Turn quadratic time on number of impl blocks into linear time
Previously, if you had a lot of inherent impl blocks on a type like:
struct Foo;
impl Foo { fn foo_1() {} }
...
impl Foo { fn foo_100_000() {} }
The compiler would be very slow at processing it, because
an internal algorithm would run in O(n^2), where n is the number
of impl blocks. Now, we add a new algorithm that allocates but
is faster asymptotically.
If there is an overlap between multiple impl blocks in terms of
identifiers, we still run a O(m^2) algorithm on groups of impl
blocks that have overlaps, but that m refers to the size of the
connected component, which is hopefully smaller than the n
that refers to the sum of all connected components.
This commit is contained in:
est31
parent
9709ef149c
commit
7208a01cdf
1 changed files with 147 additions and 5 deletions
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@ -1,10 +1,13 @@
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use rustc_data_structures::fx::{FxHashMap, FxHashSet};
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use rustc_errors::struct_span_err;
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use rustc_hir as hir;
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use rustc_hir::def_id::{CrateNum, DefId, LOCAL_CRATE};
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use rustc_hir::itemlikevisit::ItemLikeVisitor;
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use rustc_middle::ty::{self, TyCtxt};
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use rustc_span::Symbol;
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use rustc_trait_selection::traits::{self, SkipLeakCheck};
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use smallvec::SmallVec;
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use std::collections::hash_map::Entry;
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pub fn crate_inherent_impls_overlap_check(tcx: TyCtxt<'_>, crate_num: CrateNum) {
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assert_eq!(crate_num, LOCAL_CRATE);
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@ -45,7 +48,7 @@ impl InherentOverlapChecker<'tcx> {
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false
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}
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fn compare_hygienically(&self, item1: &'tcx ty::AssocItem, item2: &'tcx ty::AssocItem) -> bool {
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fn compare_hygienically(&self, item1: &ty::AssocItem, item2: &ty::AssocItem) -> bool {
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// Symbols and namespace match, compare hygienically.
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item1.kind.namespace() == item2.kind.namespace()
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&& item1.ident.normalize_to_macros_2_0() == item2.ident.normalize_to_macros_2_0()
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@ -134,10 +137,149 @@ impl ItemLikeVisitor<'v> for InherentOverlapChecker<'tcx> {
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.map(|impl_def_id| (impl_def_id, self.tcx.associated_items(*impl_def_id)))
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.collect::<SmallVec<[_; 8]>>();
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// Perform a O(n^2) algorithm for small n,
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// otherwise switch to an allocating algorithm with
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// faster asymptotic runtime.
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if impls.len() < 30 {
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for (i, &(&impl1_def_id, impl_items1)) in impls_items.iter().enumerate() {
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for &(&impl2_def_id, impl_items2) in &impls_items[(i + 1)..] {
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if self.impls_have_common_items(impl_items1, impl_items2) {
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self.check_for_overlapping_inherent_impls(impl1_def_id, impl2_def_id);
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self.check_for_overlapping_inherent_impls(
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impl1_def_id,
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impl2_def_id,
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);
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}
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}
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}
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} else {
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// Build a set of connected regions of impl blocks.
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// Two impl blocks are regarded as connected if they share
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// an item with the same unhygienic identifier.
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// After we have assembled the connected regions,
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// run the O(n^2) algorithm on each connected region.
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// This is advantageous to running the algorithm over the
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// entire graph when there are many connected regions.
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struct ConnectedRegion {
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idents: SmallVec<[Symbol; 8]>,
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impl_blocks: FxHashSet<usize>,
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}
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// Highest connected region id
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let mut highest_region_id = 0;
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let mut connected_region_ids = FxHashMap::default();
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let mut connected_regions = FxHashMap::default();
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for (i, &(&_impl_def_id, impl_items)) in impls_items.iter().enumerate() {
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if impl_items.len() == 0 {
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continue;
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}
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// First obtain a list of existing connected region ids
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let mut idents_to_add = SmallVec::<[Symbol; 8]>::new();
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let ids = impl_items
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.in_definition_order()
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.filter_map(|item| {
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let entry = connected_region_ids.entry(item.ident.name);
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if let Entry::Occupied(e) = &entry {
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Some(*e.get())
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} else {
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idents_to_add.push(item.ident.name);
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None
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}
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})
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.collect::<FxHashSet<usize>>();
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match ids.len() {
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0 | 1 => {
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let id_to_set = if ids.len() == 0 {
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// Create a new connected region
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let region = ConnectedRegion {
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idents: idents_to_add,
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impl_blocks: std::iter::once(i).collect(),
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};
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connected_regions.insert(highest_region_id, region);
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(highest_region_id, highest_region_id += 1).0
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} else {
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// Take the only id inside the list
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let id_to_set = *ids.iter().next().unwrap();
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let region = connected_regions.get_mut(&id_to_set).unwrap();
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region.impl_blocks.insert(i);
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region.idents.extend_from_slice(&idents_to_add);
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id_to_set
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};
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let (_id, region) = connected_regions.iter().next().unwrap();
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// Update the connected region ids
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for ident in region.idents.iter() {
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connected_region_ids.insert(*ident, id_to_set);
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}
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}
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_ => {
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// We have multiple connected regions to merge.
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// In the worst case this might add impl blocks
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// one by one and can thus be O(n^2) in the size
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// of the resulting final connected region, but
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// this is no issue as the final step to check
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// for overlaps runs in O(n^2) as well.
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// Take the smallest id from the list
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let id_to_set = *ids.iter().min().unwrap();
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// Sort the id list so that the algorithm is deterministic
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let mut ids = ids.into_iter().collect::<SmallVec<[_; 8]>>();
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ids.sort();
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let mut region = connected_regions.remove(&id_to_set).unwrap();
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region.idents.extend_from_slice(&idents_to_add);
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region.impl_blocks.insert(i);
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for &id in ids.iter() {
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if id == id_to_set {
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continue;
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}
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let r = connected_regions.remove(&id).unwrap();
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// Update the connected region ids
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for ident in r.idents.iter() {
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connected_region_ids.insert(*ident, id_to_set);
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}
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region.idents.extend_from_slice(&r.idents);
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region.impl_blocks.extend(r.impl_blocks);
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}
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connected_regions.insert(id_to_set, region);
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}
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}
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}
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debug!(
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"churning through {} components (sum={}, avg={}, var={}, max={})",
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connected_regions.len(),
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impls.len(),
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impls.len() / connected_regions.len(),
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{
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let avg = impls.len() / connected_regions.len();
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let s = connected_regions
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.iter()
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.map(|r| r.1.impl_blocks.len() as isize - avg as isize)
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.map(|v| v.abs() as usize)
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.sum::<usize>();
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s / connected_regions.len()
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},
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connected_regions.iter().map(|r| r.1.impl_blocks.len()).max().unwrap()
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);
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// List of connected regions is built. Now, run the overlap check
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// for each pair of impl blocks in the same connected region.
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for (_id, region) in connected_regions.into_iter() {
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let mut impl_blocks =
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region.impl_blocks.into_iter().collect::<SmallVec<[_; 8]>>();
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impl_blocks.sort();
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for (i, &impl1_items_idx) in impl_blocks.iter().enumerate() {
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let &(&impl1_def_id, impl_items1) = &impls_items[impl1_items_idx];
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for &impl2_items_idx in impl_blocks[(i + 1)..].iter() {
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let &(&impl2_def_id, impl_items2) = &impls_items[impl2_items_idx];
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if self.impls_have_common_items(impl_items1, impl_items2) {
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self.check_for_overlapping_inherent_impls(
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impl1_def_id,
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impl2_def_id,
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);
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}
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}
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}
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}
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}
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