Significant changes and quirks

While some of the items below are already mentioned separately, this page tracks the main changes from the old trait system implementation. This also mentions some ways in which the solver significantly diverges from an idealized implementation. This document simplifies and ignores edge cases. It is recommended to add an implicit "mostly" to each statement.


The new solver uses [canonicalization] when evaluating nested goals. In case there are possibly multiple candidates, each candidate is eagerly canonicalized. We then attempt to merge their canonical responses. This differs from the old implementation which does not use canonicalization inside of the trait system.

This has a some major impacts on the design of both solvers. Without using canonicalization to stash the constraints of candidates, candidate selection has to discard the constraints of each candidate, only applying the constraints by reevaluating the candidate after it has been selected: source. Without canonicalization it is also not possible to cache the inference constraints from evaluating a goal. This causes the old implementation to have two systems: evaluate and fulfill. Evaluation is cached, does not apply inference constraints and is used when selecting candidates. Fulfillment applies inference and region constraints is not cached and applies inference constraints.

By using canonicalization, the new implementation is able to merge evaluation and fulfillment, avoiding complexity and subtle differences in behavior. It greatly simplifies caching and prevents accidentally relying on untracked information. It allows us to avoid reevaluating candidates after selection and enables us to merge the responses of multiple candidates. However, canonicalizing goals during evaluation forces the new implementation to use a fixpoint algorithm when encountering cycles during trait solving: source.

Deferred alias equality

The new implementation emits AliasRelate goals when relating aliases while the old implementation structurally relates the aliases instead. This enables the new solver to stall equality until it is able to normalize the related aliases.

The behavior of the old solver is incomplete and relies on eager normalization which replaces ambiguous aliases with inference variables. As this is not not possible for aliases containing bound variables, the old implementation does not handle aliases inside of binders correctly, e.g. #102048. See the chapter on normalization for more details.

Eagerly evaluating nested goals

The new implementation eagerly handles nested goals instead of returning them to the caller. The old implementation does both. In evaluation nested goals are eagerly handled, while fulfillment simply returns them for later processing.

As the new implementation has to be able to eagerly handle nested goals for candidate selection, always doing so reduces complexity. It may also enable us to merge more candidates in the future.

Nested goals are evaluated until reaching a fixpoint

The new implementation always evaluates goals in a loop until reaching a fixpoint. The old implementation only does so in fulfillment, but not in evaluation. Always doing so strengthens inference and is reduces the order dependence of the trait solver. See trait-system-refactor-initiative#102.

Proof trees and providing diagnostics information

The new implementation does not track diagnostics information directly, instead providing proof trees which are used to lazily compute the relevant information. This is not yet fully fleshed out and somewhat hacky. The goal is to avoid tracking this information in the happy path to improve performance and to avoid accidentally relying on diagnostics data for behavior.

Major quirks of the new implementation

Hiding impls if there are any env candidates

If there is at least one ParamEnv or AliasBound candidate to prove some Trait goal, we discard all impl candidates for both Trait and Projection goals: source. This prevents users from using an impl which is entirely covered by a where-bound, matching the behavior of the old implementation and avoiding some weird errors, e.g. trait-system-refactor-initiative#76.

NormalizesTo goals are a function

See the [normalizaton] chapter. We replace the expected term with an unconstrained inference variable before computing NormalizesTo goals to prevent it from affecting normalization. This means that NormalizesTo goals are handled somewhat differently from all other goal kinds and need some additional solver support. Most notably, their ambiguous nested goals are returned to the caller which then evaluates them. See #122687 for more details.