Positional Embedding Reuse for Long-Context Attention
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Solution Overview
Problem
Transformer-based attention operations in machine learning models have high computational complexity due to quadratic computational complexity and low performance for context lengths different from the given length, leading to increased model perplexity and out-of-distribution positional embeddings.
Innovation Solution
Implement selective or dynamic re-computation and re-use of positional embeddings (PEs) by generating new PEs for influential tokens while re-using PEs for recent tokens, reducing the number of computations required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transformer-based attention operations use query-key-value approaches to process all tokens in the sequence, then model performance is improved through accurate attention mechanisms, but computational complexity increases quadratically with sequence length
Solution Approach 1:
The patent divides the token sequence into fixed context tokens and dynamic context tokens, processing them through different attention mechanisms. The fixed context uses standard self-attention while dynamic context uses a more efficient attention mechanism, thereby segmenting the computational workload to reduce overall complexity while maintaining model performance.
Solution Approach 2:
The patent implements dynamic context handling where the model can adaptively process tokens beyond the fixed context window. This allows the attention mechanism to dynamically adjust to varying sequence lengths without requiring quadratic computation for all tokens, resolving the contradiction between handling variable-length inputs and maintaining computational efficiency.
2Reliability
If models are trained with a given context length, then attention operations work effectively for that length, but performance degrades for context lengths that differ from the given length due to out-of-distribution positional embeddings
Solution Approach 1:
The patent pre-processes fixed context tokens during training to generate their positional embeddings and attention patterns in advance. This preliminary action allows the model to efficiently handle these tokens during inference without requiring re-computation, while dynamic context tokens can be processed adaptively for different sequence lengths, improving both effectiveness and adaptability.
Solution Approach 2:
The patent changes the parameterization of positional embeddings by separating fixed and dynamic context handling. Instead of using a single positional embedding scheme for all tokens, the model uses different parameterizations for fixed context (trained positions) and dynamic context (extended positions), allowing effective attention operations across varying context lengths without out-of-distribution issues.
3Measurement precision
If positional embeddings are re-computed for all tokens in each attention operation, then accurate positional information is maintained, but computational resources including memory usage and processing time increase
Solution Approach 1:
The patent creates a copy of the fixed context tokens and their associated positional embeddings that can be reused across multiple attention operations. Instead of re-computing positional embeddings for fixed context tokens in each operation, the model copies and reuses the pre-computed embeddings, maintaining positional accuracy while significantly reducing computational resource consumption.
Solution Approach 2:
The patent discards the computation of positional embeddings for fixed context tokens after they are initially computed during training or first inference, and recovers them through caching or storage. This allows the model to maintain accurate positional information for fixed context while avoiding redundant computation, thereby reducing memory usage and processing time for subsequent operations.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, a sequence of tokens is accessed as input to an attention operation. For a first token, an attention output is generated based on a window of tokens relative to the first token, comprising generating a first positional embedding for an influential token, generating a second positional embedding for the first token, and generating the attention output based on the first and second positional embeddings. For a second token, an attention output is generated based on a window of tokens relative to the second token, where the second window of tokens includes the first token, comprising generating a third positional embedding for the influential token, generating a fourth positional embedding for the second token, and generating the attention output based on the second, third, and fourth positional embeddings.