Auto merge of #120524 - Nadrieril:rollup-67952ib, r=Nadrieril

Rollup of 9 pull requests

Successful merges:

 - #120207 (check `RUST_BOOTSTRAP_CONFIG` in `profile_user_dist` test)
 - #120321 (pattern_analysis: cleanup the contexts)
 - #120355 (document `FromIterator for Vec` allocation behaviors)
 - #120430 (std: thread_local::register_dtor fix proposal for FreeBSD.)
 - #120469 (Provide more context on derived obligation error primary label)
 - #120472 (Make duplicate lang items fatal)
 - #120490 (Don't hash lints differently to non-lints.)
 - #120495 (Remove the `abi_amdgpu_kernel` feature)
 - #120501 (rustdoc: Correctly handle attribute merge if this is a glob reexport)

r? `@ghost`
`@rustbot` modify labels: rollup

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
+    }
+}