Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
This PR removes the second separate package we were using for the libtorch wheel.
In terms of testing that this works we will look use the PRs above this in the stack.
As for sanity checking these are the wheels that are produced by running
```
python setup.py clean && BUILD_LIBTORCH_WHL=1 with-proxy python setup.py bdist_whee
l && BUILD_PYTHON_ONLY=1 with-proxy python setup.py bdist_wheel --cmake
```
```
sahanp@devgpu086 ~/pytorch ((5f15e171…))> ls -al dist/ (pytorch-3.10)
total 677236
drwxr-xr-x 1 sahanp users 188 Jun 4 12:19 ./
drwxr-xr-x 1 sahanp users 1696 Jun 4 12:59 ../
-rw-r--r-- 1 sahanp users 81405742 Jun 4 12:19 torch-2.4.0a0+gitca0a73c-cp310-cp310-linux_x86_64.whl
-rw-r--r-- 1 sahanp users 612076919 Jun 4 12:19 libtorch-2.4.0a0+gitca0a73c-py3-none-any.whl
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127934
Approved by: https://github.com/atalman
As FindPythonInterp and FindPythonLibs has been deprecated since cmake-3.12
Replace `PYTHON_EXECUTABLE` with `Python_EXECUTABLE` everywhere (CMake variable names are case-sensitive)
This makes PyTorch buildable with python3 binary shipped with XCode on MacOS
TODO: Get rid of `FindNumpy` as its part of Python package
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124613
Approved by: https://github.com/cyyever, https://github.com/Skylion007
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127124
Approved by: https://github.com/Skylion007
ghstack dependencies: #127122, #127123
This PR adds a linker script optimization based on prioritized symbols that can be extracted from the profiles of popular workloads. The present linker script was generated to target ARM+CUDA and later can be extended if necessary. The reason we target ARM is shown below:
> PyTorch and other applications that access more than 24x 2MB code regions in quick succession can result in performance bottlenecks in the CPU front-end. The link-time optimization improves executable code locality and improve performance. We recommend turning on the optimization always for PyTorch and other application that behaves similarly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121975
Approved by: https://github.com/ptrblck, https://github.com/atalman
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
The warning complains that `TORCH_CUDA_ARCH_LIST` is set on the environment
instead of being defined as a build variable, which is fixed by the change to
`tools/setup_helpers/cmake.py`.
However, I still see the warning even with this fix because
```cmake
if((NOT EXISTS ${TORCH_CUDA_ARCH_LIST}) ...
```
is actually checking whether a file exists called "7.5" (or whatever arch is
being requested). Instead we want to check if the variable is defined.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104680
Approved by: https://github.com/albanD
If `CMAKE_GENERATOR=Visual Studio 16 2019` then the build will fail if `USE_NINJA=False` not set.
This PR changes that if CMAKE_GENERATOR is set an not equal to ninja then it won't use Ninja.
This is just for easier setting another generator.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98605
Approved by: https://github.com/kit1980
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
Summary: Currently, the model tracer build is broken because of 2 reasons:
1. A few source files are missing, resulting in missing link time symbols
2. The `TRACING_BASED` flag isn't passed correctly from the command line (specified as an evnironment variable) as a CMake flag
Both these issues were fixed.
Test Plan: Ran this command: `USE_CUDA=0 TRACING_BASED=1 python setup.py develop --cmake`
and saw that the tracer binary was built at `build/bin/model_tracer` - also ran it to ensure that it can generate a YAML file.
Differential Revision: [D39391270](https://our.internmc.facebook.com/intern/diff/D39391270)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84755
Approved by: https://github.com/cccclai
Fix use-dict-literal pylint suggestions by changing `dict()` to `{}`. This PR should do the change for every Python file except test/jit/test_list_dict.py, where I think the intent is to test the constructor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83718
Approved by: https://github.com/albanD
To fix#78540 I committed #78983 which is reverted due to internal CI failure. Then I comitted #79215 which was only fixing the failure but didn't have the full feature of #78983. This PR is another try.
This PR adds script to dump all operators from test models and automatically write into `lightweight_dispatch_ops.yaml`. This way we don't have to manually update the yaml file.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80791
Approved by: https://github.com/raziel
With ufmt in place https://github.com/pytorch/pytorch/pull/81157, we can now use it to gradually format all files. I'm breaking this down into multiple smaller batches to avoid too many merge conflicts later on.
This batch (as copied from the current BLACK linter config):
* `tools/**/*.py`
Upcoming batchs:
* `torchgen/**/*.py`
* `torch/package/**/*.py`
* `torch/onnx/**/*.py`
* `torch/_refs/**/*.py`
* `torch/_prims/**/*.py`
* `torch/_meta_registrations.py`
* `torch/_decomp/**/*.py`
* `test/onnx/**/*.py`
Once they are all formatted, BLACK linter will be removed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81285
Approved by: https://github.com/suo
This PR introduces selective build to lightweight dispatch CI job. By doing so we can't run the `test_lite_intepreter_runtime` test suite anymore because it requires some other operators.
From now on, if we are adding a new unit test in `test_codegen_unboxing`, we will have to export the operators for the unit test model and add them into `lightweight_dispatch_ops.yaml`. This can be automated by introducing tracing based selective build, but that's for next PR to do.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78983
Approved by: https://github.com/kit1980
Allows to choose the BLAS backend with Eigen. Previously this was a CMake option only and the env variable was ignored.
Related to f1f3c8b0fa
The claimed options BLAS=BLIS WITH_BLAS=blis are misleading: When BLAS=BLIS is set the WITH_BLAS option does not matter at all, it would only matter for BLAS=Eigen hence this issue went undetected so far.
Supersedes #59220
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78037
Approved by: https://github.com/adamjstewart, https://github.com/janeyx99
