Speculative Decoding Technique
(Redirected from speculative decoding)
Jump to navigation
Jump to search
Speculative Decoding Technique is an AI model inference acceleration technique designed to speed up LLM output generation by using a small draft LLM without altering the final outputs.
- Context:
- It can (typically) generate multiple tokens in parallel, which are then verified by the larger target model.
- It can (often) involve the use of speculative sampling, a method that ensures the output distribution matches that of the target model alone.
- ...
- Example(s):
- a Google's speculative decoding implementation that leverages a small draft model to generate candidate tokens, which are then checked by a larger LLM.
- a DeepMind's SpS (Speculative Sampling) method that achieves 2-2.5x decoding speedups in large language models while maintaining output fidelity.
- a HuggingFace's assisted generation (AsG) approach that assists in speeding up generation by using a draft model for initial token prediction.
- ...
- Counter-Example(s):
- Standard Autoregressive Decoding, which decodes tokens sequentially without the use of a draft model, leading to slower inference times.
- Non-speculative Beam Search, a decoding method that explores multiple sequences of tokens but does not involve parallel draft models for acceleration.
- See: Inference Acceleration, Autoregressive Decoding, Speculative Sampling, Large Language Models
== References
2024
- (Leviathan et al., 2023) ⇒ Yaniv Leviathan, Matan Kalman, and Yossi Matias. (2023). "Fast Inference from Transformers via Speculative Decoding." In: Proceedings of the 40th International Conference on Machine Learning.
** NOTE: It introduces speculative decoding as a method to speed up the inference of transformer-based models by using a draft model to generate tokens that are then verified by the main model. ** NOTE: It demonstrates that this technique can achieve up to 3x speedups in inference while maintaining the quality of the outputs, making it suitable for real-time applications.
2024
- (Kim et al., 2023) ⇒ Sehoon Kim, Karttikeya Mangalam, Suhong Moon, Jitendra Malik, Michael W. Mahoney, Amir Gholami, and Kurt Keutzer. (2023). "Speculative Decoding with Big Little Decoder." In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023).
- NOTE: It proposes the Big Little Decoder (BiLD) framework, which combines a small draft model and a larger target model to reduce inference latency in text generation tasks.
- NOTE: It achieves a 2.12x speedup in various NLP tasks with minimal degradation in output quality, demonstrating its potential for efficient deployment in real-time systems.
2024
- (Chen et al., 2023) ⇒ Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. (2023). "Accelerating Large Language Model Decoding with Speculative Sampling." Published by Hugging Face.
- NOTE: It presents speculative sampling as a technique for accelerating the decoding process in large language models by generating multiple tokens simultaneously and using a rejection sampling scheme to ensure output accuracy.
- NOTE: It achieves a 2-2.5x speedup in decoding for large models like Chinchilla, without requiring changes to the model itself, making it a practical method for faster inference.
2024
- (Anonymous, 2024) ⇒ Anonymous. (2024). "Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding." In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024).
- NOTE: It introduces self-speculative decoding, where a draft model generates tokens that are subsequently verified by the main model, achieving speedup without loss of output quality.
- NOTE: It demonstrates that this technique is effective across various tasks, providing a reliable method for accelerating LLM inference in practical applications.