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
This is a lot of files changed! Don't panic! Here's how it works:
* Previously, we set `follow_imports = silent` for our mypy.ini configuration. Per https://mypy.readthedocs.io/en/stable/running_mypy.html#follow-imports, what this does is whenever we have an import to a module which is not listed as a file to be typechecked in mypy, we typecheck it as normal but suppress all errors that occurred in that file.
* When mypy is run inside lintrunner, the list of files is precisely the files covered by the glob in lintrunner.toml, but with files in excludes excluded.
* The top-level directive `# mypy: ignore-errors` instructs mypy to typecheck the file as normal, but ignore all errors.
* Therefore, it should be equivalent to set `follow_imports = normal`, if we put `# mypy: ignore-errors` on all files that were previously excluded from the file list.
* Having done this, we can remove the exclude list from .lintrunner.toml, since excluding a file from typechecking is baked into the files themselves.
* torch/_dynamo and torch/_inductor were previously in the exclude list, because they were covered by MYPYINDUCTOR. It is not OK to mark these as `# mypy: ignore-errors` as this will impede typechecking on the alternate configuration. So they are temporarily being checked twice, but I am suppressing the errors in these files as the configurations are not quite the same. I plan to unify the configurations so this is only a temporary state.
* There were some straggler type errors after these changes somehow, so I fixed them as needed. There weren't that many.
In the future, to start type checking a file, just remove the ignore-errors directive from the top of the file.
The codemod was done with this script authored by GPT-4:
```
import glob
exclude_patterns = [
...
]
for pattern in exclude_patterns:
for filepath in glob.glob(pattern, recursive=True):
if filepath.endswith('.py'):
with open(filepath, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write('# mypy: ignore-errors\n\n' + content)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118414
Approved by: https://github.com/thiagocrepaldi, https://github.com/albanD
`@torch.jit.unused` and `@torch.jit.ignore` do not allow to keep in torch scripted class member function, that has non scriptable declaration (e.g. return type)
Adding FunctionModifier _DROP to allow fully skip those functions from scripting and keep them in the code of the scripted class.
E.g. it can be used for:
```
@torch.jit._drop
def __fx_create_arg__(self, tracer: torch.fx.Tracer) -> torch.fx.node.Argument:
# torch.fx classes are not scriptable
return tracer.create_node(
"call_function",
CFX,
args=(tracer.create_arg(self.features),),
kwargs={},
)
def __iter__(self) -> Iterator[torch.Tensor]:
return iter(self.a)
```
Testing:
Added test case in `test/jit/test_types.py` with non-scriptable type annotations (fx.* classes) that fails before fix and passes after.
```
python test/test_jit.py
```
Differential Revision: [D42774830](https://our.internmc.facebook.com/intern/diff/D42774830)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93012
Approved by: https://github.com/davidberard98
Summary:
Python's function parsing from the `ast` module records the line number of the function definition, not the first decorator. So this diff fixes crashes like this:
```
IndexError: vector::_M_range_check: __n (which is 10) >= this->size() (which is 8)
```
Test Plan: New unit test
Differential Revision: D40726352
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87804
Approved by: https://github.com/tugsbayasgalan, https://github.com/davidberard98
Generator comprehensions with any/all are less verbose and potentially help to save memory/CPU : https://eklitzke.org/generator-comprehensions-and-using-any-and-all-in-python
To make JIT work with this change, I added code to convert GeneratorExp to ListComp. So the whole PR is basically NoOp for JIT, but potentially memory and speed improvement for eager mode.
Also I removed a test from test/jit/test_parametrization.py. The test was bad and had a TODO to actually implement and just tested that UnsupportedNodeError is thrown, and with GeneratorExp support a different error would be thrown.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78142
Approved by: https://github.com/malfet, https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74353
Repatched `d00de0d43598522b8f6ab2de553b6aaf6768faa5` by Nora Belrose (norabelrose). With following changes:
* Register fake source of generated methods in linecache so that inspect.get_source will succeed.
* this patching is only triggered if the given dataclass passed to torch.jit.script previously. Effectively we make this feature opt-in.
## Original Summary:
Fixes https://github.com/pytorch/pytorch/issues/72901.
Since we can't get access to the source code for synthesized magic methods on dataclasses, we have to synthesize our own versions. torch/jit/_dataclass_impls.py has the code that does this.
What's supported
Synthesized __init__, __eq__, and the comparison magic methods when order=True is set on the dataclass decorator
Default values for fields
__post_init__, including using InitVar fields inside of __post_init__, on Python 3.8+
Overriding __eq__ or any of the comparison magic methods to provide your own implementation
What's not supported
Default factory initializers for fields
Frozen dataclasses
InitVar on Python 3.7
__repr__ and __hash__ (these are actually implemented, but the TorchScript interpreter won't call them)
Using the != operator on dataclasses inside TorchScript; this is because TorchScript requires that you implement __ne__ to use this operator, whereas in regular Python the != operator will resolve to the negation of whatever is returned by __eq__ if there's no __ne__. Dataclasses don't actually synthesize an __ne__ method for this reason. I've been toying with different ways to fix this but != is not working in this PR at the moment.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74889
Test Plan:
unittest
Also run previously failed test:
```
buck test mode/dev-nosan //fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests -- --exact 'fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests - test_mixmatch_multiclass (fblearner.flow.projects.fluent2.definition.transformers.contrib.faim.test.faim_mixmatch_test.TestFaimTransformerMixMatch)'
```
passes
Reviewed By: zhxchen17
Differential Revision: D35206262
Pulled By: qihqi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76771
Approved by: https://github.com/seemethere
For me, this manifested as the test failing when my folder
had a hyphen in it. But this should fix it once and for
all.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75564
Approved by: https://github.com/suo
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74353
Repatched `d00de0d43598522b8f6ab2de553b6aaf6768faa5` by Nora Belrose (norabelrose). With following changes:
* Register fake source of generated methods in linecache so that inspect.get_source will succeed.
* this patching is only triggered if the given dataclass passed to torch.jit.script previously. Effectively we make this feature opt-in.
## Original Summary:
Fixes#72901.
Since we can't get access to the source code for synthesized magic methods on dataclasses, we have to synthesize our own versions. torch/jit/_dataclass_impls.py has the code that does this.
What's supported
Synthesized __init__, __eq__, and the comparison magic methods when order=True is set on the dataclass decorator
Default values for fields
__post_init__, including using InitVar fields inside of __post_init__, on Python 3.8+
Overriding __eq__ or any of the comparison magic methods to provide your own implementation
What's not supported
Default factory initializers for fields
Frozen dataclasses
InitVar on Python 3.7
__repr__ and __hash__ (these are actually implemented, but the TorchScript interpreter won't call them)
Using the != operator on dataclasses inside TorchScript; this is because TorchScript requires that you implement __ne__ to use this operator, whereas in regular Python the != operator will resolve to the negation of whatever is returned by __eq__ if there's no __ne__. Dataclasses don't actually synthesize an __ne__ method for this reason. I've been toying with different ways to fix this but != is not working in this PR at the moment.
Test Plan:
unittest
Also run previously failed test:
```
buck test mode/dev-nosan //fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests -- --exact 'fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests - test_mixmatch_multiclass (fblearner.flow.projects.fluent2.definition.transformers.contrib.faim.test.faim_mixmatch_test.TestFaimTransformerMixMatch)'
```
passes
Differential Revision: D35206262
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74889
Approved by: https://github.com/zhxchen17
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74353
Repatched `d00de0d43598522b8f6ab2de553b6aaf6768faa5` by Nora Belrose (norabelrose). With following changes:
* Register fake source of generated methods in linecache so that inspect.get_source will succeed.
* this patching is only triggered if the given dataclass passed to torch.jit.script previously. Effectively we make this feature opt-in.
## Original Summary:
Fixes#72901.
Since we can't get access to the source code for synthesized magic methods on dataclasses, we have to synthesize our own versions. torch/jit/_dataclass_impls.py has the code that does this.
What's supported
Synthesized __init__, __eq__, and the comparison magic methods when order=True is set on the dataclass decorator
Default values for fields
__post_init__, including using InitVar fields inside of __post_init__, on Python 3.8+
Overriding __eq__ or any of the comparison magic methods to provide your own implementation
What's not supported
Default factory initializers for fields
Frozen dataclasses
InitVar on Python 3.7
__repr__ and __hash__ (these are actually implemented, but the TorchScript interpreter won't call them)
Using the != operator on dataclasses inside TorchScript; this is because TorchScript requires that you implement __ne__ to use this operator, whereas in regular Python the != operator will resolve to the negation of whatever is returned by __eq__ if there's no __ne__. Dataclasses don't actually synthesize an __ne__ method for this reason. I've been toying with different ways to fix this but != is not working in this PR at the moment.
Test Plan:
unittest
Also run previously failed test:
```
buck test mode/dev-nosan //fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests -- --exact 'fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests - test_mixmatch_multiclass (fblearner.flow.projects.fluent2.definition.transformers.contrib.faim.test.faim_mixmatch_test.TestFaimTransformerMixMatch)'
```
passes
Reviewed By: zhxchen17
Differential Revision: D34808842
fbshipit-source-id: 02f807cff1ea99e606333960225c71a239743a4b
(cherry picked from commit ec885a2bc04f9e5f65838fa5704d9a05815ebd37)
Summary:
Original commit changeset: f5a792555c88
Original Phabricator Diff: D34398107 (d00de0d435)
Backing out as this broke fluent2 tests
Test Plan: sandcastle
Reviewed By: qihqi
Differential Revision: D34597363
fbshipit-source-id: 26bbe64b981aeb53b901cda61557614d9f28700e
(cherry picked from commit f17adfed8125ef84efaf2c8923c11a751eb7fb98)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/72901.
Since we can't get access to the source code for synthesized magic methods on dataclasses, we have to synthesize our own versions. `torch/jit/_dataclass_impls.py` has the code that does this.
What's supported
- Synthesized `__init__`, `__eq__`, and the comparison magic methods when `order=True` is set on the dataclass decorator
- Default values for fields
- `__post_init__`, including using `InitVar` fields inside of `__post_init__`, on Python 3.8+
- Overriding `__eq__` or any of the comparison magic methods to provide your own implementation
What's not supported
- Default factory initializers for fields
- Frozen dataclasses
- `InitVar` on Python 3.7
- `__repr__` and `__hash__` (these are actually implemented, but the TorchScript interpreter won't call them)
- Using the `!=` operator on dataclasses inside TorchScript; this is because TorchScript requires that you implement `__ne__` to use this operator, whereas in regular Python the `!=` operator will resolve to the negation of whatever is returned by `__eq__` if there's no `__ne__`. Dataclasses don't actually synthesize an `__ne__` method for this reason. I've been toying with different ways to fix this but `!=` is not working in this PR at the moment.
qihqi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73066
Reviewed By: mrshenli
Differential Revision: D34398107
Pulled By: qihqi
fbshipit-source-id: f5a792555c88f3631f97837a96687e4890660a32
(cherry picked from commit ea7f077dc49a4ee75ca0d1409aedd85228952881)
Summary:
**Summary**: This commit solves the first part of https://github.com/pytorch/pytorch/issues/52306, which disallows type annotations on instance attributes inside any method other than the constructor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67051
Test Plan:
Added test to test_types.py.
**Reviewers**: Zhengxu Chen
**Subscribers**: Zhengxu Chen, Yanan Cao, Peng Wu, Yining Lu
**Tasks**: T103941984
**Tags**: pytorch
**Fixes** https://github.com/pytorch/pytorch/issues/52306
Reviewed By: zhxchen17
Differential Revision: D31843527
Pulled By: andrewor14
fbshipit-source-id: 624879ae801621e367c59228be8b0581ecd30ef4
Summary:
This PR is created to replace https://github.com/pytorch/pytorch/pull/53180 PR stack, which has all the review discussions. Reason for needing a replacement is due to a messy Sandcastle issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64234
Reviewed By: gmagogsfm
Differential Revision: D30656444
Pulled By: ansley
fbshipit-source-id: 77536c8bcc88162e2c72636026ca3c16891d669a
Summary:
Creates a helper function to refine the types into a torchScript compatible format in the monkeytype config for profile directed typing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62444
Reviewed By: malfet
Differential Revision: D30548159
Pulled By: nikithamalgifb
fbshipit-source-id: 7c09ce5f5e043d069313b87112837d7e226ade1f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59956
Issue #50175. Basically two things need to be checked and are lacking currently:
1. Overload declarations should always have a single `pass` statement as the body.
2. There should be always an implementation provided for decls which doesn't
have the torch.jit._overload decorator. So in this case we need to check
whether we are actually compiling a function body with decorator ahead.
Test Plan:
python test/test_jit.py TestScript.test_function_overloads
Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D29106555
fbshipit-source-id: 2d9d7df2fb51ab6db0e1b726f9644e4cfbf733d6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55172
Description:
This is part 1 of series of PRs for supporting torch.jit.ignore as context manager. Following features are implemented in this PR:
- Unique name for the registered function under torch.jit.frontend module. The unique name is generated based on the file name and line number of context manager
- Forcing user to explicitly annotate the input and outputs.
- No side effects are considered.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D27895283
Pulled By: tugsbayasgalan
fbshipit-source-id: 5d36d9aa5d457055a6bb1676f264647a745ec36a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52881
**This PR adds:**
1. logic to parse complex constants (complex literals of the form `bj`)
2. logic to parse complex lists
3. support for complex constructors: `complex(tensor/int/float/bool, tensor/int/float/bool)`
4. Limited operator support
- `add`, `sub`, `mul`, `torch.tensor`, `torch.as_tensor`
**Follow-up work:**
1. Add complex support for unary and other registered ops.
2. support complex constructor with string as input (this is supported in Python eager mode).
3. Test all emitXYZ for all XYZ in `ir_emitter.cpp` (currently only emitConst, emitValueToTensor are tested). e.g., test loops etc.
4. onnx doesn't support complex tensors, so we should error out with a clear and descriptive error message.
Test Plan: Imported from OSS
Reviewed By: bdhirsh
Differential Revision: D27245059
Pulled By: anjali411
fbshipit-source-id: af043b5159ae99a9cc8691b5a8401503fa8d6f05
