This PR enables all PIE rules on ruff, there are already some enabled rules from this family, the new added rules are
```
PIE796 Enum contains duplicate value: {value}
PIE808 Unnecessary start argument in range
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165814
Approved by: https://github.com/ezyang
This PR enables all PIE rules on ruff, there are already some enabled rules from this family, the new added rules are
```
PIE796 Enum contains duplicate value: {value}
PIE808 Unnecessary start argument in range
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165814
Approved by: https://github.com/ezyang
This should help stabilize some flaky test behavior where miopen would pick different solutions for different parts of the same test and the test expects bitwise identical results.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164598
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
It was found that the integration of miopen batchnorm was causing the output to always be in default contig memory format even when the input was channels last. This also unskips a number of related unit tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162112
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
Co-authored-by: Dmitry Nikolaev <dmitry.nikolaev@amd.com>
Co-authored-by: Jithun Nair <37884920+jithunnair-amd@users.noreply.github.com>
This would allow me to enable more opinfo tests against MPS device eventually and supposed to be a very simple test, but actually required minor adjustments to lots of test files, namely:
- Introduce `all_mps_types_and` that is very similar to `all_types_and`, but skips `float64`
- Decorate lots of tests with `@dtypesIfMPS(*all_mps_types())`
- Skip `test_from_dlpack_noncontinguous` as it currently crashes (need to be fixed)
- Add lots of `expectedFailureIfMPS`
- Delete all `@onlyNativeDeviceTypesAnd("mps")`
<sarcasm> I love how well documented this variable are </sarcasm>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153835
Approved by: https://github.com/Skylion007
Fixes#154615
Enables using ConvTranspose3D since it seems support exists both on MacOS 14 and 15.
For the half dtypes the discrepancy of CPU and GPU implementations is too large to conclude whether there is a bug in the implementation or not without a more rigorous study on what bounds are there to the expected error. So they are left unsupported for now and an assert is added to notify the user if the op is called with fp16 or bf16 inputs.
Tests for ConvTranspose3D were enabled for the supported data types.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154696
Approved by: https://github.com/malfet
The default benchmark setting is now false. The new miopen behavior means when benchmarking is disabled, for any shape that doesn't have a find hit, then it will do a quick search (same behavior as the prior default), and use that result. Now when benchmark is enabled, it will perform an exhaustive search and update any DBs. miopen immediate mode is still available and is used when deterministic is true and benchmark is false.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145294
Approved by: https://github.com/BrianHarrisonAMD, https://github.com/malfet
Triton 2.2 and greater have a bug where allowing TF32 generation for a GPU that does not support TF32 will cause code generation errors. Patch around this problem by:
1. Adding a function to `torch.cuda` that determines whether CUDA hardware is capable of using the TF32 format.
2. Using that function to explicitly disable TF32 generation when calling Triton, where needed.
To demonstrate that this fix works, try running `test/inductor/test_max_autotune.py` on a GPU with CUDA compute capability < 8 (e.g. any NVIDIA consumer GPU) without this fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145684
Approved by: https://github.com/eqy
Fixes https://github.com/pytorch/pytorch/issues/142466.
Remove the `weight.numel() != 0` check to align the behavior with CUDA for `ConvTranspose` when `out_channels=0`. After removing this check, the existing code is already able to give an empty output in such case.
Test plan:
```
python -u test/nn/test_convolution.py -k test_ConvTranspose_output_channels_0_cpu_float32
python -u test/nn/test_convolution.py -k test_ConvTranspose_output_channels_0_cuda_float32
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142859
Approved by: https://github.com/mingfeima, https://github.com/malfet
Fixes https://github.com/pytorch/pytorch/issues/142466.
Remove the `weight.numel() != 0` check to align the behavior with CUDA for `ConvTranspose` when `out_channels=0`. After removing this check, the existing code is already able to give an empty output in such case.
Test plan:
```
python -u test/nn/test_convolution.py -k test_ConvTranspose_output_channels_0_cpu_float32
python -u test/nn/test_convolution.py -k test_ConvTranspose_output_channels_0_cuda_float32
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142859
Approved by: https://github.com/mingfeima, https://github.com/malfet
Functionally two decorators are very similar, but one should rely on expectedFailure as much as possible to get signal when something is fixed.
- Move `product_version` variable from `test_mps` to common_utils, but call it `MACOS_VERSION`
- Introduce `skipIfMPSOnMacOS13` to decorate the hard crashes that happens only on MacOS13 (which at this point will not get any fixes and will be deprecated soon)
- Add `device_type='mps'` to all `skipIfMPS` per https://github.com/pytorch/pytorch/issues/140560
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139940
Approved by: https://github.com/janeyx99, https://github.com/huydhn
