We introduce a novel paradigm in compiler optimization powered by Large
Language Models with compiler feedback to optimize the code size of LLVM
assembly. The model takes unoptimized LLVM IR as input and produces optimized
IR, the best optimization passes, and instruction counts of both unoptimized
and optimized IRs. Then we compile the input with generated optimization passes
and evaluate if the predicted instruction count is correct, generated IR is
compilable, and corresponds to compiled code. We provide this feedback back to
LLM and give it another chance to optimize code. This approach adds an extra
0.53% improvement over -Oz to the original model. Even though, adding more
information with feedback seems intuitive, simple sampling techniques achieve
much higher performance given 10 or more samples.

使用具有编译器反馈的大型语言模型介绍了编译器优化的新范例，旨在优化 LLVM 汇编的代码大小。该模型以未优化的 LLVM IR 作为输入，生成优化的 IR、最佳优化过程以及未优化和优化的 IR 的指令计数。然后，将输入与生成的优化过程进行编译，并评估预测的指令计数是否正确、生成的 IR 是否可编译并且是否对应编译后的代码。将此反馈返回到 LLM，使其有机会再次优化代码。这种方法相对于原始模型的 - Oz 额外提供了 0.53% 的改进，尽管使用反馈的更多信息似乎是直观的，但根据 10 个或更多的样本，简单的抽样技术实现了更高的性能。