## The problem.
[A commit from three weeks ago](82d00acfee) appears to have broken five tests but was not caught by CI.
[A later commit](https://github.com/pytorch/pytorch/commit/e05ea2b1797) which added a decomposition of `transpose_copy` added another broken test, also seemingly not detected, making six total (listed below).
They came to my attention when I updated some pending decomposition pull requests which passed CI, and started getting failures like [this](https://hud.pytorch.org/pr/134319) for a test unrelated to any of these pull requests, `TestCommonCPU.test_out__refs_transpose_copy_cpu_float32`
Running `python test/test_ops.py -k _copy` on `viable/strict` found failures for six `_refs` ops: `copysign`, `expand_copy`, `index_copy`, `t_copy`, `transpose_copy`, `view_copy`
## The solution
The original commit did actually cause breakage by slightly changing user-visible behavior (in a special case involving scalar tensors being copied between different devices).
This pull request fixes that breakage in a reasonable way, but I don't understand why this error didn't appear in CI until I made later changes in the same area.
## To reproduce
To reproduce the six cases in your own client:
```
PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=5 python test/test_ops.py TestCommonCPU.test_out__refs_view_copy_cpu_float32
PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=2 python test/test_ops.py TestCommonCPU.test_out__refs_t_copy_cpu_float32
PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=0 python test/test_ops.py TestCommonCPU.test_out__refs_index_copy_cpu_float32
PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=7 python test/test_ops.py TestCommonCPU.test_out__refs_expand_copy_cpu_float32
PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=0 python test/test_ops.py TestCommonCPU.test_out__refs_copysign_cpu_float32
PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=4 python test/test_ops.py TestCommonCPU.test_out__refs_transpose_copy_cpu_float32
```
@amjames
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136653
Approved by: https://github.com/zou3519
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:
The assertion is causing build failures when running Pysa, our security-focused static analyzer.
This is because we run `pyre infer` on the source code before analyzing it, which introduces annotations such as `def foo() -> 'torch._tensor.Tensor'`.
This does not work with the `out_wrapper` decorator which relies on inspecting the signature of the decorated function.
Let's skip the check on the return type if we detect that it was introduced by `pyre infer`.
Test Plan: eyes
Differential Revision: D50976601
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112870
Approved by: https://github.com/ZainRizvi
Fixes https://github.com/pytorch/pytorch/issues/112449
elementwise_type_promotion_wrapper will promote `aten.normal` to the dtypes of `mean`, `std` args.
But this is incorrect if we provide the dtype param. Hence, we allow overriding the result_dtype if a specified dtype arg is available.
This problem is unique to `aten.normal`, all other ops decorated do not have a dtype param.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112467
Approved by: https://github.com/lezcano
- Extend `test_torch_dispatch_meta_outplace` to test torch ops that do not have an out parameter but have aten op overloads that have out parameters. Additionally, Python decompositions may register `OpOverloadPacket`'s so decompositions need to be tested to ensure all `OpOverloads` still function for the `Meta` key (e.g. if a python decomposition is registered for an aten op `aten.foo` with overloads `[default, out]`, the python function needs to support receiving out arguments)
- Add out parameter wrappers to python decomps for aten ops that have out overloads
CC. @ezyang @albanD @lezcano
Fixes#107713
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107707
Approved by: https://github.com/lezcano
Python decompositions wrapped by `out_wrapper` need to be unwrapped before compiling with TorchScript since:
- `out_wrapper` extends the decompositions signature with an out parameter, however this `out` parameter is not present in the source code of the original decomposition so the resulting `ScriptFunction` will not have an `out` parameter
- `out_wrapper` is in the `torch._prims_common.wrappers` module so its `globals()` are different to the globals of the decomposition to be wrapped. This may cause symbol resolution to fail with the TorchScript compiler since it is compiling the unwrapped decomps source code rather than the wrapper
The python decomposition for `aten.trace` is wrapped as an example, other decompositions are to be fixed in https://github.com/pytorch/pytorch/pull/107707
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109367
Approved by: https://github.com/lezcano
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04:
- Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh`
- Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR adds a `py_context_manager_DEPRECATED` that converts a C++ RAII
guard to an object that may be either used as Python context manager or
as a "Python RAII guard".
We don't convert all of them to Python context manager only due to BC
reasons; people in OSS and internally actually rely on these APIs and I
don't want to break them. We are justified in breaking BC if we wanted
to, but it seemed like too much work for not a lot of gain.
The API is postfixed with "DEPRECATED" to indicate that people should
really use `py_context_manager` (converts C++ RAII guard to Python
context manager) instead.
Test Plan:
- this PR converts all PyTorch usages of _AutoDispatchBelowAutograd to
context manager. I can do the rest in follow-ups.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102579
Approved by: https://github.com/bdhirsh, https://github.com/albanD
It turns out that we *do* need to update *_scatter ops to return the exact same strides as their inputs. I added a test to `test/test_functionalization.py`, which now trips thanks to Ed's functionalization stride debugging check. It only actually ends up tripping silent correctness if you try to .backward() on that function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91029
Approved by: https://github.com/ezyang
The previous behaviour would call `resize_` on 0-sized elements even
when their size was correct. This would make some test fail, as resize_
may be an in-place operation and it's not supported by some subsystems
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88116
Approved by: https://github.com/mruberry
