Introducing TriForce: A Hierarchical Speculative Decoding AI System
Bringing Practical AI Solutions to Long Sequence Generation
As the demand for efficient long-sequence inference support grows, the deployment of large language models (LLMs) like GPT-4, Gemini, and LWM has become widespread. However, the auto-regressive nature of these models and the increasing memory footprint of the key-value (KV) cache present significant challenges in serving them efficiently.
TriForce, developed by researchers from Carnegie Mellon University and Meta AI (FAIR), is a hierarchical speculative decoding system designed to address these challenges and enable scalable long sequence generation. By utilizing the original model weights and dynamic sparse KV cache via retrieval as a draft model, TriForce serves as an intermediate layer in the hierarchy, allowing for superior KV cache selection and lossless drafting.
The implementation of TriForce utilizes Transformers, FlashAttention, and PyTorch CUDA graphs to maintain full layer sparsity while minimizing kernel launching overhead. The evaluation of TriForce has shown significant speedups, with remarkable efficiency achieved on consumer GPUs.
TriForce achieves a speed of 0.108s/token, showcasing its potential for revolutionizing long-context model serving. It also offers a 1.9× speedup with large batches, making it a practical AI solution for long-context model serving.
For more information about TriForce, you can check out the paper.
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