microsoft / microsoft/onnxruntime

Optimizing BART: encoder/decoder attention

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feature request
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

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. 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

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