Summary:
Update pass that handles prim::ListUnpack in peephole file, so that it also covers the case when input to the node is of ListType.
Fixes https://github.com/pytorch/pytorch/issues/45816
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46264
Reviewed By: mrshenli
Differential Revision: D24566070
Pulled By: bzinodev
fbshipit-source-id: 32555487054f6a7fe02cc17c66bcbe81ddf9623e
Summary:
Currently, no test reports are uploaded to CI because the paths for the `onnx` runs are incorrect. This PR attempts to change that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47315
Reviewed By: malfet
Differential Revision: D24727607
Pulled By: janeyx99
fbshipit-source-id: f6d91698fdb15a39e01ef812032d4cd30621f864
Summary:
* Support propagating `dim_param` in ONNX by encoding as `ShapeSymbol` in `SymbolicShape` of outputs. If export is called with `dynamic_axes` provided, shape inference will start with these axes set as dynamic.
* Add new test file `test_pytorch_onnx_shape_inference.py`, reusing all test cases from `test_pytorch_onnx_onnxruntime.py`, but focus on validating shape for all nodes in graph. Currently this is not enabled in the CI, since there are still quite some existing issues and corner cases to fix. The test is default to run only at opset 12.
* Bug fixes, such as div, _len, and peephole.cpp passes for PackPadded, and LogSoftmaxCrossEntropy.
* This PR depends on existing PR such as 44332.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44920
Reviewed By: eellison
Differential Revision: D23958398
Pulled By: bzinodev
fbshipit-source-id: 00479d9bd19c867d526769a15ba97ec16d56e51d
Summary:
Among all ONNX tests, ONNXRuntime tests are taking the most time on CI (almost 60%).
This is because we are testing larger models (mainly torchvision RCNNs) for multiple onnx opsets.
I decided to divide tests between two jobs for older/newer opsets. This is now reducing the test time from 2h to around 1h10mins.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33242
Reviewed By: hl475
Differential Revision: D19866498
Pulled By: houseroad
fbshipit-source-id: 446c1fe659e85f5aef30efc5c4549144fcb5778c
Summary:
ONNX uses virtualenv, and PyTorch doesn't. So --user flag is causing problems in ONNX ci...
Fixing it by moving it to pytorch only scripts. And will install ninja in onnx ci separately.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22946
Reviewed By: bddppq
Differential Revision: D16297781
Pulled By: houseroad
fbshipit-source-id: 52991abac61beaf3cfbcc99af5bb1cd27b790485
Summary:
This is an extension to the original PR https://github.com/pytorch/pytorch/pull/21765
1. Increase the coverage of different opsets support, comments, and blacklisting.
2. Adding backend tests for both caffe2 and onnxruntime on opset 7 and opset 8.
3. Reusing onnx model tests in caffe2 for onnxruntime.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22421
Reviewed By: zrphercule
Differential Revision: D16225518
Pulled By: houseroad
fbshipit-source-id: 01ae3eed85111a83a0124e9e95512b80109d6aee
Summary:
- Fix typo in ```torch/onnx/utils.py``` when looking up registered custom ops.
- Add a simple test case
1. Register custom op with ```TorchScript``` using ```cpp_extension.load_inline```.
2. Register custom op with ```torch.onnx.symbolic``` using ```register_custom_op_symbolic```.
3. Export model with custom op, and verify with Caffe2 backend.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21321
Differential Revision: D16101097
Pulled By: houseroad
fbshipit-source-id: 084f8b55e230e1cb6e9bd7bd52d7946cefda8e33
Summary:
So far, we only have py2 ci for onnx. I think py3 support is important. And we have the plan to add onnxruntime backend tests, which only supports py3.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21715
Reviewed By: bddppq
Differential Revision: D15796885
Pulled By: houseroad
fbshipit-source-id: 8554dbb75d13c57b67ca054446a13a016983326c
Summary:
bypass-lint
- Change all Caffe2 builds to use setup.py instead of cmake
- Add a -cmake- Caffe2 build configuration that uses cmake and only builds cpp
- Move skipIfCI logic from onnx test scripts to the rest of CI logic
- Removal of old PYTHONPATH/LD_LIBRARY_PATH/etc. env management
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15917
Reviewed By: orionr
Differential Revision: D13637583
Pulled By: pjh5
fbshipit-source-id: c5c5639db0251ba12b6e4b51b2ac3b26a8953153
Summary:
Building caffe2 and pytorch separately will end up duplicated symbols as they now share some basic libs. And it's especially bad for registry. This PR fixes our CI and build them in one shot with shared symbols.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10427
Reviewed By: bddppq
Differential Revision: D9282372
Pulled By: yinghai
fbshipit-source-id: 0514931ea88277029a68fa5368ff4336472f132e
* Run onnx integration tests in caffe2 CI
* verbose log
* turn off onnx verbose installation log
* can not install ninja
* Do not use all cores to build pytorch
* install tests require
* pip install to user dir
* use determined path to improve (s)ccache hit
* Do not change path in test.sh
* Add the compile cache hit trick to conda install as well
* cover jenkins in CI environment detection
* Move ONNX integration tests from onnx-fb-universe to PyTorch repo
* Switch to use torchvision
* Delete single rnn operator tests, they have been covered in e2e tests in test_caffe2.py
* Mirror the fix in onnx-fb-universe to bypass cuda check
667326d84b
