Summary:
Starting ONNX IR version 4, the initializers in the ONNX graph do not have to be inputs of the graphs. This constraint, which existed in IR version 3 and earlier, was relaxed in IR version 4. This PR provides an API level argument to allow ONNX export with the relaxed constraint of IR version 4, i.e. provides the option to not include initializers as inputs. This allows backends/runtimes to do certain optimizations, such as constant folding, better.
*Edit*: After discussion with houseroad we have the following behavior. For any OperatorExportType, except OperatorExportTypes.ONNX, the current status of export is maintained in this PR by default. However, the user can override it by setting the `keep_initializers_as_inputs` argument to the export API. But when exporting to ONNX, i.e. OperatorExportType is OperatorExportTypes.ONNX, the current status is changed in that by default the initializers are NOT part of the input. Again, the default can be overridden by setting the `keep_initializers_as_inputs` argument.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23284
Differential Revision: D16459961
Pulled By: bddppq
fbshipit-source-id: b8f0270dfaba47cdb8e04bd4cc2d6294f1cb39cf
Summary:
Without metadata(datatype) for the new output, exporter won't be able to perform implicit scalar datatype casting. This PR covers a large portion of this common issue seen in many exported models, e.g. https://github.com/pytorch/pytorch/issues/23724
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23809
Reviewed By: ezyang
Differential Revision: D16707640
Pulled By: bddppq
fbshipit-source-id: 3de985c6b580b9c9ebaec08085c7443bd8d9c7f8
Summary:
Added a number of opset10 tests from Caffe2 to ORT
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22993
Differential Revision: D16467954
Pulled By: bddppq
fbshipit-source-id: 0b92694c7c0213bdf8e77e6f8e07e6bc8a85170a
Summary:
Support exporting
* Standard tensor indexing like
```
x = torch.ones(4, 5)
ind = torch.tensor([0, 1])
return x[ind]
```
* [Advanced indexing](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing) like
```
x = torch.ones(4,5,6,7,8)
ind1 = torch.tensor([0, 1])
ind2 = torch.tensor([[3], [2]])
ind3 = torch.tensor([[2, 2], [4, 5]])
return x[2:4, ind1, None, ind2, ind3, :]
```
It would be ideal if ONNX can natively support indexing in future opsets, but for opset <= 10 it will always need this kind of workarounds.
There are still various limitations, such as not supporting advanced indexing with negative indices, not supporting mask indices of rank > 1, etc. My feeling is that these are less common cases that requires great effort to support using current opset, and it's better to not make the index export more cumbersome than it already is.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21716
Reviewed By: zrphercule
Differential Revision: D15902199
Pulled By: houseroad
fbshipit-source-id: 5f1cc687fc9f97da18732f6a2c9dfe8f6fdb34a6
Summary:
Some overlap with https://github.com/pytorch/pytorch/pull/21716 regarding caffe2 nonzero. Will rebase the other one accordingly whichever gets merged first.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22601
Reviewed By: zrphercule
Differential Revision: D16224660
Pulled By: houseroad
fbshipit-source-id: dbfd1b8776cb626601e0bf83b3fcca291806e653
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:
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
