Auto merge of #118490 - Nadrieril:arena-alloc-matrix, r=nnethercote

Exhaustiveness: allocate memory better

Exhaustiveness is a recursive algorithm that allocates a bunch of slices at every step. Let's see if I can improve performance by improving allocations.

Already just using `Vec::with_capacity` is showing impressive improvements on my local measurements.

r? `@ghost`
This commit is contained in:
bors 2023-12-04 07:06:36 +00:00
commit cf8d81213c
2 changed files with 33 additions and 31 deletions

View File

@ -197,23 +197,24 @@ impl<T> TypedArena<T> {
start_ptr
}
/// Allocates the elements of this iterator into a contiguous slice in the `TypedArena`.
///
/// Note: for reasons of reentrancy and panic safety we collect into a `SmallVec<[_; 8]>` before
/// storing the elements in the arena.
#[inline]
pub fn alloc_from_iter<I: IntoIterator<Item = T>>(&self, iter: I) -> &mut [T] {
// This implementation is entirely separate to
// `DroplessIterator::alloc_from_iter`, even though conceptually they
// are the same.
// Despite the similarlty with `DroplessArena`, we cannot reuse their fast case. The reason
// is subtle: these arenas are reentrant. In other words, `iter` may very well be holding a
// reference to `self` and adding elements to the arena during iteration.
//
// `DroplessIterator` (in the fast case) writes elements from the
// iterator one at a time into the allocated memory. That's easy
// because the elements don't implement `Drop`. But for `TypedArena`
// they do implement `Drop`, which means that if the iterator panics we
// could end up with some allocated-but-uninitialized elements, which
// will then cause UB in `TypedArena::drop`.
// For this reason, if we pre-allocated any space for the elements of this iterator, we'd
// have to track that some uninitialized elements are followed by some initialized elements,
// else we might accidentally drop uninitialized memory if something panics or if the
// iterator doesn't fill all the length we expected.
//
// Instead we use an approach where any iterator panic will occur
// before the memory is allocated. This function is much less hot than
// `DroplessArena::alloc_from_iter`, so it doesn't need to be
// hyper-optimized.
// So we collect all the elements beforehand, which takes care of reentrancy and panic
// safety. This function is much less hot than `DroplessArena::alloc_from_iter`, so it
// doesn't need to be hyper-optimized.
assert!(mem::size_of::<T>() != 0);
let mut vec: SmallVec<[_; 8]> = iter.into_iter().collect();
@ -485,8 +486,9 @@ impl DroplessArena {
/// # Safety
///
/// The caller must ensure that `mem` is valid for writes up to
/// `size_of::<T>() * len`.
/// The caller must ensure that `mem` is valid for writes up to `size_of::<T>() * len`, and that
/// that memory stays allocated and not shared for the lifetime of `self`. This must hold even
/// if `iter.next()` allocates onto `self`.
#[inline]
unsafe fn write_from_iter<T, I: Iterator<Item = T>>(
&self,
@ -516,6 +518,8 @@ impl DroplessArena {
#[inline]
pub fn alloc_from_iter<T, I: IntoIterator<Item = T>>(&self, iter: I) -> &mut [T] {
// Warning: this function is reentrant: `iter` could hold a reference to `&self` and
// allocate additional elements while we're iterating.
let iter = iter.into_iter();
assert!(mem::size_of::<T>() != 0);
assert!(!mem::needs_drop::<T>());
@ -524,7 +528,7 @@ impl DroplessArena {
match size_hint {
(min, Some(max)) if min == max => {
// We know the exact number of elements the iterator will produce here
// We know the exact number of elements the iterator expects to produce here.
let len = min;
if len == 0 {
@ -532,10 +536,15 @@ impl DroplessArena {
}
let mem = self.alloc_raw(Layout::array::<T>(len).unwrap()) as *mut T;
// SAFETY: `write_from_iter` doesn't touch `self`. It only touches the slice we just
// reserved. If the iterator panics or doesn't output `len` elements, this will
// leave some unallocated slots in the arena, which is fine because we do not call
// `drop`.
unsafe { self.write_from_iter(iter, len, mem) }
}
(_, _) => {
outline(move || -> &mut [T] {
// Takes care of reentrancy.
let mut vec: SmallVec<[_; 8]> = iter.collect();
if vec.is_empty() {
return &mut [];

View File

@ -599,9 +599,9 @@ impl<'p, 'tcx> PatStack<'p, 'tcx> {
// an or-pattern. Panics if `self` is empty.
fn expand_or_pat<'a>(&'a self) -> impl Iterator<Item = PatStack<'p, 'tcx>> + Captures<'a> {
self.head().flatten_or_pat().into_iter().map(move |pat| {
let mut new_pats = smallvec![pat];
new_pats.extend_from_slice(&self.pats[1..]);
PatStack { pats: new_pats }
let mut new = self.clone();
new.pats[0] = pat;
new
})
}
@ -732,18 +732,11 @@ impl<'p, 'tcx> Matrix<'p, 'tcx> {
}
/// Build a new matrix from an iterator of `MatchArm`s.
fn new<'a>(
cx: &MatchCheckCtxt<'p, 'tcx>,
iter: impl Iterator<Item = &'a MatchArm<'p, 'tcx>>,
scrut_ty: Ty<'tcx>,
) -> Self
where
'p: 'a,
{
fn new(cx: &MatchCheckCtxt<'p, 'tcx>, arms: &[MatchArm<'p, 'tcx>], scrut_ty: Ty<'tcx>) -> Self {
let wild_pattern = cx.pattern_arena.alloc(DeconstructedPat::wildcard(scrut_ty, DUMMY_SP));
let wildcard_row = PatStack::from_pattern(wild_pattern);
let mut matrix = Matrix { rows: vec![], wildcard_row };
for (row_id, arm) in iter.enumerate() {
let mut matrix = Matrix { rows: Vec::with_capacity(arms.len()), wildcard_row };
for (row_id, arm) in arms.iter().enumerate() {
let v = MatrixRow {
pats: PatStack::from_pattern(arm.pat),
parent_row: row_id, // dummy, we won't read it
@ -806,7 +799,7 @@ impl<'p, 'tcx> Matrix<'p, 'tcx> {
ctor: &Constructor<'tcx>,
) -> Matrix<'p, 'tcx> {
let wildcard_row = self.wildcard_row.pop_head_constructor(pcx, ctor);
let mut matrix = Matrix { rows: vec![], wildcard_row };
let mut matrix = Matrix { rows: Vec::new(), wildcard_row };
for (i, row) in self.rows().enumerate() {
if ctor.is_covered_by(pcx, row.head().ctor()) {
let new_row = row.pop_head_constructor(pcx, ctor, i);
@ -1386,7 +1379,7 @@ pub(crate) fn compute_match_usefulness<'p, 'tcx>(
arms: &[MatchArm<'p, 'tcx>],
scrut_ty: Ty<'tcx>,
) -> UsefulnessReport<'p, 'tcx> {
let mut matrix = Matrix::new(cx, arms.iter(), scrut_ty);
let mut matrix = Matrix::new(cx, arms, scrut_ty);
let non_exhaustiveness_witnesses =
compute_exhaustiveness_and_reachability(cx, &mut matrix, true);