Lecture 3: Bottom Up Explicit Search.

Simple bottom up search

The simplest bottom up synthesis algorithm works by explicitly constructing all possible programs from a grammar starting with the terminals in the language. As one can imagine, this can be very inefficient, since the space of all expressions grows very large even with very small programs. The key idea behind this algorithm is to prune the set of primitives at every step by eliminating those that are deemed to be "observationally equivalent"; i.e. those which produce the same outputs on those inputs that were given as a specification. The key ideas of this algorithm were first presented in a paper by Albarghouthi, Gulwani and Kinkaid AlbarghouthiGK13, although a very similar algorithm was discovered independently and presented only a few months later by Udupa et al.Udupa:2013.

The high-level algorithm is shown below. Synthesize(inputs, outputs): plist := set of all terminals while(true): plist := grow(plist); plist := elimEquvalents(plist, inputs); forall( p in plist) if(isCorrect(p, inputs, outputs)): return p; The key steps in the algorithm are the grow operation, which uses the non-terminals in the grammar to construct new terms from all the terms in plist, and the elimEquivalents step, which eliminates all terms that are deemed to be redundant by virtue of being equivalent to other terms in the list. A key idea behind this algorithm is that the check of equivalence is not an real equivalence check, which would be expensive. Instead, the expressions are tested on the target inputs, and any two expression that produce the same outputs on these inputs are deemed equivalent, regardless of whether they are truly equivalent or not. This is what is referred to as "observational equivalence", the idea being that since we only care about the behavior of the synthesized program on the given inputs, any behavior difference on other inputs is irrelevant.

Lecture3:Slide4 As an example, consider again the language introduced earlier. Initially, the plist will contain all the terminals in the grammar: in, [0], 0. After the first call to grow, the set of expressions grows quite dramatically, as it now includes all expressions that can be creating by composing the original terminals using the different production rules in the grammar. As we can see in the figure, however, many of these expressions are clearly equivalent. For example, sort([0]) is equivalent to [0], and firstZero([0]) is equivalent to 0. More interestingly, in[0,0] and [0][0,0] may not be equivalent in general, but if I only have two inputs [0,7,3,2,5,6,3] and [0,2,13,5,9,1,0], then in[0,0] and [0][0,0] are observationally equivalent, since they are equivalent on all available inputs; on any program that uses in[0,0], we can replace it with [0][0,0], and it will produce the same output on the given inputs.

For each equivalence class of observationally equivalent programs, elimEquivalents eliminates all but one of them. Each equivalent program that is eliminated actually leads to exponential savings, since it spares us from having to construct the exponentially many programs that could be constructed from that sub-program.

This algorithm as described is extremely simple, but it is already quite powerful. First, it naturally explores small programs before large programs, so it automatically finds the smallest program satisfying the specification. Additionally, it is easy to introduce heuristics into grow and elimEquivalents to direct the search for programs so that programs that are deemed more desirable are discovered first, or to speed up the search based on prior knowledge of which programs are more likely to be correct. A second benefit of this algorithm is that it works with black-box language building blocks. For example, for the language above, there is no need to have source code for sort or firstZero; the algorithm just needs to be able to execute them. The algorithm also does a good job of coping with symmetries, and is able to exploit properties of the building blocks even without access to their source code; for example, even without being told that sort is idempotent, the algorithm would immediately discard sort(sort(in)) after deeming it equivalent to sort(in).

That said, there are a few major limitations of this simple algorithm. The most important one is scalability. Even with very aggressive prunning it is hard to scale the algorithm beyond a handful of terms. Moreover, while the algorithm does a good job at discovering how to connect discrete components, it does very poorly when there is a need to discover constants in a program, whether integers or reals. Finally, the pruning through observational equivalence fails when the language has context dependent semantics. In the language from the running example, the expression in[0,0] evaluates to the same thing wherever it appears in the expression, so if it is deemed to be observationally equivalent to another expression, it can be safely eliminated. However, if the language had the ability to mutate in, then the same expression could have a different value in different contexts, so we would not be able to easily tell whether it is equivalent to another expression. This can be a problem even in functional languages, for example if you have lambdas, or lets, which can introduce new scopes for variables.

Synthesis through Unification (STUN)

One way to address the scalability challenge is to modularize the search. One way to do this is that rather than trying to synthesize a program that works for all inputs in one shot, one can search for multiple programs that work for different situations and then find a way of combining them together into a program that works for all inputs. This idea was first formalized by Alur, Cerny and Radhakrishna Alur2015 in an algorithm called Synthesis through Unification(STUN). To illustrate the idea, suppose you are given the following input output pairs: input -> output (4, 5) -> 4 (12,14) -> 12 (13, 10)-> 10 (5, 1) -> 1 And suppose you are given the following grammar: The simple bottom-up search would require enumerating a fairly large set of programs, before hitting on the correct program, but we can also observe that the program $fst$ actually works for half of the given inputs, and the program snd works for all the others. Moreover, we can see that all the inputs on which fst produces the correct answer have the property that fst < snd, so from that we can construct the program if(fst < snd) fst else snd. The STUN approach makes this intuition systematic, by providing a strategy for synthesizing programs that work for subsets of the inputs and then discovering how to combine them into a complete program that works for all inputs. The general strategy works even for cases where we cannot simply introduce arbitrary branches, but there is also a more specialized version of the approach that works particularly well in cases where you can introduce branches Alur2017.

Lecture3:Slide10 The figure on the right illustrates a simplified version of the full algorithm. The procedure takes as input a set of inputs on which we wish the program to work correctly. As a first step, we do a first best-effort synthesis attempt and produce a program that works correctly on some inputs and incorrectly on others (if we happen to get a program that works on all inputs, we are done and we can just return that program). If the produced program fails on some input, we have two choices, we can either try to improve on the current program by demanding that it continues to work correctly on the current set of good inputs, but also on some of the other inputs on which it currently fails, or we can recursively call the STUN procedure on those inputs on which the current program Prog does not work. This will produce a new program Prog', which works on all those inputs. Then the two programs Prog and Prog' need to be unified into a single program that works for all inputs.

The full algorithm also needs to deal with the scenario where either the recursive call to STUN or the generation of a better program fail to find a program. In some cases, unification may require the programs to satisfy some additional conditions in order to succeed, so the algorithm needs to track those as well. Both of these are low-level details not captured by the figure. In the paper, the crucial decision of whether to try to continue to refine the current solution, or to recursively call the STUN procedure is handled by a simple heuristic: pick a random input, if that input fails, use that input to search for a better solution, if it succeeds, then perform the recursive call. This is a crude heuristic based on the intuition that the recursive call only happens when the current solution already works for a high-enough fraction of the current inputs.

This is the general framework, but any specific instance of the algorithm needs to address the question of how to actually perform the unification of the two programs that work on different sets of inputs.

Example: Arithmetic with top-level branches

For this example, consider the following problem. We have a range of values, represented by its two extremes $(a,b)$, and we want to discover the lower bound of the new range when we multiply this range by another value $c$. Below are a few examples which will become the input to our algorithm. Lecture3:Slide11; Lecture3:Slide12; Lecture3:Slide13; Lecture3:Slide14; Lecture3:Slide15; Lecture3:Slide16; Lecture3:Slide17 (5,10) * 3 => 15 (8, 11)* -1 => -11 (3,6)*4 => 12 (-3, 8) *4 => -12 The goal is to synthesize a small program that computes this given the following grammar. In the animation above, it is possible to see how after reaching only depth 2 of an explicit search, we can already find an expression that works for a subset of the inputs. In fact, we can find expressions that together work for all the inputs. So now, the challenge is to unify them into a single expression that works for all inputs. In this case, we do the unification by discovering a branch that separates the inputs that work correctly for one branch from the inputs that work correctly for another. In this case, we can easily find that the expression c > 0 precisely separates the two cases, so we can unify the two solutions into a general program if(c > 0) a * c else b * c.

What if the language does not have top level branches?

Consider the following alternative variant of the language above. The language is very similar, but now we are left without the option of performing unification by introducing top-level branches. This means we need a different mechanism for performing unification, that is, for joining the two programs that work for different subsets of the input into a new program that works for all inputs. In order to do this, we will use something that in the literature is known as antiunification (even though the STUN paper Alur2015 does not use that terminology).

Unification and Antiunification

In the literature, the term unification generically means finding a common structure for two different expressions. More precisely, however, the literature distinguishes between Unification, where you find the common structure by replacing variables with expressions, and Antiunification, where you find the common structure by replacing expressions with variables.

So for example, if I have two expressions $x + 5$ and $7 + y$, unification finds the common structure as $Unify(x+5, 7+y) = [7 + 5, (x->7, y->5)]$. That is, it tells us that the two expressions can be turned into the same expression by replacing $x$ with 7 and $y$ with 5. By contrast, if you have two expressions $7*3 + 2$ and $5*3+2$, antiunification identifies the common structure by introducing variables as $Antiunify(7*3+2, 5*3+2) = x*3+2$.

The STUN paper does not actually use this terminology, but it is actually fairly standard, particularly in the inductive logic programming literature. So just keep in mind when reading the STUN paper that it uses Unification in the more generic sense that can potentially mean antiunification depending on the context. Lecture3:Slide20; Lecture3:Slide21; Lecture3:Slide22

STUN without top-level branches

Without the ability to introduce branches, we need an alternative way of combining together two expressions. In the example above, once we discover that there are two expressions a*c and b*c that together cover all the inputs, we can use antiunification to produce a common expression $v$*c where $v$ stands for a fragment of code, so now we need to discover what this missing code fragment is by recursively solving a (hopefully smaller) synthesis problem. In this case, we will discover that $v$=if(c>0) a else b.

One thing to note is that when the expression $b*c$ was discovered, the synthesizer could have just as easily discovered the expression $-b$. This would have been a problem, because whereas antiunification of $b*c$ and $a*c$ worked exactly as expected, the expression $-b$ cannot be antiunified with $a*c$ except by making the whole expression a variable. The way STUN deals with this is that when a recursive call to STUN is performed, the algorithm can also pass additional constraints that the expression discovered by the recursive call must satisfy. Therefore, when recursively calling STUN with the example (8, 11)* -1 => -11, we can impose the additional constraint that the discovered expression must be antiunified with $a*c$. This would force the recursive call to STUN to produce $b*c$ instead of $-b$.

The original STUN paperAlur2015 describes other examples of the Unification operation $\oplus$. In particular, it describes an approach for bit-vectors that is also based on antiunification, but we will not describe it here.

Hierarchical Search

The approaches described earlier provide a way of modularizing the search by first discovering components that work on some fragments of the input space and then discovering how to weave them together to cover the entire space. This input-based modularization is only one of many different approaches of breaking up the search into independent search spaces. An alternative approach to improve the scalability of bottom-up search is to search the space hierarchically. This can be done in cases where the program can be split into different levels of abstraction, and where one can perform the search at each level independently.

One example of this is the recent work by Wang, Cheung and Bodik Wang:2017. In this case, their goal is to synthesize complex SQL queries from examples. The key insight in their paper is that the problem can be decomposed in a hierarchical way. To understand how this can work, consider the following query language, which is a much simplified version of the language their system actually supports.

In the language above, a relation is either the name of a table $T$, a query on a relation, or a cross product on two different relations. Now, suppose you are given some tables, for example: Employee Depts --------- ------------- Name, Dept Dept, Building --------- ------------- Todd, Sales Sales, A1 Joe, Engineering Engineering, A2 Alice, Engineering Operations, A1 Sally, Operations And suppose you are told the expected output of a query is this: Output --- XX --- Todd Sally The interesting thing to notice is that there are two aspects of the query that need to be discovered, and that are mostly independent of each other. On the one hand, there is the structure of the query, which is going to determine the "shape" of the output; the fact that there is a single column containing records that originally came from the Employee table. And on the other, there are the selection predicates. These include both the fact that we are selecting records where Building=A1, or the fact that we are joining on matching the Dept code.

The key idea in their hierarchical search approach is to perform the search in two levels. First, use a simple bottm-up search to discover the structure of the query, in a language that replaces all predicates with holes as shown below. For each query in that language that produces output in the right shape, we can now search to see if there is a set of predicates that can be inserted into the holes that will cause the query to produce the right set of records.

An important ingredient in making this work is that we need to be able to evaluate the queries with holes and tell whether they can potentially produce an output with the right shape. The key idea is to define a semantics for queries with holes that is guaranteed to produce a superset of the records that any instantiation of the holes may produce. This is easy to do in the language above by simply treating every hole as $True$, although in the paper it gets a little more tricky because they also support aggregates. In general, though, if the queries with holes are guaranteed to produce a superset of the results, then we can search the space of queries with holes and safely rule out any query that does not produce a superset of our desired results. In the case of the example above, we may find that both the queries below produce results of the right shape. Select name as XX from Employee where _ Select name as XX from Employee, Depts where _ Having found some candidate queries with holes, the synthesizer can then search for predicates that lead the queries to select the correct set of records. Just as in the case of STUN, breaking the search into smaller search problems has a significant performance benefit. In this case, we are exploiting something about the structure of the search space, the ability to tell that a query structure will produce incorrect outputs before we even know what predicates are, in order to break the search into somewhat independent search problems. Just like in the STUN case, the partition is not perfect, so we may need to try many potential solutions to the high-level problem to see which one can actually be extended to a complete solution.