# Visualization

In PyTorch, if the attribute requires_grad of a tensor is True, the computation graph will be created if we use the tensor to do any operations. The computation graph is implemented like a link list – Tensors are nodes and they are linked by their attribute gran_fn. PyTorchViz is a Python package that uses Graphviz as a backend for plotting computation graphs. TorchOpt uses PyTorchViz as the blueprint and provides more easy-to-use visualization functions on the premise of supporting all its functions.

## Usage

Let’s start with a simple multiplication computation graph. We declared the variable x with the flag requires_grad=True and compute y = 2 * x. Then we visualize the computation graph of y.

We provide the function make_dot() which takes a tensor as input. The visualization code is shown as follows:

from IPython.display import display
import torch
import torchopt

y = 2 * x
display(torchopt.visual.make_dot(y)) The figure shows y is connected by the multiplication edge. The gradient of y will flow through the multiplication backward function and then accumulate on x. Note that we pass a dictionary for adding node labels.

To add auxiliary notes to the computation graph, we can pass a dictionary as argument params to make_dot(). The keys are the notes which would be shown in the computation figure and the values are the tensors that need to be noted. So the code above can be modified as follows:

from IPython.display import display
import torch
import torchopt

y = 2 * x
display(torchopt.visual.make_dot(y, params={'x': x, 'y': y}))


Then let’s plot a neural network. Note that we can pass the generator returned by the method named_parameters for adding node labels.

from IPython.display import display
import torch
from torch import nn
import torchopt

class Net(nn.Module):
def __init__(self, dim):
super().__init__()
self.fc = nn.Linear(dim, 1, bias=True)

def forward(self, x):
return self.fc(x)

dim = 5
batch_size = 2
net = Net(dim)
xs = torch.ones((batch_size, dim))
ys = torch.ones((batch_size, 1))
pred = net(xs)
loss = F.mse_loss(pred, ys)

display(torchopt.visual.make_dot(loss, params=(net.named_parameters(), {'loss': loss}))) The computation graph of meta-learning algorithms will be much more complex. Our visualization tool allows users to take as input the extracted network state for better visualization.

from IPython.display import display
import torch
from torch import nn
import torchopt

class MetaNet(nn.Module):
def __init__(self, dim):
super().__init__()
self.fc = nn.Linear(dim, 1, bias=True)

def forward(self, x, meta_param):
return self.fc(x) + meta_param

dim = 5
batch_size = 2
net = MetaNet(dim)

xs = torch.ones((batch_size, dim))
ys = torch.ones((batch_size, 1))

optimizer = torchopt.MetaSGD(net, lr=1e-3)

# Set enable_visual
net_state_0 = torchopt.extract_state_dict(net, enable_visual=True, visual_prefix='step0.')

pred = net(xs, meta_param)
loss = F.mse_loss(pred, ys)
optimizer.step(loss)

# Set enable_visual
net_state_1 = torchopt.extract_state_dict(net, enable_visual=True, visual_prefix='step1.')

pred = net(xs, meta_param)
loss = F.mse_loss(pred, torch.ones_like(pred))

# Draw computation graph
display(
torchopt.visual.make_dot(
loss, [net_state_0, net_state_1, {'meta_param': meta_param, 'loss': loss}]
)
) ## Notebook Tutorial

Check the notebook tutorial at Visualization.