Depth-Bounded PCFG Induction
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README.md

Depth-Bounded PCFG Induction

UNDER CONSTRUCTION

Introduction

This is the repo for the paper Unsupervised Grammar Induction with Depth-bounded PCFG that appears in Transcations of Association for Computational Linguistics. A large part of the code is based on another system called UHHMM so some scripts may still have older names.

Dependencies include:

  • Python 3.4+
  • CUDA 8.0+
  • PyZMQ
  • Numpy & Scipy
  • Cython
  • NLTK
  • CUSP

Steps to set up

There are a few steps you need to do before the system is runnable. The first step is to compile to GPU source files. The second step is to compile the Cython files. Finally you need to set up a config file.

1. Compile the CUDA C scripts

There are a couple of compile scripts in the ./gpusrc/ folder for reference. Since it is most likely that you want to run the system on a super computer with many GPUs, the compile_osc.sh file best shows how to compile the CUDA scripts. You need to replace the paths in the file for CUDA libraries, Python and Numpy with your paths. After you have setup the paths, you only need to do source ./gpusrc/compile_osc.sh for nvcc to compile all the scripts.

2. Compile Cython scripts

After compiling the CUDA C scripts, you should be able to compile to Cython scripts by doing python3 setup.py build_ext.

3. Set up a config file

There is a sample config file in the ./config/ folder. The config file has two parts, io and params. The settings are explained below. Please see the sample file for the format in which the parameters should be written.
io.input_file: the path to the input ints file.
io.output_dir: the folder where all the outputs will be write into.
io.dict_file: the path to the input dict file.
params.random_restarts: the number of random restarts the sampler will do and evaluate before doing a chain.
params.num_samples: the number of iterations the sampler will run. params.startabp: the number of A/B/P categories given to the sampler, which is equivalent to K in the paper.
params.init_alpha: the value for the hyperparameter for the symmetric Dirichlet prior, which is equivalent to beta in the paper.
params.cpu_workers: the number of workers on CPUs. The CPU workers only do model compilation, not sampling.
params.gpu_workers: the number of workers on GPUs. The GPU workers do both model compilation and sampling.
params.depth: the maximum depth limit to the sampler.
gpu: the flag to use GPU or not.
gpu_batch_size: the size of a batch used on the GPU.

Notes:

  1. You can do make xxx.ints.txt to convert a space-delimited one-line-per-sentence file into an ints and a dict file used by the system.
  2. start_abp and depth control the size of the compiled model. The largest value one can reasonably try is 15 and 2 respectively, which is what's used in the paper. Larger than this, you may risk running out of memory on the GPU.
  3. cpu_workers and gpu_workers can both to set to 0, which is usually what you want in order to run on a super computer or a cluster. In this case, the master process will write out a masterConfig.txt file into the root directory of the package, and you can start arbitrary number of workers by doing python scripts/workers.py ..

Run the system

Now the hard work is over, and the system is runnable. By running the system, you should just do python scripts/uhhmm-trainer.py config/config.file. This launches the master process. The outputs will be written into the specified output directory. If you have specified number of GPU workers in the config file, then the system will start running now. If you have 0 specified GPU workers, please follow Note 3 to start some GPU workers for sampling.

Because most of the runs take longer than a couple of days, you may want to continue a stopped run. In order to do that, you just pass in the output directory instead of the config file to the trainer: python scripts/uhhmm-trainer.py outputs/output_dir and the master process will be started and pick up where it is stopped at. For the workers it is the same as starting a new run.

The important output files written to the output directory include a pcfg_hypparams.txt which records log-likelihoods and right-branching tendency scores at every iteration. The linetrees files are the sampled trees for the training corpus in the brackets format at every iteration. They are ready for normal unlabeled bracketing evaluation when punctuation is removed from these trees.