of scalar inputs and zero-dimensional inputs
- Implementation that does not do any copies nor sorting of any of the input tensors
- Faster and more correct implementation of the backwards (now it works as it should when `source.shape() != index.shape()`)
- Now `put_(..., accumulate=True)` is implemented correctly with atomic operations on GPU / CPU (when possible) and is deterministic (modulo the loss of precision that might happen due to the reordering of a sum of floats)
- Adds the `torch.put` function that was missing, (`index_put` exists, for example)
- Corrected docs

It also adds a much more thorough testing to the operations and their gradients.

There is a BC-breaking change, and that is that now we check that the inputs do not overlap in the `put_` operation. This was handled (some of the cases, other cases were wrong) in the TH implementation by making contiguous copies of the inputs. How should we handle this one?

**Edit.** Benchmarks:
<details>
<summary>Script</summary>

```python
from IPython import get_ipython
import torch
from itertools import product

torch.manual_seed(13)
torch.set_num_threads(1)

ipython = get_ipython()

cpu = torch.device('cpu')
cuda = torch.device('cuda')

def run_test(ndims, size, index_len, device, cmd):
    print(f"cmd: {cmd}, ndims: {ndims}, tensor_size: {size}, index_len: {index_len}, device: {device}")

    large_tensor = torch.rand(*([size] * ndims), device=device)
    small_tensor = torch.rand((index_len,), device=device)
    index = torch.randint(size * ndims, (index_len,), dtype=torch.long, device=device)
    if cmd == "put":
        command = "large_tensor.put_(index, small_tensor, accumulate=False)"
        if device == cuda:
            command += "; torch.cuda.synchronize()"
    elif cmd == "accumulate":
        command = "large_tensor.put_(index, small_tensor, accumulate=True)"
        if device == cuda:
            command += "; torch.cuda.synchronize()"
    elif cmd == "take":
        command = "torch.take(large_tensor, index)"
        if device == cuda:
            command += "; torch.cuda.synchronize()"
    ipython.magic(f"timeit {command}")
    print()

for method, device in product(["accumulate", "put", "take"], [cpu, cuda]):
    run_test(3, 1000, 10, device, method)
    run_test(3, 1000, 1000, device, method)
    run_test(3, 1000, 10000, device, method)
    run_test(2, 10000, 100000, device, method)
```
</details>

```python
put_(accumulate=False)
```

<details>
<summary>ATen CPU (1.5x - 2x speedup)</summary>

```python
cmd: put, ndims: 3, tensor_size: 1000, index_len: 10, device: cpu
1.05 µs ± 2.35 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

cmd: put, ndims: 3, tensor_size: 1000, index_len: 1000, device: cpu
3.15 µs ± 5.13 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: put, ndims: 3, tensor_size: 1000, index_len: 10000, device: cpu
21.6 µs ± 13.1 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

cmd: put, ndims: 2, tensor_size: 10000, index_len: 100000, device: cpu
238 µs ± 781 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
</details>

<details>
<summary>TH CPU</summary>

```python
cmd: put, ndims: 3, tensor_size: 1000, index_len: 10, device: cpu
722 ns ± 2.67 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

cmd: put, ndims: 3, tensor_size: 1000, index_len: 1000, device: cpu
4.89 µs ± 18.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: put, ndims: 3, tensor_size: 1000, index_len: 10000, device: cpu
42.5 µs ± 96.3 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

cmd: put, ndims: 2, tensor_size: 10000, index_len: 100000, device: cpu
428 µs ± 774 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
</details>
<details>
<summary>ATen GPU (same speed)</summary>

```python
cmd: put, ndims: 3, tensor_size: 1000, index_len: 10, device: cuda
8.99 µs ± 16 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: put, ndims: 3, tensor_size: 1000, index_len: 1000, device: cuda
10.4 µs ± 24.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: put, ndims: 3, tensor_size: 1000, index_len: 10000, device: cuda
10.4 µs ± 11.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: put, ndims: 2, tensor_size: 10000, index_len: 100000, device: cuda
15.6 µs ± 1.12 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
</details>

<details>
<summary>TH GPU</summary>

```python
cmd: put, ndims: 3, tensor_size: 1000, index_len: 10, device: cuda
8.44 µs ± 31.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: put, ndims: 3, tensor_size: 1000, index_len: 1000, device: cuda
9.09 µs ± 4.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: put, ndims: 3, tensor_size: 1000, index_len: 10000, device: cuda
9.77 µs ± 0.998 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: put, ndims: 2, tensor_size: 10000, index_len: 100000, device: cuda
15.8 µs ± 5.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
</details>

```python
put_(accumulate=True)
```

<details>
<summary>ATen CPU (x2 speedup)</summary>

```python
cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 10, device: cpu
1.12 µs ± 2.91 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 1000, device: cpu
3.14 µs ± 2.05 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 10000, device: cpu
20.8 µs ± 25.9 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

cmd: accumulate, ndims: 2, tensor_size: 10000, index_len: 100000, device: cpu
264 µs ± 263 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
</details>

<details>
<summary>TH CPU</summary>

```python
cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 10, device: cpu
814 ns ± 1.87 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 1000, device: cpu
5.11 µs ± 6.02 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 10000, device: cpu
43.9 µs ± 49.4 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

cmd: accumulate, ndims: 2, tensor_size: 10000, index_len: 100000, device: cpu
442 µs ± 1.07 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
</details>
<details>
<summary>ATen GPU (3x - 11x speedup)</summary>

```python
cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 10, device: cuda
9.01 µs ± 14.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 1000, device: cuda
10.4 µs ± 15.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 10000, device: cuda
10.3 µs ± 44.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: accumulate, ndims: 2, tensor_size: 10000, index_len: 100000, device: cuda
12.6 µs ± 19 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
</details>

<details>
<summary>TH GPU</summary>

```python
cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 10, device: cuda
34.7 µs ± 131 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 1000, device: cuda
38.2 µs ± 116 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

cmd: accumulate, ndims: 3, tensor_size: 1000, index_len: 10000, device: cuda
61.2 µs ± 50.4 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

cmd: accumulate, ndims: 2, tensor_size: 10000, index_len: 100000, device: cuda
140 µs ± 24.2 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
</details>

```python
take()
```

<details>
<summary>ATen CPU (1.1x speedup)</summary>

```python
cmd: take, ndims: 3, tensor_size: 1000, index_len: 10, device: cpu
1.18 µs ± 2.34 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

cmd: take, ndims: 3, tensor_size: 1000, index_len: 1000, device: cpu
2.79 µs ± 2.96 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: take, ndims: 3, tensor_size: 1000, index_len: 10000, device: cpu
16.6 µs ± 10.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: take, ndims: 2, tensor_size: 10000, index_len: 100000, device: cpu
161 µs ± 984 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
</details>

<details>
<summary>TH CPU</summary>

```python
cmd: take, ndims: 3, tensor_size: 1000, index_len: 10, device: cpu
1.1 µs ± 3.14 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

cmd: take, ndims: 3, tensor_size: 1000, index_len: 1000, device: cpu
2.93 µs ± 7.31 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: take, ndims: 3, tensor_size: 1000, index_len: 10000, device: cpu
18.6 µs ± 14.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: take, ndims: 2, tensor_size: 10000, index_len: 100000, device: cpu
178 µs ± 139 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
</details>
<details>
<summary>ATen GPU (same speed)</summary>

```python
cmd: take, ndims: 3, tensor_size: 1000, index_len: 10, device: cuda
9.38 µs ± 23.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: take, ndims: 3, tensor_size: 1000, index_len: 1000, device: cuda
10.7 µs ± 9.77 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: take, ndims: 3, tensor_size: 1000, index_len: 10000, device: cuda
10.6 µs ± 107 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: take, ndims: 2, tensor_size: 10000, index_len: 100000, device: cuda
11.5 µs ± 21.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
</details>

<details>
<summary>TH GPU</summary>

```python
cmd: take, ndims: 3, tensor_size: 1000, index_len: 10, device: cuda
9.31 µs ± 7.57 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: take, ndims: 3, tensor_size: 1000, index_len: 1000, device: cuda
9.52 µs ± 5.78 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: take, ndims: 3, tensor_size: 1000, index_len: 10000, device: cuda
9.73 µs ± 17.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

cmd: take, ndims: 2, tensor_size: 10000, index_len: 100000, device: cuda
11.7 µs ± 5.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
</details>

cc mruberry

Pull Request resolved: https://github.com/pytorch/pytorch/pull/53356

Reviewed By: mruberry

Differential Revision: D27520243

Pulled By: ngimel

fbshipit-source-id: e3979349c2c62d2949e09fb05e5fd4883fbc9093
 2021-04-05 18:05:38 -07:00
Heitor Schueroff
6d87b3667f Added support for TensorList inputs in OpInfo (#54922) 
Summary:
Stack:
* https://github.com/pytorch/pytorch/issues/54954 Fixed OpInfo jit tests failing for TensorList inputs
* __#54922 Added support for TensorList inputs in OpInfo__

Updated OpInfo to accept either a `Tensor` or `TensorList` as `sample.input` and added workarounds to make this work with gradcheck.

Note: JIT testing support for TensorList inputs will be added in a follow up PR.

Fixes https://github.com/pytorch/pytorch/issues/51996

Pull Request resolved: https://github.com/pytorch/pytorch/pull/54922

Reviewed By: H-Huang

Differential Revision: D27448952

Pulled By: heitorschueroff

fbshipit-source-id: 3f24a56f6180eb2d044dcfc89ba59fce8acfe278
 2021-03-31 04:42:10 -07:00
Edward Yang
b5ab348253 Fix missing format string qualifier (#54705) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54705

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Test Plan: Imported from OSS

Reviewed By: bdhirsh

Differential Revision: D27338808

Pulled By: ezyang

fbshipit-source-id: b21c931c2306e525bc444766bc203bb303868dbf
 2021-03-27 11:55:36 -07:00
Nikita Vedeneev
61b074581c torch.prod backward for complex types. (#48125) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/53511
torch.det does depend on torch.prod, which in turn depends on several other functions, and they also depend on torch.prod, so there is a circular relationship, hence this PR will enable complex backward support for several functions at once.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/48125

Reviewed By: pbelevich

Differential Revision: D27188589

Pulled By: anjali411

fbshipit-source-id: bbb80f8ecb83a0c3bea2b917627d3cd3b84eb09a
 2021-03-19 09:44:08 -07:00
Edward Yang
a2a7179695 Fix bug in assertRaises NotImplemented handling when no exception is thrown (#54126) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54126

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Test Plan: Imported from OSS

Reviewed By: agolynski, mruberry

Differential Revision: D27109510

Pulled By: ezyang

fbshipit-source-id: ba5a4de85ca00f81724f3d4e645797e8f32aa3b1
 2021-03-17 12:30:51 -07:00
Edward Yang
c2f41b6b84 Add meta device to generic device testing framework, skip NotImplementedError (#53682) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53682

With this, under the meta device, 101 tests passed and 16953 skipped.
It ain't much, but it's a start.

Some various bits and bobs:
- NotImplementedError suppression at test level is implemented
  in the same way as CUDA memory leak check, i.e., by wrapping
  test methods and monkeypatching them back in.
- I had to reimplement assertRaises/assertRaisesRegex from scratch to
  ignore NotImplementedError when _ignore_not_implemented_error is True.
  The implementation relies on a small amount of private API that hasn't
  changed since 2010
- expectedAlertNondeterministic doesn't really work so I skipped them
  all; there's probably a way to do it better

I tested this using `pytest --disable-warnings --tb=native -k meta --sw
test/*.py` and a pile of extra patches to make collection actually work
(lol).

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Test Plan: Imported from OSS

Reviewed By: mruberry

Differential Revision: D26955539

Pulled By: ezyang

fbshipit-source-id: ac21c8734562497fdcca3b614a28010bc4c03d74
 2021-03-14 20:41:19 -07:00
Edward Yang
d47d246206 Add 'noarch' tests which only run in one CI config (#53747) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53747

Fixes #53743

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Test Plan: Imported from OSS

Reviewed By: albanD

Differential Revision: D26971343

Pulled By: ezyang

fbshipit-source-id: cee7aa10063ae674f741406a3af830e4b4f128df
 2021-03-14 20:39:07 -07:00
kshitij12345
00771eff8e [reland] Add OpInfo for bitwise_not (#53181) 
Summary:
Reference: https://github.com/pytorch/pytorch/issues/42515

Note: Reland https://github.com/pytorch/pytorch/issues/51944

Pull Request resolved: https://github.com/pytorch/pytorch/pull/53181

Reviewed By: albanD

Differential Revision: D27004695

Pulled By: mruberry

fbshipit-source-id: 92b4e8c60bb6f3c302907716de040b5c81c8db69
 2021-03-12 14:43:56 -08:00
Nikita Vedeneev
afa1ff8e04 Implements torch.linalg.lstsq (#49093) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/44378 by providing a wider range of drivers similar to what SciPy is doing.

The supported CPU drivers are `gels, gelsy, gelsd, gelss`.
The CUDA interface has only `gels` implemented but only for overdetermined systems.

The current state of this PR:
- [x] CPU interface
- [x] CUDA interface
- [x] CPU tests
- [x] CUDA tests
- [x] Memory-efficient batch-wise iteration with broadcasting which fixes https://github.com/pytorch/pytorch/issues/49252
- [x] docs

Pull Request resolved: https://github.com/pytorch/pytorch/pull/49093

Reviewed By: albanD

Differential Revision: D26991788

Pulled By: mruberry

fbshipit-source-id: 8af9ada979240b255402f55210c0af1cba6a0a3c
 2021-03-12 13:25:55 -08:00
Mike Ruberry
1e992810b5 Revert D26811466: [pytorch][PR] [reland] Add OpInfo for bitwise_not and make ROCM and CUDA OpInfo tests consistent 
Test Plan: revert-hammer

Differential Revision:
D26811466 (a5ada2127d)

Original commit changeset: 8434a7515d83

fbshipit-source-id: 9c2c760e18154a88cf7531e45843a802e3f3d19c
 2021-03-08 15:47:47 -08:00
kshitij12345
a5ada2127d [reland] Add OpInfo for bitwise_not and make ROCM and CUDA OpInfo tests consistent (#53181) 
Summary:
Reference: https://github.com/pytorch/pytorch/issues/42515

This PR also enables the OpInfo tests on ROCM to check the same dtypes that of CUDA.

Note: Reland https://github.com/pytorch/pytorch/issues/51944

Pull Request resolved: https://github.com/pytorch/pytorch/pull/53181

Reviewed By: zhangguanheng66

Differential Revision: D26811466

Pulled By: mruberry

fbshipit-source-id: 8434a7515d83ed859db1b2f916fad81a9deaeb9b
 2021-03-08 03:39:01 -08:00
mattip
54a2498919 Modify tests to use assertWarnsOnceRegex instead of maybeWarnsRegex (#52387) 
Summary:
Related to https://github.com/pytorch/pytorch/issues/50006

Follow on for https://github.com/pytorch/pytorch/issues/48560 to ensure TORCH_WARN_ONCE warnings are caught. Most of this is straight-forward find-and-replace, but I did find one place where the TORCH_WARN_ONCE warning was not wrapped into a python warning.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/52387

Reviewed By: albanD

Differential Revision: D26773387

Pulled By: mruberry

fbshipit-source-id: 5be7efbc8ab4a32ec8437c9c45f3b6c3c328f5dd
 2021-03-08 03:32:14 -08:00
Sam Estep
8c798e0622 Forbid trailing whitespace (#53406) 
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857

These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
  - `GLOSSARY.md`
  - `aten/src/ATen/core/op_registration/README.md`
  - `scripts/README.md`
  - `torch/csrc/jit/codegen/fuser/README.md`

The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```

I looked over the auto-generated changes and didn't see anything that looked problematic.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406

Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377

This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348

Reviewed By: walterddr, seemethere

Differential Revision: D26856620

Pulled By: samestep

fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
 2021-03-05 17:22:55 -08:00
Mike Ruberry
9c2673df46 Revert D26723384: [pytorch][PR] Implements torch.linalg.lstsq 
Test Plan: revert-hammer

Differential Revision:
D26723384 (3ac9013235)

Original commit changeset: c9866a95f140

fbshipit-source-id: 3e5263d71facdc91ca09d7dcbbbe3ba818ee2821
 2021-03-03 15:24:25 -08:00
Nikita Vedeneev
3ac9013235 Implements torch.linalg.lstsq (#49093) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/44378 by providing a wider range of drivers similar to what SciPy is doing.

The supported CPU drivers are `gels, gelsy, gelsd, gelss`.
The CUDA interface has only `gels` implemented but only for overdetermined systems.

The current state of this PR:
- [x] CPU interface
- [x] CUDA interface
- [x] CPU tests
- [x] CUDA tests
- [x] Memory-efficient batch-wise iteration with broadcasting which fixes https://github.com/pytorch/pytorch/issues/49252
- [x] docs

Pull Request resolved: https://github.com/pytorch/pytorch/pull/49093

Reviewed By: H-Huang

Differential Revision: D26723384

Pulled By: mruberry

fbshipit-source-id: c9866a95f14091955cf42de22f4ac9e2da009713
 2021-03-02 19:00:07 -08:00
Howard Huang
2444b4d122 Add wait_for_worker param to TCPStore and fix port in use flaky test failures (#52888) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/52888

Test Plan: Imported from OSS

Reviewed By: glaringlee

Differential Revision: D26678707

Pulled By: H-Huang

fbshipit-source-id: 5662e60c4d06d88d2e57834f496b52fb7600de29
 2021-03-02 14:31:33 -08:00
Jane Xu
f71d9e28f9 Store test filename in test report path (#52791) 
Summary:
This way, we can have a mapping from the test files we directly execute (the tests [here](https://github.com/pytorch/pytorch/blob/master/test/run_test.py#L20)) to the test suites that we store data for in XML reports.

This will come in use later for categorizing the tests we run in CI.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/52791

Reviewed By: samestep

Differential Revision: D26655086

Pulled By: janeyx99

fbshipit-source-id: 94be32f80d7bc0ea1a7a11d4c4b1d3d8e774c5ea
 2021-02-25 07:53:30 -08:00
Heitor Schueroff
08d7f29601 Add discontiguous kwarg to make_tensor (#51985) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/51985

Test Plan: Imported from OSS

Reviewed By: mruberry

Differential Revision: D26375733

Pulled By: heitorschueroff

fbshipit-source-id: bb7831dc28c24b90c6f83885681eeccfdbb83438
 2021-02-24 08:57:24 -08:00
Rong Rong (AI Infra)
e8ab58bfc7 [reland] Early terminate CUDA on common_utils TestCases (#52126) 
Summary:
Take 2 of https://github.com/pytorch/pytorch/issues/50914
This change moves the early termination logic into common_utils.TestCase class.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/52126

Test Plan: CI with ci-all tag

Reviewed By: malfet

Differential Revision: D26391762

Pulled By: walterddr

fbshipit-source-id: a149ecc47ccda7f2795e107fb95915506ae060b4
 2021-02-12 07:32:42 -08:00
Nikita Shulga
9f1f5636d7 Revert D26019289: [pytorch][PR] Early terminate CUDA on common_utils TestCases 
Test Plan: revert-hammer

Differential Revision:
D26019289 (c1b7ca8062)

Original commit changeset: ddc7c1c0d00d

fbshipit-source-id: 6902d03fa06cda5d03191846bc4dd98af501b594
 2021-02-10 17:29:10 -08:00
Rong Rong (AI Infra)
c1b7ca8062 Early terminate CUDA on common_utils TestCases (#50914) 
Summary:
This is a follow up on https://github.com/pytorch/pytorch/issues/49869.

Previously CUDA early termination only happens for generic test classes that extends from `DeviceTypeTestBase`. However, JIT test cases which extends from common_utils.TestCase cannot benefit from the early termination.

This change moves the early termination logic into common_utils.TestCase class.
- all tests extended from common_utils.TestCase now should early terminate if CUDA assert occurs.
- For TestCases that extends from common_device_type.DeviceTypeTestBase, still only do torch.cuda.synchronize() when RTE is thrown.
- For TestCases extends common_utils.TestCase, regardless of whether a test case uses GPU or not, it will always synchronize CUDA as long as `torch.cuda.is_initialize()` returns true.
- Disabling this on common_distributed.py

Pull Request resolved: https://github.com/pytorch/pytorch/pull/50914

Reviewed By: malfet

Differential Revision: D26019289

Pulled By: walterddr

fbshipit-source-id: ddc7c1c0d00db4d073a6c8bc5b7733637a7e77d1
 2021-02-10 07:15:40 -08:00
Mike Ruberry
594a66d778 Warn about floor_divide performing incorrect rounding (#50281) (#50281) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50281

Pull Request resolved: https://github.com/pytorch/pytorch/pull/51745

Test Plan: Imported from OSS

Reviewed By: ngimel

Pulled By: mruberry

Differential Revision: D26257855

fbshipit-source-id: e5d497cf07b0c746838ed081c5d0e82fb4cb701b
 2021-02-10 03:13:34 -08:00
mattip
b97a040f71 ENH: toggle TORCH_WARN_ONCE to TORCH_WARN for tests (#48560) 
Summary:
Toward fixing https://github.com/pytorch/pytorch/issues/47624

~Step 1: add `TORCH_WARN_MAYBE` which can either warn once or every time in c++, and add a c++ function to toggle the value.
Step 2 will be to expose this to python for tests. Should I continue in this PR or should we take a different approach: add the python level exposure without changing any c++ code and then over a series of PRs change each call site to use the new macro and change the tests to make sure it is being checked?~

Step 1: add a python and c++ toggle to convert TORCH_WARN_ONCE into TORCH_WARN so the warnings can be caught in tests
Step 2: add a python-level decorator to use this toggle in tests
Step 3: (in future PRs): use the decorator to catch the warnings instead of `maybeWarnsRegex`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/48560

Reviewed By: ngimel

Differential Revision: D26171175

Pulled By: mruberry

fbshipit-source-id: d83c18f131d282474a24c50f70a6eee82687158f
 2021-02-08 08:21:19 -08:00
Peter Bell
b150f150ba Add division overload with rounding_mode selection (#51706) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51706

Pull Request resolved: https://github.com/pytorch/pytorch/pull/50280

As mentioned in gh-43874, this adds a `rounding_mode={'true', 'trunc', 'floor'}`
argument so `torch.div` can be used as a replacement for `floor_divide` during
the transitional period.

I've included dedicated kernels for truncated and floor division which
aren't strictly necessary for float, but do perform significantly better (~2x) than
doing true division followed by a separate rounding kernel.

Note: I introduce new overloads for `aten::div` instead of just adding a default
`rounding_mode` because various JIT passes rely on the exact operator schema.

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D26123271

Pulled By: mruberry

fbshipit-source-id: 51a83717602114597ec9c4d946e35a392eb01d46
 2021-02-04 13:08:36 -08:00
Jeffrey Wan
c0966914bc Internal gradcheck wrapper in testing._internal that sets certain flags to True (#51133) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/49409

There are many call sites where, gradcheck/gradgradcheck is now being implicitly invoked with `check_batched_grad` as True, but they were previously False. Cases fall into two basic categories:
1) the call site was previously using `torch.autograd.gradcheck` but is now changed to use the globally imported function instead
3) the call site was already using globally imported function, but does not explicitly pass `check_batched_grad` flag

Only in the _assertGradAndGradgradChecks cases, which are infrequent, I assumed that the the author is aware that omitting the flag means not applying check_batched_grad=True. (but maybe that is not the case?)

Overall this PR in its current state assumes that unless the author explicitly specified `check_batched_grad=False`, they were just probably not aware of this flag and did not mean to have this flag as False.

So far exceptions to the above (as discovered by CI) include:
 - Mkldnn (opaque tensors do not have strides) https://app.circleci.com/pipelines/github/pytorch/pytorch/264416/workflows/e4d87886-6247-4305-8526-2696130aa9a4/jobs/10401882/tests
 - all cases in test_sparse (https://app.circleci.com/pipelines/github/pytorch/pytorch/264553/workflows/3c1cbe30-830d-4acd-b240-38d833dccd9b/jobs/10407103)
 - all cases in test_overrides (https://app.circleci.com/pipelines/github/pytorch/pytorch/264553/workflows/3c1cbe30-830d-4acd-b240-38d833dccd9b/jobs/10407236)
 - test_autograd (test_LSTM_grad_and_gradgrad) - (https://app.circleci.com/pipelines/github/pytorch/pytorch/264553/workflows/3c1cbe30-830d-4acd-b240-38d833dccd9b/jobs/10407235)
 - test_data_parallel (test_data_parallel_buffers_requiring_grad) - *SIGSEGV* (https://app.circleci.com/pipelines/github/pytorch/pytorch/264820/workflows/14d89503-040d-4e3d-9f7b-0bc04833589b/jobs/10422697)
 - test_nn (https://app.circleci.com/pipelines/github/pytorch/pytorch/264919/workflows/df79e3ed-8a31-4a8e-b584-858ee99686ff/jobs/10427315)

Possible TODO is to prevent new tests from invoking external gradcheck.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/51133

Reviewed By: ezyang

Differential Revision: D26147919

Pulled By: soulitzer

fbshipit-source-id: dff883b50f337510a89f391ea2fd87de2d531432
 2021-01-29 09:13:37 -08:00
Kurt Mohler
8ab1a1495d Rename set_deterministic to use_deterministic_algorithms (#49904) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/49100

Pull Request resolved: https://github.com/pytorch/pytorch/pull/49904

Reviewed By: ezyang, mrshenli

Differential Revision: D25956761

Pulled By: mruberry

fbshipit-source-id: 86a59289d50825a0ebbd7c358b483c8d8039ffa6
 2021-01-22 11:27:07 -08:00
Peter Bell
0436ea125b OpInfo: Remove promotes_integers_to_float and infer it instead (#50279) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50279

This allows different sample inputs to have different behavior for the same
operator. For example, `div(..., rounding_mode='true')` will promote but other
rounding modes don't. The current boolean flag is too restrictive to allow this.

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D25950011

Pulled By: mruberry

fbshipit-source-id: 7e82b82bedc626b2b6970d92d5b25676183ec384
 2021-01-22 09:32:37 -08:00
Richard Zou
c7d348fea6 Turn on batched grad testing for non-autogenerated tests in test_nn.py (#50739) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50739

This does not turn on batched grad testing for autogenerated NewModuleTest
tests and CriterionTest tests. Those are coming later.

Test Plan: - run tests

Reviewed By: ejguan

Differential Revision: D25997677

Pulled By: zou3519

fbshipit-source-id: b4b2d68e0f99c3d573faf237e1e531d0b3fced40
 2021-01-22 07:40:20 -08:00
Ivan Yashchuk
f9a5ba7398 Added linalg.slogdet (#49194) 
Summary:
This PR adds `torch.linalg.slogdet`.

Changes compared to the original torch.slogdet:

- Complex input now works as in NumPy
- Added out= variant (allocates temporary and makes a copy for now)
- Updated `slogdet_backward` to work with complex input

Ref. https://github.com/pytorch/pytorch/issues/42666

Pull Request resolved: https://github.com/pytorch/pytorch/pull/49194

Reviewed By: VitalyFedyunin

Differential Revision: D25916959

Pulled By: mruberry

fbshipit-source-id: cf9be8c5c044870200dcce38be48cd0d10e61a48
 2021-01-19 07:28:12 -08:00
Jeffrey Wan
6e3e57095c Add complex support for torch.nn.L1Loss (#49912) 
Summary:
Building on top of the work of anjali411 (https://github.com/pytorch/pytorch/issues/46640)

Things added in this PR:
1. Modify backward and double-backward formulas
2. Add complex support for `new module tests` and criterion tests (and add complex tests for L1)
3. Modify some existing tests to support complex

Pull Request resolved: https://github.com/pytorch/pytorch/pull/49912

Reviewed By: zhangguanheng66

Differential Revision: D25853036

Pulled By: soulitzer

fbshipit-source-id: df619f1b71c450ab2818eb17804e0c55990aa8ad
 2021-01-15 15:53:15 -08:00
Rong Rong (AI Infra)
0abe7f5ef6 [BE] fix subprocess wrapped test cases reported as failure (#50515) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/49901.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/50515

Reviewed By: janeyx99

Differential Revision: D25907836

Pulled By: walterddr

fbshipit-source-id: f6f3aa4c1222bf866077275d28ba637eeaef10c5
 2021-01-14 08:05:40 -08:00
Richard Barnes
b89827b73f Drop unused imports (#49972) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49972

From
```
./python/libcst/libcst codemod remove_unused_imports.RemoveUnusedImportsWithGlean --no-format caffe2/
```

Test Plan: Standard sandcastle tests

Reviewed By: xush6528

Differential Revision: D25727352

fbshipit-source-id: 6b90717e161aeb1da8df30e67d586101d35d7d5f
 2021-01-13 12:26:17 -08:00
Rong Rong (AI Infra)
71766d89ea [BE] unified run_process_no_exception code (#49774) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49774

Reviewed By: janeyx99

Differential Revision: D25756811

Pulled By: walterddr

fbshipit-source-id: 4d2b3bd772572764ff96e5aad70323b58393e332
 2021-01-04 13:43:09 -08:00
peter
8d7338e820 Enable tests using named temp files on Windows (#49640) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49640

Reviewed By: ngimel

Differential Revision: D25681548

Pulled By: malfet

fbshipit-source-id: 0e2b25817c98d749920cb2b4079033a2ee8c1456
 2020-12-29 09:57:35 -08:00
Nikita Shulga
12942ea52b [BE] Introduce set_cwd context manager (#49657) 
Summary:
Used to temporarily change working directory, but restore it even if exception is raised
Use it in test_type_hints and during code coverage collection

Pull Request resolved: https://github.com/pytorch/pytorch/pull/49657

Reviewed By: walterddr

Differential Revision: D25660543

Pulled By: malfet

fbshipit-source-id: 77f08d57e4b60b95daa4068d0dacf7c25f978526
 2020-12-21 12:08:48 -08:00
Xiong Wei
3779bdec56 Implementing NumPy-like function torch.broadcast_to (#48997) 
Summary:
Related https://github.com/pytorch/pytorch/issues/38349

Implement NumPy-like function `torch.broadcast_to` to broadcast the input tensor to a new shape.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/48997

Reviewed By: anjali411, ngimel

Differential Revision: D25663937

Pulled By: mruberry

fbshipit-source-id: 0415c03f92f02684983f412666d0a44515b99373
 2020-12-21 11:24:50 -08:00
Xiang Gao
2ea1d97e3b Add BFloat16 support for isinf and isfinite (#49356) 
Summary:
Also fix some tests.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/49356

Reviewed By: mruberry

Differential Revision: D25604364

Pulled By: ngimel

fbshipit-source-id: 9efdd83aaa96cacc66e9689db9f9d8c24175a693
 2020-12-16 22:36:14 -08:00
Chester Liu
3a943e9f82 Use Unicode friendly API on Win32 in THAllocator (#47905) 
Summary:
This replaces the narrow character set APIs with the wide character set ones in `THAllocator.cpp`. This fixes the potential crashes caused by passing non-ASCII characters in `torch::from_file` on Windows.

See: https://github.com/pytorch/pytorch/issues/47422

Pull Request resolved: https://github.com/pytorch/pytorch/pull/47905

Reviewed By: zhangguanheng66

Differential Revision: D25399146

Pulled By: ezyang

fbshipit-source-id: 0a183b65de171c48ed1718fa71e773224eaf196f
 2020-12-14 14:24:20 -08:00
Rong Rong
69522410fa add user vs internal msg support in common_utils.TestCase (#48935) 
Summary:
should fixes https://github.com/pytorch/pytorch/issues/48879.

To test the effect of the messages: make test break, such as add `self.assertEqual(1, 2, "user_msg")` to any test
* Before:
```
AssertionError: False is not true : user_msg
```
* After
```
AssertionError: False is not true : Scalars failed to compare as equal! Comparing 1 and 2 gives a difference of 1, but the allowed difference with rtol=0 and atol=0 is only 0!
user_msg;
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/48935

Reviewed By: samestep

Differential Revision: D25382153

Pulled By: walterddr

fbshipit-source-id: 95633a9f664f4b05a28801786b12a10bd21ff431
 2020-12-10 15:25:46 -08:00
Jeff Daily
d5c4a80cfd Allow ROCm CI to use non-default stream. (#48424) 
Summary:
Revert https://github.com/pytorch/pytorch/issues/26394. Fixes https://github.com/pytorch/pytorch/issues/27356.  Not all MIOpen handles were setting their stream to the current stream prior to running the op.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/48424

Reviewed By: H-Huang

Differential Revision: D25420384

Pulled By: mruberry

fbshipit-source-id: 051683ba9e3d264b71162bd344031a0c58bf6a41
 2020-12-10 09:55:11 -08:00
Ivan Yashchuk
85121a7a0f Added CUDA support for complex input for torch.cholesky_solve (#47047) 
Summary:
`torch.cholesky_solve` now works for complex inputs on GPU.
I moved the existing tests to `test_linalg.py` and modified them to test complex and float32 dtypes.
Differentiation also works correctly with complex inputs now.

Ref. https://github.com/pytorch/pytorch/issues/33152

Pull Request resolved: https://github.com/pytorch/pytorch/pull/47047

Reviewed By: ngimel

Differential Revision: D24730020

Pulled By: mruberry

fbshipit-source-id: 95402da5789c56e5a682019790985207fa28fa1f
 2020-12-05 20:18:30 -08:00