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use std::cmp::Ordering;
use std::collections::HashMap as StdHashMap;
use dary_heap::OctonaryHeap;
use fancy_regex::Regex;
use pyo3::prelude::*;
use ahash::{AHashMap, AHashSet};
use compact_str::CompactString;
use rayon::prelude::*;
// Default GPT-4 style regex pattern for splitting text
const GPT4_PATTERN: &str = r"'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+";
type Pair = (u32, u32);
/// A Byte Pair Encoding tokenizer that matches the GPT-4 style implementation
#[pyclass]
pub struct Tokenizer {
/// Maps pairs of token IDs to their merged token ID
pub merges: StdHashMap<Pair, u32>,
/// The regex pattern used for text splitting
pub pattern: String,
/// Compiled regex for efficiency
compiled_pattern: Regex,
}
// ------------------------ internal helpers ------------------------
#[derive(Clone, Debug)]
struct Word {
ids: Vec<u32>,
}
impl Word {
#[inline]
fn new(ids: Vec<u32>) -> Self {
Self { ids }
}
#[inline]
fn pairs<'a>(&'a self) -> impl Iterator<Item = Pair> + 'a {
self.ids.windows(2).map(|w| (w[0], w[1]))
}
/// Merge all non-overlapping occurrences of pair -> new_id.
/// Returns a small Vec of local pair-count deltas for THIS word only:
/// -1 for removed pairs, +1 for newly created pairs.
///
/// NOTE: this version deliberately avoids a HashMap in the hot loop.
fn merge_pair(&mut self, pair: Pair, new_id: u32) -> Vec<(Pair, i32)> {
let (a, b) = pair;
let n = self.ids.len();
if n < 2 {
return Vec::new();
}
let mut out: Vec<u32> = Vec::with_capacity(n);
let mut deltas: Vec<(Pair, i32)> = Vec::with_capacity(6);
let mut i = 0;
while i < n {
if i + 1 < n && self.ids[i] == a && self.ids[i + 1] == b {
let left = out.last().copied();
let right = if i + 2 < n { Some(self.ids[i + 2]) } else { None };
// remove old pairs
if let Some(x) = left {
deltas.push(((x, a), -1));
deltas.push(((x, new_id), 1));
}
deltas.push(((a, b), -1));
if let Some(y) = right {
deltas.push(((b, y), -1));
deltas.push(((new_id, y), 1));
}
// write merged token
out.push(new_id);
i += 2; // skip 'a' and 'b'
} else {
out.push(self.ids[i]);
i += 1;
}
}
self.ids = out;
deltas
}
}
#[derive(Debug, Eq)]
struct MergeJob {
pair: Pair,
count: u64,
/// set of word indices where this pair may occur and needs processing
pos: AHashSet<usize>,
}
impl PartialEq for MergeJob {
fn eq(&self, other: &Self) -> bool {
self.count == other.count && self.pair == other.pair
}
}
impl PartialOrd for MergeJob {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
Some(self.cmp(other))
}
}
impl Ord for MergeJob {
fn cmp(&self, other: &Self) -> Ordering {
// Max-heap by count; tie-break to ascending pair order (deterministic)
if self.count != other.count {
self.count.cmp(&other.count)
} else {
// ascending order on the pair when counts tie
other.pair.cmp(&self.pair)
}
}
}
#[inline]
fn count_pairs_parallel(
words: &[Word],
counts: &[i32],
) -> (AHashMap<Pair, i32>, AHashMap<Pair, AHashSet<usize>>) {
words
.par_iter()
.enumerate()
.map(|(i, w)| {
let mut local_pc: AHashMap<Pair, i32> = AHashMap::new();
let mut local_wtu: AHashMap<Pair, AHashSet<usize>> = AHashMap::new();
if w.ids.len() >= 2 && counts[i] != 0 {
for (a, b) in w.pairs() {
*local_pc.entry((a, b)).or_default() += counts[i];
local_wtu.entry((a, b)).or_default().insert(i);
}
}
(local_pc, local_wtu)
})
.reduce(
|| (AHashMap::new(), AHashMap::new()),
|(mut acc_pc, mut acc_wtu), (pc, wtu)| {
for (k, v) in pc {
*acc_pc.entry(k).or_default() += v;
}
for (k, s) in wtu {
acc_wtu.entry(k).or_default().extend(s);
}
(acc_pc, acc_wtu)
},
)
}
// ------------------------ END helpers ------------------------
impl Tokenizer {
/// Core incremental BPE training given unique words and their counts.
/// `words`: one entry per unique chunk (Vec<u32> of token-ids/bytes).
/// `counts`: same length as `words`, count per chunk.
fn train_core_incremental(&mut self, mut words: Vec<Word>, counts: Vec<i32>, vocab_size: u32) {
assert!(vocab_size >= 256, "vocab_size must be at least 256");
let num_merges = vocab_size - 256;
log::info!("Starting BPE training: {} merges to compute", num_merges);
self.merges.clear();
// ---- Initial pair_counts and where_to_update (parallel) ----
log::info!("Computing initial pair counts from {} unique sequences", words.len());
let (mut pair_counts, mut where_to_update) = count_pairs_parallel(&words, &counts);
// ---- Build heap ----
log::info!("Building heap with {} unique pairs", pair_counts.len());
let mut heap = OctonaryHeap::with_capacity(pair_counts.len());
for (pair, pos) in where_to_update.drain() {
let c = *pair_counts.get(&pair).unwrap_or(&0);
if c > 0 {
heap.push(MergeJob {
pair,
count: c as u64,
pos,
});
}
}
// ---- Merge loop ----
log::info!("Starting merge loop");
let mut merges_done = 0u32;
let mut last_log_percent = 0u32;
while merges_done < num_merges {
let Some(mut top) = heap.pop() else { break; };
// Lazy refresh
let current = *pair_counts.get(&top.pair).unwrap_or(&0);
if top.count != current as u64 {
top.count = current as u64;
if top.count > 0 {
heap.push(top);
}
continue;
}
if top.count == 0 {
break;
}
// Record merge
let new_id = 256 + merges_done;
self.merges.insert(top.pair, new_id);
// Merge this pair in all words where it occurs
let mut local_pos_updates: AHashMap<Pair, AHashSet<usize>> = AHashMap::new();
for &word_idx in &top.pos {
// Apply merge to this word and collect pair-count deltas
let changes = words[word_idx].merge_pair(top.pair, new_id);
// Update global pair counts based on this word's count
for (pair, delta) in changes {
let delta_total = delta * counts[word_idx];
if delta_total != 0 {
*pair_counts.entry(pair).or_default() += delta_total;
if delta > 0 {
local_pos_updates.entry(pair).or_default().insert(word_idx);
}
}
}
}
// Add the updated pair counts back to the heap
for (pair, pos) in local_pos_updates {
let cnt = *pair_counts.get(&pair).unwrap_or(&0);
if cnt > 0 {
heap.push(MergeJob {
pair,
count: cnt as u64,
pos,
});
}
}
merges_done += 1;
// Log progress every 1%
let current_percent = (merges_done * 100) / num_merges;
if current_percent > last_log_percent {
log::info!(
"Progress: {}% ({}/{} merges) - Last merge: {:?} -> {} (frequency: {})",
current_percent, merges_done, num_merges, top.pair, new_id, top.count
);
last_log_percent = current_percent;
}
}
log::info!("Finished training: {} merges completed", merges_done);
}
}
/// Public methods for the Tokenizer class that will be exposed to Python.
#[pymethods]
impl Tokenizer {
/// Create a new Tokenizer
#[new]
pub fn new() -> Self {
Self {
merges: StdHashMap::new(),
pattern: String::new(),
compiled_pattern: Regex::new("").expect("Empty regex should be valid"),
}
}
/// Train from a streaming iterator (parallel ingestion).
/// We refill a Rust Vec<String> buffer under the GIL, then release the GIL
/// to do the heavy splitting and counting **in parallel** with rayon.
#[pyo3(signature = (iterator, vocab_size, buffer_size=8192, pattern=None))]
#[pyo3(text_signature = "(self, iterator, vocab_size, buffer_size=8192, pattern=None)")]
pub fn train_from_iterator(
&mut self,
py: pyo3::Python<'_>,
iterator: &pyo3::Bound<'_, pyo3::PyAny>,
vocab_size: u32,
buffer_size: usize,
pattern: Option<String>,
) -> PyResult<()> {
// Use provided pattern or default to GPT-4 pattern
let pattern_str = pattern.unwrap_or_else(|| GPT4_PATTERN.to_string());
// Update the stored pattern and compile it
self.pattern = pattern_str.clone();
self.compiled_pattern = Regex::new(&pattern_str)
.map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("Invalid regex pattern: {}", e)))?;
// Prepare a true Python iterator object
let py_iter: pyo3::Py<pyo3::PyAny> = unsafe {
pyo3::Bound::from_borrowed_ptr_or_err(py, pyo3::ffi::PyObject_GetIter(iterator.as_ptr()))?
.into()
};
// Global chunk counts
let mut counts: AHashMap<CompactString, i32> = AHashMap::new();
// Temporary buffer we refill under the GIL
let mut buf: Vec<String> = Vec::with_capacity(buffer_size);
log::info!("Processing sequences from iterator (buffer_size: {})", buffer_size);
let mut total_sequences = 0u64;
// Helper: refill `buf` with up to `buffer_size` strings from the Python iterator.
// Returns Ok(true) if the iterator is exhausted, Ok(false) otherwise.
let refill = |buf: &mut Vec<String>| -> PyResult<bool> {
pyo3::Python::with_gil(|py| {
buf.clear();
let it = py_iter.bind(py);
loop {
if buf.len() >= buffer_size {
return Ok(false);
}
// next(it)
let next_obj = unsafe {
pyo3::Bound::from_owned_ptr_or_opt(py, pyo3::ffi::PyIter_Next(it.as_ptr()))
};
match next_obj {
Some(obj) => {
let s: String = obj.extract()?;
buf.push(s);
}
None => {
if pyo3::PyErr::occurred(py) {
return Err(pyo3::PyErr::fetch(py));
} else {
return Ok(true); // exhausted
}
}
}
}
})
};
// Stream ingestion loop: refill under GIL, process without GIL (parallel)
loop {
let exhausted = refill(&mut buf)?;
if buf.is_empty() && exhausted {
break;
}
total_sequences += buf.len() as u64;
let pattern = self.compiled_pattern.clone();
let local: AHashMap<CompactString, i32> = py.allow_threads(|| {
buf.par_iter()
.map(|s| {
let mut m: AHashMap<CompactString, i32> = AHashMap::new();
for mat in pattern.find_iter(s) {
let piece = mat.expect("regex match failed").as_str();
*m.entry(CompactString::from(piece)).or_default() += 1;
}
m
})
.reduce(
|| AHashMap::new(),
|mut a, b| {
for (k, v) in b {
*a.entry(k).or_default() += v;
}
a
},
)
});
// Merge local into global (single-threaded)
for (k, v) in local {
*counts.entry(k).or_default() += v;
}
if exhausted {
break;
}
}
log::info!("Processed {} sequences total, {} unique", total_sequences, counts.len());
// Materialize words & counts
let mut words = Vec::with_capacity(counts.len());
let mut cvec = Vec::with_capacity(counts.len());
for (chunk, c) in counts.into_iter() {
words.push(Word::new(chunk.as_bytes().iter().map(|&b| b as u32).collect()));
cvec.push(c);
}
self.train_core_incremental(words, cvec, vocab_size);
Ok(())
}
/// Return the regex pattern
pub fn get_pattern(&self) -> String {
self.pattern.clone()
}
/// Return the mergeable ranks (token bytes -> token id / rank)
pub fn get_mergeable_ranks(&self) -> Vec<(Vec<u8>, u32)> {
let mut mergeable_ranks = Vec::new();
// Build vocabulary incrementally from low to high token IDs
let mut token_bytes: Vec<Vec<u8>> = (0..256_u32).map(|i| vec![i as u8]).collect();
for (i, bytes) in token_bytes.iter().enumerate() {
mergeable_ranks.push((bytes.clone(), i as u32));
}
// Sort merges by token id (so we can reconstruct bytes progressively)
let mut sorted_merges: Vec<_> = self.merges.iter().collect();
sorted_merges.sort_by_key(|&(_, &token_id)| token_id);
for (&pair, &merged_id) in sorted_merges {
let (left, right) = pair;
let mut merged_bytes = token_bytes[left as usize].clone();
merged_bytes.extend(&token_bytes[right as usize]);
if token_bytes.len() <= merged_id as usize {
token_bytes.resize(merged_id as usize + 1, Vec::new());
}
token_bytes[merged_id as usize] = merged_bytes.clone();
mergeable_ranks.push((merged_bytes, merged_id));
}
mergeable_ranks
}
/// Encode a string into token IDs
pub fn encode(&self, text: &str) -> Vec<u32> {
let mut all_ids = Vec::new();
// Split text using the regex pattern
for m in self.compiled_pattern.find_iter(text) {
let chunk = m.expect("regex match failed").as_str();
// Convert chunk to bytes then to u32 IDs
let mut ids: Vec<u32> = chunk.bytes().map(|b| b as u32).collect();
// Apply merges iteratively
while ids.len() >= 2 {
// Find the best pair to merge
let mut best_pair: Option<(usize, Pair, u32)> = None;
for i in 0..ids.len() - 1 {
let pair: Pair = (ids[i], ids[i + 1]);
if let Some(&new_id) = self.merges.get(&pair) {
if best_pair.is_none() || new_id < best_pair.unwrap().2 {
best_pair = Some((i, pair, new_id));
}
}
}
// If we found a pair to merge, apply it
if let Some((idx, _pair, new_id)) = best_pair {
ids[idx] = new_id;
ids.remove(idx + 1);
} else {
// No more merges possible
break;
}
}
all_ids.extend(ids);
}
all_ids
}
}
#[pymodule]
fn rustbpe(m: &Bound<'_, PyModule>) -> PyResult<()> {
pyo3_log::init(); // forwards Rust `log` to Python's `logging`
m.add_class::<Tokenizer>()?;
Ok(())
}
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