This does not introduce a new test but is tested by checking that all the classes we already have still behave as before now that they don't explicitly disable torch_function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120632
Approved by: https://github.com/ezyang
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
Currently we have 2 ways of doing the same thing for torch dispatch and function modes:
`with push_torch_dispatch_mode(X)` or `with X.push(...)`
is now the equivalent of doing
`with X()`
This removes the first API (which is older and private so we don't need to go through a deprecation cycle)
There is some risk here that this might land race with a PR that uses the old API but in general it seems like most are using the `with X()` API or `enable_torch_dispatch_mode(X())` which isn't getting removed.
EDIT: left the `with X.push(...)` API since there were ~3 land races with that over the past day or so. But made it give a warning and ask users to use the other API
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78215
Approved by: https://github.com/ezyang
Summary:
Reland of https://github.com/pytorch/pytorch/pull/72623 that was reverted for the tls cleanup was removed.
From close inspection on the counting of the number of available keys, I think there is one more since the guard is actually one after the last usable key. With this update assert, the last updated key will still be <=63 which will fit just fine.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72832
Reviewed By: H-Huang
Differential Revision: D34228571
Pulled By: albanD
fbshipit-source-id: ce5e10a841ea87386727346cfc8d9327252574c4
(cherry picked from commit 59d3b863534a37ac3463e2814bc9599c322669ee)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72620
Clarify how LoggingTensor works with autograd.
The updated comment should cover the semantic changes.
Test Plan: Imported from OSS
Reviewed By: samdow
Differential Revision: D34214956
Pulled By: albanD
fbshipit-source-id: 730d0a68f4228d2a84758e6807d869a34cbc1b31
(cherry picked from commit 66110bf16bbe17d52781d05077eb73192e0fe3c4)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66101
Updated description:
This PR tests the functionalization pass in python in two ways. For each of the test programs that I have in `test_functionalization.py`, it:
- runs the program with and without functionalization, and asserts the outputs and (potentially mutated) inputs are equal in both cases
- runs the program with `LoggingTensor`, and uses expecttests on the resulting graph. I manually confirm that the graphs look reasonable and only contain functional ops.
Mechanically, the changes include:
- factoring out `LoggingTensor` into a testing util so it can be re-used in multiple tests
- adding some private python api's in the `torch` namespace as hooks that I can use during testing
In the original version of this PR, I also added some fixes to the `_make_subclass()` function in python: allowing you to pass in strides and storage_offset. I kept them in mainly because the changes were already there.
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D31942095
Pulled By: bdhirsh
fbshipit-source-id: 90ff4c88d461089704922e779571eee09c21d707
