Update QONNX parsing for 1.0

[jmitrevs]

Description

This change updates the ONNX parser and adds support for QONNX. It replaces PR #832. It only supports ONNX that has been cleaned by the qonnx package, including converting convolutions to be channels-last and changing Gemm to MatMul and Add.

In QONNX Quant nodes can act on constants as well as the datapath. To make handling this easier, we explicitly put constants in the initial graph. There are also some helper nodes like MatMul and Conv that are introduced to support the explicit constant nodes. After the convert flow, no special ONNX nodes remain in the graph, though.

Generally Quant nodes that have power-of-2 scales and no zero-offset get converted to fixed data types either by setting the types of constants or adding a linear activation that is usually merged into preceding nodes. Non-power-of-2 scales result in ApplyAlpha nodes beings added to scale and unscale, with propagation across some layers. This can be further optimized and has generally been tested less.

This includes the changes from PR #855 with a few updates that will be backported and discussed there. Therefore, this PR needs to wait till that PR is merged, which is why I am making it draft.

Note: for the config_from_onnx_model I made the default granularity be "name" because that enables automatic precision inference, which you need for QONNX. The way that I did that is that I set config['Model']['Precision'] to the default (e.g. fixed<16,6>), but all the precisions filled by config['Model'] are auto. These can be overriden if, for example, the accumulator becomes too wide. In general, though, they are set by the infer_precision.py optimizer.

Binary networks are not yet supported.

Type of change

• New feature (non-breaking change which adds functionality)
• A new research paper code implementation

Tests

The pytest, test_qonnx.py, is the main test, building some models from the QONNX model zoo

Checklist

• I have read the guidelines for contributing.
• I have commented my code, particularly in hard-to-understand areas.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have installed and run pre-commit on the files I edited or added.
• I have added tests that prove my fix is effective or that my feature works.