nz::graph Namespace Reference

Contains classes and functions for managing and executing computation graphs in deep learning workflows. More...

Classes
class	ComputeGraph
	Represents a computational graph, which manages nodes and the computation flow. More...

Functions
std::ostream &	operator<< (std::ostream &os, ComputeGraph &graph)
	Overloads the stream insertion operator to print the details of the computational graph.
void	CreateNode (ComputeGraph *graph, const std::string &type, const std::string &name, std::vector< int > pre, const Tensor::shape_type &shape, float *data, const bool requires_grad, float *grad)
	Creates and adds a node to the computational graph based on the specified type.

Detailed Description

Contains classes and functions for managing and executing computation graphs in deep learning workflows.

The nz::graph namespace provides essential tools for creating, managing, and executing computation graphs in deep learning models. It facilitates the construction of neural networks, supports forward and backward propagation, and allows for gradient computation and optimization steps. This namespace is integral to the workflow of deep learning frameworks, ensuring efficient execution on GPU devices.

Key components within this namespace include:

• ComputeGraph: A class representing the computation graph that holds nodes and manages data flow between them.
• Nodes: A collection of different computational nodes, such as layers (e.g., AddNode, MatMulNode) and activation functions (e.g., ReLUNode, SigmoidNode).
• Optimizers: Utilities for updating the parameters of the nodes (e.g., through stochastic gradient descent).
• Forward and Backward Propagation: Functions to propagate inputs through the network and compute gradients for model optimization.
• Graph Persistence: Methods to save and load the computation graph, preserving model state.

The nz::graph namespace is designed with performance in mind, utilizing CUDA to accelerate computation on GPUs.

Note

The components in this namespace rely on CUDA for GPU-based operations. Ensure that CUDA-compatible hardware and software are properly configured.

Author

Mgepahmge(https://github.com/Mgepahmge)

Date

2024/11/29

Function Documentation

CreateNode()

void nz::graph::CreateNode	(	ComputeGraph *	graph,
		const std::string &	type,
		const std::string &	name,
		std::vector< int >	pre,
		const Tensor::shape_type &	shape,
		float *	data,
		const bool	requires_grad,
		float *	grad )

Creates and adds a node to the computational graph based on the specified type.

This function is used to create various types of nodes in a computational graph based on the provided node type, and then adds the created node to the ComputeGraph object. The node is initialized with the specified shape, data, and gradient information if needed. It also ensures that the nodes are connected to their previous nodes as specified by the pre vector.

Parameters

graph	The ComputeGraph object to which the new node will be added.
type	A string representing the type of node to be created. Supported types include "Input", "Output", "Add", "MatMul", "Sub", "ReLU", "Sigmoid", "Tanh", "LeakyReLU", "Swish", "ELU", "HardSigmoid", "HardSwish", "Softmax", "MeanSquaredError", "BinaryCrossEntropy".
name	The name of the node to be added to the graph.
pre	A vector of integers specifying the indices of the previous nodes (input nodes) that this node depends on. The number of elements in pre and the type of node may vary.
shape	A vector representing the shape of the node's output tensor.
data	A pointer to the data to initialize the node's output tensor.
requires_grad	A boolean flag indicating whether the node requires gradients for backpropagation.
grad	A pointer to the gradient data for the node's output tensor if requires_grad is true.

Exceptions

std::runtime_error

If an unsupported node type is provided or if there is a mismatch in node dependencies.

Note

• The CreateNode function automatically handles the creation of nodes, their connection to previous nodes, and the addition of the new node to the graph.
• The pre vector is used to specify which nodes are required as inputs for the current node, and it may differ in size based on the node type.
• Some node types, such as "ScalarMul", "ScalarDiv", "ScalarAdd", and "ScalarSub", are not supported and will throw a runtime error.

Usage Example:

ComputeGraph graph;
std::vector<int> pre = {0, 1};  // Specify the input nodes for the current node
std::vector<int> shape = {3, 3};  // Specify the shape of the output tensor
float data[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f};  // Example data
CreateNode(&graph, "Add", "add_node", pre, shape, data, true, nullptr);  // Create an "Add" node

See also

ComputeGraph for more details on graph structure and node management.

nodes::Node for information on individual node types and their operations.

Author

Mgepahmge (https://github.com/Mgepahmge)

Date

2024/12/09

Definition at line 108 of file ComputeGraph.cu.

operator<<()

std::ostream & nz::graph::operator<<	(	std::ostream &	os,
		ComputeGraph &	graph )

Overloads the stream insertion operator to print the details of the computational graph.

This function overloads the << operator to provide an easy and intuitive way to print the details of a ComputeGraph object. It calls the print method of ComputeGraph to output the graph's nodes, their connections, data, gradients, and loss to the provided output stream.

Parameters

os	The output stream to which the graph details will be printed (e.g., std::cout).
graph	The ComputeGraph object whose details will be printed.

Returns

The output stream after printing the graph details, enabling method chaining.

See also

ComputeGraph::print() for more information about the internal printing process.

Author

Mgepahmge (https://github.com/Mgepahmge)

Date

2024/12/09

Definition at line 56 of file ComputeGraph.cu.