f25d7b6cd6
Adds Def::Class(ClassDef) and Def::Instance(InstanceDef) variants per Decision 11 §"Form-A schema". All optional fields (superclass, doc, default body) carry skip_serializing_if so canonical-JSON bytes of pre-22b fixtures stay bit-identical. Downstream match-on-Def sites are extended with explicit Class/Instance arms. Behaviour for 22b.1 is placeholder (skip in typecheck/codegen, one-line summary in pretty/manifest, placeholder marker in prose/print) with TODO comments naming the deferred sub-iters (22b.2 typecheck, 22b.3 codegen, 22b.4 prose-projection arms).
543 lines
21 KiB
Rust
543 lines
21 KiB
Rust
//! Iter 18c.3: uniqueness inference over the typed AST.
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//!
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//! This pass classifies every binder of every fn body as either
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//! [`Uniqueness::Unique`] or [`Uniqueness::Shared`]. Unlike the
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//! [`crate::linearity`] check it is **not** a user-facing diagnostic
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//! pass — it is internal codegen input. Codegen consults the side
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//! table to decide where to emit `ailang_rc_inc` and `ailang_rc_dec`
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//! calls under `--alloc=rc` (Iter 18c.3).
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//!
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//! ## Why a separate pass from `linearity`
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//!
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//! - Different scope. `linearity` is opt-in by signature (every param
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//! mode must be explicit) and is emitted as user diagnostics.
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//! Uniqueness inference runs unconditionally on every fn body and
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//! is never reported.
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//! - Different output. `linearity` emits `Diagnostic`s; uniqueness
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//! produces a [`UniquenessTable`] keyed by `(def_name, binder_name)`.
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//! - Different consumption point. `linearity` runs from
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//! [`crate::check_workspace`]; the uniqueness side table is built
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//! on demand from codegen via [`infer_module`].
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//!
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//! ## What "unique" means here
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//!
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//! A binder `x` is `Unique` if every reachable path from its
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//! introduction to the end of its scope passes through **at most one**
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//! consume use of `x`. Borrow-position uses (Match scrutinee,
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//! `(clone)` argument, `Borrow`-mode call argument) do not count as
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//! consumes. A binder is `Shared` otherwise.
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//!
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//! For the codegen consumer, a closely related side-output is more
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//! useful: [`UniquenessInfo::consume_count`] — the maximum, across
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//! all reachable paths in the binder's scope, of the number of
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//! consume uses of the binder. `consume_count == 0` means the binder
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//! is exclusively borrowed (or unused) inside its scope, which is
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//! the case where emitting `dec` at scope close is unambiguously
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//! safe under `--alloc=rc` (no callee / outer term consumed the
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//! value first; the binder owns the only outstanding ref to its
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//! allocation).
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//!
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//! ## Position model
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//!
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//! Identical to [`crate::linearity`]'s, modulo that this pass has no
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//! activation gate (it runs on every fn). See that module's docs for
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//! the propagation rules. `Term::Lam` captures are conservatively
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//! treated as Consume — the same conservative decision the linearity
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//! check makes.
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//!
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//! ## What this pass does NOT do
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//!
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//! - **Cross-fn reasoning.** The inference is whole-fn local. It does
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//! not reason about a callee's body when classifying a binder passed
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//! into the callee. Cross-fn ownership currently flows through
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//! explicit mode annotations on the callee's signature (18a).
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//! - **Per-type drop fns and recursive `dec` cascades.** Those are
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//! emitted by codegen (`emit_drop_fn_for_type`), not consulted from
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//! this side table. The inference's role is "should codegen emit a
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//! dec on this binder leaving scope at all?"; the *shape* of the
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//! dec (uniform `drop_<m>_<T>` call vs inlined partial drop) is a
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//! codegen-side decision driven by `moved_slots`.
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use ailang_core::ast::{Arm, Def, FnDef, Module, ParamMode, Pattern, Term, Type};
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use std::collections::BTreeMap;
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/// Per-binder classification produced by the inference. See module
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/// docs for semantics.
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#[derive(Copy, Clone, Debug, PartialEq, Eq)]
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pub enum Uniqueness {
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/// Every reachable path from the binder's introduction to the end
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/// of its scope passes through at most one consume use.
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Unique,
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/// Some reachable path consumes the binder more than once.
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Shared,
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}
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/// Per-binder result of the inference. The codegen consumer uses
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/// both fields: `uniqueness` for the high-level classification and
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/// `consume_count` for the precise "is anyone else going to dec
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/// this" answer at scope close.
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#[derive(Copy, Clone, Debug, PartialEq, Eq)]
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pub struct UniquenessInfo {
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pub uniqueness: Uniqueness,
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/// Maximum number of consume-position uses of this binder
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/// across any reachable path in its scope. `0` means the binder
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/// is only borrowed (or unused) — under `--alloc=rc` the only
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/// outstanding reference at scope close is the one the binder
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/// itself holds, so dec at scope close is safe and necessary.
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pub consume_count: u32,
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}
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/// Side table indexed by `(def_name, binder_name)`. Built fresh for
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/// each codegen invocation; the side table is not cached.
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///
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/// Note: binder names within a single fn shadow lexically. Each
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/// shadow is recorded in turn; the **last** insert wins (which is
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/// the innermost binding by pop-order). Codegen consumers look up
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/// by current-scope binder name, which always matches the innermost
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/// classification — fine in practice because every shipping fixture
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/// has unique binder names per fn.
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pub type UniquenessTable = BTreeMap<(String, String), UniquenessInfo>;
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/// Top-level entry: walk every fn def in `m` and produce per-binder
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/// classifications keyed by `(fn_name, binder_name)`.
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///
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/// Pre-condition: `m` is fully typechecked. Inputs that have not
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/// been type-checked (unresolved `Var` lookups, mismatched ctor
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/// arities) may produce a noisy / incorrect side table — the
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/// codegen never runs on such inputs because `ail build` runs
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/// typecheck first.
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pub fn infer_module(m: &Module) -> UniquenessTable {
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let mut globals: BTreeMap<String, Type> = BTreeMap::new();
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for def in &m.defs {
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match def {
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Def::Fn(f) => {
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let ty = strip_forall(&f.ty).clone();
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globals.insert(f.name.clone(), ty);
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}
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Def::Const(c) => {
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globals.insert(c.name.clone(), strip_forall(&c.ty).clone());
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}
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Def::Type(_) => {}
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// Iter 22b.1: class/instance defs are skipped here. Class
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// method signatures contribute global names once
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// monomorphisation (22b.3) materialises them; the
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// pre-monomorphisation form has no concrete `Type` to
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// hand to the uniqueness pass.
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Def::Class(_) | Def::Instance(_) => {}
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}
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}
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let mut table: UniquenessTable = BTreeMap::new();
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for def in &m.defs {
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if let Def::Fn(f) = def {
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infer_fn(f, &globals, &mut table);
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}
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}
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table
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}
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fn strip_forall(t: &Type) -> &Type {
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match t {
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Type::Forall { body, .. } => body,
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other => other,
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}
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}
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/// Per-fn driver: install the parameters as binders, walk the body,
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/// snapshot the per-binder consume counts into the side table.
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fn infer_fn(f: &FnDef, globals: &BTreeMap<String, Type>, out: &mut UniquenessTable) {
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let inner = strip_forall(&f.ty);
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if !matches!(inner, Type::Fn { .. }) {
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return;
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}
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let param_states = {
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let mut walker = Walker {
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globals,
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binders: BTreeMap::new(),
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def_name: f.name.clone(),
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out,
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};
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// Install fn parameters. Their consume counter starts at 0;
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// the walk over the body increments it.
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for name in f.params.iter() {
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walker.binders.insert(name.clone(), BinderState::default());
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}
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walker.walk(&f.body, Position::Consume);
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// Snapshot params before dropping the walker.
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f.params
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.iter()
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.filter_map(|name| walker.binders.get(name).map(|s| (name.clone(), s.clone())))
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.collect::<Vec<_>>()
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};
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// Walker dropped — `out` is borrowable again.
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for (name, s) in param_states {
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let info = UniquenessInfo {
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uniqueness: classify(s.consume_count),
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consume_count: s.consume_count,
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};
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out.insert((f.name.clone(), name), info);
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}
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}
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#[inline]
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fn classify(consume_count: u32) -> Uniqueness {
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if consume_count <= 1 {
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Uniqueness::Unique
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} else {
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Uniqueness::Shared
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}
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}
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/// Position in which a [`Term::Var`] occurs. Consume uses count
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/// against the binder's `consume_count`; Borrow uses do not.
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#[derive(Copy, Clone, Debug, PartialEq, Eq)]
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enum Position {
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Consume,
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Borrow,
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}
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/// Per-binder mutable state during the walk. Future extensions
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/// (e.g. last-use markers, per-arm split) can grow more fields.
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#[derive(Default, Clone, Debug)]
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struct BinderState {
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consume_count: u32,
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}
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struct Walker<'a> {
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globals: &'a BTreeMap<String, Type>,
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binders: BTreeMap<String, BinderState>,
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def_name: String,
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out: &'a mut UniquenessTable,
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}
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impl<'a> Walker<'a> {
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fn walk(&mut self, t: &Term, pos: Position) {
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match t {
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Term::Lit { .. } => {}
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Term::Var { name } => self.use_var(name, pos),
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Term::App { callee, args, .. } => {
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self.walk(callee, Position::Consume);
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let arg_modes = self.callee_arg_modes(callee, args.len());
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for (i, arg) in args.iter().enumerate() {
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let arg_pos = match arg_modes.get(i) {
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Some(ParamMode::Borrow) => Position::Borrow,
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_ => Position::Consume,
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};
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self.walk(arg, arg_pos);
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}
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}
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Term::Let { name, value, body } => {
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self.walk(value, Position::Consume);
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let prev = self.binders.insert(name.clone(), BinderState::default());
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self.walk(body, pos);
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let final_state = self.binders.remove(name).unwrap_or_default();
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if let Some(p) = prev {
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self.binders.insert(name.clone(), p);
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}
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self.record_binder(name, &final_state);
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}
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Term::LetRec { name, body, in_term, .. } => {
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let prev = self.binders.insert(name.clone(), BinderState::default());
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self.walk(body, Position::Consume);
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self.walk(in_term, pos);
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let final_state = self.binders.remove(name).unwrap_or_default();
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if let Some(p) = prev {
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self.binders.insert(name.clone(), p);
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}
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self.record_binder(name, &final_state);
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}
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Term::If { cond, then, else_ } => {
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self.walk(cond, Position::Consume);
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let saved = self.binders.clone();
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self.walk(then, pos);
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let after_then = std::mem::replace(&mut self.binders, saved);
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self.walk(else_, pos);
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merge_states(&mut self.binders, &after_then);
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}
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Term::Match { scrutinee, arms } => {
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self.walk(scrutinee, Position::Borrow);
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let saved = self.binders.clone();
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let mut acc: Option<BTreeMap<String, BinderState>> = None;
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for arm in arms {
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self.binders = saved.clone();
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self.walk_arm(arm, pos);
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match acc.as_mut() {
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None => acc = Some(self.binders.clone()),
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Some(m) => merge_states(m, &self.binders),
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}
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}
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if let Some(merged) = acc {
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self.binders = merged;
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} else {
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self.binders = saved;
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}
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}
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Term::Seq { lhs, rhs } => {
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self.walk(lhs, Position::Consume);
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self.walk(rhs, pos);
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}
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Term::Ctor { args, .. } => {
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for a in args {
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self.walk(a, Position::Consume);
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}
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}
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Term::Do { args, .. } => {
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for a in args {
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self.walk(a, Position::Consume);
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}
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}
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Term::Lam { params, body, .. } => {
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// Lam captures cross the linearity boundary — every
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// free var of the body is conservatively a consume.
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// Walking the body with each param as a fresh binder
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// produces the right intra-Lam classification.
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let mut saved_for_params: BTreeMap<String, Option<BinderState>> = BTreeMap::new();
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for p in params {
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saved_for_params.insert(p.clone(), self.binders.remove(p));
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self.binders.insert(p.clone(), BinderState::default());
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}
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self.walk(body, Position::Consume);
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for p in params {
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let final_state = self.binders.remove(p).unwrap_or_default();
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if let Some(prev) = saved_for_params.remove(p).flatten() {
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self.binders.insert(p.clone(), prev);
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}
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self.record_binder(p, &final_state);
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}
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}
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Term::Clone { value } => {
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self.walk(value, Position::Borrow);
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}
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Term::ReuseAs { source, body } => {
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// Iter 18d.1: `(reuse-as SRC BODY)` is a Consume of
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// `SRC` (the binder's slot is being taken over) and
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// a normal walk of `BODY`. So a bare-Var SRC bumps
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// its `consume_count` by one. Linearity is the
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// user-visible enforcement; this pass only feeds the
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// RC codegen its bookkeeping.
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self.walk(source, Position::Consume);
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self.walk(body, pos);
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}
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}
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}
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fn walk_arm(&mut self, arm: &Arm, pos: Position) {
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let names = collect_pattern_binders(&arm.pat);
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let mut saved: BTreeMap<String, Option<BinderState>> = BTreeMap::new();
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for n in &names {
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saved.insert(n.clone(), self.binders.remove(n));
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self.binders.insert(n.clone(), BinderState::default());
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}
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self.walk(&arm.body, pos);
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for n in &names {
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let final_state = self.binders.remove(n).unwrap_or_default();
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if let Some(prev) = saved.remove(n).flatten() {
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self.binders.insert(n.clone(), prev);
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}
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self.record_binder(n, &final_state);
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}
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}
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fn use_var(&mut self, name: &str, pos: Position) {
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if let Some(s) = self.binders.get_mut(name) {
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if matches!(pos, Position::Consume) {
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s.consume_count = s.consume_count.saturating_add(1);
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}
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}
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}
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fn callee_arg_modes(&self, callee: &Term, n_args: usize) -> Vec<ParamMode> {
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let name = match callee {
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Term::Var { name } => name,
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_ => return vec![],
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};
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let ty = match self.globals.get(name) {
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Some(t) => t,
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None => return vec![],
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};
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let inner = strip_forall(ty);
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match inner {
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Type::Fn { param_modes, .. } => {
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if param_modes.is_empty() {
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vec![ParamMode::Implicit; n_args]
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} else {
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param_modes.clone()
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}
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}
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_ => vec![],
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}
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}
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fn record_binder(&mut self, name: &str, s: &BinderState) {
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let info = UniquenessInfo {
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uniqueness: classify(s.consume_count),
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consume_count: s.consume_count,
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};
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self.out
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.insert((self.def_name.clone(), name.to_string()), info);
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}
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}
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/// Conservative branch merge: for each binder name, the merged
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/// `consume_count` is the **max** of the two branches. Max is the
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/// right operator because the classification cares about the
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/// worst-case path.
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fn merge_states(into: &mut BTreeMap<String, BinderState>, other: &BTreeMap<String, BinderState>) {
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for (name, s) in other {
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let entry = into.entry(name.clone()).or_default();
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entry.consume_count = entry.consume_count.max(s.consume_count);
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}
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}
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fn collect_pattern_binders(p: &Pattern) -> Vec<String> {
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match p {
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Pattern::Wild | Pattern::Lit { .. } => vec![],
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Pattern::Var { name } => vec![name.clone()],
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Pattern::Ctor { fields, .. } => fields.iter().flat_map(collect_pattern_binders).collect(),
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use ailang_core::ast::{FnDef, Literal};
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fn fn_simple(name: &str, params: Vec<&str>, body: Term) -> Def {
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Def::Fn(FnDef {
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name: name.into(),
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ty: Type::Fn {
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params: params.iter().map(|_| Type::int()).collect(),
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param_modes: vec![],
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ret: Box::new(Type::int()),
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ret_mode: ParamMode::Implicit,
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effects: vec![],
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},
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params: params.into_iter().map(|s| s.to_string()).collect(),
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body,
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suppress: vec![],
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doc: None,
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})
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}
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/// `(let x 0 0)` — `x` is unique, consume_count == 0.
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#[test]
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fn let_unused_binder_is_unique_zero_consume() {
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let body = Term::Let {
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name: "x".into(),
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value: Box::new(Term::Lit { lit: Literal::Int { value: 0 } }),
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body: Box::new(Term::Lit { lit: Literal::Int { value: 0 } }),
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};
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let m = Module {
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schema: ailang_core::SCHEMA.into(),
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name: "t".into(),
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imports: vec![],
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defs: vec![fn_simple("f", vec![], body)],
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};
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let table = infer_module(&m);
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let info = table.get(&("f".into(), "x".into())).copied().expect("x recorded");
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assert_eq!(info.uniqueness, Uniqueness::Unique);
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assert_eq!(info.consume_count, 0);
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}
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/// `(let x 0 x)` — `x` consumed once; classified Unique with
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/// consume_count == 1.
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#[test]
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fn let_singly_consumed_is_unique_one() {
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let body = Term::Let {
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name: "x".into(),
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value: Box::new(Term::Lit { lit: Literal::Int { value: 0 } }),
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body: Box::new(Term::Var { name: "x".into() }),
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};
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let m = Module {
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schema: ailang_core::SCHEMA.into(),
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name: "t".into(),
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imports: vec![],
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defs: vec![fn_simple("f", vec![], body)],
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};
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|
let table = infer_module(&m);
|
|
let info = table.get(&("f".into(), "x".into())).copied().expect("x recorded");
|
|
assert_eq!(info.uniqueness, Uniqueness::Unique);
|
|
assert_eq!(info.consume_count, 1);
|
|
}
|
|
|
|
/// `(let x 0 (let y 0 (app + x x)))` — `x` consumed twice in
|
|
/// distinct positions of the same call → Shared, consume_count
|
|
/// == 2.
|
|
#[test]
|
|
fn let_doubly_consumed_is_shared_two() {
|
|
let body = Term::Let {
|
|
name: "x".into(),
|
|
value: Box::new(Term::Lit { lit: Literal::Int { value: 0 } }),
|
|
body: Box::new(Term::App {
|
|
callee: Box::new(Term::Var { name: "+".into() }),
|
|
args: vec![
|
|
Term::Var { name: "x".into() },
|
|
Term::Var { name: "x".into() },
|
|
],
|
|
tail: false,
|
|
}),
|
|
};
|
|
let m = Module {
|
|
schema: ailang_core::SCHEMA.into(),
|
|
name: "f".into(),
|
|
imports: vec![],
|
|
defs: vec![fn_simple("f", vec![], body)],
|
|
};
|
|
let table = infer_module(&m);
|
|
let info = table.get(&("f".into(), "x".into())).copied().expect("x recorded");
|
|
assert_eq!(info.uniqueness, Uniqueness::Shared);
|
|
assert_eq!(info.consume_count, 2);
|
|
}
|
|
|
|
/// `(match scr ((pat-var x) x))` — `x` (a pattern binder) is
|
|
/// consumed once → Unique with consume_count == 1.
|
|
#[test]
|
|
fn pattern_binder_is_recorded() {
|
|
let body = Term::Match {
|
|
scrutinee: Box::new(Term::Lit { lit: Literal::Int { value: 0 } }),
|
|
arms: vec![Arm {
|
|
pat: Pattern::Var { name: "x".into() },
|
|
body: Term::Var { name: "x".into() },
|
|
}],
|
|
};
|
|
let m = Module {
|
|
schema: ailang_core::SCHEMA.into(),
|
|
name: "t".into(),
|
|
imports: vec![],
|
|
defs: vec![fn_simple("f", vec![], body)],
|
|
};
|
|
let table = infer_module(&m);
|
|
let info = table.get(&("f".into(), "x".into())).copied().expect("x recorded");
|
|
assert_eq!(info.uniqueness, Uniqueness::Unique);
|
|
assert_eq!(info.consume_count, 1);
|
|
}
|
|
|
|
/// `(clone x)` is a Borrow — does NOT increment consume_count.
|
|
/// Body: `(let x 0 (clone x))` → x consume_count == 0.
|
|
#[test]
|
|
fn clone_is_borrow_not_consume() {
|
|
let body = Term::Let {
|
|
name: "x".into(),
|
|
value: Box::new(Term::Lit { lit: Literal::Int { value: 0 } }),
|
|
body: Box::new(Term::Clone {
|
|
value: Box::new(Term::Var { name: "x".into() }),
|
|
}),
|
|
};
|
|
let m = Module {
|
|
schema: ailang_core::SCHEMA.into(),
|
|
name: "t".into(),
|
|
imports: vec![],
|
|
defs: vec![fn_simple("f", vec![], body)],
|
|
};
|
|
let table = infer_module(&m);
|
|
let info = table.get(&("f".into(), "x".into())).copied().expect("x recorded");
|
|
assert_eq!(info.uniqueness, Uniqueness::Unique);
|
|
assert_eq!(info.consume_count, 0);
|
|
}
|
|
}
|