Modulated Softmax Attention for Novel-Input Accuracy
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Solution Overview
Problem
Existing attention mechanisms in transformer networks struggle with reduced accuracy when encountering novel inputs due to fixed weight matrices and high computational costs, leading to inefficiencies in inference operations.
Innovation Solution
Implement a modulated softmax attention mechanism that calculates learnable scaling and bias vectors to adjust attention weights based on global importance, independent of pairwise token relationships, increasing model accuracy with minimal additional computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fixed weight matrices are used in attention mechanisms, then model training is simplified, but accuracy decreases when encountering novel inputs during inference
Solution Approach 1:
The patent introduces dynamic attention modulation through learnable scaling and bias vectors that are updated during training but applied differently during inference. The scaling vectors dynamically adjust the magnitude of attention weights while bias vectors shift the attention distribution, enabling the model to adapt to novel inputs without requiring complete retraining of the weight matrices.
2Productivity
If computational costs are reduced through kernel approximations or token bucket grouping, then inference efficiency improves, but model accuracy decreases
Solution Approach 1:
The patent modifies the attention computation parameters by introducing per-token scaling values and per-token bias values that modulate the attention scores. These parameter changes allow the model to maintain accurate attention distribution without requiring computationally expensive exponential dot product operations or complex token grouping strategies, achieving both efficiency and accuracy improvements.
3Ease of operation
If attention mechanism computes weighted sum of values with compatibility function, then attention distribution is achieved, but computational cost scales quadratically with model parameters
Solution Approach 1:
The patent segments the attention computation into independent per-token operations by introducing per-token scaling vectors and per-token bias vectors. Instead of computing a single global attention distribution, the model computes separate modulation parameters for each token, which can then be applied independently to reduce the quadratic scaling of computational costs while maintaining attention distribution accuracy.
Data Source
AI summary
The present invention sets forth techniques for generating attention values via a modulated softmax attention mechanism. The techniques include calculating key, query, and value matrices associated with an input matrix including one or more input tokens, calculating a first vector including one or more per-token scaling values and a second vector including one or more per-token bias values. The techniques also include generating an attention prior matrix based at least on the first and second vectors, and calculating, for each of the one or more input tokens, a modulated attention score associated with the input token. The techniques further include calculating a matrix including one or more modulated attention values associated with the one or more input tokens, and transmitting the one or more modulated attention values to at least one stage included in a transformer network.