BrainCore

BrainCore is a simple but fast neural network framework written in Swift. It uses Metal which makes it screamin' fast. If you want to see it in action check out InfiniteMonkeys—an app that uses a recursive neural network to generate poems.

Features

Requirements

iOS 8.0+ / Mac OS X 10.11+
Xcode 7.2+
A device that supports Metal (doesn't work on the iOS simulator)

Usage

Network Definition

Before you build your network, start by building all the layers. This is as simple as calling each constructor:

let dataLayer = MyDataLayer()
let lstmLayer = LSTMLayer(weights: lstmWeights, biases: lstmBiases)
let ipLayer = InnerProductLayer(weights: ipWeights, biases: ipBiases)
let reluLayer = ReLULayer(size: ipBiases.count)
let sinkLayer = MySinkLayer()

BrainCore uses overloaded operators to make network definitions more concise. To connect layers together simply use the => operator inside a Net.build {} closure:

let net = Net.build {
    dataLayer => lstmLayer => ipLayer => reluLayer => sinkLayer
}

If you need to concatenate the output of two layers put them inside square brackets:

let net = Net.build {
    [dataLayer1, dataLayer2] => lstmLayer => ipLayer => reluLayer => sinkLayer
}

Similarly, if you need to split the output of one layer put its target layers in square brackets:

let net = Net.build {
    dataLayer => lstmLayer => ipLayer => reluLayer => [sinkLayer1, sinkLayer2]
}

When splitting, the inputSize of the target layers will determine where to split. If the sum of the target layers' inputSizes doesn't match the source layer's outputSize and error will be thrown.

If you want to continue on separate branches after a split you have to split the definition into separate lines:

let net = Net.build {
    dataLayer => lstmLayer => [ipLayer1, ipLayer2]
    ipLayer1 => reluLayer1 => sinkLayer1
    ipLayer2 => reluLayer2 => sinkLayer2
}

Finally if you want send multiple copies of the output of a layer to different layers use the =>> operator:

let net = Net.build {
    dataLayer => lstmLayer
    lstmLayer =>> ipLayer1 => reluLayer1 => sinkLayer1
    lstmLayer =>> ipLayer2 => reluLayer2 => sinkLayer2
}

Evaluating

Currently BrainCore only supports executing pre-trained networks. Ideally you would train your network on a server using one of the well-established neural network frameworks and import the trained weights into BrainCore. We are working on implementing solvers so that you can do everything inside BrainCore, stay posted.

Let's start by creating the layers.

// Load weights and biases from a pre-trained network
let lstmWeights = ...
let lstmBiases = ...
let ipWeights = ...
let ipBiases = ...

// Create layers
let dataLayer = MyDataLayer()
let lstmLayer = LSTMLayer(weights: lstmWeights, biases: lstmBiases)
let ipLayer = InnerProductLayer(weights: ipWeights, biases: ipBiases)
let reluLayer = ReLULayer(size: ipBiases.count)
let sinkLayer = MySinkLayer()

Next we'll build the net.

let net = Net.build {
    dataLayer => lstmLayer => ipLayer => reluLayer => sinkLayer
}

And finally execute! You need to provide a Metal device to the runner which is usually just the default device.

guard let device = MTLCreateSystemDefaultDevice() else {
    fatalError("Failed to create a Metal device.")
}

let evaluator: Evaluator
do {
    evaluator = try Evaluator(net: net, device: device)
} catch let e {
    fatalError("Failed to create an Evaluator: \(e)")
}

evaluator.evaluate { snapshot in
    print("Feed-forward pass complete!")
}

The evaluator may fail to build if there is any problem creating the buffers or initializing all the Metal code, that's why there is a try.

Calling evaluate() will execute a single forward pass, but you can call this as often as you want. In fact you will want to call evaluate() multiple times before you get any results back so that you maximise the GPU bandwidth. You can also increase the batch size to execute multiple passes in parallel.

Your data layer will most likely want to provide new data every time you call evaluate(). So your code may look something like

while !shouldStop {
    dataLayer.gather()
    evaluator.evaluate(completion)
}

Note: both the sink layer's consume() function and the completion closure will be called from a background thread. Make sure you synchronize access to the data as needed and try not to block on either of those calls for too long.

License

Upsurge is available under the MIT license. See the LICENSE file for more info.