Reauthoring and Converting models for edge inference: MambaV2 on LiteRT
7 days ago
- #LiteRT
- #edge-inference
- #MambaV2
- The article discusses the process of reauthoring and converting models for edge inference, specifically focusing on MambaV2 on LiteRT.
- The Granite 4 Nano models by IBM incorporate MambaV2, which uses convolution and SSM states for efficiency, mixed with attention layers for better quality.
- Re-authoring involves optimizing PyTorch code for on-device inference, including representing operations in an edge-friendly way and handling novel concepts like KV cache efficiently.
- Testing the re-authored model ensures the underlying math remains unchanged, with weight checkpoints remapped to new patterns.
- Conversion challenges include auto-detecting complex patterns in large LLMs and optimizing for performance, memory, and power usage.
- Key features for lowering models include custom ops and packing multiple functions into the same Program for different use-case prompt sizes.
- The article provides a practical demo of running the model on an M1 Pro Macbook, comparing performance between dense and hybrid MambaV2 versions.
- Follow-ups for production include testing across different accelerators, writing quality tests, and exploring compression schemes.
- The author praises ai-edge-torch for its abstraction level, making it easier to implement novel concepts like Mamba without deep diving into multiple layers of abstraction.