E-graph

In computer science, an e-graph is a data structure that stores an equivalence relation over terms of some language.

Definition and operations

Let $\Sigma$ be a set of uninterpreted functions, where $\Sigma _{n}$ is the subset of $\Sigma$ consisting of functions of arity $n$. Let $\mathbb {id}$ be a countable set of opaque identifiers that may be compared for equality, called e-class IDs. The application of $f\in \Sigma _{n}$ to e-class IDs $i_{1},i_{2},\ldots ,i_{n}\in \mathbb {id}$ is denoted $f(i_{1},i_{2},\ldots ,i_{n})$ and called an e-node.

The e-graph then represents equivalence classes of e-nodes, using the following data structures:

• A union-find structure $U$ representing equivalence classes of e-class IDs, with the usual operations $\mathrm {find}$, $\mathrm {add}$ and $\mathrm {merge}$. An e-class ID $e$ is canonical if $\mathrm {find} (U,e)=e$; an e-node $f(i_{1},\ldots ,i_{n})$ is canonical if each $i_{j}$ is canonical ($j$ in $1,\ldots ,n$).
• An association of e-class IDs with sets of e-nodes, called e-classes. This consists of
  • a hashcons $H$ (i.e. a mapping) from canonical e-nodes to e-class IDs, and
  • an e-class map $M$ that maps e-class IDs to e-classes, such that $M$ maps equivalent IDs to the same set of e-nodes: $\forall i,j\in \mathbb {id} ,M[i]=M[j]\Leftrightarrow \mathrm {find} (U,i)=\mathrm {find} (U,j)$

Invariants

In addition to the above structure, a valid e-graph conforms to several data structure invariants. Two e-nodes are equivalent if they are in the same e-class. The congruence invariant states that an e-graph must ensure that equivalence is closed under congruence, where two e-nodes $f(i_{1},\ldots ,i_{n}),f(j_{1},\ldots ,j_{n})$ are congruent when $\mathrm {find} (U,i_{k})=\mathrm {find} (U,j_{k}),k\in \{1,\ldots ,n\}$. The hashcons invariant states that the hashcons maps canonical e-nodes to their e-class ID.

Operations

E-graphs expose wrappers around the $\mathrm {add}$, $\mathrm {find}$, and $\mathrm {merge}$ operations from the union-find that preserve the e-graph invariants. The last operation, e-matching, is described below.

Equivalent formulations

An e-graph can also be formulated as a bipartite graph $G=(N\uplus \mathrm {id} ,E)$ where

• $\mathrm {id}$ is the set of e-class IDs (as above),
• $N$ is the set of e-nodes, and
• $E\subseteq (\mathrm {id} \times N)\cup (N\times \mathrm {id} )$ is a set of directed edges.

There is a directed edge from each e-class to each of its members, and from each e-node to each of its children.

E-matching

Let $V$ be a set of variables and let $\mathrm {Term} (\Sigma ,V)$ be the smallest set that includes the 0-arity function symbols (also called constants), includes the variables, and is closed under application of the function symbols. In other words, $\mathrm {Term} (\Sigma ,V)$ is the smallest set such that $V\subset \mathrm {Term} (\Sigma ,V)$, $\Sigma _{0}\subset \mathrm {Term} (\Sigma ,V)$, and when $x_{1},\ldots ,x_{n}\in \mathrm {Term} (\Sigma ,V)$ and $f\in \Sigma _{n}$, then $f(x_{1},\ldots ,x_{n})\in \mathrm {Term} (\Sigma ,V)$. A term containing variables is called a pattern, a term without variables is called ground.

An e-graph $E$ represents a ground term $t\in \mathrm {Term} (\Sigma ,\emptyset )$ if one of its e-classes represents $t$. An e-class $C$ represents $t$ if some e-node $f(i_{1},\ldots ,i_{n})\in C$ does. An e-node $f(i_{1},\ldots ,i_{n})\in C$ represents a term $g(j_{1},\ldots ,j_{n})$ if $f=g$ and each e-class $M[i_{k}]$ represents the term $j_{k}$ ($k$ in $1,\ldots ,n$).

e-matching is an operation that takes a pattern $p\in \mathrm {Term} (\Sigma ,V)$ and an e-graph $E$, and yields all pairs $(\sigma ,C)$ where $\sigma \subset V\times \mathbb {id}$ is a substitution mapping the variables in $p$ to e-class IDs and $C\in \mathbb {id}$ is an e-class ID such that the term $\sigma (p)$ is represented by $C$. There are several known algorithms for e-matching, the relational e-matching algorithm is based on worst-case optimal joins and is worst-case optimal.

Extraction

Given an e-class and a cost function that maps each function symbol in $\Sigma$ to a natural number, the extraction problem is to find a ground term with minimal total cost that is represented by the given e-class. This problem is NP-hard. There is also no constant-factor approximation algorithm for this problem, which can be shown by reduction from the set cover problem. However, for graphs with bounded treewidth, there is a linear-time, fixed-parameter tractable algorithm.

Complexity

• An e-graph with n equalities can be constructed in O(n log n) time.

Equality saturation

Equality saturation is a technique for building optimizing compilers using e-graphs. It operates by applying a set of rewrites using e-matching until the e-graph is saturated, a timeout is reached, an e-graph size limit is reached, a fixed number of iterations is exceeded, or some other halting condition is reached. After rewriting, an optimal term is extracted from the e-graph according to some cost function, usually related to AST size or performance considerations.

Applications

E-graphs are used in automated theorem proving. They are a crucial part of modern SMT solvers such as Z3 and CVC4, where they are used to decide the empty theory by computing the congruence closure of a set of equalities, and e-matching is used to instantiate quantifiers. In DPLL(T)-based solvers that use conflict-driven clause learning (also known as non-chronological backtracking), e-graphs are extended to produce proof certificates. E-graphs are also used in the Simplify theorem prover of ESC/Java.

Equality saturation is used in specialized optimizing compilers, e.g. for deep learning and linear algebra. Equality saturation has also been used for translation validation applied to the LLVM toolchain.

E-graphs have been applied to several problems in program analysis, including fuzzing, abstract interpretation, and library learning.

References

• de Moura, Leonardo; Bjørner, Nikolaj (2007). "Efficient E-Matching for SMT Solvers". In Pfenning, Frank (ed.). Automated Deduction – CADE-21. Lecture Notes in Computer Science. Vol. 4603. Berlin, Heidelberg: Springer. pp. 183–198. doi:10.1007/978-3-540-73595-3_13. ISBN 978-3-540-73595-3.
• Willsey, Max; Nandi, Chandrakana; Wang, Yisu Remy; Flatt, Oliver; Tatlock, Zachary; Panchekha, Pavel (2021-01-04). "egg: Fast and extensible equality saturation". Proceedings of the ACM on Programming Languages. 5 (POPL): 23:1–23:29. arXiv:2004.03082. doi:10.1145/3434304. S2CID 226282597.
• Tate, Ross; Stepp, Michael; Tatlock, Zachary; Lerner, Sorin (2009-01-21). "Equality saturation". Proceedings of the 36th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages. POPL '09. Savannah, GA, USA: Association for Computing Machinery. pp. 264–276. doi:10.1145/1480881.1480915. ISBN 978-1-60558-379-2. S2CID 2138086.
• Flatt, Oliver; Coward, Samuel; Willsey, Max; Tatlock, Zachary; Panchekha, Pavel (October 2022). "Small Proofs from Congruence Closure". In A. Griggio; N. Rungta (eds.). Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022. TU Wien Academic Press. pp. 75–83. doi:10.34727/2022/isbn.978-3-85448-053-2_13. ISBN 978-3-85448-053-2. S2CID 252118847.
• Goharshady, Amir Kafshdar; Lam, Chun Kit; Parreaux, Lionel (2024-10-08). "Fast and Optimal Extraction for Sparse Equality Graphs". Proceedings of the ACM on Programming Languages. 8 (OOPSLA2): 361:2551–361:2577. doi:10.1145/3689801.

External links

• The Egg Project
• A Colab notebook explaining e-graphs