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Differentiable Neural Computers and Sparse Differentiable Neural Computers, for Pytorch

Build Status PyPI version

This is an implementation of Differentiable Neural Computers, described in the paper Hybrid computing using a neural network with dynamic external memory, Graves et al. and the Sparse version of the DNC (the SDNC) described in Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes.

Install

pip install dnc

From source

git clone https://github.com/ixaxaar/pytorch-dnc
cd pytorch-dnc
pip install -r ./requirements.txt
pip install -e .

pytest is required to run the test

Architecure

Usage

DNC

Constructor Parameters:

Following are the constructor parameters:

Following are the constructor parameters:

Argument Default Description
input_size None Size of the input vectors
hidden_size None Size of hidden units
rnn_type 'lstm' Type of recurrent cells used in the controller
num_layers 1 Number of layers of recurrent units in the controller
num_hidden_layers 2 Number of hidden layers per layer of the controller
bias True Bias
batch_first True Whether data is fed batch first
dropout 0 Dropout between layers in the controller
bidirectional False If the controller is bidirectional (Not yet implemented
nr_cells 5 Number of memory cells
read_heads 2 Number of read heads
cell_size 10 Size of each memory cell
nonlinearity 'tanh' If using 'rnn' as rnn_type, non-linearity of the RNNs
gpu_id -1 ID of the GPU, -1 for CPU
independent_linears False Whether to use independent linear units to derive interface vector
share_memory True Whether to share memory between controller layers

Following are the forward pass parameters:

Argument Default Description
input - The input vector (B*T*X) or (T*B*X)
hidden (None,None,None) Hidden states (controller hidden, memory hidden, read vectors)
reset_experience False Whether to reset memory
pass_through_memory True Whether to pass through memory

Example usage:

from dnc import DNC

rnn = DNC(
  input_size=64,
  hidden_size=128,
  rnn_type='lstm',
  num_layers=4,
  nr_cells=100,
  cell_size=32,
  read_heads=4,
  batch_first=True,
  gpu_id=0
)

(controller_hidden, memory, read_vectors) = (None, None, None)

output, (controller_hidden, memory, read_vectors) = \
  rnn(torch.randn(10, 4, 64), (controller_hidden, memory, read_vectors, reset_experience=True))

Debugging:

The debug option causes the network to return its memory hidden vectors (numpy ndarrays) for the first batch each forward step. These vectors can be analyzed or visualized, using visdom for example.

from dnc import DNC

rnn = DNC(
  input_size=64,
  hidden_size=128,
  rnn_type='lstm',
  num_layers=4,
  nr_cells=100,
  cell_size=32,
  read_heads=4,
  batch_first=True,
  gpu_id=0,
  debug=True
)

(controller_hidden, memory, read_vectors) = (None, None, None)

output, (controller_hidden, memory, read_vectors), debug_memory = \
  rnn(torch.randn(10, 4, 64), (controller_hidden, memory, read_vectors, reset_experience=True))

Memory vectors returned by forward pass (np.ndarray):

Key Y axis (dimensions) X axis (dimensions)
debug_memory['memory'] layer * time nr_cells * cell_size
debug_memory['link_matrix'] layer * time nr_cells * nr_cells
debug_memory['precedence'] layer * time nr_cells
debug_memory['read_weights'] layer * time read_heads * nr_cells
debug_memory['write_weights'] layer * time nr_cells
debug_memory['usage_vector'] layer * time nr_cells

SDNC

Constructor Parameters:

Following are the constructor parameters:

Argument Default Description
input_size None Size of the input vectors
hidden_size None Size of hidden units
rnn_type 'lstm' Type of recurrent cells used in the controller
num_layers 1 Number of layers of recurrent units in the controller
num_hidden_layers 2 Number of hidden layers per layer of the controller
bias True Bias
batch_first True Whether data is fed batch first
dropout 0 Dropout between layers in the controller
bidirectional False If the controller is bidirectional (Not yet implemented
nr_cells 5000 Number of memory cells
read_heads 4 Number of read heads
sparse_reads 10 Number of sparse memory reads per read head
cell_size 10 Size of each memory cell
nonlinearity 'tanh' If using 'rnn' as rnn_type, non-linearity of the RNNs
gpu_id -1 ID of the GPU, -1 for CPU
independent_linears False Whether to use independent linear units to derive interface vector
share_memory True Whether to share memory between controller layers

Following are the forward pass parameters:

Argument Default Description
input - The input vector (B*T*X) or (T*B*X)
hidden (None,None,None) Hidden states (controller hidden, memory hidden, read vectors)
reset_experience False Whether to reset memory
pass_through_memory True Whether to pass through memory

Example usage:

from dnc import SDNC

rnn = SDNC(
  input_size=64,
  hidden_size=128,
  rnn_type='lstm',
  num_layers=4,
  nr_cells=100,
  cell_size=32,
  read_heads=4,
  sparse_reads=4,
  batch_first=True,
  gpu_id=0
)

(controller_hidden, memory, read_vectors) = (None, None, None)

output, (controller_hidden, memory, read_vectors) = \
  rnn(torch.randn(10, 4, 64), (controller_hidden, memory, read_vectors, reset_experience=True))

Debugging:

The debug option causes the network to return its memory hidden vectors (numpy ndarrays) for the first batch each forward step. These vectors can be analyzed or visualized, using visdom for example.

from dnc import SDNC

rnn = SDNC(
  input_size=64,
  hidden_size=128,
  rnn_type='lstm',
  num_layers=4,
  nr_cells=100,
  cell_size=32,
  read_heads=4,
  batch_first=True,
  sparse_reads=4,
  gpu_id=0,
  debug=True
)

(controller_hidden, memory, read_vectors) = (None, None, None)

output, (controller_hidden, memory, read_vectors), debug_memory = \
  rnn(torch.randn(10, 4, 64), (controller_hidden, memory, read_vectors, reset_experience=True))

Memory vectors returned by forward pass (np.ndarray):

Key Y axis (dimensions) X axis (dimensions)
debug_memory['memory'] layer * time nr_cells * cell_size
debug_memory['visible_memory'] layer * time sparse_reads+1 * nr_cells
debug_memory['read_positions'] layer * time sparse_reads+1
debug_memory['read_weights'] layer * time read_heads * nr_cells
debug_memory['write_weights'] layer * time nr_cells
debug_memory['usage'] layer * time nr_cells

Example copy task

The copy task, as descibed in the original paper, is included in the repo.

From the project root:

python ./tasks/copy_task.py -cuda 0 -optim rmsprop -batch_size 32 -mem_slot 64 # (like original implementation)

python3 ./tasks/copy_task.py -cuda 0 -lr 0.001 -rnn_type lstm -nlayer 1 -nhlayer 2 -dropout 0 -mem_slot 32 -batch_size 1000 -optim adam -sequence_max_length 8 # (faster convergence)

For SDNCs:
python3 -B ./tasks/copy_task.py -cuda 0 -lr 0.001 -rnn_type lstm -memory_type sdnc -nlayer 1 -nhlayer 2 -dropout 0 -mem_slot 100 -mem_size 10  -read_heads 1 -sparse_reads 10 -batch_size 20 -optim adam -sequence_max_length 10

and for curriculum learning for SDNCs:
python3 -B ./tasks/copy_task.py -cuda 0 -lr 0.001 -rnn_type lstm -memory_type sdnc -nlayer 1 -nhlayer 2 -dropout 0 -mem_slot 100 -mem_size 10  -read_heads 1 -sparse_reads 4 -batch_size 20 -optim adam -sequence_max_length 4 -curriculum_increment 2 -curriculum_freq 10000

For the full set of options, see:

python ./tasks/copy_task.py --help

The copy task can be used to debug memory using Visdom.

Additional step required:

pip install visdom
python -m visdom.server

Open http://localhost:8097/ on your browser, and execute the copy task:

python ./tasks/copy_task.py -cuda 0

The visdom dashboard shows memory as a heatmap for batch 0 every -summarize_freq iteration:

Visdom dashboard

General noteworthy stuff

  1. DNCs converge faster with Adam and RMSProp learning rules, SGD generally converges extremely slowly. The copy task, for example, takes 25k iterations on SGD with lr 1 compared to 3.5k for adam with lr 0.01.
  2. nans in the gradients are common, try with different batch sizes

Repos referred to for creation of this repo: