Early-Exit Decoding With Adapter Verification for Lower LLM Latency
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Solution Overview
Problem
Existing machine learning models for sequence processing, such as large language models (LLMs) and vision-language models (VLMs), suffer from high computational and memory overhead due to the need for separate models for drafting and verification, leading to increased latency and resource consumption.
Innovation Solution
A single model architecture is modified to include an adapter layer that performs early exit decoding using lower layers for drafting, while higher layers verify the accuracy of the drafts, reducing the need for separate models and optimizing computational and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for drafting and verification, then model accuracy can be maintained, but computational overhead and memory consumption increase significantly
Solution Approach 1:
The patent combines separate drafting and verification models into a single unified model architecture. The drafting layers and verification layers are integrated within one model, sharing common parameters and computation pathways. This merging eliminates the need for separate model instances, thereby reducing computational overhead and memory consumption while maintaining the functional distinction between drafting and verification through internal layer specialization.
Solution Approach 2:
The unified model serves multiple functions through its different layers: the drafting layers generate candidate tokens efficiently, while the verification layers validate these tokens for accuracy. This multi-functionality within a single model structure allows the system to perform both drafting and verification operations without requiring separate specialized models, thus reducing overall system complexity while preserving accuracy requirements.
2Adaptability or versatility
If separate models are used for drafting and verification, then task specialization can be achieved, but memory consumption increases
Solution Approach 1:
The patent merges drafting and verification capabilities into a single model that shares parameter space and computation resources. By integrating the drafting head and verification head within one model instance, the system maintains task specialization through functional separation in different layers while avoiding the memory overhead of loading separate model weights and parameters for each function.
Solution Approach 2:
The verification layers are nested within the overall model structure, utilizing the intermediate representations produced by the drafting layers. This nesting allows the verification function to operate on the outputs of the drafting function without requiring a completely separate model instance, thereby reducing memory consumption while preserving the specialized verification capability.
3Reliability
If traditional decoding is used, then model accuracy is maintained, but decoding latency is high
Solution Approach 1:
The patent segments the decoding process into distinct drafting and verification phases with different computational requirements. The drafting layers use simplified computations to rapidly generate candidate tokens, while the verification layers apply more rigorous validation. This segmentation allows the system to achieve high-speed initial generation followed by targeted verification, reducing overall decoding latency while maintaining output quality through the verification step.
Solution Approach 2:
The drafting layers perform a simplified, partial version of the full verification process to quickly generate candidate tokens. Rather than applying the complete verification computation to every token generation step, the system uses the lighter drafting process for speed and applies the fuller verification only when needed, thereby reducing average decoding latency while preserving output quality through selective verification.
Data Source
AI summary
One example aspect is directed to a computer-implemented method for performing model decoding with reduced latency. The method includes obtaining a pre-trained sequence processing model comprising a plurality of layers. The method includes modifying the sequence processing model to contain an adapter layer that is configured to receive and process an intermediate representation generated by a particular intermediate layer of the plurality of layers to predict an output token. The method includes training the adapter layer while holding the plurality of layers of the sequence processing model frozen. The method includes deploying the sequence processing model for speculative decoding in which the adapter layer, the particular intermediate layer, and the plurality of layers that precede the particular intermediate layer perform speculative token decoding and the plurality of layers that are subsequent to the particular intermediate layer perform token verification.