pytorch

6daf60be - [ONNX] Add setType from user into InferredType and Reliable in ConstantValueMap (#88622)

[ONNX] Add setType from user into InferredType and Reliable in ConstantValueMap (#88622) `setType` API is not respected in current exporter because the graph-level shape type inference simply overrides every NOT ONNX Op shape we had from node-level shape type inference. To address this issue, this PR (1) makes custom Op with `setType` **reliable** in ConstantValueMap to secure its shape/type information in pass: _C._jit_pass_onnx. (2) If an invalid Op with shape/type in pass: _C._jit_pass_onnx_graph_shape_type_inference(graph-level), we recognize it as reliable. 1. In #62856, The refactor in onnx.cpp made regression on custom Op, as that was the step we should update custom Op shape/type information into ConstantValueMap for remaining Ops. 2. Add another condition besides IsValidONNXNode for custom Op setType in shape_type_inference.cpp. If all the node output has shape (not all dynamic), we say it's custom set type. 3. ~However, this PR won't solve the [issue](https://github.com/pytorch/pytorch/issues/87738#issuecomment-1292831219) that in the node-level shape type inference, exporter invokes the warning in terms of the unknow custom Op, since we process its symbolic_fn after this warning, but it would have shape/type if setType is used correctly. And that will be left for another issue to solve. #84661~ Add `no_type_warning` in UpdateReliable() and it only warns if non ONNX node with no given type appears. Fixes #81693 Fixes #87738 NOTE: not confident of this not breaking anything. Please share your thoughts if there is a robust test on your mind. Pull Request resolved: https://github.com/pytorch/pytorch/pull/88622 Approved by: https://github.com/BowenBao