bjoernager/rust
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Turn quadratic time on number of impl blocks into linear time

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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 2020-10-24 10:42:40 +02:00
parent 9709ef149c
commit 7208a01cdf
1 changed files with 147 additions and 5 deletions
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152
compiler/rustc_typeck/src/coherence/inherent_impls_overlap.rs
View file

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