# wordchipper - HPC Rust BPE Tokenizer
[](https://crates.io/crates/wordchipper)
[](https://docs.rs/wordchipper/latest/wordchipper/)
[](https://github.com/crutcher/wordchipper/actions/workflows/ci.yml)
[](https://deepwiki.com/crutcher/wordchipper)
## Overview
This is a high-performance rust BPE tokenizer trainer/encoder/decoder.
The current status is productionization towards an alpha release.
## wordchipper vs tiktoken
Details:
- MacBook Pro 2023 Apple M2 Pro
- Each shard is ~90MB parquet file.
- Each encode/decode is compared for equality.
```terminaloutput
% cargo run --release -p sample-timer -- --dataset-dir ~/datasets
Compiling sample-timer v0.0.0 (/Users/crutcher/git/wordchipper/examples/sample-timer)
Finished `release` profile [optimized] target(s) in 1.16s
Running `target/release/sample-timer --dataset-dir /Users/crutcher/datasets`
Model: "oa:o200k_harmony"
- shards: [0, 1, 2, 3]
- batch_size: 512
Samples Summary:
- num batches: 208
- avg bytes/sample: 4777
- avg bytes/token: 4.8
Encoder Times:
- wordchipper
- batch: 31.0ms
- sample: 60.5µs
- bps: 75.31 MiB/s
- tiktoken
- batch: 30.5ms
- sample: 59.6µs
- bps: 76.39 MiB/s
Decoder Times:
- wordchipper
- batch: 2.0ms
- sample: 3.9µs
- bps: 1.14 GiB/s
- tiktoken
- batch: 1.8ms
- sample: 3.5µs
- bps: 1.29 GiB/s
```
## Client Usage
### Pretrained Vocabularies
* [OpenAI OATokenizer](https://docs.rs/wordchipper/latest/wordchipper/vocab/public/openai/enum.OATokenizer.html)
### Encoders and Decoders
* [Token Encoders](https://docs.rs/wordchipper/latest/wordchipper/encoders/index.html)
* [Token Decoders](https://docs.rs/wordchipper/latest/wordchipper/decoders/index.html)
## Example Usage
```rust,no_run
use wordchipper::decoders::{TokenDictDecoder, TokenDecoder};
use wordchipper::encoders::{DefaultTokenEncoder, TokenEncoder};
use wordchipper::concurrency::rayon::{ParallelRayonDecoder, ParallelRayonEncoder};
use wordchipper::regex::{regex_pool_supplier, RegexWrapperPattern};
use wordchipper::spanning::{TextSpanningConfig, TextSpanner};
use wordchipper::pretrained::openai::OATokenizer;
use wordchipper::vocab::UnifiedTokenVocab;
use wordchipper::disk_cache::WordchipperDiskCache;
type T = u32;
let mut disk_cache = WordchipperDiskCache::default();
let vocab: UnifiedTokenVocab<T> = OATokenizer::0200kHarmony::load(&mut disk_cache).unwrap();
let encoder: DefaultTokenEncoder<T> = DefaultTokenEncoder::init(vocab.clone(), None);
let encoder = ParallelRayonEncoder::new(encoder);
let decoder = TokenDictDecoder::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`.
```rust,no_run
use wordchipper::vocab::UnifiedTokenVocab;
use wordchipper::encoders::DefaultTokenEncoder;
use wordchipper::encoders::TokenEncoder;
use wordchipper::types::TokenType;
fn example<T: TokenType>(
vocab: &UnifiedTokenVocab<T>,
batch: &[&str],
) -> Vec<Vec<T>> {
let encoder = DefaultTokenEncoder::<T>::init(vocab.clone(), None);
#[cfg(feature = "rayon")]
let encoder = wordchipper::concurrency::rayon::ParallelRayonEncoder::new(encoder);
encoder.try_encode_batch(batch).unwrap()
}
```
### TokenDecoder Clients
Decoder clients should use:
* `TokenDictDecoder` - the fastest `TokenDecoder`.
* `ParallelRayonDecoder` - a batch parallelism wrapper around any `TokenDecoder`.
```rust,no_run
use wordchipper::vocab::UnifiedTokenVocab;
use wordchipper::decoders::DefaultTokenDecoder;
use wordchipper::decoders::TokenDecoder;
use wordchipper::types::TokenType;
fn example<T: TokenType>(
vocab: &UnifiedTokenVocab<T>,
batch: &[Vec<T>],
) -> Vec<String> {
let decoder = DefaultTokenDecoder::<T>::from_unified_vocab(vocab);
#[cfg(feature = "rayon")]
let decoder = wordchipper::concurrency::rayon::ParallelRayonDecoder::new(decoder);
decoder.try_decode_batch_to_strings(batch).unwrap().unwrap()
}
```
## 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.
```rust,no_run
use wordchipper::training::bpe_trainer::{BinaryPairVocabTrainer, BinaryPairVocabTrainerOptions};
use wordchipper::vocab::io::tiktoken_io::save_span_map_to_tiktoken_path;
use wordchipper::pretrained::openai::patterns::OA_GPT3_CL100K_WORD_PATTERN;
use wordchipper::vocab::{ByteMapVocab, UnifiedTokenVocab};
use wordchipper::encoders::DefaultTokenEncoder;
use wordchipper::decoders::DefaultTokenDecoder;
use wordchipper::concurrency::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 = DefaultTokenDecoder::from_unified_vocab(vocab.clone());
let decoder = ParallelRayonDecoder::new(decoder);
}
```
#### Running `examples/tokenizer_trainer`
Each shard is ~90MB parquet file.
```terminaloutput
$ 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](https://github.com/karpathy)
and [nanochat](https://github.com/karpathy/nanochat)
for the work on `rustbpe`.
* Thank you to [tiktoken](https://github.com/openai/tiktoken) for their initial work in the rust
tokenizer space.