v1.14.0

[yuanyao-nv]

ONNX v1.14.0 is now available with exciting new features! We would like to thank everyone who contributed to this release! Please visit onnx.ai to learn more about ONNX and associated projects.

New operators

DeformConv added in #4783

Operator extensions

Equal - Support for string data type added in #4828
AveragePool - New attribute dilations #4790
Pad - Added new wrap to the mode attribute to support circular padding #4793
Resize - Added half_pixel_symmetric to the coordinate_transformation_mode attribute #4862

IR updates (bump to 9)

• Support attributes with default values: proto to support attributes with default values #4911
• Added 4 new 8-bit floating point data types: Introduce float 8 types, FLOATE4M3, FLOATE5M2 #4805

Backend tests

Replaced real models with light models in backend tests. #4861 #4960

Support Protobuf v21

Now ONNX supports Protobuf v21: #4956

Deprecation notice

• Python 3.7 support will be deprecated due to EOL in next release: Deprecate Python 3.7 due to EOL #5191
• onnx-weekly package will be deprecated in TestPyPI. Please use them from PyPI instead: [Announce] Move onnx-weekly package from TestPyPI to PyPI #4930
• Properties in FormalParameter will be deprecated in future release. Please use newer properties name: [Tracking] Remove deprecated pybind fields in objects #5074
• Variables from mapping.py will be deprecated and become private implementation details. Please use public functions to get corresponding types from helper.py instead: [Tracking] Move all variables in mapping.py into _mapping.py #4554

Installation Notice

You can upgrade to the latest release using pip install onnx --upgrade or build from source following the README instructions.

Contributors

Thanks to these individuals for their contributions in this release since last 1.13.0 release: @jcwchen, @andife, @gramalingam, @xadupre, @justinchuby, @liqunfu, @yuanyao-nv, @jbachurski, @p-wysocki, @prasanthpul, @jantonguirao, @take-cheeze, @smk2007, @AlexandreEichenberger, @snnn, @daquexian, @linkerzhang.

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This discussion was created from the release v1.14.0.

[take-cheeze]

Thank you for the release!
IR version update should be noted since it may break some code relying on onnx.helper.make_model

And after #4911 the IR version docs isn't updated
https://github.com/onnx/onnx/blame/v1.14.0/onnx/onnx.proto#L106

[jcwchen]

@cbourjau You are right. onnx.helper can be disruptive sometimes, because currently onnx.helper.make_model will always use the latest IR_VERSION and the latest opset_version by default, although it can specify the opset_version (and also ir_version). Other than that, onnx.helper.make_model_gen_version can generate models with specified opset_version and corresponding IR_VERSION, which is typically a safer way to get a stable model.

Now users can only manually fix the IR_VERSION after onnx.helper.make_model. However, it seems reasonable to me that onnx should enable helper.make_model to get specified IR_VERSION.

[gramalingam]

A correction: my reading of this line is that if the caller explicitly specifies ir_version via ir_version=... in the call that takes precedence in make_model (both versions). Only if the caller does not specify ir_version, is make_model going to have a release-dependent behavior.

[jcwchen]

Thank you @gramalingam for the correction! Sorry I misread that. Yes, current onnx.helper.make_model is able to created a model with specified ir_version by onnx.helper.make_model(graph, ir_version=8). I have revised my previous comment to prevent confusion.

[take-cheeze]

Thank you for updating note!
I hope onnxruntime supporting new IR would be released soon.

[cbourjau]

Thanks for the clarification!