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I was hoping to see a more ambitious language model based project. Rather than just picking compiler arguments, an LLM could parse, and then transform AST into

I was hoping to see a more ambitious language model based project. Rather than just picking compiler arguments, an LLM could parse, and then transform AST into its more optimized form in a way that does not rely on a very formal way it is treated by compilers of today, apply guesstimate-based branch reordering and inlining heuristics not dissimilar to how a programmer would do so manually. Once done, a compiler could perform final AST validation and either route it back to LLM to fix it or auto-fix most common cases.

haltist 3y ago
How do you validate semantic equivalence?
boomanaiden154 3y ago
What's the benefit of having an LLM do those things in a way that guesstimates? There are big wins to be had in code-size and some wins to be had in performance related to inlining [1][2], but I think the implementation in the references directly tied into the compiler's inlining heuristic is a much better way to do that as it guarantees correctness. In addition, there's a reason that compilers basically ignore the `inline` keyword these days. For branch reordering, techniques like BOLT [5] are pretty effectively able to reorder code layout at the binary level for big performance gains by using profile information. ML models can sometimes synthesize that information [3], but if I recall correctly, the performance of those models wasn't as good. Neural compilation (like what you're describing) has been tried with LLMs [4], but has a lot of correctness problems currently, and I don't think it's going to be feasible anytime soon to do reinforcement learning for performance/code-size improvements. 1. https://arxiv.org/abs/2101.04808 https://arxiv.org/abs/2101.04808 2. https://arxiv.org/abs/2207.08389 https://arxiv.org/abs/2207.08389 3. https://arxiv.org/abs/2112.14679 https://arxiv.org/abs/2112.14679 4. https://ieeexplore.ieee.org/document/9926313 https://ieeexplore.ieee.org/document/9926313 5. https://arxiv.org/abs/1807.06735 https://arxiv.org/abs/1807.06735

haltist 3y ago

How do you validate semantic equivalence?

boomanaiden154 3y ago

What's the benefit of having an LLM do those things in a way that guesstimates? There are big wins to be had in code-size and some wins to be had in performance related to inlining [1][2], but I think the implementation in the references directly tied into the compiler's inlining heuristic is a much better way to do that as it guarantees correctness. In addition, there's a reason that compilers basically ignore the `inline` keyword these days. For branch reordering, techniques like BOLT [5] are pretty effectively able to reorder code layout at the binary level for big performance gains by using profile information. ML models can sometimes synthesize that information [3], but if I recall correctly, the performance of those models wasn't as good. Neural compilation (like what you're describing) has been tried with LLMs [4], but has a lot of correctness problems currently, and I don't think it's going to be feasible anytime soon to do reinforcement learning for performance/code-size improvements. 1. https://arxiv.org/abs/2101.04808 https://arxiv.org/abs/2101.04808 2. https://arxiv.org/abs/2207.08389 https://arxiv.org/abs/2207.08389 3. https://arxiv.org/abs/2112.14679 https://arxiv.org/abs/2112.14679 4. https://ieeexplore.ieee.org/document/9926313 https://ieeexplore.ieee.org/document/9926313 5. https://arxiv.org/abs/1807.06735 https://arxiv.org/abs/1807.06735