Summary:
Desugar prim::shape to aten::size so that passes don't need to reason about both ops. Serialized models still resolve to `prim::shape` so this doesn't break BC.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34286
Differential Revision: D20316818
Pulled By: eellison
fbshipit-source-id: d1585687212843f51e9396e07c108f5c08017818
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30892
Fixes all outstanding lints and actually installs a properly configured
flake8
Test Plan: Imported from OSS
Differential Revision: D18862825
Pulled By: suo
fbshipit-source-id: 08e9083338a7309272e17bb803feaa42e348aa85
Summary:
PyTorch dim and ONNX axis have different meanings.
ONNX only supports log_softmax with dim = -1. Transpose must be added before and after log_softmax to support other cases.
This requires input rank to be known at export time.
Fixes https://github.com/pytorch/pytorch/issues/17918
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30433
Reviewed By: hl475
Differential Revision: D18723520
Pulled By: houseroad
fbshipit-source-id: d0ed3b3f051d08d46495a7abfa854edd120dca3a
Summary:
The PyTorch exporter does not add any name to the ONNX operators in the exported graph. A common request is to add names to op nodes by default. This helps the readability of the graph in visualization tools such a Netron, or when the ONNX graph is printed as a string. Also, it helps with the debuggability of the ONNX graph.
Therefore this PR adds name to operators in the exporters. The names follow a simple format, <op_type>_<index>. Expect files for tests in `test/onnx/test_operators.py` have been updated.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27342
Reviewed By: hl475
Differential Revision: D17790979
Pulled By: houseroad
fbshipit-source-id: 1eaae88b5f51f152735a2ff96e22827837e34d9d
Summary:
This should resolve https://github.com/pytorch/pytorch/issues/29008. This flag has two effects on the tracer.
- Remove the underscroll for inplace operators. E.g.: index_put_ ==> index_put. This is handled in utils.py separately as well.
- Add out as input for backward computation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29466
Reviewed By: hl475
Differential Revision: D18422815
Pulled By: houseroad
fbshipit-source-id: 317b6a3c8a5751fe6fe49d7543e429d281ed0d6d
Summary:
This is a fix for batch norm 2D with affine=False.
Repro: https://github.com/pytorch/pytorch/issues/29271
Error is because the output of the unsqueeze op does not have scalar type information. So I moved the references to scalar type after the unsqueeze line.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29458
Reviewed By: hl475
Differential Revision: D18400975
Pulled By: houseroad
fbshipit-source-id: f5c5633857c584edcef3b9e9946861dcfccccd75
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29694
This PR adds preliminary support required to be able to run quantized pytorch models on a C2 backend.
For quantized ops we use a custom domain name 'caffe2' to register the ops if they are in the "quantized" namespace.
The change also adds JIT pass to unpack the quantized weights and insert the unpacked values into the graph.
The actual tensor values are looked up from the params dict.
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2.py TestQuantizedOps
Imported from OSS
Reviewed By: houseroad
Differential Revision: D18467130
fbshipit-source-id: 53ebd8c43935f7d7e74305dad6c231a2247df176
Summary:
Fix Slice/Select trace arguments. This PR stashes arguments to functions in order to avoid tracing them as constants.
This PR depends on a fix for select op in PR:
https://github.com/pytorch/pytorch/pull/25273
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26549
Reviewed By: hl475
Differential Revision: D17623851
Pulled By: houseroad
fbshipit-source-id: ae314004266688d2c25c5bada2dcedbfc4f39c5b
Summary:
Exporting torch.select when index = negative one (x[:,-1]) was broken. This PR has the fix in symbolic function for select.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25273
Reviewed By: hl475
Differential Revision: D17159707
Pulled By: houseroad
fbshipit-source-id: 2c3b275421082758f1b63c1c9b6e578f03ca9f76
Summary:
Currently, we export invalid ONNX models when size() is used with a negative dim.
This PR fixes the issue and allows exporting these models to ONNX (ex: input.size(-1)).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26436
Reviewed By: hl475
Differential Revision: D17565905
Pulled By: houseroad
fbshipit-source-id: 036bc384b25de77506ef9fbe24ceec0f7e3cff8b
Summary:
Added support for gelu in symbolic opset9 + op and ORT tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24475
Reviewed By: hl475
Differential Revision: D17088708
Pulled By: houseroad
fbshipit-source-id: 9d2f9d7d91481c57829708793d88f786d6c3956f
Summary:
This pass tries to resolve scalar type mismatch issues between input tensors introduced by the implicit type conversions on scalars.
e.g. https://github.com/pytorch/pytorch/issues/23724
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24378
Reviewed By: hl475
Differential Revision: D17088682
Pulled By: houseroad
fbshipit-source-id: 3de710f70c3b70b9f76fd36a7c4c76e168dbc756
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:
Don't automatically unwrap top layer DataParalllel for users. Instead, we provide useful error information and tell users what action to take.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23365
Reviewed By: zrphercule
Differential Revision: D16514273
Pulled By: houseroad
fbshipit-source-id: f552de5c53fb44807e9d9ad62126c98873ed106e
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:
cosine_similarity has two non-tensor parameters, needs some special handling. Add the support for its export in this diff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21884
Reviewed By: zrphercule
Differential Revision: D15866807
Pulled By: houseroad
fbshipit-source-id: a165fbc00c65c44b276df89ae705ca8960349d48
