Transformer Attention Distillation for Long-Context Sparse Training
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Solution Overview
Problem
Neural network models, particularly transformers, face increased memory and computation complexity as input data length grows, limiting context size and degrading performance, and existing approximated attention mechanisms fail to utilize pre-trained knowledge, leading to degraded training accuracy.
Innovation Solution
A method using knowledge distillation to train a neural network model by generating a compressed attention matrix, acquiring an attention mask, and performing an attention operation on a sparse attention matrix, with parameters adjusted to minimize loss values, leveraging a pre-trained model to guide a student model's training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the length of input data (context size) is increased in a transformer-based neural network model, then the model can process more tokens and improve performance, but memory complexity and computation complexity increase in proportion to the square of the input data length
Solution Approach 1:
The patent divides the attention mechanism into multiple segments or blocks, processing the input sequence in chunks rather than as a single large matrix. This segmentation reduces the computational burden from O(n²) to approximately O(n²/b) where b is the block size, allowing longer context processing with manageable complexity
Solution Approach 2:
The patent introduces intermediate representations and auxiliary structures (such as position encodings, key-value caches, and attention scores) that mediate between the input tokens and final outputs. These intermediaries enable efficient computation by breaking down the direct n×n attention matrix computation into manageable steps with reusable intermediate results
2Device complexity
If existing approximated attention mechanisms are used to reduce complexity, then computation complexity is reduced, but the ability to utilize pre-trained neural network knowledge is lost, degrading training accuracy
Solution Approach 1:
The patent creates simplified copies or approximations of the full attention mechanism that preserve the essential knowledge transfer functionality. Student models are trained to replicate the attention patterns and knowledge representations of teacher models, maintaining accuracy while reducing computational requirements in deployment
Solution Approach 2:
The patent modifies attention parameters such as dimensionality reduction projections, quantization levels, or sparsity patterns while maintaining the fundamental knowledge distillation objective. These parameter changes allow the model to operate with reduced complexity while still capturing the essential knowledge from pre-trained models through adjusted attention weights and softmax operations
Data Source
AI summary
A method for training a neural network model, using knowledge distillation, comprises receiving, by a pre-trained first neural network model, sequence data including one or more tokens, as input; performing knowledge distillation of one or more attention parameters for an attention operation to a second neural network model; receiving, by the second neural network model, the sequence data, as input; and training the second neural network to output an attention operation result for the sequence data based on the one or more attention parameters.