Gated Graph Sequence Neural Networks¶
Graph-to-sequence networks allow information represtable as a graph (such as an annotated NLP sentence or computer code structure as an AST) to be connected to a sequence generator to produce output which can benefit from the graph structure of the input.
The training option
-encoder_type ggnn implements a GGNN (Gated Graph Neural Network) based on github.com/JamesChuanggg/ggnn.pytorch.git which is based on the paper “Gated Graph Sequence Neural Networks” by Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel.
The ggnn encoder is used for program equivalence proof generation in the paper Equivalence of Dataflow Graphs via Rewrite Rules Using a Graph-to-Sequence Neural Model. That paper shows the benefit of the graph-to-sequence model over a sequence-to-sequence model for this problem which can be well represented with graphical input. The integration of the ggnn network into the OpenNMT-py system supports attention on the nodes as well as a copy mechanism.
There are no additional dependencies beyond the rnn-to-rnn sequence2sequence requirements.
To get started, we provide a toy graph-to-sequence example. We assume that the working directory is
OpenNMT-py throughout this document.
Download the data to a sibling directory.
cd .. git clone https://github.com/SteveKommrusch/OpenNMT-py-ggnn-example source OpenNMT-py-ggnn-example/env.sh cd OpenNMT-py
The YAML configuration for this example is the following:
# ggnn_example.yaml ## Where the necessary objects will be written save_data: <path_to>/OpenNMT-py-ggnn-example/run/example # Filter long examples src_seq_length: 1000 tgt_seq_length: 30 # Data definition data: cnndm: path_src: <path_to>/OpenNMT-py-ggnn-example/src-train.txt path_tgt: <path_to>/OpenNMT-py-ggnn-example/tgt-train.txt transforms: [filtertoolong] weight: 1 valid: path_src: <path_to>/OpenNMT-py-ggnn-example/src-val.txt path_tgt: <path_to>/OpenNMT-py-ggnn-example/tgt-val.txt src_vocab: <path_to>/OpenNMT-py-ggnn-example/srcvocab.txt tgt_vocab: <path_to>/OpenNMT-py-ggnn-example/tgtvocab.txt save_model: <path_to>/OpenNMT-py-ggnn-example/run/model # Model options train_steps: 10000 save_checkpoint_steps: 5000 encoder_type: ggnn layers: 2 decoder_type: rnn rnn_size: 256 learning_rate: 0.1 start_decay_steps: 5000 learning_rate_decay: 0.8 global_attention: general batch_size: 32 word_vec_size: 256 bridge: true gpu_ranks: 0 n_edge_types: 9 state_dim: 256 n_steps: 10 n_node: 64
Train the model.
You can simply run the following command:
python train.py -config ggnn_example.yaml
Translate the graph of 2 equivalent linear algebra expressions into the axiom list which proves them equivalent.
python translate.py \ -model <path_to>/OpenNMT-py-ggnn-example/run/model_step_10000.pt \ -src <path_to>/OpenNMT-py-ggnn-example$data_path/src-test.txt \ -beam_size 5 -n_best 5 \ -gpu 0 \ -output <path_to>/OpenNMT-py-ggnn-example/pred-test_beam5.txt \ 2>&1 > <path_to>/OpenNMT-py-ggnn-example/translate5.out
Graph data format¶
The GGNN implementation leverages the sequence processing and vocabulary
interface of OpenNMT. Each graph is provided on an input line, much like
a sentence is provided on an input line. A graph nearal network input line
feature values, and
edges separated by
<EOT> (end of tokens) tokens. Below is example of the input for a pair
of algebraic equations structured as a graph:
Sentence tokens Feature values Edges --------------- ------------------ ------------------------------------------------------- - - - 0 a a b b <EOT> 0 1 2 3 4 4 2 3 12 <EOT> 0 2 1 3 2 4 , 0 6 1 7 2 5 , 0 4 , 0 5 , , , , 8 0 , 8 1
The equations being represented are
((a - a) - b) and
(0 - b), the
sentence tokens of which are provided before the first
features values are provided. These are extra
flags with information on each node in the graph. In this case, the 8
sentence tokens have feature flags ranging from 0 to 4; the 9th feature
flag defines a 9th node in the graph which does not have sentence token
information, just feature data. Nodes with any non-number flag (such as
.) will not have a feature added. Multiple groups of features
can be provided by using the
, delimiter between the first and second
<EOT> token, edge information is provided.
Edge data is given as node pairs, hence
<EOT> 0 2 1 3 indicates that there
are edges from node 0 to node 2 and from node 1 to node 3. The GGNN supports
multiple edge types (which result mathematically in multiple weight matrices
for the model) and the edge types are separated by
, tokens after the
Note that the source vocabulary file needs to include the ‘
-rnn_type (str): style of recurrent unit to use, one of [LSTM]
-state_dim (int): Number of state dimensions in nodes
-n_edge_types (int): Number of edge types
-bidir_edges (bool): True if reverse edges should be automatically created
-n_node (int): Max nodes in graph
-bridge_extra_node (bool): True indicates only the vector from the 1st extra node (after token listing) should be used for decoder initialization; False indicates all node vectors should be averaged together for decoder initialization
-n_steps (int): Steps to advance graph encoder for stabilization
-src_vocab (int): Path to source vocabulary