Summary:
Addresses this network risk mitigation mentioned in https://github.com/pytorch/pytorch/issues/65439#issuecomment-924627239.
I didn't include any mobile app/benchmarking changes because I think the pretrained matters there.
I ended up removing the changes in test_utils because those were sensitive to the pretrained variable.
I am saving the quantization test changes for another PR because they are currently disabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66312
Reviewed By: ejguan
Differential Revision: D31542992
Pulled By: janeyx99
fbshipit-source-id: 57b4f70247af25cc96c57abd9e689c34641672ff
Summary:
*Context:* https://github.com/pytorch/pytorch/issues/53406 added a lint for trailing whitespace at the ends of lines. However, in order to pass FB-internal lints, that PR also had to normalize the trailing newlines in four of the files it touched. This PR adds an OSS lint to normalize trailing newlines.
The changes to the following files (made in 54847d0adb9be71be4979cead3d9d4c02160e4cd) are the only manually-written parts of this PR:
- `.github/workflows/lint.yml`
- `mypy-strict.ini`
- `tools/README.md`
- `tools/test/test_trailing_newlines.py`
- `tools/trailing_newlines.py`
I would have liked to make this just a shell one-liner like the other three similar lints, but nothing I could find quite fit the bill. Specifically, all the answers I tried from the following Stack Overflow questions were far too slow (at least a minute and a half to run on this entire repository):
- [How to detect file ends in newline?](https://stackoverflow.com/q/38746)
- [How do I find files that do not end with a newline/linefeed?](https://stackoverflow.com/q/4631068)
- [How to list all files in the Git index without newline at end of file](https://stackoverflow.com/q/27624800)
- [Linux - check if there is an empty line at the end of a file [duplicate]](https://stackoverflow.com/q/34943632)
- [git ensure newline at end of each file](https://stackoverflow.com/q/57770972)
To avoid giving false positives during the few days after this PR is merged, we should probably only merge it after https://github.com/pytorch/pytorch/issues/54967.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54737
Test Plan:
Running the shell script from the "Ensure correct trailing newlines" step in the `quick-checks` job of `.github/workflows/lint.yml` should print no output and exit in a fraction of a second with a status of 0. That was not the case prior to this PR, as shown by this failing GHA workflow run on an earlier draft of this PR:
- https://github.com/pytorch/pytorch/runs/2197446987?check_suite_focus=true
In contrast, this run (after correcting the trailing newlines in this PR) succeeded:
- https://github.com/pytorch/pytorch/pull/54737/checks?check_run_id=2197553241
To unit-test `tools/trailing_newlines.py` itself (this is run as part of our "Test tools" GitHub Actions workflow):
```
python tools/test/test_trailing_newlines.py
```
Reviewed By: malfet
Differential Revision: D27409736
Pulled By: samestep
fbshipit-source-id: 46f565227046b39f68349bbd5633105b2d2e9b19
Summary:
There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports:
```2to3 -f future -w caffe2```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033
Reviewed By: seemethere
Differential Revision: D23808648
Pulled By: bugra
fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
Summary:
This PR makes Caffe2 compatible with TensorRT 6. To make sure it works well, new unit test is added. This test checks PyTorch->ONNX->TRT6 inference flow for all classification models from TorhchVision Zoo.
Note on CMake changes: it has to be done in order to import onnx-tensorrt project. See https://github.com/pytorch/pytorch/issues/18524 for details.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26426
Reviewed By: hl475
Differential Revision: D17495965
Pulled By: houseroad
fbshipit-source-id: 3e8dbe8943f5a28a51368fd5686c8d6e86e7f693
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18494
Today we have some C2 end2end test run requiring reading model data from external filesystem (for example, Gluster and AWS). This could be a source for flaky test when the external filesystems are not reachable during the tests.
In this diff, we add try/catch logic around where we download models and open model files from external system. In case such attempts fails, we will catch the excption and let the unittest skip the current test instead of failure.
I also refactor the code a little bit by removing some duplicated logic on downloading and build the c2 model data. It has been duplicated in two classes and a few functions...
Reviewed By: yinghai
Differential Revision: D14442241
fbshipit-source-id: da8bf56c8d096efa34ca2070de5cd10a18aad70c
Summary:
According to https://docs.python.org/3/tutorial/inputoutput.html, it is good practice to use the "with" keyword when dealing with file objects. If not, you should call f.close() to close the file and immediately free up any system resources used by it. Thus, I adjust the open file function to "with open() as f".
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18017
Differential Revision: D14475112
Pulled By: ezyang
fbshipit-source-id: d1c0821e39cb8a09f86d6d08b437b4a99746416c
Summary:
This is follow up on #13945 where we had to turn off some TRT tests because some ops were not ready to accept ONNX opset 9+ models. This PR fixes Reshape.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15380
Differential Revision: D13649825
Pulled By: houseroad
fbshipit-source-id: b72e62803de5b63cc001c3fe4b3bf64dfa996e94
Summary:
ONNX-TensorRT is still using old opset (<7). Patch it for now.
Future fix would be expose versioning in onnx exporter.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9285
Reviewed By: houseroad
Differential Revision: D8775268
Pulled By: yinghai
fbshipit-source-id: c272073f80cce35ebd971e44ec9472e3c8fd4b9e
* Add support to TensorRT
* Removed License header
* Bind input/output by position
* Comments
* More comments
* Add benchmark
* Add warning for performance degradation on large batch
* Address comments
* comments
