DeepSynth

General-purpose program synthesiser.

This is the repository for the code of the paper "Scaling Neural Program Synthesis with Distribution-based Search".

Authors: Anonymous

Abstract

We consider the problem of automatically constructing computer programs from input-output examples. We investigate how to augment probabilistic and neural program synthesis methods with new search algorithms, proposing a framework called distribution-based search. Within this framework, we introduce two new search algorithms: HEAP SEARCH, an enumerative method, and SQRT SAMPLING, a probabilistic method. We prove certain optimality guarantees for both methods, show how they integrate with probabilistic and neural techniques, and demonstrate how they can operate at scale across parallel compute environments. Collectively these findings offer theoretical and applied studies of search algorithms for program synthesis that integrate with recent developments in machine-learned program synthesizers.

Usage

Installation

# clone this repository
git clone https://github.com/nathanael-fijalkow/DeepSynth.git

# create your new env
conda create -n deep_synth python>=3.7 
# activate it
conda activate deep_synth
# install pip
yes | conda install pip
# install this package and the dependencies
pip install torch cython tqdm numpy matplotlib
pip install git+https://github.com/MaxHalford/vose
# For flashfill dataset
pip install sexpdata
# If you want to do the parallel experiments
pip install ray

# You are good to go :)
# To test your installation you can run the following tests:
python unit_test_algorithms.py
python unit_test_programs.py
python unit_test_algorithms.py
python unit_test_predictions.py
# Only if you installed ray
python unit_test_parallel.py

File structure

./
        Algorithms/      # the search algorithms + parallel pipeline
        DSL/             # DSL: dreamcoder, deepcoder, flashfill
        list_dataset/    # DreamCoder dataset in pickle format
        Predictions/     # all files related to the ANN for prediction of the grammars 

Reproducing the experiments

All of the files mentioned in this section are located in the root folder and follow this pattern run_*_experiments*.py.

Here is a short summary of each experiment:

• run_random_PCFG_search.py produce a list of all programs generated under Xsec of search time by all algorithms.
• run_random_PCFG_search_parallel.py same experiment but iwth the grammar_splitter and multiple CPUs.
• run_experiments_ .py try to find solutions using an ANN to predict the grammar and for each algorithm logs the search data for the corresponding . The suffix parallel can also be found indicating that the algorithms are run in parallel. The semantics experiments in the paper used a trained model thatn can be obtained using produce_network.py or directly in the repository. The results can be plotted using plot_results_semantics.py.

Note that for the DreamCoder experiment in our paper, we did not use the cached evaluation of HeapSearch, this can be reproduced by setting use_heap_search_cached_eval to False in run_experiment.py.

Quick guide to using ANN to predict a grammar

Is it heavily inspired by the file model_loader.py.

First we create a prediction model:

############################
##### Hyperparameters ######
############################

max_program_depth = 4

size_max = 10  # maximum number of elements in a list (input or output)
nb_inputs_max = 2  # maximum number of inputs in an IO
lexicon = list(range(30))  # all elements of a list must be from lexicon
# only useful for VariableSizeEncoding
encoding_output_dimension = 30  # fixing the dimension

embedding_output_dimension = 10
# only useful for RNNEmbedding
number_layers_RNN = 1

size_hidden = 64

############################
######### PCFG #############
############################

deepcoder = DSL(semantics, primitive_types)
type_request = Arrow(List(INT), List(INT))
deepcoder_cfg = deepcoder.DSL_to_CFG(
    type_request, max_program_depth=max_program_depth)
deepcoder_pcfg = deepcoder_cfg.CFG_to_Uniform_PCFG()

############################
###### IO ENCODING #########
############################

# IO = [[I1, ...,Ik], O]
# I1, ..., Ik, O are lists
# IOs = [IO1, IO2, ..., IOn]
# task = (IOs, program)
# tasks = [task1, task2, ..., taskp]

#### Specification: #####
# IOEncoder.output_dimension: size of the encoding of one IO
# IOEncoder.lexicon_size: size of the lexicon
# IOEncoder.encode_IO: outputs a tensor of dimension IOEncoder.output_dimension
# IOEncoder.encode_IOs: inputs a list of IO of size n
# and outputs a tensor of dimension n * IOEncoder.output_dimension

IOEncoder = FixedSizeEncoding(
    nb_inputs_max=nb_inputs_max,
    lexicon=lexicon,
    size_max=size_max,
)


# IOEncoder = VariableSizeEncoding(
#     nb_inputs_max = nb_inputs_max,
#     lexicon = lexicon,
#     output_dimension = encoding_output_dimension,
#     )

############################
######### EMBEDDING ########
############################

# IOEmbedder = SimpleEmbedding(
#     IOEncoder=IOEncoder,
#     output_dimension=embedding_output_dimension,
#     size_hidden=size_hidden,
# )
 
IOEmbedder = RNNEmbedding(
    IOEncoder=IOEncoder,
    output_dimension=embedding_output_dimension,
    size_hidden=size_hidden,
    number_layers_RNN=number_layers_RNN,
)

#### Specification: #####
# IOEmbedder.output_dimension: size of the output of the embedder
# IOEmbedder.forward_IOs: inputs a list of IOs
# and outputs the embedding of the encoding of the IOs
# which is a tensor of dimension
# (IOEmbedder.input_dimension, IOEmbedder.output_dimension)
# IOEmbedder.forward: same but with a batch of IOs

############################
######### MODEL ############
############################

model = RulesPredictor(
    cfg=deepcoder_cfg,
    IOEncoder=IOEncoder,
    IOEmbedder=IOEmbedder,
    size_hidden=size_hidden,
)

# model = LocalRulesPredictor(
#     cfg = deepcoder_cfg,
#     IOEncoder = IOEncoder,
#     IOEmbedder = IOEmbedder,
#     # size_hidden = size_hidden,
#     )

Now we can produce the grammars:

dsl = DSL(semantics, primitive_types)
batched_grammars = model(batched_examples)
if isinstance(model, RulesPredictor):
    batched_grammars = model.reconstruct_grammars(batched_grammars)

Quick guide to train a neural network

Just copy the model initialisation used in your experiment in the file produce_network.py or use the ones provided that correspond to our experiments. You can change the hyperparameters, then run the script. A .weights file should appear at the root folder. This will train a neural network on random generated programs as described in Appendix F in the paper.

Quick guide to using a search algorithm for a grammar

There are already functions for that in run_experiment.py, namely run_algorithm and run_algorithm_parallel. The former enables you to run the specified algorithm in a single thread while the latter in parallel with a grammar splitter. To produce a is_correct function you can use make_program_checker in experiment_helper.py.

How to download the DeepCoder dataset?

First, download the archive from here (Deepcoder repo): https://storage.googleapis.com/deepcoder/dataset.tar.gz in a folder deepcoder_dataset at the root of DeepSynth. Then you simply need to:

gunzip dataset.tar.gz
tar -xf dataset.tar

You should see a few JSON files.