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https://github.com/fan-tom/liquid-rust

Liquid Types implementation for Rust
https://github.com/fan-tom/liquid-rust

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Liquid Types implementation for Rust

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README 

        
**Liquid Rust**



This project is attempt to implement Liquid Types type checker

for Rust code.



**Abstract**

-



The basic idea of refined types is to attach predicates to types, i.e

`type NonZero = { v: int | v != 0}`. Each value of that type satisfies that predicate,

so if we want function to accept only non-zero values(safe division, for example), we declare it's type as

`NonZero -> `. We may also use refined type in return position of function signature,

so we can use function invocation as source of refinements of variables.

The whole satisfiability of restriction and refinements checking machinery is implemented in SMT solvers, such as Z3.  



**Concrete**

-



To implement Liquid Types for rust, we need to allow programmer to supply restrictions of

function signatures and variables in source code and then check that that restrictions are always satisfied.



The key question here are:



 1) how to provide restrictions?



 2) how to analyze Rust code?



 3) how to interop with SMT solver?



The current answers are:



 1) We may use custom attributes, i.e

     ```rust

     //main.rs

     #![feature(custom_attribute)]

     

     #[require(a > 30)]

     #[ensure(result == a)]

     fn foo(a: i32) -> i32 {

         a

     }

     ```

     

     This way we may annotate functions, methods, variables, fields and may be other interesting syntax constructions.

     Another possibility is to create annotated type alias and use it anywhere type name is allowed.

     

     ```rust

     //main.rs

     #![feature(custom_attribute)]

     

     #[require(NonZero != 0)]

     type NonZero = i32;



     fn accept_refined(a: NonZero) -> i32 {

         a

     }

     fn return_refined(a: i32) -> NonZero {

         a

     }

     fn declare_refined() {

        let a: NonZero = 5;

     }

     

     fn ascript_refined() -> NonZero {

        let mut x;

        // init and mutate x

        x: NonZero + 5

     }

     ```

     

     After we get annotation we need to parse restriction, typecheck it and translate to SMT expression.

     The attribute is given as path (**require** or **ensure** above) + TokenStream of the rest.

     Currently I want to convert TokenStream to string and parse it with Pest(or rust-peg) parser.

     Question here is to discover what are all possible syntax constructs we may and want to support.

     

 2) Current approach of analyzing Rust code is to use Rust compiler lint infrastructure and 

    declare rustc plugin, that will use LatePass lint phase, where typed representation of AST is accessible (called HIR).

 

    The problem here is that this representation is very "high-level", because there are high-level constructs here,

    such as pattern matching, method calls, and so on. Ideally we want representation, where all high-level

    constructs are lowered to assignments (SSA?) and function calls. ~~There is such representation in compiler,

    called MIR, but it seems that there is no such phase in linting where we work with MIR.

    Generally speaking, we need to find all assignments to refined variables, that may come from

    actual assignment, initialization, function call (actual parameters assigned to formal parameters) and

    function return (result expression is assigned to implicit result variable).~~

    We can easily get MIR of function or constexpr if we know substitution of all type variables within that fun/const.

    It means however that we cannot analyze generic functions in abstract, only when they are called from monomorphized function.

    Possible approach here may be to maintain map from (``, ``) -> 

    in LintPass and query for it any time we want to refine function call from inside another function.

    

    How to check that restrictions required by function argument refined type is satisfied?

    We need to analyze what values actual argument may be equal to(we call them refinements), and pass them as preconditions

    to SMT solver.

    How to collect that refinements?

    Refinements may come from assignment of refined variable, if-check, match, reachability analysis

    ```rust

        let x = ...;

        if x == 5 {

            panic/return/break/continue;

        }

        // here x != 5

    ```

    and WHAT ELSE?

    

 3) Interaction may be done by adaptor libraries, such as rsmt2 and z3. What one to use?

    We want to translate parsing tree to AST accepted by library. z3 provides functions to

    construct different AST nodes (such as `x.eq(y)`), but that AST types are opaque so we

    cannot construct them by hands. rsmt2 has no AST type, but requires to provide type

    that may be converted to SMT solver predicate string and another type to parse SMT output

    (model) from string.

    There exists rustproof-libsmt that seems appropriate for our needs



**Links**



* Liquid Types original paper: https://goto.ucsd.edu/~rjhala/liquid/liquid_types.pdf

* Liquid Haskell project, the most mature implementation of Liquid Types: https://ucsd-progsys.github.io/liquidhaskell-blog/

* Another attempt to implement refinement types for Rust code: https://github.com/Rust-Proof/rustproof/

(seems isn't maintained and doesn't compile as of May 2019)



19.12.2019



Currently I decided to analyze MIR in LatePass, deferring polymorphic functions analysis to call sites.

Notes about original paper:

* We don't need to perform HM type inference as we already have type information from rust compiler

* We don't need (sure? what about closures, including as arguments?) to handle function types.

* We don't need to infer types of functions as our analysis is local, we don't infer liquid types globally,

the same way rust does it. So we only must check explicitly provided type restrictions.

* We cannot analyze polymorphic functions alone, as we cannot get MIR without type substitutions, so we defer analysis

of them until they are called and we can get their MIR. It may be very inefficient, as a lot of rust code uses

generics, but we can cache analysis results using pair of (function id, type arguments) as key.

So we don't need the notion of 'type schema' (all our schemas are monotypes)

* Original paper describes algorithms using functional nature of analyzed language, i.e program consists of declarations

and expressions. There is no notion of expression in MIR, however it seems that we may replace it with notion of

SSA variable, that has type as once-assigned value. Environment of each that assignment consists of all the variables,

introduced earlier in that block or any of its predecessors, whereas in functional language each expr is in another expression and

there is notion of 'sibling' expressions.



28.01.2020



Currently plugin can infer types in simple cases, from assignment and arithmetic operations, can handle conditions in 

branches, check preconditions of called functions and use their postconditions to refine variables. The main problem

now is to infer most common type **after** branches, as we need to somehow handle the fact that refienements in

different branches are valid under different conditions. For example, consider next piece of code:

 

```rust

#[ensures(return > 0)]

fn returns_positive(x: i32) -> i32 {

    if x < 0 {          // 1

        1               // 2

    } else if x == 0 {  // 3

        x+1             // 4

    } else {            // 5

        x               // 6

    }                   // 7

                        // 8

}

```

As you can see, postcondition is always satisfied, but plugin infers types after `if` (line 8) as

`(x < 0 \/ x = 0 \/ not (x < 0 \/ x = 0)) /\ (return = 1 \/ return = x + 1 \/ return = x)` and so postcondition check fails, because

we lost connection between condition over `x` and corresponding `return` value.

Instead, we need to infer most common type for all variables among all branches. Here it may be `return > 0`. But that 

type is imprecise, as we lose information that `return = x` if `x > 0` or `return = 1` otherwise. Can we handle this?



02.02.2020



I just realized that MIR representation is not SSA actually, as there are multiple assignments

to mutable variables, including function arguments. It breaks almost the whole idea, as we cannot precisely

track variable's refinements anymore. For example, in the following piece of code

```rust

let mut a = 1;

a += 1;

let b = a;

```

we cannot proof that `b = 2`, because we get such SMT asserts:

```

(a = 1 \/ a = a + 1) /\ (b = a)

```

so we can only conclude that `b = 1 \/ a + 1`.

We need to introduce new version of variable `a` when we mutate it and use that new version in all the statements

that follow this reassignment. However, there may be cases where reassignment is conditional, so we also need so-called

φ-statements, that "merge" different versions of the same variable together, therefore previous code gives next asserts:

```

(a0 = 1) /\ (a1 = a0 + 1) /\ (b = a1)

```

which lets us proof that `b = 2`.

The question is: do we need our own true-SSA code representation, created from MIR,

or we can somehow encode that variables versioning and versions merging into InferenceCtx directly?