Baby Steps

A blog about programming, and tiny ways to improve it.

Borrowing Errors

I implemented a simple, non-flow-sensitive version of the reference checker which I described in my previous post. Of course it does not accept the Rust codebase; however, the lack of flow-sensitivity is not the problem, but rather our extensive use of unique vectors. I thought I’d write a post first showing the problem that you run into and then the various options for solving it.

Errors

The single most common error involves vec:len(). There are many variations, but mostly it boils down to code code like this, taken from the io package:

type mem_buffer = @{mut buf: [mut u8],
                    mut pos: uint};

impl of writer for mem_buffer {
    fn write(v: [const u8]/&) {
        if self.pos == vec::len(self.buf) { ... }
        ...
    }
}

The problem lies in vec::len(self.buf). This is considered illegal because the vector self.buf resides in a mutable field of a task-local box. Therefore, the algorithm assumes that vec::len() may have access to it and could, potentially, mutate it, which would cause the vector to be freed. Bad. This call would be fine if the field buf were not mutable. In that case, even if vec::len() had access to the mem_buffer, it could not be able to overwrite the field.

In fact, all of the errors I see right now (about 46 of them across the standard library and rustc) are calls to vec::len() or vec::each() with the vector in question living in mutable, aliasable memory. It is, currently, the only way to accumulate items in a vector, after all. However, I haven’t implemented the full check—in particular, I didn’t implement the check that pattern matching a variant or through a box requires immutable memory, and so I imagine there will be some more errors related to that once I do that.

Solution #1: Swapping

Of course, this problem is not really a surprise. The solution I had in mind for handling unique data that is located in mutable, aliasable memory is to swap that unique data into your stack frame, where the compiler can track it (inspired by Haller and Odersky’s work on uniqueness, though I’m sure the technique predates them). So the code from the io package could be rewritten as:

type mem_buffer = @{mut buf: [mut u8],
                    mut pos: uint};

impl of writer for mem_buffer {
    fn write(v: [const u8]/&) {
        let mut buf = [];
        buf <-> self.buf;
        if self.pos == vec::len(buf) { ... }
        ...
        self.buf <- buf;
    }
}

This makes use of the little known swap (<->) and move (<-) assignment forms. Now the buffer being passed to vec::len() is in the local variable buf, not the contents of some @ box; this means that vec::len() could not possily reassign it because there are no aliases to the local variable buf.

It’s a bit of a pain to write this swapping code each time. It could of course be packaged up in a library (here, I’ve included various mode declarations, though these would be unnecessary in a purely region-ified world, as ownership would be done by default):

type swappable<T> = {mut val: option<T>};
impl methods<T> for swappable<T> {
    fn swap(f: fn(+T) -> T) {
        let mut v = none;
        v <-> self.buf;
        if v.is_none() { fail "already swapped"; }
        self.val <- some(f(option::unwrap(v)));
    }
}

Swappable could then be used to build up a dynamically growable vector library:

type dvec<T> = {buf: swappable<T>};
impl methods<T> for dvec<T> {
    fn add(+e: T) {
        self.buf.swap { |v| v + [e] }
    }

    fn add_all(v2: [T]) {
        self.buf.swap { |v| v + v2 }
    }

    fn each(f: fn(T) -> bool) {
        self.buf.swap { |v| vec::each(v, f); v }
    }
}

Attempts to add to a vector that is being iterated over would fail dynamically (basically a more reliable version of Java’s “fail-fast iterators”).

Solution #2: Pure functions…?

Still, it’d be nice if one could invoke vec::len() and vec::each() even when the data is in a mutable location. After all, neither of those functions make any changes, and we know that. One solution I considered was that we could make use of the pure annotation in a kind of lightweight effect system.

The basic idea would be that pure functions are functions which do not modify any aliasable state (today pure functions disallow mutation of any state, including data interior to the stack frame; we should fix this regardless). However, drawing on more work by the Scala folks, we can actually generalize pure functions somewhat farther: we could allow them to invoke closures so long as those closures are given in the arguments. The idea is basically that a pure function is one which does not make any modifications to aliasable state except possibly through closures which the caller itself provided.

These changes would allow us to declare vec::len() and vec::each() as pure. In the case of vec::len(), that would be sufficient to ensure safety without any form of alias check. Horray!

But don’t get too excited: even if vec::each() is declared pure, we still cannot accept calls like the ones we saw before:

vec::each(self.buf) { |e|
    ...
}

The reason is that buf is still stored in aliasable, mutable state, and so we have to be sure that the loop body is safe. This can be achieved when the vector is stored in a local variable, as we can monitor for writes to that variable. But if the vector is in an @ box, we have to consider any possible alias of that box. And this leads us to our next possible solution, alias analysis.

Solution #3: Alias analysis

As I said in my previous post, I am not 100% sure of what analysis we are doing today. But if I were to design an alias-based analysis to address this shortcoming, I imagine if would work something like this:

  • Each callee is guaranteed that every reference is stable (points at memory which will not be freed) no matter what actions is takes. This means that the callee is free to call any functions it likes, including closures, because the caller has guaranteed that all functions which the callee has access to are harmless.

In particular, vec::each(v, f) could safely invoke the f() on each item in v without fear of v being freed. It’s up to the caller to guarantee that f will not have any harmful effects.

But how can the caller do this? There are two basic techniques. The first is to rely on the guarantees it gets from the outside. So, if you have a function like:

fn map<T,U>(v: [T]/&, m: fn(T) -> U) -> [U] {
    let mut r = [];
    for vec::each(v) { |e|
        r += m(e);
    }
    ret r;
}

Here, the call to m() is known to be safe because both v and m were given as parameters, so it actually the job of the caller of map() to ensure that they do not conflict.

If we can’t rely on a guarantee from the outside, then, we have to look at the types. For example, going back to our example of the buffer, if we had a loop like:

for self.buf.each { |e|
    some_ptr.buf = [];
}

Here the assignment to some_ptr.buf would be disallowed if some_ptr had the same type as self: after all, maybe it is an alias of self.

We can apply similar reasoning to functions that are invoked:

for self.buf.each { |e|
    clear_buf(some_ptr);
}

Without knowing what set_buf() does, we’d have to reject this because it has access to data of the same type as self (and hence, potentially to self itself).

The cool thing about an analysis like this is that it would allow most of the examples in the standard library to compile mostly as is. But there are some downsides.

First, it’s not clear to me that an analysis like this “scales well”. By scales well I do not mean performance but rather that, while library code tends to pass, I am not sure that uses of library code will pass. For example, suppose I have a shared, growable vector that encapsulates a unique pointer, rather like Java’s ArrayBuffer. And now I have some library code that does:

 my_vec.each { |e| do_some_processing(e); }

where my_vec is one of these array buffers. Using an alias check, it is possible to define the ArrayBuffer.each() method, but that essentially pushes the requirement to the caller to validate that the body of the each() loop will not modify my_vec. Since my_vec is aliasable, this means that do_some_processing() must not use any array buffers of its own.

Admittedly, we haven’t run into these scaling problems so much, but I am not sure how much to draw from that. For one thing, the analysis is buggy today, so it may be that we should be seeing more errors than we are. For another, all vectors are unique now, but this is causing us scaling problems, and we are starting to move away from that.

A second concern about the analysis is that it is anti-encapsulation. It requires the compiler to have full details about the types of all data that may be accessed. When you have types like closures or interfaces types, this information is not available, and so the more we use these abstractions, the worse the analysis performs. Furthermore, it becomes impossible for modules to “hide” the implementation of a type—whenever any type definition anywhere changes, all downstream code must be recompiled or else the memory safety can no longer be guaranteed. Admittedly, due to Rust’s support for interior types (not everything is a pointer) and inlining, this is already often the case, but it should still be possible to define modules that make use of opaque pointer types in the future, allowing for changes to the implementation where no recompile is necessary.

UPDATE: A further thought on this matter. This is a bit different from requiring recompilation as a matter of course (e.g., because the size of a record changed)—that is, there is no guarantee that the downstream compilation will succeed. Now, if I add a use of some vector library in the upstream code, downstream code may fail to compile, even if the use of the vector library is purely internal and not exposed through the interface.

Summary

I am still leaning towards solution #1, though I appreciate our alias analysis more and more. Actually, the fact that I only encountered 46 errors seems pretty decent, especially since most of them are clustered together. However, I do expect more such errors when I implement the pattern matching safety checks, but there we can make better use of fine-grained copies and so I expect that to be less of a problem.

Oh, and a final note regarding flow sensitivity: I think I will implement a flow-sensitive variant of the checker (it’s a small change from what I have today), but since we never take the address of locals today, it’s a moot point anyhow.

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