Compiler generated feedback for Large Language Models
Dejan Grubisic Chris Cummins Volker Seeker Hugh Leather
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.