Equations are ubiquitous in mathematical reasoning. Often, however, they only hold under certain conditions. As these conditions are usually clear from context, mathematicians regularly omit them when performing equational reasoning on paper. In contrast, interactive theorem provers pedantically insist on every detail to be convinced that a theorem holds, hindering equational reasoning at the more abstract level of pen-andpaper mathematics. In this paper, we address this issue by raising the level of equational reasoning to enable pen-and-paper style in interactive theorem provers. We achieve this by interpreting theorems as conditional rewrite rules, and use equality saturation to automatically derive equational proofs. Conditions that cannot be automatically proven may be surfaced as proof obligations. Concretely, we present how to interpret theorems as conditional rewrite rules for a significant class of theorems. Handling these theorems goes beyond simple syntactic rewriting, and deals with aspects like propositional conditions and type classes. We evaluate our approach by implementing it as a tactic in Lean, using the egg library for equality saturation with e-graphs. We show four use cases demonstrating the efficacy of this higher level of abstraction for equational reasoning.

Equality saturation has gained significant interest as a powerful optimization and reasoning technique. At its heart is the e-graph data structure, that space-efficiently represents equal sub-terms uniquely. An important open problem in this context is extending this efficient representation to languages featuring (bound) variables. Independent of how we represent variables in e-graphs, either as names or nameless (using de Bruijn indices), sharing is broken as sub-terms that differ only in the names of their variables are represented separately. This results in aggressive e-graph growth, bad performance, as well as reduced expressiveness. In this paper, we present a novel approach to representing bound variables in e-graphs by making them a first-class built-in feature of the data structure. Our slotted e-graph represents terms that differ only by (bound or free) variable names uniquely. To do so, e-classes that represent equivalent terms via e-nodes are parameterized by slots , abstracting over free variables of the represented terms. Referring to an e-class from an e-node now requires relating the variables from its context to the slots of the e-class. Our evaluation of slotted e-graph uses two case studies from compiler optimization and theorem proving to show that performing equality saturation for languages with bound variables is greatly simplified and that we can solve practically relevant problems that cannot be solved with e-graphs using de Bruijn indices.

Rewriting is a principled term transformation technique with uses across theorem proving and compilation. In theorem proving, each rewrite is a proof step; in compilation, rewrites optimize a program term. While developing rewrite sequences manually is possible, this process does not scale to larger rewrite sequences. Automated rewriting techniques, like greedy simplification or equality saturation, work well without requiring human input. Yet, they do not scale to large search spaces, limiting the complexity of tasks where automated rewriting is effective, and meaning that just a small increase in term size or rewrite length may result in failure. This paper proposes a semi-automatic rewriting technique as a means to scale rewriting by allowing human insight at key decision points. Specifically, we propose guided equality saturation that embraces human guidance when fully automated equality saturation does not scale. The rewriting is split into two simpler automatic equality saturation steps: from the original term to a human-provided intermediate guide , and from the guide to the target. Complex rewriting tasks may require multiple guides, resulting in a sequence of equality saturation steps. A guide can be a complete term, or a sketch containing undefined elements that are instantiated by the equality saturation search. Such sketches may be far more concise than complete terms. We demonstrate the generality and effectiveness of guided equality saturation using two case studies. First, we integrate guided equality saturation in the Lean 4 proof assistant. Proofs are written in the style of textbook proof sketches, as a series of calculations omitting details and skipping steps. These proofs conclude in less than a second instead of minutes when compared to unguided equality saturation, and can find complex proofs that previously had to be done manually. Second, in the compiler of the RISE array language, where unguided equality saturation fails to perform optimizations within an hour and using 60 GB of memory, guided equality saturation performs the same optimizations with at most 3 guides, within seconds using less than 1 GB memory.

Halide and many similar projects have demonstrated the great potential of domain specific optimizing compilers. They enable programs to be expressed at a convenient high-level, while generating high-performance code for parallel architectures. As domains of interest expand towards deep learning, probabilistic programming and beyond, it becomes increasingly clear that it is unsustainable to redesign domain specific compilers for each new domain. In addition, the rapid growth of hardware architectures to optimize for poses great challenges for designing these compilers. In this paper, we show how to extend a unifying domain-extensible compiler with domain-specific as well as hardware-specific optimizations. The compiler operates on generic patterns that have proven flexible enough to express a wide range of computations. Optimizations are not hard-coded into the compiler but are expressed as user-defined rewrite rules that are composed into strategies controlling the optimization process. Crucially, both computational patterns and optimization strategies are extensible without modifying the core compiler implementation. We demonstrate that this domain-extensible compiler design is capable of expressing image processing pipelines and well-known image processing optimizations. Our results on four mobile ARM multi-core CPUs, often used for image processing tasks, show that the code generated for the Harris operator outperforms the image processing library OpenCV by up to 16× and achieves performance close to - or even up to 1.4 × better than - the state-of-the-art image processing compiler Halide.

Optimizing programs to run efficiently on modern parallel hardware is hard but crucial for many applications. The predominantly used imperative languages - like C or OpenCL - force the programmer to intertwine the code describing functionality and optimizations. This results in a portability nightmare that is particularly problematic given the accelerating trend towards specialized hardware devices to further increase efficiency. Many emerging DSLs used in performance demanding domains such as deep learning or high-performance image processing attempt to simplify or even fully automate the optimization process. Using a high-level - often functional - language, programmers focus on describing functionality in a declarative way. In some systems such as Halide or TVM, a separate schedule specifies how the program should be optimized. Unfortunately, these schedules are not written in well-defined programming languages. Instead, they are implemented as a set of ad-hoc predefined APIs that the compiler writers have exposed. In this functional pearl, we show how to employ functional programming techniques to solve this challenge with elegance. We present two functional languages that work together - each addressing a separate concern. RISE is a functional language for expressing computations using well known functional data-parallel patterns. ELEVATE is a functional language for describing optimization strategies. A high-level RISE program is transformed into a low-level form using optimization strategies written in ELEVATE . From the rewritten low-level program high-performance parallel code is automatically generated. In contrast to existing high-performance domain-specific systems with scheduling APIs, in our approach programmers are not restricted to a set of built-in operations and optimizations but freely define their own computational patterns in RISE and optimization strategies in ELEVATE in a composable and reusable way. We show how our holistic functional approach achieves competitive performance with the state-of-the-art imperative systems Halide and TVM.