diff options
Diffstat (limited to 'compiler/rustc_pattern_analysis/src/pat_column.rs')
| -rw-r--r-- | compiler/rustc_pattern_analysis/src/pat_column.rs | 90 |
1 files changed, 90 insertions, 0 deletions
diff --git a/compiler/rustc_pattern_analysis/src/pat_column.rs b/compiler/rustc_pattern_analysis/src/pat_column.rs new file mode 100644 index 00000000000..3cacfc491b9 --- /dev/null +++ b/compiler/rustc_pattern_analysis/src/pat_column.rs @@ -0,0 +1,90 @@ +use crate::constructor::{Constructor, SplitConstructorSet}; +use crate::pat::{DeconstructedPat, PatOrWild}; +use crate::{Captures, MatchArm, TypeCx}; + +/// A column of patterns in a match, where a column is the intuitive notion of "subpatterns that +/// inspect the same subvalue/place". +/// This is used to traverse patterns column-by-column for lints. Despite similarities with the +/// algorithm in [`crate::usefulness`], this does a different traversal. Notably this is linear in +/// the depth of patterns, whereas `compute_exhaustiveness_and_usefulness` is worst-case exponential +/// (exhaustiveness is NP-complete). The core difference is that we treat sub-columns separately. +/// +/// This is not used in the usefulness algorithm; only in lints. +#[derive(Debug)] +pub struct PatternColumn<'p, Cx: TypeCx> { + /// This must not contain an or-pattern. `expand_and_push` takes care to expand them. + patterns: Vec<&'p DeconstructedPat<'p, Cx>>, +} + +impl<'p, Cx: TypeCx> PatternColumn<'p, Cx> { + pub fn new(arms: &[MatchArm<'p, Cx>]) -> Self { + let patterns = Vec::with_capacity(arms.len()); + let mut column = PatternColumn { patterns }; + for arm in arms { + column.expand_and_push(PatOrWild::Pat(arm.pat)); + } + column + } + /// Pushes a pattern onto the column, expanding any or-patterns into its subpatterns. + /// Internal method, prefer [`PatternColumn::new`]. + fn expand_and_push(&mut self, pat: PatOrWild<'p, Cx>) { + // We flatten or-patterns and skip algorithm-generated wildcards. + if pat.is_or_pat() { + self.patterns.extend( + pat.flatten_or_pat().into_iter().filter_map(|pat_or_wild| pat_or_wild.as_pat()), + ) + } else if let Some(pat) = pat.as_pat() { + self.patterns.push(pat) + } + } + + pub fn head_ty(&self) -> Option<&Cx::Ty> { + self.patterns.first().map(|pat| pat.ty()) + } + pub fn iter<'a>(&'a self) -> impl Iterator<Item = &'p DeconstructedPat<'p, Cx>> + Captures<'a> { + self.patterns.iter().copied() + } + + /// Do constructor splitting on the constructors of the column. + pub fn analyze_ctors( + &self, + cx: &Cx, + ty: &Cx::Ty, + ) -> Result<SplitConstructorSet<Cx>, Cx::Error> { + let column_ctors = self.patterns.iter().map(|p| p.ctor()); + let ctors_for_ty = cx.ctors_for_ty(ty)?; + Ok(ctors_for_ty.split(column_ctors)) + } + + /// Does specialization: given a constructor, this takes the patterns from the column that match + /// the constructor, and outputs their fields. + /// This returns one column per field of the constructor. They usually all have the same length + /// (the number of patterns in `self` that matched `ctor`), except that we expand or-patterns + /// which may change the lengths. + pub fn specialize( + &self, + cx: &Cx, + ty: &Cx::Ty, + ctor: &Constructor<Cx>, + ) -> Vec<PatternColumn<'p, Cx>> { + let arity = ctor.arity(cx, ty); + if arity == 0 { + return Vec::new(); + } + + // We specialize the column by `ctor`. This gives us `arity`-many columns of patterns. These + // columns may have different lengths in the presence of or-patterns (this is why we can't + // reuse `Matrix`). + let mut specialized_columns: Vec<_> = + (0..arity).map(|_| Self { patterns: Vec::new() }).collect(); + let relevant_patterns = + self.patterns.iter().filter(|pat| ctor.is_covered_by(cx, pat.ctor())); + for pat in relevant_patterns { + let specialized = pat.specialize(ctor, arity); + for (subpat, column) in specialized.into_iter().zip(&mut specialized_columns) { + column.expand_and_push(subpat); + } + } + specialized_columns + } +} |
