Overhaul shape inference for custom ops

[maltanar]

Details

Currently, QONNX custom ops need to implement the make_shape_compatible_op function for shape inference. This function is supposed to return a single, standard ONNX node which has the desired shape inference behavior as the custom op. Finding a single-node shape inference equivalent can be challenging if the custom op has non-trivial shape inference behavior. Several custom ops overcome this by assuming that their input shape is available, computing the desired output shape, and using the make_const_shape_op.

However, this requires that the shapes for the inputs to all the custom op are already specified. In cases where they are not, bugs arise e.g. #152 works around one such bug. We should have a more flexible custom op shape inference system.

New behavior

There are several paths forward which should be evaluated in more detail.

1. Enhance the custom op shape inference system such that custom ops can return multi-node subgraphs instead of just single-node ones. The ONNX abstractions for subgraphs or onnx.compose may come in handy here. The system in InferShapes for replacing custom nodes with single standard nodes will also need an overhaul, replacing a single node with a subgraph and then back again.
2. Keep the current make_shape_compatible_op single-node interface, and keep the assumptions about input shape being available. Rework InferShapes to replace custom ops one at a time in topological order and calling ONNX shape inference at each step, instead of replacing all at the same time before calling ONNX shape inference.
3. Explore possibilities with PyOp in onnxruntime-extensions to switch out a larger portion of how QONNX handles custom ops. This goes beyond just rehauling shape inference, but may have other benefits. See https://github.com/microsoft/onnxruntime-extensions/blob/main/tutorials/pytorch_custom_ops_tutorial.ipynb

Motivation

Avoid shape inference related bugs for custom ops.

Parts of QONNX being affected

Depends on the approach chosen.