This page was generated from docs/advanced/graph_overview.ipynb. Interactive online version:

# Computational graphs in Rockpool

Graphs is Rockpool are used to convert the structure of an arbitrary network to deploy on neuromorphic hardware. The graphs cannot be used for simulation, and are generally more constrained than networks used for training and simulation.

```
[1]:
```

```
# - Switch off warnings
import warnings
warnings.filterwarnings("ignore")
```

## Graph base classes

Graphs consist of modules (derived from the class `graph.GraphModule`

), connected over nodes (derived from the class `graph.GraphNode`

).

`graph.GraphModule`

s are units of computation. For example, a set of linear weights; a population of spiking neurons.

`graph.GraphModule`

s contain links to sets of input nodes and output nodes, which define the channels of information flowing in and out of a module. These nodes are `graph.GraphNode`

objects.

```
[2]:
```

```
from IPython.display import Image
from pathlib import Path
Image(Path("images", "GraphModule.png"))
```

```
[2]:
```

The figure above shows the conceptual contents of a `graph.GraphModule`

. `input_nodes`

and `output_nodes`

are ordered lists containing references to zero or more `graph.GraphNode`

objects.

`graph.GraphModule`

subclasses define specific computational units. They contain all additional parameters needed to define the configuration of the computational unit. For example, see the `graph.LIFNeuronWithSynsRealValue`

class, which contains the configuration parameters needed to define a standard LIF neuron with exponential synapses.

A `graph.GraphNode`

is an object that holds connections between `graph.GraphModule`

s. Each node can have multiple sources and sinks of information.

```
[3]:
```

```
Image(Path("images", "GraphNode.png"))
```

```
[3]:
```

The figure above shows the conceptual contents of a `graph.GraphNode`

object. It simply acts as plumbing between `graph.GraphModule`

objects, holding references to source and sink `graph.GraphModule`

s that are connected over this `graph.GraphNode`

.

## Building graphs

You can generate `graph.GraphModule`

s by providing existing input and output nodes explicitly, as well as other parameters. Or you can use the `_factory()`

method, which generates new input and output nodes for the module.

When you instantiate a `graph.GraphModule`

subclass, you can provide additional arguments to the constructor or the `_factory()`

method to fill in extra attributes of the object.

You should connect modules using the `graph.utils.connect_modules()`

function. This function wires up two modules by merging the output nodes from the source module with the input nodes of the destination module.

```
[4]:
```

```
Image(Path("images", "connect.png"))
```

```
[4]:
```

`graph.utils.connect_modules()`

also supports connecting subsets of output / input nodes. See the help for `graph.utils.connect_modules()`

for more details.

Once you have built up a graph, you can can conveniently encapsulate complex graphs and subgraphs using the `graph.GraphHolder`

class.

```
[5]:
```

```
Image(Path("images", "GraphHolder.png"))
```

```
[5]:
```

This class only needs the input_nodes and output_nodes arguments, and conceptually holds an entire subgraph linked from those nodes.

You can also use the `as_GraphHolder()`

function to easily encapsulate a `GraphModule`

as a `GraphHolder`

.

## Exploring graphs

Graphs can be traversed by iterating over `GraphModule.input_nodes`

, `GraphNode.sink_modules`

, etc.

```
[6]:
```

```
import rockpool.graph as rg
mod1 = rg.GraphModule._factory(1, 2, "Mod1")
mod2 = rg.GraphModule._factory(2, 1, "Mod2")
rg.connect_modules(mod1, mod2)
graph = rg.GraphHolder(mod1.input_nodes, mod2.output_nodes, "Graph", None)
graph
```

```
[6]:
```

```
GraphHolder "Graph" with 1 input nodes -> 1 output nodes
```

```
[7]:
```

```
graph.input_nodes[0]
```

```
[7]:
```

```
GraphNode 4461578656 with 0 source modules and 1 sink modules
```

```
[8]:
```

```
graph.input_nodes[0].sink_modules[0]
```

```
[8]:
```

```
GraphModule "Mod1" with 1 input nodes -> 2 output nodes
```

```
[9]:
```

```
graph.input_nodes[0].sink_modules[0].output_nodes[0]
```

```
[9]:
```

```
GraphNode 4461584544 with 1 source modules and 1 sink modules
```

```
[10]:
```

```
graph.input_nodes[0].sink_modules[0].output_nodes[0].sink_modules[0]
```

```
[10]:
```

```
GraphModule "Mod2" with 2 input nodes -> 1 output nodes
```

And so on. More convenient traversal and graph search algorithms are provided by the .graph.utils package.

### Bags of graphs

`graph.utils.bag_graph()`

converts a graph into two collections: a list of unique `GraphModule`

s in the graph, and a list of unique `GraphNode`

s. It does so by recursively traversing a graph from input to output. Note that any unreachable modules or nodes will not be returned.

```
[11]:
```

```
rg.bag_graph(graph)
```

```
[11]:
```

```
([GraphNode 4461578656 with 0 source modules and 1 sink modules,
GraphNode 4461580192 with 1 source modules and 0 sink modules,
GraphNode 4461584544 with 1 source modules and 1 sink modules,
GraphNode 4461577792 with 1 source modules and 1 sink modules],
[GraphModule "Mod1" with 1 input nodes -> 2 output nodes,
GraphModule "Mod2" with 2 input nodes -> 1 output nodes])
```

### Searching by module class

If you know the specific `graph.GraphModule`

subclass you are looking for, then the helper function `graph.utils.find_modules_of_subclass()`

will be useful. It searches an entire graph for modules of the desired class, and returns a list of unique modules.

```
[12]:
```

```
import rockpool.graph as rg
import numpy as np
lin = rg.LinearWeights._factory(1, 2, "Lin", None, np.zeros((1, 2)))
rate = rg.RateNeuronWithSynsRealValue._factory(2, 2, "Rate")
rg.connect_modules(lin, rate)
graph = rg.GraphHolder(lin.input_nodes, rate.output_nodes, "Graph", None)
print(rg.find_modules_of_subclass(graph, rg.LinearWeights))
print(rg.find_modules_of_subclass(graph, rg.RateNeuronWithSynsRealValue))
```

```
[LinearWeights "Lin" with 1 input nodes -> 2 output nodes]
[RateNeuronWithSynsRealValue "Rate" with 2 input nodes -> 2 output nodes]
```

### Searching for recurrent modules

Loopy graphs are permitted, as are recurrently connected modules. Recurrent modules are `GraphModule`

s where at least two modules are connected back to back (see below)

```
[13]:
```

```
Image(Path("images", "recurrent_modules.png"))
```

```
[13]:
```

The helper function `graph.utils.find_recurrent_modules()`

will return any recurrently-connected (in the sense above) `GraphModule`

objects in the graph. Note that it will not return loops with one or more modules in between.

```
[14]:
```

```
import rockpool.graph as rg
rec_mod1 = rg.GraphModule._factory(2, 2, "Recurrent 1")
# - Use the existing graph nodes to directly connect the two modules recurrently
rec_mod2 = rg.GraphModule(
rec_mod1.output_nodes,
rec_mod1.input_nodes,
"Recurrent 2",
None,
)
graph = rg.GraphHolder(rec_mod1.input_nodes, rec_mod2.output_nodes, "Graph", None)
rg.find_recurrent_modules(graph)
```

```
[14]:
```

```
[GraphModule "Recurrent 2" with 2 input nodes -> 2 output nodes,
GraphModule "Recurrent 1" with 2 input nodes -> 2 output nodes]
```

## Integrating custom `nn.Module`

s into the computational graph

If you make a custom neuron or custom network architecture, and want that class to participate in the graph extraction API, you need to implement the `Module.as_graph()`

method. This method must return a `GraphModule`

or `GraphHolder`

containing a graph of your module.

Combinators such as `ModSequential`

know how to assemble these sub-graphs into a complete computational graph of the whole network.

Below is an example of a simple custom spiking neuron class that represents itself as an LIF module in the computational graph. It returns an `graph.LIFNeuronWithSynsRealValue`

object, with parameters filled in from the rockpool module.

```
[15]:
```

```
import rockpool.graph as rg
from rockpool.nn.modules import LIFTorch
class MyLIFNeuron(LIFTorch):
def as_graph(self) -> rg.GraphModuleBase:
return rg.LIFNeuronWithSynsRealValue._factory(
size_in=self.size_in,
size_out=self.size_out,
name=self.name,
computational_module=self,
tau_mem=self.tau_mem,
tau_syn=self.tau_syn,
bias=self.bias,
dt=self.dt,
)
mod = MyLIFNeuron(4)
mod.as_graph()
```

```
Could not import package: No module named 'iaf_nest'
```

```
[15]:
```

```
LIFNeuronWithSynsRealValue with 4 input nodes -> 4 output nodes
```

Other graph module classes that may be useful are `LinearWeights`

, `GenericNeurons`

, `AliasConnection`

and `RateNeuronWithSynsRealValue`

.

More complex network architectures can be encapsulated by `GraphHolder`

objects to return. For example, the below `Module`

subclass contains two submodules — weights and neurons.

```
[16]:
```

```
from rockpool.nn.modules import Linear, Rate, Module
import rockpool.graph as rg
class WeightedRate(Module):
def __init__(self, shape: tuple, *args, **kwargs):
# - Initialise superclass
super().__init__(shape, *args, **kwargs)
# - Define two sub-modules
self.lin = Linear(shape[0:2])
self.rate = Rate(shape[1])
def evolve(self, input_data, record: bool = False):
# - Push data through the sub-modules
out, ns_lin, rd_lin = self.lin(input_data, record)
out, ns_rate, rd_rate = self.rate(out, record)
ns = dict("lin", ns_lin, "rate", ns_rate)
rd = dict("lin", rd_lin, "rate", rd_rate)
return out, ns, rd
```

Now we add a `as_graph()`

override method. This returns a graph that represents the computational graph of this module. The structure of the subgraph must match the computation that takes place in the `evolve()`

method above, since it cannot be extracted automatically.

However, since each sub-module also implements the `as_graph()`

interface, we can quickly and conveniently build a computational graph for our custom module.

```
[17]:
```

```
class WeightedRate(WeightedRate):
def as_graph(self) -> rg.GraphModuleBase:
# - Get a graph module for the two sub-modules
glin = self.lin.as_graph()
grate = self.rate.as_graph()
# - Connnect the two modules
rg.connect_modules(glin, grate)
# - Return a graph holder
return rg.GraphHolder(glin.input_nodes, grate.output_nodes, self.name, self)
```

```
[18]:
```

```
mod = WeightedRate((1, 2))
mod.as_graph()
```

```
[18]:
```

```
GraphHolder with 1 input nodes -> 2 output nodes
```

The goal with graphs an sub-graphs is to build a flat structure of computational modules that can be examined programmatically to map the graph onto neuromorphic hardware, or convert its structure.

## Next steps

To learn about creating your own mappers onto your own HW, and how to subclass `GraphModule`

, see Graph mapping.