OpenCL-accelerated 2D convolutions for Rust

Documentation:

This library provides 2D convolutions accelerated with OpenCL. Convolutions are particularly useful for deep learning tasks, such as image recognition; they are a basic building block for convolutional neural networks.

The library is intended mostly for quick-and-dirty hacking in deep learning research, in which one needs a separate spatial convolution primitive. Full-scale DL frameworks (TensorFlow, PyTorch, etc.) will most probably be a more robust and scalable tool for more high-level tasks.

Usage

Add this to your Crate.toml:

[dependencies]
ocl-convolution = "0.3.0"

Basic floating-point convolution can be implemented as follows:

use ndarray::Array4;
use rand::{Rng, thread_rng};
use ocl_convolution::{Convolution, FeatureMap, Params};

let convolution = Convolution::f32(3)?.build(Params {
    strides: [1, 1],
    pads: [0; 4],
    dilation: [1, 1],
    groups: 1,
})?;

// Generate random signal with 6x6 spatial dims and 3 channels.
let mut rng = thread_rng();
let signal = Array4::from_shape_fn([1, 6, 6, 3], |_| rng.gen_range(-1.0..=1.0));
// Construct two 3x3 spatial filters.
let filters = Array4::from_shape_fn([2, 3, 3, 3], |_| rng.gen_range(-1.0..=1.0));
// Perform the convolution. The output must have 4x4 spatial dims
// and contain 2 channels (1 per each filter). The output layout will
// be the same as in the signal.
let output = convolution.compute(
    // `FeatureMap` wraps `ArrayView4` with information about
    // memory layout (which is "channels-last" / NHWC in this case).
    FeatureMap::nhwc(&signal),
    &filters,
)?;
assert_eq!(output.shape(), [1, 4, 4, 2]);

Ok::<_, ocl::Error>(())

See crate docs for more examples of usage.

Installing OpenCL

OpenCL has a variety of implementations. For quick testing, one may use POCL; it is open source and not tied to hardware (at the cost of being CPU-based, i.e., orders of magnitude slower than OpenCL implementations by GPU vendors). POCL can be installed from sources with the commands like in the installation script (tested on Ubuntu 22.04).

License

Licensed under either of Apache License, Version 2.0 or MIT license at your option.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in ocl-convolution by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.