Neural Network Code Generation with Hierarchical Dependency Attention

The patent segments the code generation process into hierarchical levels (function level, class level, module level, file level) and applies self-attention operations at each level separately. This segmentation allows the model to focus on dependencies at appropriate granularities, improving code conformity without requiring a monolithic complex architecture.

The patent introduces an attention chain mechanism that acts as an intermediary between the input specification and the generated code. The attention chain performs self-attention operations across hierarchical levels and cross-attention with external codebases, mediating the generation process to ensure dependency conformity while managing architectural complexity.

The patent divides the computational workload by segmenting self-attention operations into discrete hierarchical levels. Instead of performing a single massive self-attention operation on all code tokens, the model performs smaller, targeted self-attention operations at each hierarchy level, reducing peak computational complexity while preserving contextual information.

The patent adds a hierarchical dimension to the code generation process by organizing self-attention operations across multiple levels (function, class, module, file). This dimensional organization allows contextual information to be retained through structured processing rather than requiring computationally intensive flat-processing of all tokens simultaneously.