greedtok 0.13

Partition Cover Approach to Tokenization

A partition cover approach to tokenization

In this work, we formulate tokenization as an optimization objective, show that it is NP-hard via a simple reduction from vertex cover, and propose a polynomial-time greedy algorithm GreedTok. Our formulation naturally relaxes to the well-studied weighted maximum coverage problem which has a simple $(1 - 1/e)$-approximation greedy algorithm.

GreedTok

1. If using python wrapper

   a. Using pip (use the lightweight source code w/o data/notebooks):

   wget "https://github.com/PreferredAI/pcatt/archive/refs/tags/v0.13.tar.gz"
   unzip pcatt-0.13.zip -d pcatt
   cd pcatt
   pip install -r requirements.txt
   pip install .
   

   b. Or compile manually e.g. (have to specify links)

   c++ -O3 -Wall -shared -std=c++20 \
   -fPIC $(python3 -m pybind11 --includes) \
   -I$CONDA_PREFIX/include/ \
   -I$CONDA_PREFIX/include/tbb \
   -I$CONDA_PREFIX/include/oneapi \
   -L$CONDA_PREFIX/lib/ \
   -l tbb \
   ./pcatt/greedy_builder.cpp \
   -o ./pcatt/greedy_builder$(python3-config --extension-suffix) 
   

   c. import and use! Examples in eval_tokenizer_example.ipynb

2. If using C++ files directly

   a. Install dependencies for C++ code, we use oneTBB to parallelize the code, simplest way is to use Conda or pip:

   conda install tbb-devel
   

   b. Compile greedy_cache.py e.g.:

   c++ -O3 -std=c++20 \
   -I$CONDA_PREFIX/include/ \
   -I$CONDA_PREFIX/include/tbb \
   -I$CONDA_PREFIX/include/oneapi \
   -L$CONDA_PREFIX/lib/ \
   -l tbb \
   pcatt/greedy_cache.cpp \
   -o pcatt/greedy.exe 
   

   c. Prepare inputs (refer to cpp_inputs for examples):

   • counts: a file with '\n' delimited integers
   • words: a file with ' ' (space) delimited words

   d. Run compiled program (currently looks for domain inputs in fixed path under cpp_inputs/*) ./greedy.exe <domain> <k> e. Now we obtained our ranked token sequence (refer to cpp_outputs for examples):

   • merges: the number of covers at each step, delimited by '\n'
   • tokens: byte sequences in hex-format, delimited by '\n'

Evaluations in eval_notebook.ipynb

Citation

TBD