Update ruff to 0.4.1 .
This version fixes a lot false negatives/false positives, is 20-40% faster, and has various other bug fixes.
Below is a before and after table showing the execution time of ruff lint and ruff format in milliseconds courtesy of https://astral.sh/blog/ruff-v0.4.0
| Repository | Linter (v0.3) | Linter (v0.4) | Formatter (v0.3) | Formatter (v0.4) |
|----------------------------------------------------|---------------|---------------|------------------|------------------|
| [pytorch/pytorch](https://github.com/pytorch/pytorch) | 328.7 | 251.8 | 351.1 | 274.9 |
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124549
Approved by: https://github.com/ezyang
Summary:
As this diff shows, currently there are a couple hundred instances of raw `noqa` in the codebase, which just ignore all errors on a given line. That isn't great, so this PR changes all existing instances of that antipattern to qualify the `noqa` with respect to a specific error code, and adds a lint to prevent more of this from happening in the future.
Interestingly, some of the examples the `noqa` lint catches are genuine attempts to qualify the `noqa` with a specific error code, such as these two:
```
test/jit/test_misc.py:27: print(f"{hello + ' ' + test}, I'm a {test}") # noqa E999
test/jit/test_misc.py:28: print(f"format blank") # noqa F541
```
However, those are still wrong because they are [missing a colon](https://flake8.pycqa.org/en/3.9.1/user/violations.html#in-line-ignoring-errors), which actually causes the error code to be completely ignored:
- If you change them to anything else, the warnings will still be suppressed.
- If you add the necessary colons then it is revealed that `E261` was also being suppressed, unintentionally:
```
test/jit/test_misc.py:27:57: E261 at least two spaces before inline comment
test/jit/test_misc.py:28:35: E261 at least two spaces before inline comment
```
I did try using [flake8-noqa](https://pypi.org/project/flake8-noqa/) instead of a custom `git grep` lint, but it didn't seem to work. This PR is definitely missing some of the functionality that flake8-noqa is supposed to provide, though, so if someone can figure out how to use it, we should do that instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56272
Test Plan:
CI should pass on the tip of this PR, and we know that the lint works because the following CI run (before this PR was finished) failed:
- https://github.com/pytorch/pytorch/runs/2365189927
Reviewed By: janeyx99
Differential Revision: D27830127
Pulled By: samestep
fbshipit-source-id: d6dcf4f945ebd18cd76c46a07f3b408296864fcb
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
Summary:
solves most of gh-38011 in the framework of solving gh-32703.
These should only be formatting fixes, I did not try to fix grammer and syntax.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41068
Differential Revision: D22411919
Pulled By: zou3519
fbshipit-source-id: 25780316b6da2cfb4028ea8a6f649bb18b746440
Summary:
Fixes errors when importing the module. The import is used by sphinx in documentation builds.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38921
Differential Revision: D21722144
Pulled By: ezyang
fbshipit-source-id: 5f31d4750325f1753de93754a009006cbc13655e
Summary:
This was previously buggy and not being displayed on master. This fixes
the issues with the script to generate the builtin function schemas and
moves it to its own page (it's 6000+ lines of schemas)
Sphinx looks like it will just keep going if it hits errors when importing modules, we should find out how to turn that off and put it in the CI
This also includes some other small fixes:
* removing internal only args from `script()` and `trace()` docs, this also requires manually keeping these argument lists up to date but I think the cleanliness is worth it
* removes outdated note about early returns
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24056
Pulled By: driazati
Differential Revision: D16742406
fbshipit-source-id: 9102ba14215995ffef5aaafcb66a6441113fad59
Summary:
resize_ and resize_as resize the input tensor. because our shape analysis
is flow invariant, we don't do shape analysis on any op that relies on a Tensor that can alias a resized Tensor.
E.g. in the following graph the x += 10 x may have been resized.
```
torch.jit.script
def test(x, y):
for i in range(10):
x += 10
x.resize_as_([1 for i in int(range(torch.rand())))
return x
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17518
Differential Revision: D14249835
Pulled By: eellison
fbshipit-source-id: f281b468ccb8c29eeb0f68ca5458cc7246a166d9
Summary:
Documents what is supported in the script standard library.
* Adds `my_script_module._get_method('forward').schema()` method to get function schema from a `ScriptModule`
* Removes `torch.nn.functional` from the list of builtins. The only functions not supported are `nn.functional.fold` and `nn.functional.unfold`, but those currently just dispatch to their corresponding aten ops, so from a user's perspective it looks like they work.
* Allow printing of `IValue::Device` by getting its string representation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14912
Differential Revision: D13385928
Pulled By: driazati
fbshipit-source-id: e391691b2f87dba6e13be05d4aa3ed2f004e31da
Summary:
In addition to documentation, this cleans up a few error message formats.
It also adds infra to find which operators are supported by the JIT automatically, which is then used in the generation of the docs.
The wording and formatting of the docs is not yet polished, but having this will allow our document writers to make faster progress.
Followup PRs will polish the docs and fix formatting issues.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11357
Differential Revision: D9721277
Pulled By: zdevito
fbshipit-source-id: 153a0d5be1efb314511bcfc0cec48643d78ea48b
