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Decoupled Neural Interfaces using Synthetic Gradients for PyTorch

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Decoupled Neural Interfaces for PyTorch

This tiny library is an implementation of 

Decoupled Neural Interfaces using Synthetic Gradients
_ for 
PyTorch
_. It's very simple to use as it was designed to enable researchers to integrate DNI into existing models with minimal amounts of code.

To install, run::

$ python setup.py install

Description of the library and how to use it in some typical cases is provided below. For more information, please read the code.

Terminology

This library uses a message passing abstraction introduced in the paper. Some terms used in the API (matching those used in the paper wherever possible):

  • Interface
    - A Decoupled Neural Interface that decouples two parts (let's call them part A and part B) of the network and lets them communicate via 
    message
    passing. It may be 
    Forward
    , 
    Backward
    or 
    Bidirectional
    .
  • BackwardInterface
    - A type of 
    Interface
    that the paper focuses on. It can be used to prevent update locking by predicting gradient for part A of the decoupled network based on the activation of its last layer.
  • ForwardInterface
    - A type of 
    Interface
    that can be used to prevent forward locking by predicting input for part B of the network based on some information known to both parts - in the paper it's the input of the whole network.
  • BidirectionalInterface
    - A combination of 
    ForwardInterface
    and 
    BackwardInterface
    , that can be used to achieve a complete unlock.
  • message
    - Information that is passed through an 
    Interface
    - activation of the last layer for 
    ForwardInterface
    or gradient w.r.t. that activation for 
    BackwardInterface
    . Note that no original information passes through. A 
    message
    is consumed by one end of the 
    Interface
    and used to update a 
    Synthesizer
    . Then the 
    Synthesizer
    can be used produce a synthetic 
    message
    at the other end of the 
    Interface
    .
  • trigger
    - Information based on which 
    message
    is synthesized. It needs to be accessible by both parts of the network. For 
    BackwardInterface
    , it's activation of the layer w.r.t. which gradient is to be synthesized. For 
    ForwardInterface
    it can be anything - in the paper it's the input of the whole network.
  • context
    - Additional information normally not shown to the network at the forward pass, that can condition an 
    Interface
    to provide a better estimate of the 
    message
    . The paper uses labels for this purpose and calls DNI with context cDNI.
  • send
    - A method of an 
    Interface
    , that takes as input 
    message
    and 
    trigger
    , based on which that 
    message
    should be generated, and updates 
    Synthesizer
    to improve the estimate.
  • receive
    - A method of an 
    Interface
    , that takes as input 
    trigger
    and returns a 
    message
    generated by a 
    Synthesizer
    .
  • Synthesizer
    - A regression model that estimates 
    message
    based on 
    trigger
    and 
    context
    .

Typical use cases

Synthetic Gradient for Feed-Forward Networks ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In this case we want to decouple two parts A and B of a neural network to achieve an update unlock, so that there is a normal forward pass from part A to B, but part A learns using synthetic gradient generated by the DNI.

.. image:: images/feedforward-update-unlock.png

Following the paper's convention, solid black arrows are update-locked forward connections, dashed black arrows are update-unlocked forward connections, green arrows are real error gradients and blue arrows are synthetic error gradients. Full circles denote synthetic gradient loss computation and 

Synthesizer
update.

We can use a 

BackwardInterface
to do that:

.. code-block:: python

class Network(torch.nn.Module):


def __init__(self):
    # ...

    # 1. create a BackwardInterface, assuming that dimensionality of
    #    the activation for which we want to synthesize gradients is
    #    activation_dim
    self.backward_interface = dni.BackwardInterface(
        dni.BasicSynthesizer(output_dim=activation_dim, n_hidden=1)
    )

    # ...

def forward(self, x):
    # ...

    # 2. call the BackwardInterface at the point where we want to
    #    decouple the network
    x = self.backward_interface(x)

    # ...

    return x

That's it! During the forward pass, 

BackwardInterface
will use a 
Synthesizer
to generate synthetic gradient w.r.t. activation, backpropagate it and add to the computation graph a node that will intercept the real gradient during the backward pass and use it to update the 
Synthesizer
's estimate.

The 

Synthesizer
used here is 
BasicSynthesizer
- a multi-layer perceptron with ReLU activation function. Writing a custom 
Synthesizer
is described at 
Writing custom Synthesizers
_.

You can specify a 

context
by passing 
context_dim
(dimensionality of the context vector) to the 
BasicSynthesizer
constructor and wrapping all DNI calls in the 
dni.synthesizer_context
context manager:

.. code-block:: python

class Network(torch.nn.Module):


def __init__(self):
    # ...

    self.backward_interface = dni.BackwardInterface(
        dni.BasicSynthesizer(
            output_dim=activation_dim, n_hidden=1,
            context_dim=context_dim
        )
    )

    # ...

def forward(self, x, y):
    # ...

    # assuming that context is labels given in variable y
    with dni.synthesizer_context(y):
        x = self.backward_interface(x)

    # ...

    return x

Example code for digit classification on MNIST is at 

examples/mnist-mlp
_.

Complete Unlock for Feed-Forward Networks ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In this case we want to decouple two parts A and B of a neural network to achieve forward and update unlock, so that part B receives synthetic input and part A learns using synthetic gradient generated by the DNI.

.. image:: images/feedforward-complete-unlock.png

Red arrows are synthetic inputs.

We can use a 

BidirectionalInterface
to do that:

.. code-block:: python

class Network(torch.nn.Module):


def __init__(self):
    # ...

    # 1. create a BidirectionalInterface, assuming that dimensionality of
    #    the activation for which we want to synthesize gradients is
    #    activation_dim and dimensionality of the input of the whole
    #    network is input_dim
    self.bidirectional_interface = dni.BidirectionalInterface(
        # Synthesizer generating synthetic inputs for part B, trigger
        # here is the input of the network
        dni.BasicSynthesizer(
            output_dim=activation_dim, n_hidden=1,
            trigger_dim=input_dim
        ),
        # Synthesizer generating synthetic gradients for part A,
        # trigger here is the last activation of part A (no need to
        # specify dimensionality)
        dni.BasicSynthesizer(
            output_dim=activation_dim, n_hidden=1
        )
    )

    # ...

def forward(self, input):
    x = input

    # ...

    # 2. call the BidirectionalInterface at the point where we want to
    #    decouple the network, need to pass both the last activation
    #    and the trigger, which in this case is the input of the whole
    #    network
    x = self.backward_interface(x, input)

    # ...

    return x

During the forward pass, 

BidirectionalInterface
will receive real activation, use it to update the input 
Synthesizer
, generate synthetic gradient w.r.t. that activation using the gradient 
Synthesizer
, backpropagate it, generate synthetic input using the input 
Synthesizer
and attach to it a computation graph node that will intercept the real gradient w.r.t. the synthetic input and use it to update the gradient 
Synthesizer
.

Example code for digit classification on MNIST is at 

examples/mnist-full-unlock
_.

Writing custom Synthesizers ^^^^^^^^^^^^^^^^^^^^^^^^^^^

This library includes only 

BasicSynthesizer
- a very simple 
Synthesizer
based on a multi-layer perceptron with ReLU activation function. It may not be sufficient for all cases, for example for classifying MNIST digits using a CNN the paper uses a 
Synthesizer
that is also a CNN.

You can easily write a custom 

Synthesizer
by subclassing 
torch.nn.Module
with method 
forward
taking 
trigger
and 
context
as arguments and returning a synthetic 
message
:

.. code-block:: python

class CustomSynthesizer(torch.nn.Module):


def forward(self, trigger, context):
    # synthesize the message
    return message



trigger
will be a 
torch.autograd.Variable
and 
context
will be whatever is passed to the 
dni.synthesizer_context
context manager, or 
None
if 
dni.synthesizer_context
is not used.

Example code for digit classification on MNIST using a CNN is at 

examples/mnist-cnn
_.

Synthetic Gradient for Recurrent Networks ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In this case we want to use DNI to approximate gradient from an infinitely-unrolled recurrent neural network and feed it to the last step of the RNN unrolled by truncated BPTT.

.. image:: images/rnn-update-unlock.png

We can use methods 

make_trigger
and 
backward
of 
BackwardInterface
to do that:

.. code-block:: python

class Network(torch.nn.module):


def __init__(self):
    # ...

    # 1. create a BackwardInterface, assuming that dimensionality of
    #    the RNN hidden state is hidden_dim
    self.backward_interface = dni.BackwardInterface(
        dni.BasicSynthesizer(output_dim=hidden_dim, n_hidden=1)
    )

    # ...

def forward(self, input, hidden):
    # ...

    # 2. call make_trigger on the first state of the unrolled RNN
    hidden = self.backward_interface.make_trigger(hidden)
    # run the RNN
    (output, hidden) = self.rnn(input, hidden)
    # 3. call backward on the last state of the unrolled RNN
    self.backward_interface.backward(hidden)

    # ...


in the training loop:


with dni.defer_backward():
    (output, hidden) = model(input, hidden)
    loss = criterion(output, target)
    dni.backward(loss)


BackwardInterface.make_trigger
marks the first hidden state as a 
trigger
used to update the gradient estimate. During the backward pass, gradient passing through the 
trigger
will be compared to synthetic gradient generated based on the same 
trigger
and the 
Synthesizer
will be updated. 
BackwardInterface.backward
computes synthetic gradient based on the last hidden state and backpropagates it.

Because we are passing both real and synthetic gradients through the same nodes in the computation graph, we need to use 

dni.defer_backward
and 
dni.backward
. 
dni.defer_backward
is a context manager that accumulates all gradients passed to 
dni.backward
(including those generated by 
Interfaces
) and backpropagates them all at once in the end. If we don't do that, PyTorch will complain about backpropagating twice through the same computation graph.

Example code for word-level language modeling on Penn Treebank is at 

examples/rnn
_.

Distributed training with a Complete Unlock ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The paper describes distributed training of complex neural architectures as one of the potential uses of DNI. In this case we have a network split into parts A and B trained independently, perhaps on different machines, communicating via DNI. We can use methods 

send
and 
receive
of 
BidirectionalInterface
to do that:

.. code-block:: python

class PartA(torch.nn.Module):


def forward(self, input):
    x = input

    # ...

    # send the intermediate results computed by part A via DNI
    self.bidirectional_interface.send(x, input)


class PartB(torch.nn.Module):


def forward(self, input):
    # receive the intermediate results computed by part A via DNI
    x = self.bidirectional_interface.receive(input)

    # ...

    return x



PartA
and 
PartB
have their own copies of the 
BidirectionalInterface
. 
BidirectionalInterface.send
will compute synthetic gradient w.r.t. 
x
(intermediate results computed by 
PartA
) based on 
x
and 
input
(input of the whole network), backpropagate it and update the estimate of 
x
. 
BidirectionalInterface.receive
will compute synthetic 
x
based on 
input
and in the backward pass, update the estimate of the gradient w.r.t. 
x
. This should work as long as 
BidirectionalInterface
parameters are synchronized between 
PartA
and 
PartB
once in a while.

There is no example code for this use case yet. Contributions welcome!

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