This work is a self-contained implementation of reverse mode automatic differentiation, based on that of PyTorch's and made way more simpler with the intention to understand the basics of how it works.

This is a Rust implementation of the code I found in A Gentle Introduction to torch.autograd tutorial, below further readings, a notebook named Example implementation of reverse-mode autodiff.

This is my first Rust project. Having read 55% of the book I felt I needed to put my readings into practice following these principles:

1. Implement an existing program with few modifications as possible, so I could focus in Rust.
2. Do not look other implementations in beforehand. There are plenty of Rust autograds projects outhere, innocent inspiration would have ended in shameless no-brain copy and paste.
3. Do not improve it further. Transforming this development into a deep learning framework is tempting, not only there are better ones but I shall not forget the main reason to start it: to practice some Rust.

It can take gradients of gradients.

let op = Ops::new();

let a = op.named_var(Tensor::new(&[0.4605, 0.4061, 0.9422, 0.3946], &Device::Cpu).unwrap(), "a");
let b = op.named_var(Tensor::new(&[0.0850, 0.3296, 0.9888, 0.6494], &Device::Cpu).unwrap(), "b");

let simple = | a, b | { op.mul( &op.add(a, b), b ) };

let l0 = op.sum( &simple(&a, &b) , Some("L0") );
op.grad(&l0, &[&a, &b]);

let dl0_da = a.grad().unwrap();
let dl0_db = b.grad().unwrap();

let l1 = op.sum( &op.add( &op.mul(&dl0_da, &dl0_da), &op.mul(&dl0_db, &dl0_db) ), Some("L1") );
op.grad(&l1, &[&a, &b]);

let dl1_da = a.grad().unwrap();
let dl1_db = b.grad().unwrap();

println!("d{} = {}", a.name, dl1_da);
println!("d{} = {}", b.name, dl1_db);

Output:

a = [0.4605, 0.4061, 0.9422, 0.3946]
b = [0.0850, 0.3296, 0.9888, 0.6494]
v0 = a + b
v1 = v0 * b
L0 = v1.sum()
dL0 --------------
v3 = v2.expand([4])
v4 = v3 * b
v5 = v3 * v0
v6 = v5 + v4
dL0_dv1 = v3
dL0_dv0 = v4
dL0_dL0 = v2
dL0_da = v4
dL0_db = v6
------------------
v7 = v4 * v4
v8 = v6 * v6
v9 = v7 + v8
L1 = v9.sum()
dL1 --------------
v11 = v10.expand([4])
v12 = v11 * v6
v13 = v11 * v6
v14 = v12 + v13
v15 = v11 * v4
v16 = v11 * v4
v17 = v15 + v16
v18 = v17 + v14
v19 = v14 * v0
v20 = v14 * v3
v21 = v18 * b
v22 = v18 * v3
v23 = v19 + v21
v24 = v23.sum()
v25 = v22 + v20
dL1_dv0 = v20
dL1_dv4 = v18
dL1_dv2 = v24
dL1_dv5 = v14
dL1_dv9 = v11
dL1_dL1 = v10
dL1_db = v25
dL1_dv6 = v14
dL1_dv3 = v23
dL1_dv7 = v11
dL1_da = v20
dL1_dv8 = v11
------------------
da = [1.2610, 2.1306, 5.8396, 3.3868]
db = [ 2.6920,  4.9204, 13.6568,  8.0724]

I wanted to support syntax like the following (i.e. not enforcing binding of returned variables).

let y = op.mul( &op.add(&a, &b), &b );

I did not bother in making operations as variable operators, a functional approach would be enough for the time being.

It should work with tensor or ndarray data types which are expected to not support the Copy trait and which might to be expensive to clone().

type Var = Rc<Variable>;

struct Variable {
    value: Value,
    name: String,
    grad: RefCell<Option<Var>>,
}

So I put variables inside a Rc<T>.

Some people store a reference to the tape right into variables, others, choose to do it into operations, not to mention the ones who prefer a global variable. I did it in operations because it was the thing that worked the best for my current Rust knowledge.

struct Ops {
    tape: RefCell<Vec<TapeEntry>>,
    counter: RefCell<u8>,
}

I had to use RefCell<T> for the tape because there might be many references to Ops and they had to be mutable.

• rustygrad
• rustograd

• CS224n gradient notes
• CS231n vector derivatives