Adaptive Transformer With Token-Specific Inference Depth
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Solution Overview
Problem
Existing transformer models perform a fixed number of computations on all input tokens regardless of their complexity, leading to inefficient use of computational resources and potential inaccuracies due to increased model parameters.
Innovation Solution
An adaptive transformer that dynamically adjusts the number of computations per input token based on reinforcement learning models, generating new input tokens and position encodings for additional computations when necessary, optimizing computational efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of parameters of the model is increased to increase accuracy, then accuracy is improved, but the amount of inference computation increases
Solution Approach 1:
The transformer dynamically adjusts the number of computation times for each input token based on its complexity. The system uses a controller that determines whether to perform additional computations on specific tokens, allowing the computation depth to vary dynamically rather than being fixed for all tokens. This resolves the contradiction by enabling the model to use more parameters and computations only where needed for accuracy while reducing computations for simpler tokens.
Solution Approach 2:
Different input tokens receive different numbers of computations based on their individual complexity requirements. The system applies local quality by making the computation depth token-specific rather than uniform across all tokens. The controller evaluates each token and applies additional computations only to those that require them, optimizing the balance between accuracy and computational cost.
2Ease of manufacture
If a fixed neural network structure is used to perform computation, then implementation is simple, but the same amount of computation is performed on all input tokens regardless of complexity
Solution Approach 1:
The system introduces dynamic control into the transformer architecture by adding a controller that adaptively determines the number of computation times for each input token. This dynamic adjustment mechanism allows the system to maintain a relatively simple base structure while adding intelligence to optimize computational efficiency based on actual input requirements.
Solution Approach 2:
The transformer processes input tokens independently with variable computation depths. Each token can be processed with a different number of computation iterations, allowing the system to segment the computational effort according to the specific needs of each token rather than applying a uniform computation depth to all tokens.
3Measurement precision
If computation is delayed and performed two or more times, then accuracy may be improved, but the number of additional computations is not variable even when input is simple
Solution Approach 1:
The controller dynamically determines whether to perform additional computations on each input token based on its complexity assessment. This dynamic decision-making process allows the system to adaptively adjust computation times rather than using a fixed multi-computation approach for all tokens, resolving the lack of variability in computation times.
Solution Approach 2:
The system changes the parameter of computation depth on a per-token basis. Instead of maintaining a fixed number of computation iterations for all tokens, the controller adjusts this parameter dynamically based on the assessed complexity of each individual token, enabling variable computation times that adapt to input requirements.
Data Source
AI summary
A method for operating an adaptive transformer may comprise: inputting a first input token and a first position encoding corresponding to the first input token to a first model to generate a first attention module output; determining whether to perform an additional computation on the first input token, based on the first attention module output; upon determination that the additional computation is to be performed on the first input token, determining a second input token based on the first input token and the first attention module output; determining a second position encoding corresponding to the second input token; inputting the second input token and the second position encoding to the first model to generate a second attention module output; and upon determination that the additional computation is not to be performed on the first input token, generating a final output token based on the first attention module output.