Improve tagger: Return iterators over WordData, remove groups, para…

…llelize deserialization (#70)

* use vec instead of hashmap internally in tagger

* parallelize tagger deserialization

* return iterators in tagger

* chore: improve benches a bit (#71)

* remove redundant vectors in chunker

* cleanup wordidmap

* update changelog

Co-authored-by: Bernhard Schuster <[email protected]>

CHANGELOG.md

@@ -1,3 +1,21 @@
+# 0.6.4
+
+## Internal improvements
+
+- Decrease time it takes to load the `Tokenizer` by ~ 40% (#70).
+- Tag lookup is backed by a vector instead of a hashmap now.
+
+## Breaking changes
+
+- The tagger now returns iterators over tags instead of allocating a vector.
+- Remove `get_group_members` function.
+
+# 0.6.3
+
+## Fixes
+
+- Fix a bug where calling `Rule::suggest` in parallel across threads would cause a panic (#68, thanks @drahnr!)
+
 # 0.6.2
 
 ## Internal improvements

nlprule/benches/load.rs

@@ -1,15 +1,31 @@
 use criterion::{black_box, criterion_group, criterion_main, Criterion};
 use nlprule::{Rules, Tokenizer};
+use std::time::Duration;
 
-fn criterion_benchmark(c: &mut Criterion) {
+fn parse_tokenizer(c: &mut Criterion) {
     c.bench_function("load tokenizer", |b| {
         b.iter(|| Tokenizer::new(black_box("../storage/en_tokenizer.bin")).unwrap())
     });
+}
 
+fn parse_rules(c: &mut Criterion) {
     c.bench_function("load rules", |b| {
         b.iter(|| Rules::new(black_box("../storage/en_rules.bin")).unwrap())
     });
 }
 
-criterion_group!(benches, criterion_benchmark);
-criterion_main!(benches);
+fn no_warmup_criterion() -> Criterion {
+    Criterion::default()
+        .sample_size(20)
+        .warm_up_time(Duration::from_nanos(1))
+}
+
+criterion_group!(
+name = parse;
+config = no_warmup_criterion();
+targets =
+    parse_rules,
+    parse_tokenizer,
+);
+
+criterion_main!(parse);

nlprule/src/compile/impls.rs

@@ -23,7 +23,7 @@ use crate::{
     tokenizer::{
         chunk,
         multiword::{MultiwordTagger, MultiwordTaggerFields},
-        tag::{Tagger, TaggerLangOptions},
+        tag::{Tagger, TaggerLangOptions, WordIdMap},
         Tokenizer, TokenizerLangOptions,
     },
     types::*,
@@ -94,9 +94,6 @@ impl Tagger {
         common_words: &HashSet<String>,
         lang_options: TaggerLangOptions,
     ) -> std::io::Result<Self> {
-        let mut tags = DefaultHashMap::default();
-        let mut groups = DefaultHashMap::default();
-
         let mut tag_store = HashSet::new();
         let mut word_store = HashSet::new();
 
@@ -148,24 +145,25 @@ impl Tagger {
             .map(|(i, x)| (x.to_string(), PosIdInt::from_value_unchecked(i as u16)))
             .collect();
 
+        let mut tags: Vec<Option<Vec<(WordIdInt, PosIdInt)>>> = vec![None; word_store.len()];
+
         for (word, inflection, tag) in lines.iter() {
             let word_id = word_store.get_by_left(word).unwrap();
             let lemma_id = word_store.get_by_left(inflection).unwrap();
             let pos_id = tag_store.get_by_left(tag).unwrap();
 
-            let group = groups.entry(*lemma_id).or_insert_with(Vec::new);
-            if !group.contains(word_id) {
-                group.push(*word_id);
+            match &mut tags[word_id.value() as usize] {
+                Some(vec) => {
+                    vec.push((*lemma_id, *pos_id));
+                }
+                None => {
+                    tags[word_id.value() as usize] = Some(vec![(*lemma_id, *pos_id)]);
+                }
             }
-
-            tags.entry(*word_id)
-                .or_insert_with(Vec::new)
-                .push((*lemma_id, *pos_id));
         }
 
         Ok(Tagger {
-            tags,
-            groups,
+            tags: WordIdMap(tags),
             word_store,
             tag_store,
             lang_options,
@@ -453,24 +451,32 @@ pub(in crate::compile) struct ContextData {
     outcomes: Vec<usize>,
 }
 
-impl From<ContextData> for chunk::Context {
-    fn from(data: ContextData) -> Self {
-        chunk::Context {
-            parameters: data.parameters,
-            outcomes: data.outcomes,
-        }
-    }
-}
-
 impl From<ModelData> for chunk::Model {
     fn from(data: ModelData) -> Self {
+        let mut outcomes: Vec<usize> = Vec::new();
+        let mut parameters: Vec<f32> = Vec::new();
+
+        let pmap = data
+            .pmap
+            .into_iter()
+            .map(|(key, value)| {
+                assert_eq!(value.outcomes.len(), value.parameters.len());
+
+                let offset = outcomes.len();
+                let length = value.outcomes.len();
+
+                outcomes.extend(value.outcomes);
+                parameters.extend(value.parameters);
+
+                (chunk::hash::hash_str(&key), (offset, length))
+            })
+            .collect::<DefaultHashMap<_, _>>();
+
         chunk::Model {
             outcome_labels: data.outcome_labels,
-            pmap: data
-                .pmap
-                .into_iter()
-                .map(|(key, value)| (chunk::hash::hash_str(&key), value.into()))
-                .collect::<DefaultHashMap<_, _>>(),
+            outcomes,
+            parameters,
+            pmap,
         }
     }
 }

nlprule/src/compile/parse_structure.rs

@@ -380,6 +380,7 @@ fn parse_match(m: structure::Match, engine: &Engine, info: &mut BuildInfo) -> Re
         || m.postag_replace.is_some()
         || m.text.is_some()
     {
+        // this would need a fully functional PosReplacer to work
         return Err(Error::Unimplemented(
             "postag, postag_regex, postag_replace and text in `match` are not implemented.".into(),
         ));

nlprule/src/filter/mod.rs

@@ -27,12 +27,11 @@ impl Filterable for NoDisambiguationEnglishPartialPosTagFilter {
     fn keep(&self, sentence: &MatchSentence, graph: &MatchGraph) -> bool {
         graph.by_id(self.id).tokens(sentence).all(|token| {
             if let Some(captures) = self.regexp.captures(&token.word().as_str()) {
-                let tags = sentence
+                let mut tags = sentence
                     .tagger()
                     .get_tags(&captures.get(1).unwrap().as_str());
 
-                tags.iter()
-                    .any(|x| self.postag_regexp.is_match(x.pos().as_str()))
+                tags.any(|x| self.postag_regexp.is_match(x.pos().as_str()))
             } else {
                 false
             }

nlprule/src/rule/grammar.rs

@@ -51,35 +51,9 @@ pub struct PosReplacer {
 }
 
 impl PosReplacer {
-    fn apply(&self, text: &str, sentence: &MatchSentence) -> Option<String> {
-        let mut candidates: Vec<_> = sentence
-            .tagger()
-            .get_tags(text)
-            .iter()
-            .map(|x| {
-                let group_words = sentence.tagger().get_group_members(&x.lemma().as_str());
-                let mut data = Vec::new();
-                for word in group_words {
-                    if let Some(i) = sentence
-                        .tagger()
-                        .get_tags(word)
-                        .iter()
-                        .position(|x| self.matcher.is_match(x.pos()))
-                    {
-                        data.push((word.to_string(), i));
-                    }
-                }
-                data
-            })
-            .rev()
-            .flatten()
-            .collect();
-        candidates.sort_by(|(_, a), (_, b)| a.cmp(b));
-        if candidates.is_empty() {
-            None
-        } else {
-            Some(candidates.remove(0).0)
-        }
+    fn apply(&self, _text: &str, _sentence: &MatchSentence) -> Option<String> {
+        // TODO: needs to be implemented with correct ordering, currently rules which would need this are disabled
+        unimplemented!()
     }
 }
 

nlprule/src/tokenizer.rs

@@ -324,11 +324,13 @@ impl Tokenizer {
                 IncompleteToken::new(
                     Word::new(
                         self.tagger.id_word(token_text.into()),
-                        self.tagger.get_tags_with_options(
-                            token_text,
-                            if is_sentence_start { Some(true) } else { None },
-                            None,
-                        ),
+                        self.tagger
+                            .get_tags_with_options(
+                                token_text,
+                                if is_sentence_start { Some(true) } else { None },
+                                None,
+                            )
+                            .collect(),
                     ),
                     Span::new(
                         byte_start..byte_start + token_text.len(),

nlprule/src/tokenizer/chunk.rs

@@ -18,12 +18,6 @@ fn softmax(vec: &mut Vec<f32>) {
     }
 }
 
-#[derive(Debug, Serialize, Deserialize, Clone)]
-pub(crate) struct Context {
-    pub(crate) parameters: Vec<f32>,
-    pub(crate) outcomes: Vec<usize>,
-}
-
 #[derive(Debug, Clone)]
 struct Sequence<'a> {
     outcomes: Vec<&'a str>,
@@ -122,77 +116,70 @@ pub(crate) mod hash {
 #[derive(Serialize, Deserialize)]
 struct ModelFields {
     outcome_labels: Vec<String>,
-    // stores each hash and the length of the context of the hash
-    // this is kind of close to gzip compression already and it is difficult
-    // where to draw the line. The chunker model should have a custom
-    // serialization implementation anyway for bf16 compression so this is OK here.
-    cols: Vec<(u64, u8)>,
-    // stores the context outcome labels
-    rows: Vec<u8>,
-    // stores the context parameter values
-    values: Vec<bf16>,
+    pmap: Vec<(u64, u8)>,
+    outcomes: Vec<u8>,
+    parameters: Vec<bf16>,
 }
 
 impl From<Model> for ModelFields {
     fn from(model: Model) -> Self {
-        let mut cols = Vec::new();
-        let mut rows = Vec::new();
-        let mut values = Vec::new();
+        let mut pmap: Vec<_> = model.pmap.into_iter().collect();
+        pmap.sort_by_key(|(_, (offset, _))| *offset);
 
-        for (key, context) in model.pmap.iter() {
-            assert_eq!(context.outcomes.len(), context.parameters.len());
-            assert!(context.outcomes.len() <= std::u8::MAX as usize);
-            cols.push((*key, context.outcomes.len() as u8));
-
-            for (label, value) in context.outcomes.iter().zip(context.parameters.iter()) {
-                assert!(*label <= std::u8::MAX as usize);
+        let pmap = pmap
+            .into_iter()
+            .map(|(key, (_, length))| {
+                assert!(length <= u8::MAX as usize);
+                (key, length as u8)
+            })
+            .collect();
 
-                rows.push(*label as u8);
-                values.push(bf16::from_f32(*value));
-            }
-        }
+        let outcomes = model
+            .outcomes
+            .into_iter()
+            .map(|outcome| {
+                assert!(outcome <= u8::MAX as usize);
+                outcome as u8
+            })
+            .collect();
+        let parameters = model.parameters.into_iter().map(bf16::from_f32).collect();
 
         ModelFields {
             outcome_labels: model.outcome_labels,
-            cols,
-            rows,
-            values,
+            pmap,
+            outcomes,
+            parameters,
         }
     }
 }
 
 impl From<ModelFields> for Model {
     fn from(data: ModelFields) -> Self {
         let mut pmap = DefaultHashMap::new();
+        let mut offset = 0;
+
+        for (key, length) in data.pmap {
+            pmap.insert(key, (offset, length as usize));
 
-        let mut row_iter = data.rows.iter();
-        let mut value_iter = data.values.iter();
-
-        for (key, n) in data.cols.iter() {
-            let outcomes: Vec<_> = (0..*n as usize)
-                .map(|_| *row_iter.next().expect("checked in From<Model> impl") as usize)
-                .collect();
-            let parameters: Vec<_> = (0..*n as usize)
-                .map(|_| {
-                    value_iter
-                        .next()
-                        .expect("checked in From<Model> impl")
-                        .to_f32()
-                })
-                .collect();
-
-            pmap.insert(
-                *key,
-                Context {
-                    outcomes,
-                    parameters,
-                },
-            );
+            offset += length as usize;
         }
 
+        let outcomes = data
+            .outcomes
+            .into_iter()
+            .map(|outcome| outcome as usize)
+            .collect();
+        let parameters = data
+            .parameters
+            .into_iter()
+            .map(|parameter| parameter.to_f32())
+            .collect();
+
         Model {
             outcome_labels: data.outcome_labels,
             pmap,
+            outcomes,
+            parameters,
         }
     }
 }
@@ -201,16 +188,21 @@ impl From<ModelFields> for Model {
 #[serde(from = "ModelFields", into = "ModelFields")]
 pub(crate) struct Model {
     pub(crate) outcome_labels: Vec<String>,
-    pub(crate) pmap: DefaultHashMap<u64, Context>,
+    pub(crate) outcomes: Vec<usize>,
+    pub(crate) parameters: Vec<f32>,
+    pub(crate) pmap: DefaultHashMap<u64, (usize, usize)>,
 }
 
 impl Model {
     fn eval(&self, context: &[u64]) -> Vec<f32> {
         let mut prior =
             vec![(1. / (self.outcome_labels.len() as f32)).ln(); self.outcome_labels.len()];
 
-        for context in context.iter().filter_map(|x| self.pmap.get(&x)) {
-            for (idx, param) in context.outcomes.iter().zip(context.parameters.iter()) {
+        for (offset, length) in context.iter().filter_map(|x| self.pmap.get(&x)) {
+            let outcomes = &self.outcomes[*offset..*offset + length];
+            let parameters = &self.parameters[*offset..*offset + length];
+
+            for (idx, param) in outcomes.iter().zip(parameters.iter()) {
                 prior[*idx] += param;
             }
         }