ml-explore / ml-explore/mlx-examples
Feature Request - Beam Search Decoder
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- Dominant language
- Python
- Stars
- 9k
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Description
Hi MLX team,
I want to request a feature/example implementation of Beam Search Decoder for one of the text generation examples. The current implementations only cover Greedy and Top-P sampling. I currently implemented a naive beam search implementation, which runs on CPU and is slow with many for loops. It would be helpful if someone from your team could provide a reference implementation using MLX kernels and efficiently utilize GPU or vectorized CPU kernels.
I am happy to collaborate on this if I can get some guidance from your team.
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 locating the text generation examples that implement Greedy and Top-P sampling, then review how decoding is structured and where a beam-search entry point would fit. Compare the requested behavior with the existing naive CPU implementation described in the issue; done means a reference beam-search decoder using MLX kernels with GPU or vectorized CPU execution.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100