corgi 0.6.0

BLAS

• The BLAS feature can be enabled, and requires CBLAS if used.

Examples

• Fully-connected neural network (full version):

for _ in 0..iterations {
    let x = rng.gen_range(-1.0..1.0);
    let input = arr![arr![x]];
    let target = x.exp();

    let result = model.forward(input);
    let loss = model.backward(arr![target]);

    println!(
	"in: {}, out: {}, target: {}, loss: {}",
	x, result[0], target, loss
    );
}

• Dynamic computational graph:

let a = arr![5.0].tracked();
let b = arr![2.0].tracked();
let mut c = arr![0.0].tracked();

for _ in 0..10 {
    c = &c + &(&a * &b);
    if c[0] > 50.0 {
	c = &c * &a;
    }
}

assert_eq!(c, arr![195300.0]);

c.backward(None);
assert_eq!(c.gradient(), arr![1.0]);
assert_eq!(b.gradient(), arr![97650.0]);
assert_eq!(a.gradient(), arr![232420.0]);

• Custom operation (still needs some work):

// note proper implementations should handle tracked, and untracked cases
let op: array::ForwardOp = Arc::new(|x: &[&Array]| {
    Arrays::new((x[0].dimensions(), x[0].values().iter().zip(x[1].values()).map(|(x, y)| x * y).collect::<Vec<Float>>()))
});

let op_clone = Arc::clone(&op);
let backward_op: array::BackwardOp = Arc::new(move |c: &mut Vec<Array>, x: &Array| {
    vec![Some(Array::op(&vec![&c[1], x], Arc::clone(&op_clone), None)),
         Some(Array::op(&vec![&c[0], x], Arc::clone(&op_clone), None))]
});

let a = arr![1.0, 2.0, 3.0];
let b = arr![3.0, 2.0, 1.0];
let mut product = Array::op(&vec![&a, &b], op, Some(backward_op));
assert_eq!(product, arr![3.0, 4.0, 3.0]);
product.backward(None);
assert_eq!(b.gradient(), arr![1.0, 2.0, 3.0]);
assert_eq!(a.gradient(), arr![3.0, 2.0, 1.0]);

Design

• Originally worked around the ergonomics of the arr! macro (which however, currently still needs more work).
• Dynamic-as-possible computational graph.
• Did not want to have to manage any 'graph' structures when using Corgi (the Arrays should represent the graph alone).
• Graph became more, and more dependent on threading for the backward pass, and the use of Arc, and Mutex.
• Graphs do note store consumers (at the moment). They store consumer counts instead.

Tracked Arrays

• Tracked arrays are arrays which require gradients to be computed, and stored.
• For more information, see the documentation for tracked(), and untracked() in array.rs.

Backward Pass

• An informal UML sequence diagram (it's not entirely up to specs, but should give an overview of the process):

Name

• Original name was going to be 'cog-(something)', since Rust's logo is a cog, and since cognition (get it?). But as it turns out, many AI libraries are named 'cog-(something)'. Attempts at permutations of 'cog' sounded awkward, such as 'cogi', for 'cog-intelligence', so the name Corgi was chosen.

Acknowledgements

• Shields are from shields.io

Licence

• MIT