This repository contains the source code described in our paper A Fast Unified Model for Sentence Parsing and Understanding. For a more informal introduction to the ideas behind the model, see this Stanford NLP blog post.
There are three separate implementations available:
thin-stackrepresentation described in our paper
The Python code lives, quite intuitively, in the
python folder. We used this code to train and test the SPINN models before publication.
There is one enormous difference in the
fat-stack uses Theano's automatically generated symbolic backpropagation graphs, while
thin-stack generates its own optimal backpropagation graph. This makes
thin-stack oodles faster than its brother, but we have not yet implemented all SPINN variants to support this custom backpropagation.
Install all required Python dependencies using the command below. (WARNING: This installs our custom Theano fork. We recommend installing in a virtual environment in order to avoid overwriting any stock Theano install you already have.)
pip install -r python/requirements.txt
We use a modified version of Theano in order to support fast forward- and backward-prop in
thin-stack. While it isn't absolutely necessary to use this hacked Theano, it greatly improves
Alternatively, you can use a custom Docker image that we've prepared, as discussed in this CodaLab worksheet.
The easiest way to launch a train/test run is to use one of the
The Bash scripts in this directory will download the necessary data and launch train/test runs of all models reported in our paper. You can run any of the following scripts:
./checkpoints/spinn.sh ./checkpoints/spinn_pi.sh ./checkpoints/spinn_pi_nt.sh ./checkpoints/rnn.sh
All of the above scripts will by default launch a training run beginning with the recorded parameters of our best models. You can change their behavior using the arguments below:
$ ./checkpoints/spinn.sh -h spinn.sh [-h] [-e] [-t] [-s] -- run a train or test run of a SPINN model where: -h show this help text -e run in eval-only mode (evaluates on dev set by default) -t evaluate on test set -s skip the checkpoint loading; run with a randomly initialized model
To evaluate our best SPINN-PI-NT model on the test set, for example, run
$ ./checkpoints/spinn_pi_nt.sh -e -t Running command: python -m spinn.models.fat_classifier --data_type snli --embedding_data_path ../glove/glove.840B.300d.txt --log_path ../logs --training_data_path ../snli_1.0/snli_1.0_train.jsonl --experiment_name spinn_pi_nt --expanded_eval_only --eval_data_path ../snli_1.0/snli_1.0_test.jsonl --ckpt_path spinn_pi_nt.ckpt_best --batch_size 32 --embedding_keep_rate 0.828528124124 --eval_seq_length 50 --init_range 0.005 --l2_lambda 3.45058959758e-06 --learning_rate 0.000297682444894 --model_dim 600 --model_type Model0 --noconnect_tracking_comp --num_sentence_pair_combination_layers 2 --semantic_classifier_keep_rate 0.9437038157 --seq_length 50 --tracking_lstm_hidden_dim 57 --use_tracking_lstm --word_embedding_dim 300 ...  Checkpointed model was trained for 156500 steps.  Building forward pass.  Writing eval output for ../snli_1.0/snli_1.0_test.jsonl.  Written gold parses in spinn_pi_nt-snli_1.0_test.jsonl-parse.gld  Written predicted parses in spinn_pi_nt-snli_1.0_test.jsonl-parse.tst  Step: 156500 Eval acc: 0.808734 0.000000 ../snli_1.0/snli_1.0_test.jsonl
The main executable for the SNLI experiments in the paper is fat_classifier.py, whose flags specify the hyperparameters of the model. You may also need to set Theano flags through the THEANO_FLAGS environment variable, which specifies compilation mode (set it to
fast_compile during development, and delete it to use the default state for longer runs),
device, which can be set to
cuda.root, which specifies the location of CUDA when running on GPU.
floatX should always be set to
Here's a sample command that runs a fast, low-dimensional CPU training run, training and testing only on the dev set. It assumes that you have a copy of SNLI available locally.
PYTHONPATH=spinn/python \ THEANO_FLAGS=optimizer=fast_compile,device=cpu,floatX=float32 \ python2.7 -m spinn.models.fat_classifier --data_type snli \ --training_data_path snli_1.0/snli_1.0_dev.jsonl \ --eval_data_path snli_1.0/snli_1.0_dev.jsonl \ --embedding_data_path spinn/python/spinn/tests/test_embedding_matrix.5d.txt \ --word_embedding_dim 5 --model_dim 10
For full runs, you'll also need a copy of the 840B word 300D GloVe word vectors.
The C++ code lives in the
cpp folder. This code implements a basic SPINN feedforward. (This implementation corresponds to the bare SPINN-PI-NT, "parsed input / no tracking" model, described in the paper.) It has been verified to produce the exact same output as a recursive neural network with the same weights and inputs. (We used a simplified version of Ozan Irsoy's
deep-recursive project as a comparison.)
The main binary,
stacktest, simply generates random input data and runs a feedforward. It outputs the total feedforward time elapsed and the numerical result of the feedforward.
The only external dependency of the C++ code is CUDA >=7.0. The tests depend on the
googletest library, included in this repository as a Git submodule.
First install CUDA >=7.0 and ensure that
nvcc is on your
# From project root cd cpp # Pull down Git submodules (libraries) git submodule update --init # Compile C++ code make stacktest make rnntest
This should generate a binary in
cpp/bin/stacktest runs on random input data. You can time the feedforward yourself by running the following commands:
# From project root cd cpp BATCH_SIZE=512 ./bin/stacktest
You can of course set
BATCH_SIZE to whatever integer you desire. The other model architecture parameters are fixed in the code, but you can easily change them as well on this line if you desire.
cpp/bin/rnntest runs a vanilla RNN (ReLU activations) with random input data. You can run this performance test script as follows:
# From project root cd cpp BATCH_SIZE=512 ./bin/rnntest
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