AI
Kangaroo: Inference Acceleration Architecture Implementation
Introduction
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.
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Using The Target Model's Confidence Threshold To Decide Whether To Enable Speculative Decoding
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.
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Using the `assistant_model` method in HuggingFace's `transformers` library to accelerate 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.
Self-Speculative Decoding Implementation: LayerSkip Model, Bayesian Optimization, and Adaptive Draft-Exiting Mechanism (Here are gemma-2-9b-it Experiment Results)
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:
- A Decoder-only Transformer model with layer skipping (based on Llama and Gemma-2 architectures)
- Adaptive Draft Exit Mechanism
- Bayesian Optimization to discover the best layer-skipping strategy (optimizing draft model configurations)
- Self-Speculative Decoding — achieving acceleration purely through the model itself
[Paper Reading] Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding
Highlights of This Paper
- Quantization, pruning, and distillation can also accelerate models, but come with issues like changes in output distribution compared to the original model, as well as the cost of retraining.
- The original Speculative Decoding faces the issue of requiring additional memory to run the draft model, whereas Self-Speculative Decoding uses part of its own neural network as the draft model.
- The Adaptive Draft-Exiting Mechanism can automatically adjust the number of tokens predicted by the draft model based on confidence score thresholds.
Optimizing LayerSkip Models with Bayesian Search for an Effective Layer Skipping Strategy
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.
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Self-Speculative Decoding Implementation: LayerSkip Transformer
Introduction
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.
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[Paper Reading] Fast Inference from Transformers via Speculative Decoding
Abstract
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.
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KV Cache: A Caching Mechanism To Accelerate Transformer Generation
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.
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