Summary:
The reference quantized modules for linear / conv / etc fail to torchscript due to two issues
(1) The type of torch.qscheme doesn't script
(2) The "_DTYPE_TO_QVALUE_BOUNDS" values were resolving to union[float, int] instead of just int. We fix that with a hard cast.
See: <internal post> + comments for more context
Test Plan: unit tests + fixing this NB N6923590
Differential Revision: D72652616
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150870
Approved by: https://github.com/jerryzh168
It's not fully clear why these are not being created, but you can definitely
reproduce this in code. `__name__` is fun, since there appears to be no way to
explicitly set it on the pybind11 layer or c++ layer. I've set this in the
python wrapper code (which works correctly). But let me know if people feel
strongly and want us to go explicitly cast to python within the cpp functions
and set it there.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147906
Approved by: https://github.com/jansel
ghstack dependencies: #147894
Over time, a large number of the existing type ignores have become irrelevant/unused/dead as a result of improvements in annotations and type checking.
Having these `# type: ignore` linger around is not ideal for two reasons:
- They are verbose/ugly syntatically.
- They could hide genuine bugs in the future, if a refactoring would actually introduce a bug but it gets hidden by the ignore.
I'm counting over 1500 unused ignores already. This is a first PR that removes some of them. Note that I haven't touched type ignores that looked "conditional" like the import challenge mentioned in https://github.com/pytorch/pytorch/pull/60006#issuecomment-2480604728. I will address these at a later point, and eventually would enable `warn_unused_ignores = True` in the mypy configuration as discussed in that comment to prevent accumulating more dead ignores going forward.
This PR should have no effect on runtime at all.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142325
Approved by: https://github.com/Skylion007, https://github.com/janeyx99
* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
#### Issue
Tensor constant was previously lifted directly as an input in the fx graph, which results errors for multiple test cases with tensor constant. This PR introduces a fix to convert tensor constant to a `GetAttr` in the fx graph.
This PR also introduces other fixes to maintain a valid `state_dict` for exported program when there are tensor constants. In short, after tensor constants are converted as `GetAttr`, they are treated as buffers during retracing. The fix will convert those back from buffer to constant.
#### Test Plan
Add new test cases that generate tensor constants
* `pytest test/export/test_converter.py -s -k test_implicit_constant_to_tensor_handling`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128442
Approved by: https://github.com/angelayi
Fixes#127896
### Description
Add docstring to `torch/jit/frontend.py:get_default_args` function
### Checklist
- [x] The issue that is being fixed is referred in the description
- [x] Only one issue is addressed in this pull request
- [x] Labels from the issue that this PR is fixing are added to this pull request
- [x] No unnecessary issues are included into this pull request
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128408
Approved by: https://github.com/malfet
Summary: When we export already traced module, it seems to be modifying some global state causing the traced modules to fail to run. For now, we are only logging for test cases, so it is probs ok to trace fresh copy to be used in export for now.
Test Plan: CI
Differential Revision: D57983518
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127577
Approved by: https://github.com/pianpwk
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
Resolves#126888
- #126888
This PR is split from PR #126898.
- #126898
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127689
Approved by: https://github.com/Skylion007
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
UPDATE: Use `FutureWarning` instead of `DeprecationWarning`.
Resolves#126888
- #126888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126898
Approved by: https://github.com/albanD
Summary:
Expand TorchScript `__init__` annotation warning to `list` and `dict` with reference to GSD task T187638414 and annotation warning reproduction D56834720.
Currently, the TorchScript compiler ignores and throws `UserWarning`s for the following annotation types for empty values within the `__init__` function: `List`, `Dict`, `Optional`. However, the compiler should additionally cover warnings for `list` and `dict`. This diff adds support for `list` and `dict`.
Test Plan:
Added 4 new unit tests:
`test_annotated_empty_list_lowercase` and `test_annotated_empty_dict_lowercase` verify that TorchScript throws UserWarnings for the list and dict type annotations on empty values.
```
(base) [jananisriram@devvm2248.cco0 /data/users/jananisriram/fbsource/fbcode (e4ce427eb)]$ buck2 test @mode/{opt,inplace} //caffe2/test:jit -- --regex test_annotated_empty_list_lowercase
...
Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0
```
```
(base) [jananisriram@devvm2248.cco0 /data/users/jananisriram/fbsource/fbcode (e4ce427eb)]$ buck2 test @mode/{opt,inplace} //caffe2/test:jit -- --regex test_annotated_empty_dict_lowercase
...
Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0
```
`test_annotated_with_jit_empty_list_lowercase` and `test_annotated_with_jit_empty_dict_lowercase` verify that TorchScript throws UserWarnings for the list and dict type annotations on empty values with the jit annotation.
```
(base) [jananisriram@devvm2248.cco0 /data/users/jananisriram/fbsource/fbcode (e4ce427eb)]$ buck2 test @mode/{opt,inplace} //caffe2/test:jit -- --regex test_annotated_with_jit_empty_list_lowercase
...
Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0
```
```
(base) [jananisriram@devvm2248.cco0 /data/users/jananisriram/fbsource/fbcode (e4ce427eb)]$ buck2 test @mode/{opt,inplace} //caffe2/test:jit -- --regex test_annotated_with_jit_empty_dict_lowercase
...
Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0
```
Differential Revision: D57752002
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127045
Approved by: https://github.com/davidberard98
Summary: We want to track how well torch.jit.trace can be converted to export in large scale. As a first step, we log all of torch.jit.trace unittests whether we can convert the traced module to export module OR we can export the model directly
Test Plan: CI
Differential Revision: D57629682
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126900
Approved by: https://github.com/SherlockNoMad
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/127122
Approved by: https://github.com/kit1980
