Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48668
Combine tests for `fmod` and `remainder`.
## BC-breaking Note:
In order to make `remainder` operator have type promotion, we have to introduce BC breaking.
### 1.7.1:
In the case where the second argument is a python number, the result is casted to the dtype of the first argument.
```python
>>> torch.remainder(x, 1.2)
tensor([0, 0, 0, 0, 0], dtype=torch.int32)
```
### This PR:
In the case where the second argument is a python number, the dtype of result is determined by type promotion of both inputs.
```python
>>> torch.remainder(x, 1.2)
tensor([1.0000, 0.8000, 0.6000, 0.4000, 0.2000])
```
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D25869136
Pulled By: ejguan
fbshipit-source-id: 8e5e87eec605a15060f715952de140f25644008c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48278
Remove various lines from tests due to no type promotion introduced from #47323
## BC-breaking Note:
In order to make `fmod` operator have type promotion, we have to introduce BC breaking.
### 1.7.1:
In the case where the second argument is a python number, the result is casted to the dtype of the first argument.
```python
>>> torch.fmod(x, 1.2)
tensor([0, 0, 0, 0, 0], dtype=torch.int32)
```
### Prior PR:
Check the BC-breaking note of #47323
### This PR:
In the case where the second argument is a python number, the dtype of result is determined by type promotion of both inputs.
```python
>>> torch.fmod(x, 1.2)
tensor([1.0000, 0.8000, 0.6000, 0.4000, 0.2000])
```
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D25869137
Pulled By: ejguan
fbshipit-source-id: bce763926731e095b75daf2e934bff7c03ff0832
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50105
There should be no functional change here.
A couple of reasons here:
1) This function is generally an anti-pattern (https://github.com/pytorch/pytorch/issues/49758) and it is good to minimize its usage in the code base.
2) pow itself has a fair amount of smarts like not broadcasting scalar/tensor combinations and we should defer to it.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D25786172
Pulled By: gchanan
fbshipit-source-id: 89de03aa0b900ce011a62911224a5441f15e331a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49926
While investigating https://github.com/pytorch/pytorch/issues/49758, I changed the xlogy kernel to use the recommended wrapped_scaler_tensor pattern instead of moving the scalar to the GPU as a tensor.
While this doesn't avoid a synchronization (there is no synchronization in the move, as its done via fill), this does significantly speed up the GPU kernel (almost ~50%, benchmark in PR comments).
From looking at the nvprof output, it looks like this code path avoids broadcasting. Aside: this seems unnecessary, as there is nothing special from the point-of-view of broadcasting whether the Tensor
is ()-sized or marked as a wrapped_scalar. Still, this is a useful change to make as we avoid extra kernel launches and dispatches to create and fill the tensor.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D25724215
Pulled By: gchanan
fbshipit-source-id: 4adcd5d8b3297502672ffeafc77e8af80592f460
Summary:
Fixes https://github.com/pytorch/pytorch/issues/49214
**BC-Breaking**
Before this PR, `%=` didn't actually do the operation inplace and returned a new tensor.
After this PR, `%=` operation is actually inplace and the modified input tensor is returned.
Before PR,
```python
>>> import torch
>>> a = torch.tensor([11,12,13])
>>> id(a)
139627966219328
>>> a %= 10
>>> id(a)
139627966219264
```
After PR,
```python
>>> import torch
>>> a = torch.tensor([11,12,13])
>>> id(a)
139804702425280
>>> a %= 10
>>> id(a)
139804702425280
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49390
Reviewed By: izdeby
Differential Revision: D25560423
Pulled By: zou3519
fbshipit-source-id: 2b92bfda260582aa4ac22c4025376295e51f854e
Summary:
Test should verify, that all listed conditions throw, not just the first one
Refactor duplicated constants
Use `self.assertTrue()` instead of suppressing flake8 `B015: Pointless Comparison` warning
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48597
Reviewed By: mruberry
Differential Revision: D25222734
Pulled By: malfet
fbshipit-source-id: 7854f755a84f23a1a52dc74402582e34d69ff984
Summary:
Creates multiple new test suites to have fewer tests in test_torch.py, consistent with previous test suite creation like test_unary_ufuncs.py and test_linalg.py.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47356
Reviewed By: ngimel
Differential Revision: D25202268
Pulled By: mruberry
fbshipit-source-id: 75fde3ca76545d1b32b86d432a5cb7a5ba8f5bb6
