{ "cells": [ { "cell_type": "markdown", "id": "f1bfa603", "metadata": {}, "source": [ "*Copyright (C) 2022 Intel Corporation*
\n", "*SPDX-License-Identifier: BSD-3-Clause*
\n", "*See: https://spdx.org/licenses/*\n", "\n", "---" ] }, { "cell_type": "markdown", "id": "3ebce42a", "metadata": {}, "source": [ "# Walk through Lava\n", "\n", "Lava is an open-source software library dedicated to the development of algorithms for neuromorphic computation. To that end, Lava provides an easy-to-use Python interface for creating the bits and pieces required for such a neuromorphic algorithm. For easy development, Lava allows to run and test all neuromorphic algorithms on standard von-Neumann hardware like CPU, before they can be deployed on neuromorphic processors such as the Intel Loihi 1/2 processor to leverage their speed and power advantages. Furthermore, Lava is designed to be extensible to custom implementations of neuromorphic behavior and to support new hardware backends.\n", "\n", "Lava can fundamentally be used at two different levels: Either by using existing resources which can be used to create complex algorithms while requiring almost no deep neuromorphic knowledge. Or, for custom behavior, Lava can be easily extended with new behavior defined in Python and C.\n", "\n", "![lava_overview.png](https://raw.githubusercontent.com/lava-nc/lava-docs/dev/walk-through-tutorial/_static/images/tutorial00/lava_overview.png)\n", "\n", "This tutorial gives an high-level overview over the key components of Lava. For illustration, we will use a simple working example: a feed-forward multi-layer LIF network executed locally on CPU.\n", "In the first section of the tutorial, we use the internal resources of Lava to construct such a network and in the second section, we demonstrate how to extend Lava with a custom process using the example of an input generator.\n", "\n", "In addition to the core Lava library described in the present tutorial, the following tutorials guide you to use high level functionalities:\n", "- [lava-dl](https://github.com/lava-nc/lava-dl) for deep learning applications\n", "- [lava-optimization](https://github.com/lava-nc/lava-optimization) for constraint optimization\n", "- [lava-dnf](https://github.com/lava-nc/lava-dnf) for Dynamic Neural Fields" ] }, { "cell_type": "markdown", "id": "47e4bb81", "metadata": {}, "source": [ "## 1. Usage of the Process Library" ] }, { "cell_type": "markdown", "id": "910bc90a", "metadata": {}, "source": [ "In this section, we will use a simple 2-layered feed-forward network of LIF neurons executed on CPU as canonical example. \n", "\n", "The fundamental building block in the Lava architecture is the `Process`. A `Process` describes a functional group, such as a population of `LIF` neurons, which runs asynchronously and parallel and communicates via `Channels`. A `Process` can take different forms and does not necessarily be a population of neurons, for example it could be a complete network, program code or the interface to a sensor (see figure below).\n", "\n", "![process_overview.png](https://raw.githubusercontent.com/lava-nc/lava-docs/dev/walk-through-tutorial/_static/images/tutorial00/proc_overview.png)\n", "\n", "For convenience, Lava provides a growing Process Library in which many commonly used `Processes` are publicly available.\n", "In the first section of this tutorial, we will use the `Processes` of the Process Library to create and execute a multi-layer LIF network. Take a look at the [documentation](https://lava-nc.org) to find out what other `Processes` are implemented in the Process Library.\n", "\n", "Let's start by importing the classes `LIF` and `Dense` and take a brief look at the docstring." ] }, { "cell_type": "code", "execution_count": 1, "id": "f5f304d1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001B[0;31mInit signature:\u001B[0m \u001B[0mLIF\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", "\u001B[0;31mDocstring:\u001B[0m \n", "Leaky-Integrate-and-Fire (LIF) neural Process.\n", "\n", "LIF dynamics abstracts to:\n", "u[t] = u[t-1] * (1-du) + a_in # neuron current\n", "v[t] = v[t-1] * (1-dv) + u[t] + bias # neuron voltage\n", "s_out = v[t] > vth # spike if threshold is exceeded\n", "v[t] = 0 # reset at spike\n", "\n", "Parameters\n", "----------\n", "shape : tuple(int)\n", " Number and topology of LIF neurons.\n", "u : float, list, numpy.ndarray, optional\n", " Initial value of the neurons' current.\n", "v : float, list, numpy.ndarray, optional\n", " Initial value of the neurons' voltage (membrane potential).\n", "du : float, optional\n", " Inverse of decay time-constant for current decay. Currently, only a\n", " single decay can be set for the entire population of neurons.\n", "dv : float, optional\n", " Inverse of decay time-constant for voltage decay. Currently, only a\n", " single decay can be set for the entire population of neurons.\n", "bias_mant : float, list, numpy.ndarray, optional\n", " Mantissa part of neuron bias.\n", "bias_exp : float, list, numpy.ndarray, optional\n", " Exponent part of neuron bias, if needed. Mostly for fixed point\n", " implementations. Ignored for floating point implementations.\n", "vth : float, optional\n", " Neuron threshold voltage, exceeding which, the neuron will spike.\n", " Currently, only a single threshold can be set for the entire\n", " population of neurons.\n", "\n", "Example\n", "-------\n", ">>> lif = LIF(shape=(200, 15), du=10, dv=5)\n", "This will create 200x15 LIF neurons that all have the same current decay\n", "of 10 and voltage decay of 5.\n", "\u001B[0;31mInit docstring:\u001B[0m Initializes a new Process.\n", "\u001B[0;31mFile:\u001B[0m ~/lava-nc/lava/src/lava/proc/lif/process.py\n", "\u001B[0;31mType:\u001B[0m ProcessPostInitCaller\n", "\u001B[0;31mSubclasses:\u001B[0m LIFReset\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from lava.proc.lif.process import LIF\n", "from lava.proc.dense.process import Dense\n", "\n", "LIF?" ] }, { "cell_type": "markdown", "id": "b4dce60e", "metadata": {}, "source": [ "The docstring gives insights about the parameters and internal dynamics of the `LIF` neuron. `Dense` is used to connect to a neuron population in an all-to-all fashion, often implemented as a matrix-vector product.\n", "\n", "In the next box, we will create the `Processes` we need to implement a multi-layer LIF (LIF-Dense-LIF) network." ] }, { "cell_type": "code", "execution_count": 2, "id": "dbd808cb", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "# Create processes\n", "lif1 = LIF(shape=(3, ), # Number and topological layout of units in the process\n", " vth=10., # Membrane threshold\n", " dv=0.1, # Inverse membrane time-constant\n", " du=0.1, # Inverse synaptic time-constant\n", " bias_mant=(1.1, 1.2, 1.3), # Bias added to the membrane voltage in every timestep\n", " name=\"lif1\")\n", "\n", "dense = Dense(weights=np.random.rand(2, 3), # Initial value of the weights, chosen randomly\n", " name='dense')\n", "\n", "lif2 = LIF(shape=(2, ), # Number and topological layout of units in the process\n", " vth=10., # Membrane threshold\n", " dv=0.1, # Inverse membrane time-constant\n", " du=0.1, # Inverse synaptic time-constant\n", " bias_mant=0., # Bias added to the membrane voltage in every timestep\n", " name='lif2')" ] }, { "cell_type": "markdown", "id": "1fbfed43", "metadata": {}, "source": [ "As you can see, we can either specify parameters with scalars, then all units share the same initial value for this parameter, or with a tuple (or list, or numpy array) to set the parameter individually per unit.\n", "\n", "\n", "### Processes\n", "\n", "Let's investigate the objects we just created. As mentioned before, both, `LIF` and `Dense` are examples of `Processes`, the main building block in Lava.\n", "\n", "A `Process` holds three key components (see figure below):\n", "\n", "- Input ports\n", "- Variables\n", "- Output ports\n", "\n", "![process.png](https://raw.githubusercontent.com/lava-nc/lava-docs/dev/walk-through-tutorial/_static/images/tutorial00/proc.png)\n", "\n", "The `Vars` are used to store internal states of the `Process` while the `Ports` are used to define the connectivity between the `Processes`. Note that a `Process` only defines the `Vars` and `Ports` but not the behavior. This is done separately in a `ProcessModel`. To separate the interface from the behavioral implementation has the advantage that we can define the behavior of a `Process` for multiple hardware backends using multiple `ProcessModels` without changing the interface. We will get into more detail about `ProcessModels` in the second part of this tutorial.\n", "\n", "### Ports and connections\n", "\n", "Let's take a look at the `Ports` of the `LIF` and `Dense` processes we just created. The output `Port` of the `LIF` neuron is called `s_out`, which stands for 'spiking' output. The input `Port` is called `a_in` which stands for 'activation' input." ] }, { "cell_type": "code", "execution_count": 3, "id": "3f8f656a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['s_out']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lif1.out_ports.member_names" ] }, { "cell_type": "markdown", "id": "f8ed37d8", "metadata": {}, "source": [ "For example, we can see the size of the `Port` which is in particular important because `Ports` can only connect if their shape matches." ] }, { "cell_type": "code", "execution_count": 4, "id": "f77b750d", "metadata": {}, "outputs": [], "source": [ "assert(lif1.s_out.size == dense.s_in.size)" ] }, { "cell_type": "markdown", "id": "7378d13d", "metadata": {}, "source": [ "Similarly we can investigate the input port of the second `LIF` population." ] }, { "cell_type": "code", "execution_count": 5, "id": "706dc863", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['a_in']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lif2.in_ports.member_names" ] }, { "cell_type": "code", "execution_count": 6, "id": "521bf370", "metadata": {}, "outputs": [], "source": [ "assert(dense.a_out.size == lif2.a_in.size)" ] }, { "cell_type": "markdown", "id": "1c5da64b", "metadata": {}, "source": [ "Now that we know about the input and output `Ports` of the `LIF` and `Dense` `Processes`, we can `connect` the network to complete the LIF-Dense-LIF structure.\n", "\n", "![process_comm.png](https://raw.githubusercontent.com/lava-nc/lava-docs/dev/walk-through-tutorial/_static/images/tutorial00/procs.png)\n", "\n", "As can be seen in the figure above, by `connecting` two processes, a `Channel` between them is created which means that messages between those two `Processes` can be exchanged." ] }, { "cell_type": "code", "execution_count": 7, "id": "657063e9", "metadata": {}, "outputs": [], "source": [ "# Connect the OutPort of lif1 to the InPort of dense\n", "lif1.s_out.connect(dense.s_in)\n", "\n", "# Connect the OutPort of dense to the InPort of lif2\n", "dense.a_out.connect(lif2.a_in)" ] }, { "cell_type": "markdown", "id": "7f0add01", "metadata": {}, "source": [ "### Variables\n", "\n", "Similar to the `Ports`, we can investigate the `Vars` of a `Process`." ] }, { "cell_type": "code", "execution_count": 8, "id": "d6be4fa0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['bias_exp', 'bias_mant', 'du', 'dv', 'u', 'v', 'vth']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lif1.vars.member_names" ] }, { "cell_type": "markdown", "id": "971d5ed7", "metadata": {}, "source": [ "`Vars` are also accessible as member variables. We can print details of a specific `Var` to see the shape, initial value and current value. The `shareable` attribute controls whether a `Var` can be manipulated via remote memory access. Learn more about about this topic in the [remote memory access tutorial](https://github.com/lava-nc/lava/blob/main/tutorials/in_depth/tutorial07_remote_memory_access.ipynb)." ] }, { "cell_type": "code", "execution_count": 9, "id": "46c18b1f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Variable: v\n", " shape: (3,)\n", " init: 0\n", " shareable: True\n", " value: 0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lif1.v" ] }, { "cell_type": "markdown", "id": "7574279a", "metadata": {}, "source": [ "We can take a look at the random weights of `Dense` by calling the `get` function." ] }, { "cell_type": "code", "execution_count": 10, "id": "e60c16db", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.48667088, 0.24619592, 0.89903799],\n", " [0.96371252, 0.58821522, 0.37490556]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dense.weights.get()" ] }, { "cell_type": "markdown", "id": "6afa9b38", "metadata": {}, "source": [ "
\n", "Note: There is also a `set` function available to change the value of a `Var` after the network was executed.\n", "
" ] }, { "cell_type": "markdown", "id": "49a7f22e", "metadata": {}, "source": [ "### Record internal Vars over time\n", "\n", "In order to record the evolution of the internal `Vars` over time, we need a `Monitor`.\n", "For this example, we want to record the membrane potential of both `LIF` Processes, hence we need two `Monitors`." ] }, { "cell_type": "code", "execution_count": 11, "id": "635bf66b", "metadata": {}, "outputs": [], "source": [ "from lava.proc.monitor.process import Monitor\n", "\n", "monitor_lif1 = Monitor()\n", "monitor_lif2 = Monitor()" ] }, { "cell_type": "markdown", "id": "05dc0a83", "metadata": {}, "source": [ "We can define the `Var` that a `Monitor` should record, as well as the recording duration, using the `probe` function." ] }, { "cell_type": "code", "execution_count": 12, "id": "ef93825c", "metadata": {}, "outputs": [], "source": [ "num_steps = 100\n", "\n", "monitor_lif1.probe(lif1.v, num_steps)\n", "monitor_lif2.probe(lif2.v, num_steps)" ] }, { "cell_type": "markdown", "id": "84a02b97", "metadata": {}, "source": [ "
\n", "Note: Currently, the `Monitor` can only record a single `Var` per `Process` and supports only CPU. This functionality will be extended in future releases.\n", "
" ] }, { "cell_type": "markdown", "id": "ce0c6495", "metadata": {}, "source": [ "### Execution\n", "\n", "Now, that we finished to set up the network and recording `Processes`, we can execute the network by simply calling the `run` function of one of the `Processes`.\n", "\n", "The `run` function requires two parameters, a `RunCondition` and a `RunConfig`. The `RunCondition` defines *how* the network runs (i.e. for how long) while the `RunConfig` defines on which hardware backend the `Processes` should be mapped and executed." ] }, { "cell_type": "markdown", "id": "9a43d818", "metadata": {}, "source": [ "#### Run Conditions\n", "\n", "Let's investigate the different possibilities for `RunConditions`. One option is `RunContinuous` which executes the network continuously and non-blocking until `pause` or `stop` is called." ] }, { "cell_type": "code", "execution_count": 13, "id": "0cf86c34", "metadata": {}, "outputs": [], "source": [ "from lava.magma.core.run_conditions import RunContinuous\n", "run_condition = RunContinuous()" ] }, { "cell_type": "markdown", "id": "865e2ca9", "metadata": {}, "source": [ "The second option is `RunSteps`, which allows you to define an exact amount of time steps the network should run." ] }, { "cell_type": "code", "execution_count": 14, "id": "91fbce5e", "metadata": {}, "outputs": [], "source": [ "from lava.magma.core.run_conditions import RunSteps, RunContinuous\n", "\n", "run_condition = RunSteps(num_steps=num_steps)" ] }, { "cell_type": "markdown", "id": "2366d304", "metadata": {}, "source": [ "For this example. we will use `RunSteps` and let the network run exactly `num_steps` time steps.\n", "\n", "#### RunConfigs\n", "\n", "Next, we need to provide a `RunConfig`. As mentioned above, The `RunConfig` defines on which hardware backend the network is executed.\n", "\n", "For example, we could run the network on the Loihi1 processor using the `Loihi1HwCfg`, on Loihi2 using the `Loihi2HwCfg`, or on CPU using the `Loihi1SimCfg`. The compiler and runtime then automatically select the correct `ProcessModels` such that the `RunConfig` can be fulfilled.\n", "\n", "For this section of the tutorial, we will run our network on CPU, later we will show how to run the same network on the Loihi2 processor." ] }, { "cell_type": "code", "execution_count": 15, "id": "14c301f7", "metadata": {}, "outputs": [], "source": [ "from lava.magma.core.run_configs import Loihi1SimCfg\n", "\n", "run_cfg = Loihi1SimCfg(select_tag=\"floating_pt\")" ] }, { "cell_type": "markdown", "id": "baf95f1f", "metadata": {}, "source": [ "#### Execute\n", "\n", "Finally, we can simply call the `run` function of the second `LIF` process and provide the `RunConfig` and `RunCondition`." ] }, { "cell_type": "code", "execution_count": 16, "id": "331f71b7", "metadata": {}, "outputs": [], "source": [ "lif2.run(condition=run_condition, run_cfg=run_cfg)" ] }, { "cell_type": "markdown", "id": "1d8ea488", "metadata": {}, "source": [ "### Retrieve recorded data\n", "\n", "After the simulation has stopped, we can call `get_data` on the two monitors to retrieve the recorded membrane potentials." ] }, { "cell_type": "code", "execution_count": 17, "id": "582215cd", "metadata": {}, "outputs": [], "source": [ "data_lif1 = monitor_lif1.get_data()\n", "data_lif2 = monitor_lif2.get_data()" ] }, { "cell_type": "markdown", "id": "22f44fba", "metadata": {}, "source": [ "Alternatively, we can also use the provided `plot` functionality of the `Monitor`, to plot the recorded data. As we can see, the bias of the first `LIF` population drives the membrane potential to the threshold which generates output spikes. Those output spikes are passed through the `Dense` layer as input to the second `LIF` population." ] }, { "cell_type": "code", "execution_count": 18, "id": "32f48b10", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib\n", "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "\n", "# Create a subplot for each monitor\n", "fig = plt.figure(figsize=(16, 5))\n", "ax0 = fig.add_subplot(121)\n", "ax1 = fig.add_subplot(122)\n", "\n", "# Plot the recorded data\n", "monitor_lif1.plot(ax0, lif1.v)\n", "monitor_lif2.plot(ax1, lif2.v)" ] }, { "cell_type": "markdown", "id": "cf5fcea8", "metadata": {}, "source": [ "As a last step we can stop the runtime by calling the `stop` function. `Stop` will terminate the `Runtime` and all states will be lost." ] }, { "cell_type": "code", "execution_count": 19, "id": "0ddcd735", "metadata": {}, "outputs": [], "source": [ "lif2.stop()" ] }, { "cell_type": "markdown", "id": "26af5f1d", "metadata": {}, "source": [ "### Summary\n", "\n", "- There are many tools available in the Process Library to construct basic networks\n", "- The fundamental building block in Lava is the `Process`\n", "- Each `Process` consists of `Vars` and `Ports`\n", "- A `Process` defines a common interface across hardware backends, but not the behavior\n", "- The `ProcessModel` defines the behavior of a `Process` for a specific hardware backend\n", "- `Vars` store internal states, `Ports` are used to implement communication channels between processes\n", "- The `RunConfig` defines on which hardware backend the network runs " ] }, { "cell_type": "markdown", "id": "cf9529fa", "metadata": {}, "source": [ "### Learn more about\n", "- [Processes](https://github.com/lava-nc/lava/blob/main/tutorials/in_depth/tutorial02_processes.ipynb) and [hierarchical Processes](https://github.com/lava-nc/lava/blob/main/tutorials/in_depth/tutorial06_hierarchical_processes.ipynb)\n", "- [Possible connectivity patterns](https://github.com/lava-nc/lava/blob/main/tutorials/in_depth/tutorial05_connect_processes.ipynb)\n", "- [Remote memory access](https://github.com/lava-nc/lava/blob/main/tutorials/in_depth/tutorial07_remote_memory_access.ipynb)\n", "- [Execution](https://github.com/lava-nc/lava/blob/main/tutorials/in_depth/tutorial04_execution.ipynb)" ] }, { "cell_type": "markdown", "id": "be6d778c", "metadata": {}, "source": [ "## 2. Create a custom Process\n", "\n", "In the previous section of this tutorial, we used `Processes` which were available in the Process Library. In many cases, this might be sufficient and the Process Library is constantly growing. Nevertheless, the Process Library can not cover all use-cases. Hence, Lava makes it very easy to create new `Processes`. In the following section, we want to show how to implement a custom `Process` and the corresponding behavior using a `ProcessModel`.\n", "\n", "The example of the previous section implemented a bias driven LIF-Dense-LIF network. One crucial aspect which is missing this example, is the input/output interaction with sensors and actuators. Commonly used sensors would be Dynamic Vision Sensors or artificial cochleas, but for demonstration purposes we will implement a simple `SpikeGenerator`. The purpose of the `SpikeGenerator` is to output random spikes to drive the LIF-Dense-LIF network. \n", "\n", "Let's start by importing the necessary classes from Lava." ] }, { "cell_type": "code", "execution_count": 20, "id": "656ac8dc", "metadata": {}, "outputs": [], "source": [ "from lava.magma.core.process.process import AbstractProcess\n", "from lava.magma.core.process.variable import Var\n", "from lava.magma.core.process.ports.ports import OutPort" ] }, { "cell_type": "markdown", "id": "bbc3acf4", "metadata": {}, "source": [ "All `Processes` in Lava inherit from a common base class called `AbstractProcess`. Additionally, we need `Var` for storing the spike probability and `OutPort` to define the output connections for our `SpikeGenerator`." ] }, { "cell_type": "code", "execution_count": 21, "id": "00bde8ce", "metadata": {}, "outputs": [], "source": [ "class SpikeGenerator(AbstractProcess):\n", " \"\"\"Spike generator process provides spikes to subsequent Processes.\n", "\n", " Parameters\n", " ----------\n", " shape: tuple\n", " defines the dimensionality of the generated spikes per timestep\n", " spike_prob: int\n", " spike probability in percent\n", " \"\"\"\n", " def __init__(self, shape: tuple, spike_prob: int) -> None: \n", " super().__init__()\n", " self.spike_prob = Var(shape=(1, ), init=spike_prob)\n", " self.s_out = OutPort(shape=shape)\n" ] }, { "cell_type": "markdown", "id": "6f392041", "metadata": {}, "source": [ "The constructor of `Var` requires the shape of the data to be stored and some initial value. We use this functionality to store the spike data. Similarly, we define an `OutPort` for our `SpikeGenerator`. " ] }, { "cell_type": "markdown", "id": "ea8e287c", "metadata": {}, "source": [ "### Create a new ProcessModel \n", "As mentioned earlier, the `Process` only defines the interface but not the behavior of the `SpikeGenerator`. We will do that in a separate `ProcessModel` which has the advantage that we can define the behavior of a `Process` on different hardware backends without changing the interface (see figure below). More details about the different kinds of `ProcessModels` can be found in the dedicated in-depth tutorials ([here](https://github.com/lava-nc/lava/blob/main/tutorials/in_depth/tutorial03_process_models.ipynb) and [here](https://github.com/lava-nc/lava/blob/main/tutorials/in_depth/tutorial06_hierarchical_processes.ipynb)). Lava automatically selects the correct `ProcessModel` for each `Process` given the `RunConfig`.\n", "\n", "![process_models.png](https://raw.githubusercontent.com/lava-nc/lava-docs/dev/walk-through-tutorial/_static/images/tutorial00/proc_models.png)\n", "\n", "So, let's go ahead and define the behavior of the `SpikeGenerator` on a CPU in Python. Later in this tutorial we will show how to implement the same behavior on an embedded CPU in C and how to implement the behavior of a `LIF` process on a Loihi2 neuro-core.\n", "\n", "We first import all necessary classes from Lava." ] }, { "cell_type": "code", "execution_count": 22, "id": "c68411dc", "metadata": {}, "outputs": [], "source": [ "from lava.magma.core.model.py.model import PyLoihiProcessModel\n", "from lava.magma.core.resources import CPU\n", "from lava.magma.core.decorator import implements, requires\n", "from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol\n", "from lava.magma.core.model.py.type import LavaPyType\n", "from lava.magma.core.model.py.ports import PyOutPort" ] }, { "cell_type": "markdown", "id": "524df362", "metadata": {}, "source": [ "All `ProcessModels` defined to run on CPU are written in Python and inherit from the common class called `PyLoihiProcessModel`. Further, we use the decorators `requires` and `implements` to define which computational resources (i.e. CPU, GPU, Loihi1NeuroCore, Loihi2NeuroCore) are required to execute this `ProcessModel` and which `Process` it implements. Finally, we need to specify the types of `Vars` and `Ports` in our `SpikeGenerator` using `LavaPyType` and `PyOutPort`.\n", "\n" ] }, { "cell_type": "markdown", "id": "6cad5b03", "metadata": {}, "source": [ "
\n", "Note: It is important to mention that the `ProcessModel` needs to implement the exact same Vars and Ports of the parent process using the same class attribute names.\n", "
" ] }, { "cell_type": "markdown", "id": "8d45cc48", "metadata": {}, "source": [ "Additionally, we define that our `PySpikeGeneratorModel` follows the `LoihiProtocol`. The `LoihiProtocol` defines that the execution of a model follows a specific sequence of phases. For example, there is the *spiking phase* (`run_spk`) in which input spikes are received, internal `Vars` are updated and output spikes are sent. There are other phases such as the *learning phase* (`run_lrn`) in which online learning takes place, or the *post management phase* (`run_post_mgmt`) in which memory content is updated. As the `SpikeGenerator` basically just sends out spikes, the correct place to implement its behavior is the `run_spk` phase. \n", "\n", "To implement the behavior, we need to have access to the global simulation time. We can easily access the simulation time with `self.time_step` and use that to index the `spike_data` and send out the corresponding spikes through the `OutPort`." ] }, { "cell_type": "code", "execution_count": 23, "id": "068cb965", "metadata": {}, "outputs": [], "source": [ "@implements(proc=SpikeGenerator, protocol=LoihiProtocol)\n", "@requires(CPU)\n", "class PySpikeGeneratorModel(PyLoihiProcessModel):\n", " \"\"\"Spike Generator process model.\"\"\"\n", " spike_prob: int = LavaPyType(int, int)\n", " s_out: PyOutPort = LavaPyType(PyOutPort.VEC_DENSE, float)\n", "\n", " def run_spk(self) -> None:\n", " # Generate random spike data\n", " spike_data = np.random.choice([0, 1], p=[1 - self.spike_prob/100, self.spike_prob/100], size=self.s_out.shape[0])\n", " \n", " # Send spikes\n", " self.s_out.send(spike_data)" ] }, { "cell_type": "markdown", "id": "642ad797", "metadata": {}, "source": [ "
\n", "Note: For the `SpikeGenerator` we only needed an `OutPort` which provides the `send` function to send data. For the `InPort` the corresponding function to receive data is called `recv`.\n", "
" ] }, { "cell_type": "markdown", "id": "5b61fee8", "metadata": {}, "source": [ "Next, we want to redefine our network as in the example before with the exception that we turn off all biases." ] }, { "cell_type": "code", "execution_count": 24, "id": "069e687d", "metadata": {}, "outputs": [], "source": [ "# Create processes\n", "lif1 = LIF(shape=(3, ), # Number of units in this process\n", " vth=10., # Membrane threshold\n", " dv=0.1, # Inverse membrane time-constant\n", " du=0.1, # Inverse synaptic time-constant\n", " bias_mant=0., # Bias added to the membrane voltage in every timestep\n", " name=\"lif1\")\n", "\n", "dense = Dense(weights=np.random.rand(2, 3), # Initial value of the weights, chosen randomly\n", " name='dense')\n", "\n", "lif2 = LIF(shape=(2, ), # Number of units in this process\n", " vth=10., # Membrane threshold\n", " dv=0.1, # Inverse membrane time-constant\n", " du=0.1, # Inverse synaptic time-constant\n", " bias_mant=0., # Bias added to the membrane voltage in every timestep\n", " name='lif2')\n", "\n", "# Connect the OutPort of lif1 to the InPort of dense\n", "lif1.s_out.connect(dense.s_in)\n", "\n", "# Connect the OutPort of dense to the InPort of lif2\n", "dense.a_out.connect(lif2.a_in)\n", "\n", "# Create Monitors to record membrane potentials\n", "monitor_lif1 = Monitor()\n", "monitor_lif2 = Monitor()\n", "\n", "# Probe membrane potentials from the two LIF populations\n", "monitor_lif1.probe(lif1.v, num_steps)\n", "monitor_lif2.probe(lif2.v, num_steps)" ] }, { "cell_type": "markdown", "id": "a58971d8", "metadata": {}, "source": [ "### Use the custom SpikeGenerator\n", "\n", "We instantiate the `SpikeGenerator` as usual with the shape of the fist `LIF` population.\n", "\n", "To define the connectivity between the `SpikeGenerator` and the first `LIF` population, we us another `Dense` Layer.\n", "Now, we can connect its `OutPort` to the `InPort` of the `Dense` layer and the `OutPort` of the `Dense` layer to the `InPort` of the first `LIF` population.\n" ] }, { "cell_type": "code", "execution_count": 25, "id": "4e1a6b58", "metadata": {}, "outputs": [], "source": [ "# Instantiate SpikeGenerator\n", "spike_gen = SpikeGenerator(shape=(lif1.a_in.shape[0], ), spike_prob=7)\n", "\n", "# Instantiate Dense\n", "dense_input = Dense(weights=np.eye(lif1.a_in.shape[0])) # one-to-one connectivity\n", "\n", "# Connect spike_gen to dense_input\n", "spike_gen.s_out.connect(dense_input.s_in)\n", "\n", "# Connect dense_input to LIF1 population\n", "dense_input.a_out.connect(lif1.a_in)" ] }, { "cell_type": "markdown", "id": "b2d8fc88", "metadata": {}, "source": [ "### Execute and plot\n", "\n", "Now that our network is complete, we can execute it the same way as before using the `RunCondition` and `RunConfig` we created in the previous example." ] }, { "cell_type": "code", "execution_count": 26, "id": "af9da307", "metadata": {}, "outputs": [], "source": [ "lif2.run(condition=run_condition, run_cfg=run_cfg)" ] }, { "cell_type": "markdown", "id": "6227e1bd", "metadata": {}, "source": [ "And now, we can retrieve the recorded data and plot the membrane potentials of the two `LIF` populations." ] }, { "cell_type": "code", "execution_count": 27, "id": "7cbc2b21", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a subplot for each monitor\n", "fig = plt.figure(figsize=(16, 5))\n", "ax0 = fig.add_subplot(121)\n", "ax1 = fig.add_subplot(122)\n", "\n", "# Plot the recorded data\n", "monitor_lif1.plot(ax0, lif1.v)\n", "monitor_lif2.plot(ax1, lif2.v)" ] }, { "cell_type": "markdown", "id": "12243ae4", "metadata": {}, "source": [ "As we can see, the spikes provided by the `SpikeGenerator` are sucessfully sent to the first `LIF` population. which in turn sends its output spikes to the second `LIF` population." ] }, { "cell_type": "code", "execution_count": 28, "id": "a922b4aa", "metadata": {}, "outputs": [], "source": [ "lif2.stop()" ] }, { "cell_type": "markdown", "id": "a9fadbe3", "metadata": {}, "source": [ "### Summary\n", "\n", "- Define custom a `Process` by inheritance from `AbstractProcess`\n", "- `Vars` are used to store internal data in the `Process`\n", "- `Ports` are used to connect to other `Processes`\n", "- The behavior of a `Process` is defined in a `ProcessModel`\n", "- `ProcessModels` are aware of their hardware backend, they get selected automatically by the compiler/runtime\n", "- `PyProcessModels` run on CPU\n", "- Number and names of `Vars` and `Ports` of a `ProcessModel` must match those of the `Process` it implements" ] }, { "cell_type": "markdown", "id": "f90d252c", "metadata": {}, "source": [ "### Learn more about\n", "- [ProcessModels](https://github.com/lava-nc/lava/blob/main/tutorials/in_depth/tutorial03_process_models.ipynb)\n" ] }, { "cell_type": "markdown", "id": "2e335eee", "metadata": {}, "source": [ "### How to learn more?\n", "\n", "If you want to find out more about Lava, have a look at the [Lava documentation](https://lava-nc.org/) or dive deeper with the [in-depth tutorials](https://github.com/lava-nc/lava/tree/main/tutorials/in_depth).\n", "\n", "To receive regular updates on the latest developments and releases of the Lava Software Framework please subscribe to [our newsletter](http://eepurl.com/hJCyhb)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" } }, "nbformat": 4, "nbformat_minor": 5 }