Help Wanted: Implementing ONNX Ops

[antimora]

The Burn community would greatly appreciate your help in completing the missing ONNX operations in the burn-import crate. By contributing to this effort, you can help expand the functionality and usability of Burn for a wider range of machine learning models.

List of Missing Ops (Available in Burn but not in burn-import)

Top Requested Ops

• Expand
• Tile (dependent on #1715)
• Slice

Relatively Easy Ops (Similar to Existing Implemented Ops)

• ArgMax
• AveragePool1d
• ConvTranspose1d
• Gather
• Greater
• GreaterOrEqual
• Less
• LessOrEqual
• Max
• MaxPool1d
• Mean
• Min
• PRelu
• Pad
• Range
• ReduceMin
• ReduceProd
• ReduceSum
• Squeeze ¹
• Sum
• TopK
• Trilu

^{1 Current implementation only supports squeeze on a single dimension.}

Harder Ops (Not Similar to Existing Implemented Ops)

• GRU
• GroupNormalization
• If
• InstanceNormalization
• LSTM
• MatMulInteger
• OneHot
• RNN
• RandomNormal
• RandomNormalLike
• RandomUniform
• RandomUniformLike
• Resize
• Scatter

For a comprehensive list of all supported ONNX Ops, please refer to the SUPPORTED-ONNX-OPS.md file in the Burn repository.

Getting Started

To begin contributing, please follow the instructions in the contributor book:

# Build the book and open in a browser
cargo xtask books contributor open

Once the book is built, navigate to http://localhost:3011/guides/onnx-to-burn-conversion-tool.html for detailed guidance on how to implement missing ONNX operations in the burn-import crate.

Related Issues Submitted by Users

Several users have submitted issues related to missing ONNX operations. These issues can provide valuable context and help prioritize the implementation of specific ops:

1. Issue #1593
2. Issue #1560
3. Issue #1544
4. Issue #675
5. Issue #673
6. Issue #600
7. Issue #510

By tackling these missing ops, you can help address the needs of the Burn community and contribute to the growth and effectiveness of the project. Thank you for your interest in contributing to Burn!