wordchipper - HPC Rust BPE Tokenizer

Overview

This is a high-performance rust BPE tokenizer trainer/encoder/decoder.

The current status is productionization towards an alpha release.

Crate Features

feature: default

• client
• download

The default feature does not enable training.

feature: client

• ahash
• rayon
• std

The default client is focused on loading vocabularies and running high performance encoders / decoders.

feature: download

• wordchipper-disk-cache
• std

The download feature enables downloading vocabularies from the internet.

feature: training

• compact_str
• dary_heap
• std

The training feature enables the training code.

feature: std / no_std

The std feature enables the use of the std library; and the no_std feature enables deps needed when std is not enabled. (Negative feature deps are not stable yet.)

Note: I am unsure if this is complete. It is tested CI, but I'm unsure if I've fully covered it; and I haven't worked out a no_std deploy test yet.

feature: ahash

This swaps all HashMap/HashSet implementations for ``ahash`; which is a performance win on many/(most?) modern CPUs.

This is done by the types::hash_types::CommonHash{*} type alias machinery. See also the hashbrown dep used by no_std.

feature: rayon

This enables some parallelism wrappers using the rayon crate.

TODO: I intend on providing a tokio based async parallelism mechanism as well, to structure more direct regex`>`encode pipeline parallelism.

feature: tracing

This enables a number of tracing instrumentation points. This is only useful for timing tracing of the library itself.

Client Usage

UnifiedTokenVocab

The UnifiedTokenVocab is a unified representation of the vocabularies used by the TokenEncoder and TokenDecoder clients. It contains:

• SegmentationConfig - describing the span/word regex and the special token map.
• ByteMapVocab - describing the { u8 -> T } mapping.
• SpanMapVocab - describing the { Vec<u8> -> T } mapping.
• PairMapVocab - describing known { (T, T) -> T } merge pairs.

Loading Pretrained Vocabularies

This is only partially implemented; it still requires a fair amount of manual work.

A collection of metadata about known pretrained vocabularies is available:

• wordchipper::vocab::public

What is incomplete is a local URL cache plus workflow for assembling a vocab from the known metadata.

A loading example exists in the examples/token-cli crate.

use wordchipper::decoders::{DictionaryDecoder, TokenDecoder};
use wordchipper::encoders::{DefaultTokenEncoder, TokenEncoder};
use wordchipper::rayon::{ParallelRayonDecoder, ParallelRayonEncoder};
use wordchipper::regex::{regex_pool_supplier, RegexWrapperPattern};
use wordchipper::segmentation::{SegmentationConfig, TextSegmentor};
use wordchipper::vocab::public::openai::load_o200k_harmony_vocab;
use wordchipper::vocab::UnifiedTokenVocab;
use wordchipper::disk_cache::WordchipperDiskCache;

type T = u32;

let mut disk_cache = WordchipperDiskCache::default();
let vocab: Arc<UnifiedTokenVocab<T>> = load_o200k_harmony_vocab(&mut disk_cache)?.into();

let encoder: DefaultTokenEncoder<T> =
    DefaultTokenEncoder::init(vocab.clone(), None);
let encoder = ParallelRayonEncoder::new(encoder);

let decoder = DictionaryDecoder::from_unified_vocab(vocab.clone());
let decoder = ParallelRayonDecoder::new(decoder);

TokenEncoder Clients

Encoder clients should use:

• DefaultTokenEncoder - the current default (only?) TokenEncoder.
• ParallelRayonEncoder - a batch parallelism wrapper around any TokenEncoder.

use wordchipper::vocab::UnifiedTokenVocab;
use wordchipper::encoders::DefaultTokenEncoder;
use wordchipper::encoders::TokenEncoder;
use wordchipper::types::TokenType;
use std::sync::Arc;

fn example<T: TokenType>(
    vocab: Arc<UnifiedTokenVocab<T>>,
    batch: &[&str],
) -> Vec<Vec<T>> {
    let encoder: DefaultTokenEncoder<T> = DefaultTokenEncoder::init(vocab, None);

    #[cfg(feature = "rayon")]
    let encoder = wordchipper::rayon::ParallelRayonEncoder::new(encoder);

    encoder.try_encode_batch(batch).unwrap()
}

TokenDecoder Clients

Decoder clients should use:

• DictionaryDecoder - the fastest TokenDecoder.
• ParallelRayonDecoder - a batch parallelism wrapper around any TokenDecoder.

use wordchipper::vocab::UnifiedTokenVocab;
use wordchipper::decoders::DictionaryDecoder;
use wordchipper::decoders::TokenDecoder;
use wordchipper::types::TokenType;
use std::sync::Arc;

fn example<T: TokenType>(
    vocab: Arc<UnifiedTokenVocab<T>>,
    batch: &[Vec<T>],
) -> Vec<String> {
    let decoder: DictionaryDecoder<T> = DictionaryDecoder::from_unified_vocab(vocab);

    #[cfg(feature = "rayon")]
    let decoder = wordchipper::rayon::ParallelRayonDecoder::new(decoder);

    decoder.try_decode_batch_to_strings(batch).unwrap().unwrap()
}

Side-by-side Comparison to tiktoken-rs

Each shard is ~90MB parquet file.

• 128/64 Core Thread Ripper
• NOTE: there are still some tokenization differences to resolve here.

$ RAYON_NUM_THREADS=16 cargo run --release -p token-cli -- --dataset-dir /media/Data/nanochat/dataset 
   Compiling wordchipper v0.1.2 (/home/crutcher/git/wordchipper/crates/wordchipper)
   Compiling token-cli v0.0.0 (/home/crutcher/git/wordchipper/examples/token-cli)
    Finished `release` profile [optimized] target(s) in 1.87s
     Running `target/release/token-cli --dataset-dir /media/Data/nanochat/dataset`

Samples Summary:
- count: 53248
- total size: 254737840
- avg size: 4783
- avg batch size bytes: 2449402

Timing Config:
- batch size: 512
- num batches: 104

Timing Encode:
- wordchipper:      14.6ms,    160.32 MiB/s
- tiktoken-rs:      32.7ms,     71.33 MiB/s

Observed Bytes/Token Stats:
- wordchipper token count: 54749669
- wordchipper byte/token: 4.65
- tiktoken-rs token count: 53251930
- tiktoken-rs byte/token: 4.78

Timing Decode:
- wordchipper:       2.8ms,    840.54 MiB/s
- tiktoken-rs:       2.1ms,      1.08 GiB/s

Training Overview

See examples/tokenizer_trainer.

This is a code snippet overview of training.

Expect training to take ~1s/10MB of input; and to be slowed primarily by how well the stream logic of loading the training samples is parallelized.

Note: currently, training has limited logging, and no progress reporting.

A common training binary is probably a good idea; and much of the messiness of supporting many different training data sources could be hidden in the isolated deps of such a tool.

Here:

• The iterator stream for samples may be quite large.
• Training a nanochat equivalent tokenizer takes ~80 CPU minutes.

use wordchipper::training::bpe_trainer::{BinaryPairVocabTrainer, BinaryPairVocabTrainerOptions};
use wordchipper::vocab::io::tiktoken_io::save_span_map_to_tiktoken_path;
use wordchipper::vocab::public::openai::patterns::OA_GPT3_CL100K_WORD_PATTERN;
use wordchipper::vocab::{ByteMapVocab, UnifiedTokenVocab};
use wordchipper::encoders::DefaultTokenEncoder;
use wordchipper::decoders::DictionaryDecoder;
use wordchipper::rayon::{ParallelRayonEncoder, ParallelRayonDecoder};
use std::sync::Arc;

fn example<I, S>(
    vocab_size: usize,
    batches: I,
    tiktoken_save_path: Option<String>,
) where
    I: IntoIterator,
    I::Item: AsRef<[S]>,
    S: AsRef<str>,
{
    // We can pick any unsigned integer type > vocab_size;
    // See [`wordchipper::types::TokenType`].
    type T = u32;
    type K = String;
    type C = u64;

    let options = BinaryPairVocabTrainerOptions::new(
        OA_GPT3_CL100K_WORD_PATTERN,
        vocab_size,
    );

    let mut trainer: BinaryPairVocabTrainer<K, C> = options.init();

    for batch in batches {
        // The trainer has no parallelism.
        // The perceived benefits of parallelism in the trainer
        // are insignificant if the IO for the sample source is
        // fed by another thread.
        trainer.update_from_samples(batch.as_ref());
    }

    let vocab: UnifiedTokenVocab<T> = trainer
        .train(Default::default())
        .expect("training failed");

    if let Some(path) = tiktoken_save_path {
        save_span_map_to_tiktoken_path(&vocab.span_vocab.span_map(), &path)
            .expect("failed to save tiktoken vocab");
        println!("- tiktoken vocab: {path:?}");
    }

    let encoder: DefaultTokenEncoder<T> = DefaultTokenEncoder::init(vocab.clone(), None);
    let encoder = ParallelRayonEncoder::new(encoder);

    let decoder = DictionaryDecoder::from_unified_vocab(vocab.clone());
    let decoder = ParallelRayonDecoder::new(decoder);
}

Running examples/tokenizer_trainer

Each shard is ~90MB parquet file.

$ time cargo run --release -p tokenizer_trainer -- --dataset-dir ~/Data/nanochat/dataset --shards 
..8 --voc
ab-size=65536 --time-encode-decode                                        
   Compiling anyhow v1.0.100
   Compiling wordchipper-disk-cache v0.2.2 (/Users/crutcher/git/wordchipper/crates/wordchipper-disk-cache)
   Compiling wordchipper-data v0.0.0 (/Users/crutcher/git/wordchipper/crates/wordchipper-data)
   Compiling wordchipper v0.2.2 (/Users/crutcher/git/wordchipper/crates/wordchipper)
   Compiling tokenizer_trainer v0.0.0 (/Users/crutcher/git/wordchipper/examples/tokenizer_trainer)
    Finished `release` profile [optimized] target(s) in 2.68s
     Running `target/release/tokenizer_trainer --dataset-dir /Users/crutcher/Data/nanochat/dataset --shards ..8 --vocab-size=65536 --time-encode-decode`
Loading Shards: [0, 1, 2, 3, 4, 5, 6, 7]
...

Training Tokenizer on shards: [0, 1, 2, 3, 4, 5, 6, 7]
- shard: 0
- shard: 1
- shard: 2
- shard: 3
- shard: 4
- shard: 5
- shard: 6
- shard: 7
- train
- training_duration: 106.70s
- vocab_size: 65535

Samples Summary:
- count: 20480
- avg size: 4741

Timing Config:
- batch size: 512

Timing Encode:
- batch avg: 18.276894ms
- sample avg: 35.697µs
- avg bps: 132.81 MB/s

Observed Bytes/Token Stats:
- total bytes: 97103222
- total tokens: 24645141
- sample byte/token: 3.94

Timing Decode:
- batch avg: 1.829894ms
- sample avg: 3.574µs

Acknowledgements

• Thank you to @karpathy and nanochat for the work on rustbpe.
• Thank you to tiktoken for their initial work in the rust tokenizer space.