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21 July 2022 — by Facundo Domínguez
A dialogue with Liquid Haskell
haskellliquidhaskellformal-methods

Liquid Haskell (LH) is a tool that gives the Haskell programmer the ability to express what a program is meant to compute and to verify that the program does meet this expectation.

One aspect that was not easy to deal with was understanding verification failures. When a failure happened, we could get a summary of the condition that LH was trying to verify, but we wouldn’t be able to say exactly which facts LH was using to check it or which facts were missing.

In this post I will present some recent contributions of mine to LH, which give the user better access to the facts used during verification. We will go through an example of how these facts can be used to help LH prove a property in a more informed way than previously possible.

Relating programs to logical conditions

In programming, we can use verification tools by relating our programs to logical formulas that these tools can reason about. Let us bring an example from an earlier post. Suppose that we wanted to ensure that a list indexing function is only called with indices smaller than the length of the list. Using LH, we can express this expectation in a specification of the indexing function, which goes in special comments marked with {[email protected] ... @-}.

{[email protected] at :: xs:[a] -> { i:Int | i >= 0 && i < length xs } -> a @-}
at :: [a] -> Int -> a
at (x:_) 0 = x
at (_:xs) i = at xs (i - 1)

The arguments of the at function get refinement types in the specification. A refinement type is a subtype of another type, defined by a predicate characterizing the elements in the subtype. The language in which the refinement predicate is written is not Haskell, but a simpler language with constants, function symbols, application, logical connectives, and arithmetic and comparison operators.

In the function at, the predicate of the index argument states both the expectation that the index is non-negative and that it is strictly smaller than the length of the list. LH will check that these preconditions rule out receiving an empty list, and therefore the definition doesn’t need more equations. In addition, at compile time, LH will systematically check that every invocation of the at function satisfies these expectations.

In the last equation LH can assume that i > 0 because the preceding equation already checked for 0 and its refinement type says it is non-negative. LH can also assume that i < length (_:xs) because the refinement type guarantees it. Now LH will verify that i - 1 >= 0 && i - 1 < length xs which is the precondition of the recursive call.

Diagnosing verification failures

If a fact can’t be verified by LH, but we are sure it really holds, chances are that extra knowledge must be given to the tool. To do so, it is crucial to know how close the tool is to verifying it, which in turn requires retrieving some information from the tool. This is where LH got a boost recently.

To illustrate how we can now interact with LH’s diagnostics, let us prove a lemma. In LH, lemmas are just functions whose return type is (): such a function doesn’t compute any information at runtime, the only information is that the refinement specification holds (statically).

Here is what we will be proving: if i is smaller than the length of xs, then the i-th element of xs is the same as the i-th element of append xs ys.

{[email protected]
atFromAppend
  :: { i:Int | i >= 0 }
  -> { xs:[a] | i < length xs }
  -> ys:[a]
  -> { _:() | at xs i == at (append xs ys) i }
@-}
atFromAppend :: Int -> [a] -> [a] -> ()

We will also need the traditional definitions for length and append.

{[email protected] reflect length @-}
length :: [a] -> Int
length [] = 0
length (_:xs) = 1 + length xs

{[email protected] reflect append @-}
append :: [a] -> [a] -> [a]
append [] ys = ys
append (x:xs) ys = x : append xs ys

The reflect directive allows length and append to appear in the predicates of refinement types. This is accomplished by translating their definitions to the logic language that LH uses. We also need to reflect the at function.

{[email protected] reflect at @-}

As a first attempt, we could implement atFromAppend with a single equation.

atFromAppend :: Int -> [a] -> [a] -> ()
atFromAppend _i _xs _ys = ()

Since atFromAppend is expected to return a value of the () type, this definition is satisfactory to the Haskell compiler.

On the other hand, LH analyses the program and constructs a list of goals to verify, also known as constraints, which are associated to various sub-expressions in the program. These constraints are fed to Liquid Fixpoint (LF), an SMT solver wrapper with some extra enhancements for LH. When LH reports an error, it usually means that LF failed to verify some of the constraints.

In the equation above, LH is going to produce an error when checking that the returned () has the expected refinement type.

The inferred type
  {v : () | v == ()}

is not a subtype of the required type
  {v : () | at _xs _i == at (append _xs _ys) _i}

...

Constraint id 39
   |
67 | atFromAppend _i _xs _ys = ()
   | ^

We can ask LH to dump constraints to a file .liquid/<module>.fq.prettified with the directive {[email protected] LIQUID "--save" @-}. By searching for the constraint identifier at the end of the message (39 in this case), we can find the failing constraint:

constraint:
  lhs {v : () | v = ()}
  rhs {_ : () | at _xs _i = at (append _xs _ys) _i}
  // META constraint id 39 : test1.hs:67:1-28
  environment:
    _i : {_i : int | _i >= 0}
    _xs : {xs : [a] | _i < length xs}
    _ys : [a]

Constraints have a left-hand side with the inferred type of the expression, while the right-hand side has the expected type. As in most interesting errors from LH, the inferred and expected refinement types only differ in the predicate part.

Constraints also have an environment with the facts or hypotheses that LF can use to verify the right-hand side. In this case, it only contains the preconditions that we would expect from the refinement type of atFromAppend, which is insufficient for the SMT solver to establish the right-hand side of the constraint.

These explicit environments are a recent feature — before, all one could do was to guess them from the source code1 or read other dumps that are not suitable for human consumption really2.

Returning to our example, the SMT solver still needs to be informed about how to unfold the applications of at and append to have a better chance to see that the preconditions do ensure the expected property. Though LH has translated the definitions of these functions to the logic, it cannot use them yet because each one of the definitional equalities requires the arguments to be more specific than we know them to be. Let’s contemplate the right-hand side once more.

  rhs {_ : () | at _xs _i = at (append _xs _ys) _i}

We don’t know, for instance, if _i is zero or positive, which is necessary for deciding which equation of at can be applied. Furthermore, we don’t know if _xs is a non-empty list, which is also a necessary condition to make the equations applicable.

In order to move forward, we can split the verification task by cases.

atFromAppend 0 (_x:_xs) _ys = ()
atFromAppend _i (_x:_xs) _ys = ()

We don’t need equations for the case of the empty list because LH checks that the preconditions rule it out. However, LH still rejects both equations with different failing constraints. The constraint for the first equation is:

constraint:
  lhs {v : () | v = () }
  rhs {_ : () | at (_x:_xs) 0 = at (append (_x:_xs) _ys) 0 }
  // META constraint id 4 : test1.hs:67:31-32
  environment:
    _x : a
    _xs : {_xs : [a] | length (_x:_xs) >= 0}
    _ys : [a]

Note that the arguments of at in the right-hand side are sufficiently specific, as they are for append. The following equalities from the definition of at and append become applicable now.

at (_x:_xs) 0 = _x
append (_x:_xs) _ys = _x : append _xs _ys

In order to have LH generate these equalities and feed them to the SMT solver, we need to enable PLE with {[email protected] LIQUID "--ple" @-}. PLE stands for Proof by Logical Evaluation, which is one of the LF algorithms enhancing the SMT solver capabilities.

After applying the equality for append, the right-hand side becomes

  rhs {_ : () | at _xs 0 = at (_x : append _xs _ys) 0 }

Which will prompt PLE to produce yet another equality for the at function.

at (_x : append _xs _ys) 0 = _x

With all of these equalities, LH finally accepts the first equation of atFromAppend.

The second equation is rejected with another constraint.

constraint:
  lhs {v : () | v = () }
  rhs {_ : () | at (_x:_xs) _i = at (append (_x:_xs) _ys) _i }
  // META constraint id 8 : test1.hs:68:31-32
  environment:
    _i : {_i:int | _i > 0}
    _x : a
    _xs : {_xs : [a] | length (_x:_xs) >= 0}
    _ys : [a]

We have that _i > 0 because otherwise the control flow would have entered the first equation instead. The equations that PLE generated are missing because they go in a separate dump file called <module>.fq.ple. If we look for them there, we can find:

at (_x:_xs) _i = at _xs (_i - 1)
append (_x:_xs) _ys = _x : append _xs _ys
at (_x : append _xs _ys) _i = at (append _xs _ys) (_i - 1)

These are informative equalities, but something is still missing to convince the SMT solver. Let us apply these equalities to the right-hand side of the constraint.

  at (_x:_xs) _i = at (append (_x:_xs) _ys) _i
  -- apply the first equality to get
  at _xs (_i - 1) = at (append (_x:_xs) _ys) _i
  -- apply the second equality to get
  at _xs (_i - 1) = at (_x : append _xs _ys) _i
  -- apply the third equality to get
  at _xs (_i - 1) = at (append _xs _ys) (_i - 1)

To finish, we need the fact at the last line. In a pen-and-paper proof, this would be the inductive hypothesis of an induction on _i. PLE can’t help with discovering that, because it only knows how to unfold definitions. But it turns out there is a natural way to bring the missing fact into the environment: calling atFromAppend recursively.

{[email protected]
atFromAppend
  :: { i:Int | i >= 0 }
  -> { xs:[a] | i < length xs }
  -> ys:[a]
  -> { ():() | at xs i == at (append xs ys) i }
@-}
atFromAppend :: Int -> [a] -> [a] -> ()
atFromAppend 0 (_x:_xs) _ys = ()
atFromAppend i (_x:xs) ys = atFromAppend (i - 1) xs ys

And now LH accepts both equations of atFromAppend, which is sound to do since LH also checks that the recursion of atFromAppend is terminating.

Chatty PLE dumps

Having access to the list of equalities generated by PLE is another feature that I implemented recently. Before, one needed to edit the LH source code and activate tracing messages. Until very recently, however, the list of equations would be too large even for moderately complex goals, and equalities would also be larger than necessary.

To cite a couple of examples, PLE would include the expansion of a function like

at _xs 0 = if 0 == 0 then head _xs else at (tail _xs) (0 - 1)
at (_x:_xs) _i = if _i == 0 then head (_x:_xs) else at (tail (_x:_xs)) (_i - 1)
append (_x:_xs) _ys = if null (_x:_xs) then _ys else head (_x:_xs) : append (tail (_x:_xs)) _ys

The function at would be expanded like this for every invocation encountered in a constraint or the expansion of invocations in a constraint. This is for at, which has only two equations, but functions with more equations are not uncommon and would lead to further nested if statements.

Another difficulty was that PLE would also include the context in which invocations appear, which is irrelevant most of the time. For instance, whenever PLE encounters the expression f (g (at (append (_x:_xs) _ys) _i)), one would hope that telling the SMT solver how to expand append would suffice.

append (_x:_xs) _ys = _x : append _xs _ys

However, PLE would generate instead the equality

f (g (at (append (_x:_xs) _ys) _i)) = f (g (at (_x : append _xs _ys) _i))

The same append equality would apply in multiple contexts, and PLE would produce one such equality for every context. More cases where the generated equalities were more chatty than expected are discussed in the LF repo.

PLE present and future

Improving the introspection capabilities of Liquid Haskell is important for user experience. More often than not, LH will need additional help from the user to verify a property, and it is vital that the communication is fluent for applying LH at large scale.

There are many corners to improve. Integrating the information of the various dumps with cross references and consistent presentation of names still remains to be done. There is extra noise and verbosity that I have filtered when presenting the dumps in this post that could be eliminated. There are some examples where PLE still reports more equations than necessary. And beyond PLE, there are other algorithms in LF that still offer little feedback to the user when verification fails3.

However, PLE already yields vastly smaller sets of equations than it used to. This not only influences the feedback the user receives but also the amount of work that is demanded from the SMT solver, with time reductions in benchmarks that range between 10% and 50%. On the other hand, the experience of revising and reformulating the assumptions on the implementation of PLE was enlightening and rewarding on all kinds of technical details that constitute the integration of PLE in the larger context of LH4. Please, join me in celebrating this milestone. I look forward to hearing from your experience with the latest LH.


  1. In truth, the error message that LH offers also contains a summary of hypotheses that we have elided in our error snippet. In this example, the .prettified file doesn’t add any new hypotheses, but this is not always the case. In an ideal future, the .prettified dump and the hypotheses in the reported error would always match, rendering the .prettiffied dump obsolete.
  2. These other dumps are produced with --save as well, and have suffix .fq. They are still text files, but have so many cross references that it is hard to grasp their meaning for more than the smallest examples.
  3. Algorithms like Fusion for local refinement typing and REST.
  4. Special thanks to Niki Vazou and Ranjit Jhala who helped me navigate the network of design decisions at crucial times.
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