Integrate with ONNX 1.16.0 release branch

[cjvolzka]

We are releasing ONNX 1.16.0. A release branch is created (https://github.com/onnx/onnx/tree/rel-1.16.0). The planned release date is March 25, 2024. Release candidates are also available from TestPyPI: pip install -i https://test.pypi.org/simple/ --pre onnx

It is important to integrate ONNX release branch ASAP so that any issues and incompatibilities can be detected and resolved before the ONNX release.

Key updates:

• ai.onnx Opset 21
  • Update to support int4 and uint4:
    • Cast, CastLike, Constant, ConstantOfShape, Identity, If, Loop, Reshape, Scan, Shape, Size
  • Update to support float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4 and uint4:
    • Flatten, Pad, Squeeze, Transpose, Unsqueeze
  • Support blocked quantization. Support int4, uint4, int16, and uint16:
    • DequantizeLinear, QuantizeLinear,
  • Support bfloat16 and float16 scales. Support float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz quantized tensors:
    • QLinearMatMul,
  • Add stash_type attribute to GroupNormalization
• ai.onnx.ml Opset 5
  • Addeded new operator TreeEnsemble
  • Deprecated TreeEnsembleClassifier and TreeEnsembleRegressor
• IR Version 10
  • Enabled UINT4, INT4 support
• Support Python 3.12

In case a bug in ONNX is detected during integration of ONNX 1.16.0, please open a ONNX Bug Report and tag ONNX Release Manager @cjvolzka so that the bug is fixed in the ONNX release branch.