microsoft / microsoft/onnxruntime
Optimizing BART: encoder/decoder attention
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- Dominant language
- C++
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Description
**Is your feature request related to a problem? Please describe.**
I'd like to optimize BART with ONNX Runtime, but it looks like the only Attention operator currently supported is [self-attention](https://github.com/microsoft/onnxruntime/blob/master/docs/ContribOperators.md#com.microsoft.Attention), and BART requires encoder/decoder cross-attention.
**System information**
- ONNX Runtime version (you are using): 1.4.0
**Describe the solution you'd like**
A fused operator implementing encoder/decoder cross-attention
**Describe alternatives you've considered**
- In the meantime I'm planning to check out APEX's [Fast Multihead Attention](https://github.com/NVIDIA/apex/tree/master/apex/contrib/multihead_attn).
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading ONNX Runtime's existing com.microsoft.Attention operator documentation and the BART encoder/decoder cross-attention requirement described here. Confirm whether the requested fused operator is still needed, then identify the relevant operator implementation and tests before defining completion criteria for cross-attention support.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100