Adds a ruff lint rule to ban raising raw exceptions. Most of these should at the very least be runtime exception, value errors, type errors or some other errors. There are hundreds of instance of these bad exception types already in the codebase, so I have noqa'd most of them. Hopefully this error code will get commiters to rethink what exception type they should raise when they submit a PR.
I also encourage people to gradually go and fix all the existing noqas that have been added so they can be removed overtime and our exception typing can be improved.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124570
Approved by: https://github.com/ezyang
Enables PyLint error codes implemented in ruff. These are un-opinionated static analysis checks on Python code that finds common bugs. After running all the PLE error codes that are implemented in ruff, I fixed the bugs, added a few ignores for malformed Python code that is part of our JIT test script, and finally added a few ignores for a false positive on PLE0605 and submitted an issue upstream to fix in ruff https://github.com/charliermarsh/ruff/issues/4345 .
Common bugs found here include analysis for malformed logging format calls, bad string format calls, invalid escape sequences, and more.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101079
Approved by: https://github.com/malfet
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52336
**Summary**
In Python, the boolean interpretation of the return value of `__exit__` of objects that are used as context managers with `with` statements is used to determine
whether or not to propagate exceptions thrown inside the body of the with
statement. This latter feature is not possible to add to TorchScript at
the moment, but the current requirement that `__exit__` not have any
return values can make it difficult to script a context manager whose
`__exit__` *does* have a return value.
Accordingly, this commit removes the requirement that `__exit__` must
not have any return value. TorchScript does not interpret this return
value in the same way Python does (or at all), but this should make it
easier to share context managers between eager mode and script code.
**Test Plan**
This commit adds a return value to one of the context managers used in
`TestWith`.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D26504910
Pulled By: SplitInfinity
fbshipit-source-id: 2ab635a24d111ac25df4e361b716be8fada5128e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52335
**Summary**
`with` statements can only be used with objects that have `__enter__`
and `__exit__` defined. At present, any attempt to use an expression
that returns something that is not an instance of a class type results
in a cryptic internal assert failure instead of a useful error message.
This is because the code that generates IR for `with` statements uses
`Type::expect` as if it were `Type::cast`; that is, as if it returns
`nullptr` on failure.
This commit fixes this issue by checking the `kind()` of the type of the
expression used as the with item before calling `expect<ClassType>()` on
it.
**Test Plan**
This commit adds a unit test to `test_with_errors` to test this case.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D26504909
Pulled By: SplitInfinity
fbshipit-source-id: 92d108e0c010370fd45131a57120f50c0b85c401
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45106
**Summary**
This commit fixes `WithTest.test_with_exceptions`. It's been running
in regular Python this whole time; none of the functions created and
invoked for the test were scripted. Fortunately, the tests still pass
after being fixed.
**Test Plan**
Ran unit tests + continuous integration.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D23848206
Pulled By: SplitInfinity
fbshipit-source-id: fd975ee34db9441ef4e4a4abf2fb21298166bbaa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44345
As part of enhancing profiler support for RPC, when executing TorchScript functions over RPC, we would like to be able to support user-defined profiling scopes created by `with record_function(...)`.
Since after https://github.com/pytorch/pytorch/pull/34705, we support `with` statements in TorchScript, this PR adds support for `with torch.autograd.profiler.record_function` to be used within TorchScript.
This can be accomplished via the following without this PR:
```
torch.opts.profiler._record_function_enter(...)
# Script code, such as forward pass
torch.opts.profiler._record_function_exit(....)
```
This is a bit hacky and it would be much cleaner to use the context manager now that we support `with` statements. Also, `_record_function_` type operators are internal operators that are subject to change, this change will help avoid BC issues in the future.
Tested with `python test/test_jit.py TestWith.test_with_record_function -v`
ghstack-source-id: 112320645
Test Plan:
Repro instructions:
1) Change `def script_add_ones_return_any(x) -> Any` to `def script_add_ones_return_any(x) -> Tensor` in `jit/rpc_test.py`
2) `buck test mode/dev-nosan //caffe2/test/distributed/rpc:process_group_agent -- test_record_function_on_caller_rpc_async --print-passing-details`
3) The function which ideally should accept `Future[Any]` is `def _call_end_callbacks_on_future` in `autograd/profiler.py`.
python test/test_jit.py TestWith.test_with_foo -v
Reviewed By: pritamdamania87
Differential Revision: D23332074
fbshipit-source-id: 61b0078578e8b23bfad5eeec3b0b146b6b35a870
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41371
**Summary**
This commit enables the use of `torch.no_grad()` in a with item of a
with statement within JIT. Note that the use of this context manager as
a decorator is not supported.
**Test Plan**
This commit adds a test case to the existing with statements tests for
`torch.no_grad()`.
**Fixes**
This commit fixes#40259.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D22649519
Pulled By: SplitInfinity
fbshipit-source-id: 7fa675d04835377666dfd0ca4e6bc393dc541ab9
Summary:
**Summary**
This commit adds support for with statements to PyTorch JIT. Each
of the with items in a with statement is represented in the JIT IR
as a pair of `prim::Enter` and `prim::Exit` nodes that call the
`__enter__` and `__exit__` methods defined on the context manager objects
returned by the expressions in the with item.
**Testing**
This commit adds unit tests for with statements with named with items,
nameless with items, and with statements that encounter exceptions.
```
$ python test/test_jit.py TestWith.test_with_as
Fail to import hypothesis in common_utils, tests are not derandomized
.
----------------------------------------------------------------------
Ran 1 test in 0.430s
OK
```
```
$ python test/test_jit.py TestWith.test_with_no_as
Fail to import hypothesis in common_utils, tests are not derandomized
.
----------------------------------------------------------------------
Ran 1 test in 0.264s
OK
```
```
$ python test/test_jit.py TestWith.test_with_exceptions
Fail to import hypothesis in common_utils, tests are not derandomized
Couldn't download test skip set, leaving all tests enabled...
.
----------------------------------------------------------------------
Ran 1 test in 1.053s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34705
Differential Revision: D22095945
Pulled By: SplitInfinity
fbshipit-source-id: f661565a834786725259b8ea014b4d7532f9419d
