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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61931
This PR consolidates the profiling code around a new C++ implementation
(profiler_kineto.h/cpp) and uses it unconditionally from
torch.autograd.profiler/torch.profiler:
1. Always use profiler_kineto.h/cpp as the C++ implementation
2. Simplify profiler.py to remove unneeded parts depending on legacy
impl
3. Move some of the legacy logic into profiler_legacy.py (to be fully
deleted later)
Test Plan:
USE_KINETO=1 USE_CUDA=1 USE_MKLDNN=1 BLAS=MKL BUILD_BINARY=1 python setup.py develop install --cmake
python test/test_profiler.py -v
USE_KINETO=0 USE_CUDA=1 USE_MKLDNN=1 BLAS=MKL BUILD_BINARY=1 python setup.py develop install --cmake
python test/test_profiler.py -v
Imported from OSS
Reviewed By: gdankel
Differential Revision: D29801599
fbshipit-source-id: 9794d29f2af38dddbcd90dbce4481fc8575fa29e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
Summary:
**ZeroDivisionError** occurs when `cuda_prof_exec_time` is small enough.
This situation is normal for a project that has little CUDA work.
Or someone does not make his work transferred to CUDA successfully. In this time he profiles the code, this error occurs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11987
Differential Revision: D10488568
Pulled By: soumith
fbshipit-source-id: db8c1e9e88a00943c100958ebef41a1cb56e7e65
Summary:
The pytorch.org site redirects all of the http:// requests to the https:// site anyway, so the comments and error messages might as well refer directly to the https:// site. The GitHub project description should also be updated to point to https://pytorch.org
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12636
Differential Revision: D10377099
Pulled By: soumith
fbshipit-source-id: f47eaba1dd3eecc5dbe62afaf7022573dc3fd039
* Add environment collection script
Fixes#6111. This should make it easier for users to report bugs by giving
them a script to collect system environment information.
Changes include:
- Refactor out the environment collecting code from utils.bottleneck
- Add script (collect_env.py)
- Cleaned up the issues template so that it suggests using the script
and is more readable.
Testing: added expect tests to go with 4 CI configurations. Whenever one
of these configurations gets updated, the test will fail until the test
also gets updated.
* Expect tests
* Update issue template
* Fix random space
* Minor improvement to issue template; fix expect test
* Skip expect test if BUILD_ENVIRONMENT not found; test fix; split off smoke/expect test
Fixes#6312.
Changed bottleneck's arg parser to user argparse.REMAINDER. This lets
the user specify args as `python -m torch.utils.bottleneck script.py
[args]` (previously, a -- was needed after `bottleneck` and before
`script.py`).
* Implement torch.util.bottleneck
This is a tool that is intended to be used as initial exploratory
debugging of bottlenecks in user scripts. Run it with
python -m torch.utils.bottleneck /path/to/source/script.py
* Refactor and address comments
* Fix tests
* Allow passing of args to the profiled script
* Replace Variable
