# Summary
This PR made some significant changes to the scripts around Release Scripts. At a high level:
- Turned the quips into docs and updated links
- Update the common.categorizes list in the hopes to make this the source of truth for releases- This is hard since the release_notes labels can be changed at will. An alternative would be to poll from github api. However, I think that is overkill. The notebook does a set compare and will show you knew categories. I think we want this to be manual so that the release note engineer will decided how to categorize.
- Create cateogry group from speaking with folks on distributed and AO that told me these different release categories can be merged.
- I am the newest person to Core and don't use ghstack soo made token getting a lil more generic.
- Added a classifier.py file. This file will train a commit categorizer for you, hopefully with decent accuracy. I was able to achieve 75% accuracy. I drop the highest frequency class - "skip" since this creates a more useful cateogrizer.
- I updated the categorize.py script so that the prompt will be what the classifier thinks, gated by a flag.
- Added a readme that will hopefully help future release notes engineers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94560
Approved by: https://github.com/albanD
Merges startswith, endswith calls to into a single call that feeds in a tuple. Not only are these calls more readable, but it will be more efficient as it iterates through each string only once.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96754
Approved by: https://github.com/ezyang
Applies the remaining flake8-comprehension fixes and checks. This changes replace all remaining unnecessary generator expressions with list/dict/set comprehensions which are more succinct, performant, and better supported by our torch.jit compiler. It also removes useless generators such as 'set(a for a in b)`, resolving it into just the set call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94676
Approved by: https://github.com/ezyang
I applied some flake8 fixes and enabled checking for them in the linter. I also enabled some checks for my previous comprehensions PR.
This is a follow up to #94323 where I enable the flake8 checkers for the fixes I made and fix a few more of them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94601
Approved by: https://github.com/ezyang
Preferring dash over underscore in command-line options. Add `--command-arg-name` to the argument parser. The old arguments with underscores `--command_arg_name` are kept for backward compatibility.
Both dashes and underscores are used in the PyTorch codebase. Some argument parsers only have dashes or only have underscores in arguments. For example, the `torchrun` utility for distributed training only accepts underscore arguments (e.g., `--master_port`). The dashes are more common in other command-line tools. And it looks to be the default choice in the Python standard library:
`argparse.BooleanOptionalAction`: 4a9dff0e5a/Lib/argparse.py (L893-L895)
```python
class BooleanOptionalAction(Action):
def __init__(...):
if option_string.startswith('--'):
option_string = '--no-' + option_string[2:]
_option_strings.append(option_string)
```
It adds `--no-argname`, not `--no_argname`. Also typing `_` need to press the shift or the caps-lock key than `-`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94505
Approved by: https://github.com/ezyang, https://github.com/seemethere
This change introduces a mechanism to test onnx export based on sample inputs registered in OpInfo, similar to how MPS and other components of pytorch are tested. It provides test coverage on ops and dtypes previously unattainable with manually created test models. This is the best way for us to discover gaps in the exporter support, especially for ops with partial existing support.
This test is adapted from https://github.com/pytorch/pytorch/blob/master/test/test_mps.py
This PR also
- Update sqrt to support integer inputs to match pytorch behavior
- Add pytest-subtests for unittest subtests support in the new test file
I only enabled very few ops: `t`, `ceil` and `sqrt` because otherwise too many things will fail due to (1) unsupported dtypes in the exporter (2) unimplemented dtype support in onnxruntime (3) unexpected input to verification.verify.
Subsequent PRs should improve `verification.verify` first for it to accept any legal input to a pytorch model, then incrementally fix the symbolic functions to enable more test cases.
Fixes#85363
Design #88118
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86182
Approved by: https://github.com/BowenBao
Summary: Currently, build_mobile.sh doesn't allow lite interpreter builds or tracing based selective builds. build_mobile.sh is used for host builds of PyTorch for Mobile deployment.
Additionally, certain flags such as `USE_BLAS` were not being respected as they should be. This change addresses that as well.
Test Plan: Build using:
```
cat /tmp/selected_ops.yaml
- aten::add
- aten::sub
```
```
BUILD_PYTORCH_MOBILE_WITH_HOST_TOOLCHAIN=1 USE_LIGHTWEIGHT_DISPATCH=0 BUILD_LITE_INTERPRETER=1 SELECTED_OP_LIST=/tmp/selected_ops.yaml ./scripts/build_mobile.sh
```
```
cat /tmp/main.cpp
int main() {
auto m = torch::jit::_load_for_mobile("/tmp/path_to_model.ptl");
auto res = m.forward({});
return 0;
}
```
Test using:
```
g++ /tmp/main.cpp -L build_mobile/lib/ -I build_mobile/install/include/ -lpthread -lc10 -ltorch_cpu -ltorch -lXNNPACK -lpytorch_qnnpack -lcpuinfo -lclog -lpthreadpool -lgloo -lkineto -lfmt -ldl -lc10
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84647
Approved by: https://github.com/JacobSzwejbka, https://github.com/cccclai
We're no longer building Caffe2 mobile as part of our CI, and it adds a lot of clutter to our make files. Any lingering internal dependencies will use the buck build and so wont be effected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84338
Approved by: https://github.com/dreiss
This PR adds an internal wrapper on the [beartype](https://github.com/beartype/beartype) library to perform runtime type checking in `torch.onnx`. It uses beartype when it is found in the environment and is reduced to a no-op when beartype is not found.
Setting the env var `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK=ERRORS` will turn on the feature. setting `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK=DISABLED` will disable all checks. When not set and `beartype` is installed, a warning message is emitted.
Now when users call an api with invalid arguments e.g.
```python
torch.onnx.export(conv, y, path, export_params=True, training=False)
# traning should take TrainingModel, not bool
```
they get
```
Traceback (most recent call last):
File "bisect_m1_error.py", line 63, in <module>
main()
File "bisect_m1_error.py", line 59, in main
reveal_error()
File "bisect_m1_error.py", line 32, in reveal_error
torch.onnx.export(conv, y, cpu_model_path, export_params=True, training=False)
File "<@beartype(torch.onnx.utils.export) at 0x1281f5a60>", line 136, in export
File "pytorch/venv/lib/python3.9/site-packages/beartype/_decor/_error/errormain.py", line 301, in raise_pep_call_exception
raise exception_cls( # type: ignore[misc]
beartype.roar.BeartypeCallHintParamViolation: @beartyped export() parameter training=False violates type hint <class 'torch._C._onnx.TrainingMode'>, as False not instance of <protocol "torch._C._onnx.TrainingMode">.
```
when `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK` is not set and `beartype` is installed, a warning message is emitted.
```
>>> torch.onnx.export("foo", "bar", "f")
<stdin>:1: CallHintViolationWarning: Traceback (most recent call last):
File "/home/justinchu/dev/pytorch/torch/onnx/_internal/_beartype.py", line 54, in _coerce_beartype_exceptions_to_warnings
return beartyped(*args, **kwargs)
File "<@beartype(torch.onnx.utils.export) at 0x7f1d4ab35280>", line 39, in export
File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/site-packages/beartype/_decor/_error/errormain.py", line 301, in raise_pep_call_exception
raise exception_cls( # type: ignore[misc]
beartype.roar.BeartypeCallHintParamViolation: @beartyped export() parameter model='foo' violates type hint typing.Union[torch.nn.modules.module.Module, torch.jit._script.ScriptModule, torch.jit.ScriptFunction], as 'foo' not <protocol "torch.jit.ScriptFunction">, <protocol "torch.nn.modules.module.Module">, or <protocol "torch.jit._script.ScriptModule">.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/justinchu/dev/pytorch/torch/onnx/_internal/_beartype.py", line 63, in _coerce_beartype_exceptions_to_warnings
return func(*args, **kwargs)
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 482, in export
_export(
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 1422, in _export
with exporter_context(model, training, verbose):
File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/contextlib.py", line 119, in __enter__
return next(self.gen)
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 177, in exporter_context
with select_model_mode_for_export(
File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/contextlib.py", line 119, in __enter__
return next(self.gen)
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 95, in select_model_mode_for_export
originally_training = model.training
AttributeError: 'str' object has no attribute 'training'
```
We see the error is caught right when the type mismatch happens, improving from what otherwise would become `AttributeError: 'str' object has no attribute 'training'`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83673
Approved by: https://github.com/BowenBao
* Move memory heavy tests from `test_pytorch_onnx_onnxruntime.py` to
`test_models_onnxruntime.py`. The former is run in parallel in CI,
while the latter is not. A change is that the moved tests are now
only covered in default opset export.
* Refactor and create base class for tests that export model to ONNX
and verify with ONNX Runtime. The new base class are parameterized
with `opset_version` and `is_script`. Further work can be done to
refactor existing test classes in `test_pytorch_onnx_onnxruntime.py`.
See #75630
* Reduce unnecessarily large tensor size in
`test_pytorch_onnx_onnxruntime.py` to further reduce memory usage
and test time.
After this PR, the running time for `test_pytorch_onnx_onnxruntime.py`
is reduced from `1338.82s (0:22:18)` to `225.07s (0:03:45)`,
benchmarked on 10900x with `-n 10`.
Fixes#79179
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79640
Approved by: https://github.com/justinchuby, https://github.com/garymm
Should fix#78844
Custom op related tests utilize inline cpp extension to build custom
operator from c++ source snippet. Only two test cases become flaky after
parallel run, and both use inline cpp extension. Reverting to run these
tests in single process to try resolve the flakiness.
Reverts test skip added previously #78936.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78944
Approved by: https://github.com/janeyx99, https://github.com/garymm
