15 May 2017 | Jameson Nash

*Adventures in Type Inference Convergence: 2017 edition*

In my last post on type inference convergence, I described a correct inference algorithm. However, while this was a tremendous improvement over being wrong, I was unhappy with it. Indeed, I wrote that “a full discussion of these heuristics will have to wait for a future blog post”. But what I didn’t say is that I had already written many notes for that post, and had reached a point where I understood that the current algorithm wouldn’t actually permit reliable, well-tuned heuristics.

Plus, the algorithm required lots of global state (to manage the work-queue), which required tricky locks and coordination to ensure it stayed consistent.

The improved convergence algorithm in PR #21677 maintains all of the correctness guarantees of the existing algorithm, but uses a completely revised cycle-detection algorithm that provides much stronger guarantees about the order in which work will be done.

By comparison to be current algorithm, the revised one has half as many states. They are:

- processing: any leaf function of the call-stack (usually there’s just one at a time)
- on the call-stack: the tree DAG (directed-acyclic-graph) of functions with edges representing inferred potential calls
- in a cycle: groups of functions nodes discovered not to form a DAG (represented instead as an unsorted set)
- finished: done

The outline of the algorithm that manages these states is that the call-stack is always maintained as a simple tree, only permitting the existence of nodes with forward edges. During the course of running inference, if adding an edge would cause a cycle in this graph, the algorithm instead replaces all nodes that are participating in the cycle with a single node that represents that cyclic set.

Conceptually, this set is similar to either the “fixed-point” set in the current algorithm, or the “active lines” set. It differs from the “fixed-point” set because it can also guarantee that only nodes in the cycle will be in the set. The “active lines” set only allows describing the active set within a single method – the new representation expands that to iterate convergence of an entire set of methods.

Because the algorithm maintains a acyclic call-stack, it becomes now possible to depend on the inspection of an edge in inference to result in exactly one of two results: an inferred return type, or a cycle detection that replaces the current node with a convergence set. This greatly increases the options available for integrating new inference heuristics!

There’s one additional nice benefit to this new representation:
improved inlining heuristics!
It may not be obvious how improved cycle-detection during inference convergence
would impact the optimization afterwards,
but one of the unintuitive products of inference is the inlining *order*.
Deciding on the profitability of inlining is dependent upon knowing the
precise structure (ordering) of the call-stack and any cycles in it.
Since the new inference structure provides precise information on which
functions potentially participate in cycles,
the inliner order can take advantage of that information to decide which functions to inline,
and where to terminate the inlining due to recursion.

Without heuristics imposed on top of the existing inference algorithm, it would already be Turing-complete[^turing], with just the current support for recursively inspecting dispatch over computed types.

This means that (without some additional constraints added) inference might attempt to compute anything (including the halting problem, busy beaver, infinite recursion). In some cases, code is written that explicitly tries to make use of this using “lisp-y style” function application. However, code written in that manner often quickly runs into limits that are inherently required by the compiler to avoid other very similar code patterns where the inferred recursion is either unintentional (it won’t affect runtime performance / behavior) or unreachable (solving for reachability is commonly referred to as the halting problem).

It is suboptimal for this inference system to be undecidable (or even slow), since its sole purpose is to find optimizations to make your program run faster. And inference is going to be painfully slow when it is being used as an interpreter (rather than just running the program unspecialized). So, there needs to be some mechanism or heuristics for preventing this from happening.

It’s also important to realize that these heuristics need to be tuned precisely to the capabilities of inference.

Turing-completeness requires several features:

- loops
- conditionals
- arbitrarily large (but finite) memory

Removing any one of these would be sufficient to ensure the system is computable. But loops (recursion), conditionals (dispatch), and memory (apply-type) are all essential to getting useful results from inference and thus it is generally not desirable to simply remove them. Additionally, even if it was possible now, as inference becomes more capable (e.g. inter-procedural constant propagation, effect-free computation, speculative evaluation of constant arithmetic), new forms of loops and memory would appear that would also need to be addressed.

Since Julia’s inference algorithm currently only operates on Julia’s Type’s,
(and does not support inference of constant expressions like `1 + 1`

),
we only need to consider how these can be used to express an arbitrarily large memory.
Imply both that it can form arbitrarily large constants, and do arithmetic (conditional loops) on them.

There are a finite number of type names. But there are two primary mechanisms by which a type can be used to express an arbitrary value. For any type, the parameters can be arbitrarily nested in depth. Additionally, there are tuples and unions, which (in addition to depth), can have arbitrary length. We can express this complexity as:

```
depth(type) = 1 + maximum(depth, type.parameters)
length(type) = length(type.parameters)
```

It is possible, using either property (or a combination of both), to represent any number.

The current heuristics in inference prohibit recursion which increases either depth or length. without this ability, the program cannot express any number larger than the input, forcing it to (eventually) terminate. Additionally, there are various (semi-arbitrary) limits places on the lengths and depths of the types that are flowing through the system.

For predictability, it is preferable that the heuristic limits imposed should only be local properties, computable from the function itself or its forward edges (the functions that it calls). This remains an unsolved problem, since the heuristics are based on examining the call-stack (following back-edges). This means that the order in which functions enter inference can affect how precisely they are permitted to compute their results.

*(A diagram showing how the context-sensativity of the specialization type signature can create repeating patterns instead of simple recursion)*

To illustrate what I mean by wanting to only use “local properties”, consider if `fuchsia`

entered inference first,
then inference will see the widening recursion at `fuchsia´`

.
And `green´`

will not be encountered.
Thus, the result of `green`

will be different than if `fuchsia`

was not being inferred
(recursion would have been detected at `green´`

, changing the results assigned and cached for both –
including `fuchsia`

when it is later inferred, and uses the cached return type for `green`

).

An optimal limit would notice the recursion at `fuchsia´`

, but change the topmost function (`fuchsia`

)
to use an approximation of `green`

while canceling type-inference on the original attempted edges.
This would preserve the characteristic that the cached result of type-inferring `green`

shouldn’t
depend on having been called from `fuschia`

.

A downside of the current arbitrary limits is that they penalize valid code which uses complex types, even if they are not changing under recursion.

Another downside is that it only takes into account the possibility of expressing memory via the type-system “length” and “depth” definitions above.

These challenges remain, and will need to be addressed in a future update to this blog (and to the implementation!).

02 Jul 2019 | Julia Computing

Newsletter June 2019
11 Jun 2019 | Julia Computing

Julia In Excel
10 Jun 2019 | Harsha Byadarahalli Mahesh

Get the latest news about Julia delivered to your inbox.

Need help with Julia?

We provide products, training and consulting to make Julia successful in your organization. Email us: **[email protected]**

Contact us
Julia Computing was founded with a mission to make Julia easy to use, easy to deploy and easy to scale. We operate out of Boston, London and Bangalore and we serve customers worldwide.
© 2015-2019 Julia Computing, Inc. All Rights Reserved.