Code LLM Compiler Integration for Program Reasoning
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Solution Overview
Problem
Current code Large Language Models (LLMs) lack program reasoning capabilities, particularly in complex programming tasks such as code parallelization, due to their inability to analyze incomplete programs and lack of logical and mathematical reasoning skills, which limits their effectiveness in assisting developers with optimizing software for specific hardware.
Innovation Solution
Combining code LLMs with compilers to leverage the program reasoning capabilities of compilers, enabling LLMs to generate compiler-required program representations from incomplete code and perform analysis passes, thereby enhancing their ability to reason about programs and solve complex programming problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If code LLMs are used for code generation and autocompletion, then code writing efficiency is improved, but program reasoning capabilities and ability to handle complex programming tasks are insufficient
Solution Approach 1:
The patent combines code LLMs with compiler technology to create a hybrid system that merges the code generation capabilities of LLMs with the program reasoning capabilities of compilers. The LLM generates code representations from natural language or incomplete code, while the compiler analyzes these representations to perform program reasoning tasks such as optimization, parallelization, and hardware-specific transformations, thereby resolving the contradiction between improved productivity and insufficient reliability.
2Ease of operation
If LLMs generate code from incomplete programs, then ease of operation is improved, but manufacturing precision and logical reasoning accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary code representation layer that bridges the gap between incomplete user code and compiler analysis. The LLM transforms incomplete programs into structured code representations (such as abstract syntax trees or intermediate representations) that serve as intermediaries, allowing the compiler to perform accurate program analysis and reasoning even when the original input is incomplete or ambiguous.
3Device complexity
If code LLMs are used without compiler integration, then device complexity is reduced, but adaptability to handle complex programming tasks deteriorates
Solution Approach 1:
The patent segments the code processing system into distinct functional modules: an LLM module for code generation and representation creation, and a compiler module for program analysis and optimization. This segmentation allows each component to specialize in its strength while working together through well-defined interfaces, thereby maintaining manageable device complexity while achieving high adaptability for complex programming tasks.
Data Source
AI summary
Example apparatus disclosed includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to receive an input source code by a code large language model (LLM), generate one or more code representations of the input source code, analyze the one or more code representations of the input source code, and compile the one or more code representations of the input source code into one or more computer executable instructions.