Gigagrad

A small deep learning library that goes gigafast (not yet though). Gigagrad makes heavy use of C++'s operator overloading to provide an ergonomic way to define neural networks, without all the runtime overhead of Python. Eventually, Gigagrad will be able to generate executables or static libraries containing neural networks. Gigagrad's implementation takes inspiration from Tinygrad and Pytorch.

Building

This project uses the Meson build system. You can install it with pip3 with pip3 install meson. Next, building Gigagrad is as simple as

    meson setup build
    cd build
    meson compile

From there, you can run tests such as ./gigagrad-test.

Usage

// Declare a network
gg::nn::Module network;

// Add an input vector of length 4
auto x = network.AddInput(4);

// Add a weight of length 4
auto w = network.AddWeight(4);

// L1 is now the elementwise difference between w and x
auto L1 = w - x;

// Compile the training context. Currently, we only have a scalar C backend
gg::TrainingContext ctx = gg::CompileTrainingGraph<gg::codegen::BackendScalarC>(network, L1);

// Set input data
float x_data[] = { 1.0, 2.0, 3.0, 4.0 };
float w_data[] = { -0.1, 0.1, -0.001, 0.0001 };
float training_example_data[] = { 0.0, 0.0, 0.0, 0.0 };
x.data() = x_data;
w.data() = w_data;
ctx.training_example = training_example_data;

// Run twenty iterations of gradient descent, and print the loss!
for(int i = 0; i < 20; i++)
{
    ctx.Execute();
    printf("%.6f\n", ctx.loss[0]);
}
// Print your learned weight: W = { 0.98, 1.97, 2.96, 3.94 }
printf("W = { %.2f, %.2f, %.2f, %.2f }\n", w_data[0], w_data[1], w_data[2], w_data[3]);

save-buffer/gigagrad

Gigagrad

Building

Usage

Backends