**MOTIVATION**
We recently integrated support for Intel Gaudi devices (identified as 'hpu') into the common_device_type framework via the pull request at https://github.com/pytorch/pytorch/pull/126970. This integration allows tests to be automatically instantiated for Gaudi devices upon loading the relevant library. Building on this development, the current pull request extends the utility of these hooks by adapting selected CUDA tests to operate on Gaudi devices. Additionally, we have confirmed that these modifications do not interfere with the existing tests on CUDA devices.
Other accelerators can also extend the functionality by adding the device in the devices list. ( For eg: xpu )
**CHANGES**
Create a separate class for test functions running on CUDA devices
Extend the functionality of these tests to include HPUs
Use instantiate_device_type_tests with targeted attributes to generate device-specific test instances within the new classes
Apply skipIfHPU decorator to bypass tests that are not yet compatible with HPU devices
Previously we had submitted some changes in https://github.com/pytorch/pytorch/pull/140131 . However, deleted that PR due to merge conflicts and other issues.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144387
Approved by: https://github.com/ankurneog, https://github.com/EikanWang, https://github.com/yanboliang, https://github.com/guangyey
Summary:
- When a user specify `TORCHINDUCTOR_MAX_AUTOTUNE=1` env variable, we add `config.max_autotune=True` to the generated minifier_launcher
- We should do this to other inductor configs as well in a followup Diff
Currently in dynamo and aoti minifier, if a config is overwritten by an env variable, the config will not show up in the config list in the minifier_launcher.py file. As a result, when running the minifier_launcher, they need to re-apply the same env variable.
This is:
1) not convenient for the users
2) if they copy-paste the minifier_launcher.py to us without including the env variable, we could be confused and not able to reproduce the error.
Underlying implementation change:
- Add `env_default` parameter to `codegen_config()`. If set, configs overriden by the env are not considered default.
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test:utils -- -r test_codegen_config
```
Differential Revision: D67299312
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143330
Approved by: https://github.com/jansel, https://github.com/eellison
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127125
Approved by: https://github.com/Skylion007
ghstack dependencies: #127122, #127123, #127124
```
Takes in a function which has been printed with print_readable() and constructs kwargs to run it.
Currently only handles Tensor inputs and a graph module which might have tensor constants.
Example:
Consider a function `forward` defined as follows:
>>> def forward(self, primals_1: "f32[1001, 6]"):
... _tensor_constant0: "i64[4190]" = self._tensor_constant0
... # Further implementation
>>> kwargs = aot_graph_input_parser(forward)
>>> forward(**kwargs)
"""
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119409
Approved by: https://github.com/shunting314
Generating reference outputs somtimes fails because of type mismatches in the graph,
an issue which was noticed previously for `prims.convert_element_type` and fixed in #92036
but the same issue happens with other functions such as tensor constructors.
This expands the fix from #92036 to all dtype keyword arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110232
Approved by: https://github.com/ezyang
