wordchipper
Python bindings for the wordchipper BPE tokenizer library, by ZSpaceLabs.
wordchipper is a high-performance Rust byte-pair encoder tokenizer for the OpenAI GPT-2 tokenizer
family. Under Python wrappers, we see a range of ~2x-4x (4 to 64 cores) speedups over
tiktoken.
| x 64 Core | r50k python | o200k python |
|---|---|---|
| wordchipper | 110.5 MiB/s | 106.5 MiB/s |
| tiktoken | 25.5 MiB/s | 32.7 MiB/s |
| tokenizers | 20.8 MiB/s | 23.2 MiB/s |
Read the full performance paper: wordchipper: Fast BPE Tokenization with Substitutable Internals
Installation
pip install wordchipper
Usage
from wordchipper import Tokenizer
# See available models
Tokenizer.available_models()
# ['r50k_base', 'p50k_base', 'p50k_edit', 'cl100k_base', 'o200k_base', 'o200k_harmony']
# Load a tokenizer
tok = Tokenizer.from_pretrained("cl100k_base")
# Encode / decode
tokens = tok.encode("hello world") # [15339, 1917]
text = tok.decode(tokens) # "hello world"
# Batch encode / decode (parallel via rayon)
results = tok.encode_batch(["hello", "world", "foo bar"])
texts = tok.decode_batch(results)
# Vocab inspection
tok.vocab_size # 100256
tok.token_to_id("hello") # 15339
tok.id_to_token(15339) # "hello"
tok.token_to_id("nonexistent") # None
# Special tokens
tok.get_special_tokens()
# [('<|endoftext|>', 100257), ...]
# Save vocab to file (base64 tiktoken format, excludes special tokens)
tok.save_base64_vocab("vocab.tiktoken")
Drop-in compatibility
wordchipper ships drop-in replacements for both tiktoken and HuggingFace tokenizers.
Change one import line and the rest of your code stays the same.
tiktoken
# Before
import tiktoken
# After
from wordchipper.compat import tiktoken
Everything you use works out of the box:
enc = tiktoken.get_encoding("cl100k_base")
enc = tiktoken.encoding_for_model("gpt-4o")
tokens = enc.encode("hello world")
text = enc.decode(tokens)
# Special token handling (same defaults as tiktoken)
enc.encode("<|endoftext|>") # raises ValueError
enc.encode("<|endoftext|>", allowed_special="all") # [100257]
enc.encode("<|endoftext|>", disallowed_special=()) # BPE subwords
# Single-token operations
enc.encode_single_token("hello") # 15339
enc.decode_single_token_bytes(15339) # b'hello'
# Byte-level decoding
enc.decode_bytes(tokens) # b'hello world'
enc.decode_tokens_bytes(tokens) # [b'hello', b' world']
# Batch operations (parallel via Rust)
enc.encode_batch(["hello", "world"])
enc.decode_batch([[15339], [1917]])
# Inspection
enc.is_special_token(100257) # True
enc.token_byte_values() # bytes for every token in vocab
enc.n_vocab # 100277
enc.special_tokens_set # {'<|endoftext|>', ...}
Supported encodings: cl100k_base, o200k_base, p50k_base, p50k_edit, r50k_base.
Model mapping covers GPT-4o, GPT-4, GPT-3.5, o3, o1, and all legacy models.
HuggingFace tokenizers
# Before
from tokenizers import Tokenizer
# After
from wordchipper.compat.tokenizers import Tokenizer
tok = Tokenizer.from_pretrained("Xenova/gpt-4o")
enc = tok.encode("hello world")
enc.ids # [24912, 2375]
enc.tokens # ['hello', ' world']
enc.attention_mask # [1, 1]
enc.type_ids # [0, 0]
len(enc) # 2
# Sentence pairs
enc = tok.encode("hello", pair="world")
enc.type_ids # [0, 1]
# Batch (supports pairs too)
tok.encode_batch(["hello", ("a", "b")])
# Decode with special token control
tok.decode(enc.ids, skip_special_tokens=True)
tok.decode(enc.ids, skip_special_tokens=False)
# Padding and truncation
tok.enable_padding(length=128, pad_id=0)
tok.enable_truncation(max_length=512)
# Vocab
tok.get_vocab() # {'hello': 24912, ...}
tok.get_vocab_size() # 200000
tok.token_to_id("hello") # 24912
Mapped identifiers: Xenova/gpt-4o, Xenova/gpt-4, Xenova/cl100k_base, Xenova/o200k_base.
You can also pass bare encoding names like cl100k_base directly. All supported identifiers
resolve to vocabularies embedded in the binary, so from_pretrained never makes HTTP requests.
Why switch?
- 2-4x faster encoding than tiktoken and HuggingFace tokenizers (see benchmarks above)
- No network requests on load (vocabs are embedded in the binary)
- Single dependency, no C compiler needed
- Both compat layers verified with side-by-side comparison tests against the upstream libraries
A few parameters are accepted for API compatibility but not yet implemented
(e.g. is_pretokenized). These raise NotImplementedError when set to non-default values.
Development
cd bindings/python
# Set up environment and build
uv venv .venv
source .venv/bin/activate
uv pip install maturin pytest
maturin develop --features python-extension-module
# Run tests
pytest tests/ -v
After making changes to src/lib.rs, rebuild with maturin develop before re-running tests.
Benchmarks
Compares wordchipper against tiktoken and HuggingFace tokenizers for single and batch encoding
on cl100k_base and o200k_base. Uses the same corpora and methodology as the Rust benchmarks in
wordchipper-bench:
- Single-string:
english.txt/multilingual.txtrepeated 10x - Batch: 1024 samples from fineweb-edu shard 0 (~4.2 MB)
# Install benchmark dependencies
uv pip install pytest-benchmark tiktoken tokenizers pyarrow
# Build in release mode for meaningful numbers
maturin develop --release --features python-extension-module
# Run all benchmarks
pytest benchmarks/
# Run only single-encode benchmarks
pytest benchmarks/ -k "TestSingleEncode"
# Run only batch-encode benchmarks
pytest benchmarks/ -k "TestBatchEncode"
# Run only decode benchmarks
pytest benchmarks/ -k "TestSingleDecode"
# Filter by model
pytest benchmarks/ -k "cl100k_base"
License
wordchipper is distributed under the terms of both the MIT license and the Apache License (Version
2.0). See LICENSE-APACHE and
LICENSE-MIT for details.
Metadata
Release files for wordchipper 0.9.2
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Built distributions (wheels)
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