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Shrink bitset words through functional mapping

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Previously, all words in the (deduplicated) bitset would be stored raw -- a full
64 bits (8 bytes). Now, those words that are equivalent to others through a
specific mapping are stored separately and "mapped" to the original when
loading; this shrinks the table sizes significantly, as each mapped word is
stored in 2 bytes (a 4x decrease from the previous).

The new encoding is also potentially non-optimal: the "mapped" byte is
frequently repeated, as in practice many mapped words use the same base word.

Currently we only support two forms of mapping: rotation and inversion. Note
that these are both guaranteed to map transitively if at all, and supporting
mappings for which this is not true may require a more interesting algorithm for
choosing the optimal pairing.

Updated sizes:

Alphabetic     : 2622 bytes     (-  414 bytes)
Case_Ignorable : 1803 bytes     (-  330 bytes)
Cased          : 808 bytes      (-  126 bytes)
Cc             : 32 bytes
Grapheme_Extend: 1508 bytes     (-  252 bytes)
Lowercase      : 901 bytes      (-   84 bytes)
N              : 1064 bytes     (-  156 bytes)
Uppercase      : 838 bytes      (-   96 bytes)
White_Space    : 91 bytes       (-    6 bytes)
Total table sizes: 9667 bytes   (-1,464 bytes)
This commit is contained in:
Mark Rousskov
2020-03-21 10:16:01 -04:00
parent 6c7691a37b
commit b0e121d9d5
4 changed files with 1211 additions and 432 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
src/tools/unicode-table-generator/src/main.rs
View File

@@ -254,12 +254,19 @@ fn generate_tests(data_path: &str, ranges: &[(&str, Vec<Range<u32>>)]) -> String
s.push_str(
"
#[inline(always)]
fn range_search<const N: usize, const CHUNK_SIZE: usize, const N1: usize, const N2: usize>(
fn range_search<
const N: usize,
const CHUNK_SIZE: usize,
const N1: usize,
const CANONICAL: usize,
const CANONICALIZED: usize,
>(
needle: u32,
chunk_idx_map: &[u8; N],
(last_chunk_idx, last_chunk_mapping): (u16, u8),
bitset_chunk_idx: &[[u8; CHUNK_SIZE]; N1],
bitset: &[u64; N2],
bitset_canonical: &[u64; CANONICAL],
bitset_canonicalized: &[(u8, u8); CANONICALIZED],
) -> bool {
let bucket_idx = (needle / 64) as usize;
let chunk_map_idx = bucket_idx / CHUNK_SIZE;
@@ -273,8 +280,21 @@ fn range_search<const N: usize, const CHUNK_SIZE: usize, const N1: usize, const
} else {
chunk_idx_map[chunk_map_idx]
};
let idx = bitset_chunk_idx[(chunk_idx as usize)][chunk_piece];
let word = bitset[(idx as usize)];
let idx = bitset_chunk_idx[(chunk_idx as usize)][chunk_piece] as usize;
let word = if idx < CANONICAL {
bitset_canonical[idx]
} else {
let (real_idx, mapping) = bitset_canonicalized[idx - CANONICAL];
let mut word = bitset_canonical[real_idx as usize];
let should_invert = mapping & (1 << 7) != 0;
if should_invert {
word = !word;
}
// Unset the inversion bit
let rotate_by = mapping & !(1 << 7);
word = word.rotate_left(rotate_by as u32);
word
};
(word & (1 << (needle % 64) as u64)) != 0
}
",

240
src/tools/unicode-table-generator/src/raw_emitter.rs
View File

@@ -22,8 +22,9 @@
//! mapping into two separate sets; currently this is not dealt with).
//!
//! With that scheme, we now have a single byte for every 64 codepoints. We
//! further group these by 16 (arbitrarily chosen), and again deduplicate and
//! store in an array (u8 -> [u8; 16]).
//! further group these by some constant N (between 1 and 64 per group), and
//! again deduplicate and store in an array (u8 -> [u8; N]). The constant is
//! chosen to be optimal in bytes-in-memory for the given dataset.
//!
//! The indices into this array represent ranges of 64*16 = 1024 codepoints.
//!
@@ -37,9 +38,9 @@
//! down considerably.
use crate::fmt_list;
use std::collections::{BTreeSet, HashMap};
use std::collections::{BTreeMap, BTreeSet, HashMap};
use std::convert::TryFrom;
use std::fmt::Write;
use std::fmt::{self, Write};
use std::ops::Range;
#[derive(Clone)]
@@ -61,6 +62,10 @@ impl RawEmitter {
}
fn emit_bitset(&mut self, words: &[u64]) {
let mut words = words.to_vec();
// Ensure that there's a zero word in the dataset, used for padding and
// such.
words.push(0);
let unique_words =
words.iter().cloned().collect::<BTreeSet<_>>().into_iter().collect::<Vec<_>>();
if unique_words.len() > u8::max_value() as usize {
@@ -68,13 +73,9 @@ impl RawEmitter {
}
// needed for the chunk mapping to work
assert_eq!(unique_words[0], 0, "has a zero word");
let canonicalized = Canonicalized::canonicalize(&unique_words);
let word_indices = unique_words
.iter()
.cloned()
.enumerate()
.map(|(idx, word)| (word, u8::try_from(idx).unwrap()))
.collect::<HashMap<_, _>>();
let word_indices = canonicalized.unique_mapping.clone();
let compressed_words = words.iter().map(|w| word_indices[w]).collect::<Vec<u8>>();
let mut best = None;
@@ -91,14 +92,32 @@ impl RawEmitter {
}
self.emit_chunk_map(word_indices[&0], &compressed_words, best.unwrap().0);
struct Bits(u64);
impl fmt::Debug for Bits {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
write!(f, "0b{:064b}", self.0)
}
}
writeln!(
&mut self.file,
"static BITSET: [u64; {}] = [{}];",
unique_words.len(),
fmt_list(&unique_words),
"static BITSET_CANONICAL: [u64; {}] = [{}];",
canonicalized.canonical_words.len(),
fmt_list(canonicalized.canonical_words.iter().map(|v| Bits(*v))),
)
.unwrap();
self.bytes_used += 8 * unique_words.len();
self.bytes_used += 8 * canonicalized.canonical_words.len();
writeln!(
&mut self.file,
"static BITSET_MAPPING: [(u8, u8); {}] = [{}];",
canonicalized.canonicalized_words.len(),
fmt_list(&canonicalized.canonicalized_words),
)
.unwrap();
// 8 bit index into shifted words, 7 bits for shift + optional flip
// We only need it for the words that we removed by applying a shift and
// flip to them.
self.bytes_used += 2 * canonicalized.canonicalized_words.len();
}
fn emit_chunk_map(&mut self, zero_at: u8, compressed_words: &[u8], chunk_length: usize) {
@@ -170,7 +189,8 @@ impl RawEmitter {
writeln!(&mut self.file, " &BITSET_CHUNKS_MAP,").unwrap();
writeln!(&mut self.file, " BITSET_LAST_CHUNK_MAP,").unwrap();
writeln!(&mut self.file, " &BITSET_INDEX_CHUNKS,").unwrap();
writeln!(&mut self.file, " &BITSET,").unwrap();
writeln!(&mut self.file, " &BITSET_CANONICAL,").unwrap();
writeln!(&mut self.file, " &BITSET_MAPPING,").unwrap();
writeln!(&mut self.file, " )").unwrap();
writeln!(&mut self.file, "}}").unwrap();
}
@@ -196,3 +216,193 @@ pub fn emit_codepoints(emitter: &mut RawEmitter, ranges: &[Range<u32>]) {
emitter.blank_line();
emitter.emit_lookup();
}
struct Canonicalized {
canonical_words: Vec<u64>,
canonicalized_words: Vec<(u8, u8)>,
/// Maps an input unique word to the associated index (u8) which is into
/// canonical_words or canonicalized_words (in order).
unique_mapping: HashMap<u64, u8>,
}
impl Canonicalized {
fn canonicalize(unique_words: &[u64]) -> Self {
#[derive(Copy, Clone, Debug)]
enum Mapping {
Rotate(u32),
Invert,
RotateAndInvert(u32),
}
// key is the word being mapped to
let mut mappings: BTreeMap<u64, Vec<(u64, Mapping)>> = BTreeMap::new();
for &a in unique_words {
'b: for &b in unique_words {
// skip self
if a == b {
continue;
}
// All possible distinct rotations
for rotation in 1..64 {
if a.rotate_right(rotation) == b {
mappings.entry(b).or_default().push((a, Mapping::Rotate(rotation)));
// We're not interested in further mappings between a and b
continue 'b;
}
}
if (!a) == b {
mappings.entry(b).or_default().push((a, Mapping::Invert));
// We're not interested in further mappings between a and b
continue 'b;
}
// All possible distinct rotations, inverted
for rotation in 1..64 {
if (!a.rotate_right(rotation)) == b {
mappings
.entry(b)
.or_default()
.push((a, Mapping::RotateAndInvert(rotation)));
// We're not interested in further mappings between a and b
continue 'b;
}
}
}
}
// These are the bitset words which will be represented "raw" (as a u64)
let mut canonical_words = Vec::new();
// These are mapped words, which will be represented by an index into
// the canonical_words and a Mapping; u16 when encoded.
let mut canonicalized_words = Vec::new();
let mut unique_mapping = HashMap::new();
#[derive(Debug, PartialEq, Eq)]
enum UniqueMapping {
Canonical(usize),
Canonicalized(usize),
}
while let Some((&to, _)) = mappings.iter().max_by_key(|m| m.1.len()) {
// Get the mapping with the most entries. Currently, no mapping can
// only exist transitively (i.e., there is no A, B, C such that A
// does not map to C and but A maps to B maps to C), so this is
// guaranteed to be acceptable.
//
// In the future, we may need a more sophisticated algorithm to
// identify which keys to prefer as canonical.
let mapped_from = mappings.remove(&to).unwrap();
for (from, how) in &mapped_from {
// Remove the entries which mapped to this one.
// Noting that it should be associated with the Nth canonical word.
//
// We do not assert that this is present, because there may be
// no mappings to the `from` word; that's fine.
mappings.remove(from);
assert_eq!(
unique_mapping
.insert(*from, UniqueMapping::Canonicalized(canonicalized_words.len())),
None
);
canonicalized_words.push((canonical_words.len(), *how));
// Remove the now-canonicalized word from other mappings,
// to ensure that we deprioritize them in the next iteration of
// the while loop.
for (_, mapped) in &mut mappings {
let mut i = 0;
while i != mapped.len() {
if mapped[i].0 == *from {
mapped.remove(i);
} else {
i += 1;
}
}
}
}
assert!(
unique_mapping
.insert(to, UniqueMapping::Canonical(canonical_words.len()))
.is_none()
);
canonical_words.push(to);
// Remove the now-canonical word from other mappings, to ensure that
// we deprioritize them in the next iteration of the while loop.
for (_, mapped) in &mut mappings {
let mut i = 0;
while i != mapped.len() {
if mapped[i].0 == to {
mapped.remove(i);
} else {
i += 1;
}
}
}
}
// Any words which we couldn't shrink, just stick into the canonical
// words.
//
// FIXME: work harder -- there are more possibilities for mapping
// functions (e.g., multiplication, shifting instead of rotation, etc.)
// We'll probably always have some slack though so this loop will still
// be needed.
for &w in unique_words {
if !unique_mapping.contains_key(&w) {
assert!(
unique_mapping
.insert(w, UniqueMapping::Canonical(canonical_words.len()))
.is_none()
);
canonical_words.push(w);
}
}
assert_eq!(canonicalized_words.len() + canonical_words.len(), unique_words.len());
assert_eq!(unique_mapping.len(), unique_words.len());
let unique_mapping = unique_mapping
.into_iter()
.map(|(key, value)| {
(
key,
match value {
UniqueMapping::Canonicalized(idx) => {
u8::try_from(canonical_words.len() + idx).unwrap()
}
UniqueMapping::Canonical(idx) => u8::try_from(idx).unwrap(),
},
)
})
.collect::<HashMap<_, _>>();
let mut distinct_indices = BTreeSet::new();
for &w in unique_words {
let idx = unique_mapping.get(&w).unwrap();
assert!(distinct_indices.insert(idx));
}
let canonicalized_words = canonicalized_words
.into_iter()
.map(|v| {
(
u8::try_from(v.0).unwrap(),
match v.1 {
Mapping::RotateAndInvert(amount) => {
assert!(amount < (1 << 7));
1 << 7 | (amount as u8)
}
Mapping::Rotate(amount) => {
assert!(amount < (1 << 7));
amount as u8
}
Mapping::Invert => 1 << 7,
},
)
})
.collect::<Vec<(u8, u8)>>();
Canonicalized { unique_mapping, canonical_words, canonicalized_words }
}
}