# Modules¶

PyTorch uses modules to represent neural networks. Modules are:

• Building blocks of stateful computation. PyTorch provides a robust library of modules and makes it simple to define new custom modules, allowing for easy construction of elaborate, multi-layer neural networks.

• Tightly integrated with PyTorch’s autograd system. Modules make it simple to specify learnable parameters for PyTorch’s Optimizers to update.

• Easy to work with and transform. Modules are straightforward to save and restore, transfer between CPU / GPU / TPU devices, prune, quantize, and more.

This note describes modules, and is intended for all PyTorch users. Since modules are so fundamental to PyTorch, many topics in this note are elaborated on in other notes or tutorials, and links to many of those documents are provided here as well.

## A Simple Custom Module¶

To get started, let’s look at a simpler, custom version of PyTorch’s Linear module. This module applies an affine transformation to its input.

import torch
from torch import nn

class MyLinear(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.bias = nn.Parameter(torch.randn(out_features))

def forward(self, input):
return (input @ self.weight) + self.bias


This simple module has the following fundamental characteristics of modules:

• It inherits from the base Module class. All modules should subclass Module for composability with other modules.

• It defines some “state” that is used in computation. Here, the state consists of randomly-initialized weight and bias tensors that define the affine transformation. Because each of these is defined as a Parameter, they are registered for the module and will automatically be tracked and returned from calls to parameters(). Parameters can be considered the “learnable” aspects of the module’s computation (more on this later). Note that modules are not required to have state, and can also be stateless.

• It defines a forward() function that performs the computation. For this affine transformation module, the input is matrix-multiplied with the weight parameter (using the @ short-hand notation) and added to the bias parameter to produce the output. More generally, the forward() implementation for a module can perform arbitrary computation involving any number of inputs and outputs.

This simple module demonstrates how modules package state and computation together. Instances of this module can be constructed and called:

m = MyLinear(4, 3)
sample_input = torch.randn(4)
m(sample_input)
: tensor([-0.3037, -1.0413, -4.2057], grad_fn=<AddBackward0>)


Note that the module itself is callable, and that calling it invokes its forward() function. This name is in reference to the concepts of “forward pass” and “backward pass”, which apply to each module. The “forward pass” is responsible for applying the computation represented by the module to the given input(s) (as shown in the above snippet). The “backward pass” computes gradients of module outputs with respect to its inputs, which can be used for “training” parameters through gradient descent methods. PyTorch’s autograd system automatically takes care of this backward pass computation, so it is not required to manually implement a backward() function for each module. The process of training module parameters through successive forward / backward passes is covered in detail in Neural Network Training with Modules.

The full set of parameters registered by the module can be iterated through via a call to parameters() or named_parameters(), where the latter includes each parameter’s name:

for parameter in m.named_parameters():
print(parameter)
: ('weight', Parameter containing:
tensor([[ 1.0597,  1.1796,  0.8247],
[-0.5080, -1.2635, -1.1045],
[ 0.0593,  0.2469, -1.4299],
[-0.4926, -0.5457,  0.4793]], requires_grad=True))
('bias', Parameter containing:
tensor([ 0.3634,  0.2015, -0.8525], requires_grad=True))


In general, the parameters registered by a module are aspects of the module’s computation that should be “learned”. A later section of this note shows how to update these parameters using one of PyTorch’s Optimizers. Before we get to that, however, let’s first examine how modules can be composed with one another.

## Modules as Building Blocks¶

Modules can contain other modules, making them useful building blocks for developing more elaborate functionality. The simplest way to do this is using the Sequential module. It allows us to chain together multiple modules:

net = nn.Sequential(
MyLinear(4, 3),
nn.ReLU(),
MyLinear(3, 1)
)

sample_input = torch.randn(4)
net(sample_input)
: tensor([-0.6749], grad_fn=<AddBackward0>)


Note that Sequential automatically feeds the output of the first MyLinear module as input into the ReLU, and the output of that as input into the second MyLinear module. As shown, it is limited to in-order chaining of modules.

In general, it is recommended to define a custom module for anything beyond the simplest use cases, as this gives full flexibility on how submodules are used for a module’s computation.

For example, here’s a simple neural network implemented as a custom module:

import torch.nn.functional as F

class Net(nn.Module):
def __init__(self):
super().__init__()
self.l0 = MyLinear(4, 3)
self.l1 = MyLinear(3, 1)
def forward(self, x):
x = self.l0(x)
x = F.relu(x)
x = self.l1(x)
return x


This module is composed of two “children” or “submodules” (l0 and l1) that define the layers of the neural network and are utilized for computation within the module’s forward() method. Immediate children of a module can be iterated through via a call to children() or named_children():

net = Net()
for child in net.named_children():
print(child)
: ('l0', MyLinear())
('l1', MyLinear())


To go deeper than just the immediate children, modules() and named_modules() recursively iterate through a module and its child modules:

class BigNet(nn.Module):
def __init__(self):
super().__init__()
self.l1 = MyLinear(5, 4)
self.net = Net()
def forward(self, x):
return self.net(self.l1(x))

big_net = BigNet()
for module in big_net.named_modules():
print(module)
: ('', BigNet(
(l1): MyLinear()
(net): Net(
(l0): MyLinear()
(l1): MyLinear()
)
))
('l1', MyLinear())
('net', Net(
(l0): MyLinear()
(l1): MyLinear()
))
('net.l0', MyLinear())
('net.l1', MyLinear())


Sometimes, it’s necessary for a module to dynamically define submodules. The ModuleList and ModuleDict modules are useful here; they register submodules from a list or dict:

class DynamicNet(nn.Module):
def __init__(self, num_layers):
super().__init__()
self.linears = nn.ModuleList(
[MyLinear(4, 4) for _ in range(num_layers)])
self.activations = nn.ModuleDict({
'relu': nn.ReLU(),
'lrelu': nn.LeakyReLU()
})
self.final = MyLinear(4, 1)
def forward(self, x, act):
for linear in self.linears:
x = linear(x)
x = self.activations[act](x)
x = self.final(x)
return x

dynamic_net = DynamicNet(3)
sample_input = torch.randn(4)
output = dynamic_net(sample_input, 'relu')


For any given module, its parameters consist of its direct parameters as well as the parameters of all submodules. This means that calls to parameters() and named_parameters() will recursively include child parameters, allowing for convenient optimization of all parameters within the network:

for parameter in dynamic_net.named_parameters():
print(parameter)
: ('linears.0.weight', Parameter containing:
tensor([[-1.2051,  0.7601,  1.1065,  0.1963],
[ 3.0592,  0.4354,  1.6598,  0.9828],
[-0.4446,  0.4628,  0.8774,  1.6848],
[-0.1222,  1.5458,  1.1729,  1.4647]], requires_grad=True))
('linears.0.bias', Parameter containing:
tensor([ 1.5310,  1.0609, -2.0940,  1.1266], requires_grad=True))
('linears.1.weight', Parameter containing:
tensor([[ 2.1113, -0.0623, -1.0806,  0.3508],
[-0.0550,  1.5317,  1.1064, -0.5562],
[-0.4028, -0.6942,  1.5793, -1.0140],
[-0.0329,  0.1160, -1.7183, -1.0434]], requires_grad=True))
('linears.1.bias', Parameter containing:
tensor([ 0.0361, -0.9768, -0.3889,  1.1613], requires_grad=True))
('linears.2.weight', Parameter containing:
tensor([[-2.6340, -0.3887, -0.9979,  0.0767],
[-0.3526,  0.8756, -1.5847, -0.6016],
[-0.3269, -0.1608,  0.2897, -2.0829],
[ 2.6338,  0.9239,  0.6943, -1.5034]], requires_grad=True))
('linears.2.bias', Parameter containing:
tensor([ 1.0268,  0.4489, -0.9403,  0.1571], requires_grad=True))
('final.weight', Parameter containing:
tensor([[ 0.2509], [-0.5052], [ 0.3088], [-1.4951]], requires_grad=True))
('final.bias', Parameter containing:
tensor([0.3381], requires_grad=True))


It’s also easy to move all parameters to a different device or change their precision using to():

# Move all parameters to a CUDA device
dynamic_net.to(device='cuda')

# Change precision of all parameters
dynamic_net.to(dtype=torch.float64)

dynamic_net(torch.randn(5, device='cuda', dtype=torch.float64))
: tensor([6.5166], device='cuda:0', dtype=torch.float64, grad_fn=<AddBackward0>)


These examples show how elaborate neural networks can be formed through module composition. To allow for quick and easy construction of neural networks with minimal boilerplate, PyTorch provides a large library of performant modules within the torch.nn namespace that perform computation commonly found within neural networks, including pooling, convolutions, loss functions, etc.

In the next section, we give a full example of training a neural network.

For more information, check out:

## Neural Network Training with Modules¶

Once a network is built, it has to be trained, and its parameters can be easily optimized with one of PyTorch’s Optimizers from torch.optim:

# Create the network (from previous section) and optimizer
net = Net()
optimizer = torch.optim.SGD(net.parameters(), lr=1e-4, weight_decay=1e-2, momentum=0.9)

# Run a sample training loop that "teaches" the network
# to output the constant zero function
for _ in range(10000):
input = torch.randn(4)
output = net(input)
loss = torch.abs(output)
net.zero_grad()
loss.backward()
optimizer.step()


In this simplified example, the network learns to simply output zero, as any non-zero output is “penalized” according to its absolute value by employing torch.abs() as a loss function. While this is not a very interesting task, the key parts of training are present:

• A network is created.

• An optimizer (in this case, a stochastic gradient descent optimizer) is created, and the network’s parameters are associated with it.

• A training loop…
• acquires an input,

• runs the network,

• computes a loss,

• zeros the network’s parameters’ gradients,

• calls loss.backward() to update the parameters’ gradients,

• calls optimizer.step() to apply the gradients to the parameters.

After the above snippet has been run, note that the network’s parameters have changed. In particular, examining the value of l1’s weight parameter shows that its values are now much closer to 0 (as may be expected):

print(net.l1.weight)
: Parameter containing:
tensor([[-0.0013],
[ 0.0030],
[-0.0008]], requires_grad=True)


Training neural networks can often be tricky. For more information, check out:

## Module State¶

In the previous section, we demonstrated training a module’s “parameters”, or learnable aspects of computation. Now, if we want to save the trained model to disk, we can do so by saving its state_dict (i.e. “state dictionary”):

# Save the module
torch.save(net.state_dict(), 'net.pt')

...

# Load the module later on
new_net = Net()
new_net.load_state_dict(torch.load('net.pt'))
: <All keys matched successfully>


A module’s state_dict contains state that affects its computation. This includes, but is not limited to, the module’s parameters. For some modules, it may be useful to have state beyond parameters that affects module computation but is not learnable. For such cases, PyTorch provides the concept of “buffers”, both “persistent” and “non-persistent”. Following is an overview of the various types of state a module can have:

• Parameters: learnable aspects of computation; contained within the state_dict

• Buffers: non-learnable aspects of computation

• Persistent buffers: contained within the state_dict (i.e. serialized when saving & loading)

• Non-persistent buffers: not contained within the state_dict (i.e. left out of serialization)

As a motivating example for the use of buffers, consider a simple module that maintains a running mean. We want the current value of the running mean to be considered part of the module’s state_dict so that it will be restored when loading a serialized form of the module, but we don’t want it to be learnable. This snippet shows how to use register_buffer() to accomplish this:

class RunningMean(nn.Module):
def __init__(self, num_features, momentum=0.9):
super().__init__()
self.momentum = momentum
self.register_buffer('mean', torch.zeros(num_features))
def forward(self, x):
self.mean = self.momentum * self.mean + (1.0 - self.momentum) * x
return self.mean


Now, the current value of the running mean is considered part of the module’s state_dict and will be properly restored when loading the module from disk:

m = RunningMean(4)
for _ in range(10):
input = torch.randn(4)
m(input)

print(m.state_dict())
: OrderedDict([('mean', tensor([ 0.1041, -0.1113, -0.0647,  0.1515]))]))

# Serialized form will contain the 'mean' tensor
torch.save(m.state_dict(), 'mean.pt')

m_loaded = RunningMean(4)
m_loaded.load_state_dict(torch.load('mean.pt'))
assert(torch.all(m.mean == m_loaded.mean))


As mentioned previously, buffers can be left out of the module’s state_dict by marking them as non-persistent:

self.register_buffer('unserialized_thing', torch.randn(5), persistent=False)


Both persistent and non-persistent buffers are affected by model-wide device / dtype changes applied with to():

# Moves all module parameters and buffers to the specified device / dtype
m.to(device='cuda', dtype=torch.float64)


Buffers of a module can be iterated over using buffers() or named_buffers().

For more information, check out:

## Module Hooks¶

In Neural Network Training with Modules, we demonstrated the training process for a module, which iteratively performs forward and backward passes, updating module parameters each iteration. For more control over this process, PyTorch provides “hooks” that can perform arbitrary computation during a forward or backward pass, even modifying how the pass is done if desired. Some useful examples for this functionality include debugging, visualizing activations, examining gradients in-depth, etc. Hooks can be added to modules you haven’t written yourself, meaning this functionality can be applied to third-party or PyTorch-provided modules.

PyTorch provides two types of hooks for modules:

All hooks allow the user to return an updated value that will be used throughout the remaining computation. Thus, these hooks can be used to either execute arbitrary code along the regular module forward/backward or modify some inputs/outputs without having to change the module’s forward() function.

## Advanced Features¶

PyTorch also provides several more advanced features that are designed to work with modules. All these functionalities are “inherited” when writing a new module. In-depth discussion of these features can be found in the links below.

For more information, check out: