Crate coaster_nn [] [src]

Provides a Coaster Plugin, to extend Coaster with Neural Network related operations such as convolutions, pooling, ReLU, etc. A full list of operations provided by this Plugin, can be found at the provided Operations section.

Overview

This Coaster Plugin extends Coaster's Backend with NN related methods/operations. This allows you to run, these operations (and therefore your application) on your local machine as well as on servers, mobiles or any other machine (as if they were written for common CPU execution), while receiving the significant performance increases (usually one-to-two orders of magnitutde), by executing the operations on special purpose hardware such as GPUs - if they are available. Usage examples can be found in the next section.

The architecture of a Plugin is quite easy. It defines one Plugin Trait, in this case the NN trait, which implements basic functionality for initialization and multiple Plugin Operation Traits which define the methods which are going to be available on the Backed, as the Plugin Trait as well as the Plugin Operations Traits are implemented for the Coaster Backends (CUDA, OpenCL, Native). The operations take as arguments one or many SharedTensors, holding the data over which the operation should happen, and none or one Operation Configuration.

Usage

An example on how to write some data into a SharedTensor and compute the result of the sigmoid function for each value:

extern crate coaster as co;
extern crate coaster_nn as nn;
use co::prelude::*;
use co::frameworks::native::flatbox::FlatBox;
use nn::*;

fn write_to_memory<T: Copy>(mem: &mut FlatBox, data: &[T]) {
    let mut mem_buffer = mem.as_mut_slice::<T>();
    for (index, datum) in data.iter().enumerate() {
        mem_buffer[index] = *datum;
    }
}

pub fn main() {
    // Initialize a CUDA Backend.
    // Usually you would not use CUDA but let Coaster pick what is available on the machine.
    let backend = Backend::<Cuda>::default().unwrap();
    // Initialize two SharedTensors.
    let mut x = SharedTensor::<f32>::new(&(1, 1, 3));
    let mut result = SharedTensor::<f32>::new(&(1, 1, 3));
    // Fill `x` with some data.
    let payload: &[f32] = &::std::iter::repeat(1f32).take(x.capacity()).collect::<Vec<f32>>();
    let native = Native::new();
    let cpu = native.new_device(native.hardwares()).unwrap();
    write_to_memory(x.write_only(&cpu).unwrap(), payload); // Write to native host memory.
    // Run the sigmoid operation, provided by the NN Plugin, on your CUDA enabled GPU.
    backend.sigmoid(&mut x, &mut result).unwrap();
    // See the result.
    println!("{:?}", result.read(&cpu).unwrap().as_slice::<f64>());
}

Provided Operations

This Plugins provides the following operations. If not denoted otherwise, this means forward and backward A - means not yet implemented.

Operation CUDA OpenCL Native
Sigmoid cuDNN v5 or later - Rust
SigmoidPointwise cuDNN v5 or later - Rust
ReLU cuDNN v5 or later - Rust
ReLUPointwise cuDNN v5 or later - Rust
Tanh cuDNN v5 or later - Rust
TanhPointwise cuDNN v5 or later - Rust
Normalization (LRN) cuDNN v5 or later - -
Convolution cuDNN v5 or later - Rust(fwd)
Softmax cuDNN v5 or later - Rust
LogSoftmax cuDNN v5 or later - Rust
Pooling Max cuDNN v5 or later - Rust(fwd)
Pooling Avg cuDNN v5 or later - -

Modules

frameworks

Provides the specific Framework implementations for the Library Operations.

Macros

impl_ops_log_softmax_for
impl_ops_lrn_for
impl_ops_relu_for
impl_ops_sigmoid_for
impl_ops_softmax_for
impl_ops_tanh_for

Enums

ConvBackwardDataAlgo

Different algorithms to compute the gradient with respect to the filter.

ConvBackwardFilterAlgo

Different algorithms to compute the gradient with respect to the filter.

ConvForwardAlgo

Different algorithms to compute the convolution forward algorithm.

Traits

Convolution

Provides the functionality for a Backend to support Convolution operations.

ConvolutionConfig

Provides Convolution Config functionality.

LRN

Provides the functionality for a Backend to support Local Response Normalization operations.

LogSoftmax

Provides the functionality for a Backend to support LogSoftmax operations.

NN

Provides the functionality for a backend to support Neural Network related operations.

NNOperationConfig

Provides generic NN Operation Config functionality.

Pooling

Provides the functionality for a Backend to support Pooling operations.

Relu

Provides the functionality for a Backend to support ReLU operations.

ReluPointwise

Provides the functionality for pointwise ReLU operations (overwrites the input with the result of the operation).

Sigmoid

Provides the functionality for a Backend to support Sigmoid operations.

SigmoidPointwise

Provides the functionality for pointwise Sigmoid operations (overwrites the input with the result of the operation).

Softmax

Provides the functionality for a Backend to support Softmax operations.

Tanh

Provides the functionality for a Backend to support TanH operations.

TanhPointwise

Provides the functionality for pointwise ReLU operations (overwrites the input with the result of the operation).