Kangaroo is an implementation of Self-Speculative Decoding that introduces a trainable adapter layer. Over the past few weeks, I have been working on fine-tuning its adapter layer and have achieved some preliminary results, which I am documenting here.
Read More »Kangaroo: Inference Acceleration Architecture Implementation
Many of the inference acceleration techniques I have studied, such as Speculative Decoding, predominantly use a threshold for the confidence scores of the draft model. This threshold determines how many draft tokens should be decoded before passing them to the target model for verification, thereby reducing the extra computational cost when the draft model operates with low confidence.
Read More »Using The Target Model's Confidence Threshold To Decide Whether To Enable Speculative Decoding
Recently, I attempted to implement various speculative decoding acceleration methods. HuggingFace's transformers library also provides a corresponding acceleration feature called assistant_model. Today, let me take this opportunity to document it.
Over the past week, I dedicated some time to reproducing the Self-Speculative Decoding mechanism based on the ideas from the paper Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding, implementing the following modules:
In self-speculative decoding, since our draft model is derived from part of the target model’s network, finding an optimal 'Layer Skip Strategy' is crucial. We need to skip enough layers to achieve meaningful speedup while ensuring the draft model’s speculative decoding is good enough to avoid frequent rejection by the target model.
Today’s implementation focuses on optimizing my previously implemented LayerSkip model using the Bayesian optimization framework Optuna, to determine which layers to skip.
Read More »Optimizing LayerSkip Models with Bayesian Search for an Effective Layer Skipping Strategy
Self-Speculative Decoding is a variant of Speculative Decoding. The original Speculative Decoding method uses a draft model to optimize the inference of the target model. The draft model, which is typically distilled from the target model, offers similar output quality but with several times faster inference speed.
Read More »Self-Speculative Decoding Implementation: LayerSkip Transformer
In auto-regressive model decoding, if we need to decode K tokens, we must go through the process K times, which is the current bottleneck in the inference time of large language models.
Read More »[Paper Reading] Fast Inference from Transformers via Speculative Decoding
During the decoding process of large language models, especially in Auto-regressive models, decoding must be performed step-by-step until the entire sequence is generated. Within this process, there are caching techniques that can help reduce computation and improve decoding speed; one such technique is known as the KV Cache.
Read More »KV Cache: A Caching Mechanism To Accelerate Transformer Generation
When implementing various services through LLMs, do you worry about uncontrolled language generation? Recently, at a critical juncture in wrapping up a project, I used tools like Outlines to constrain LLM decoding, which effectively controlled the model's output to follow the desired patterns. However, a colleague posed a deep question: What if I want it not to generate specific words?
Read More »Using Finite State Machine (FSM) and Rollback Mechanism to Restrict LLM from Generating Banned Words
