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tensor.test.ts
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import { Tensor } from "../src/tensor";
import { expect, test, describe } from "bun:test";
describe("Tensor General Operations", () => {
test("Create tensor successfully", () => {
let t = new Tensor(10, [2, 5]);
expect(t.at(0, 0)).toBe(0);
});
test("Detect invalid shape", () => {
expect(() => new Tensor(20, [3, 4])).toThrowError();
});
test("Tensor 2D > not supported", () => {
expect(() => new Tensor(60, [3, 4, 5])).toThrowError();
});
test("Pre-existing array gets copied successfully", () => {
let a = [
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
0, 0, 0, 1,
];
let t = new Tensor(a, [4, 4]);
expect(t.data).toBeArray();
expect(t.data).toEqual(a);
});
})
describe("CPU TensorXTensor Operations", () => {
test("Tensor mul (Dot) with a tensor", () => {
let t = new Tensor([
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
0, 0, 0, 1,
], [4, 4]);
let o = new Tensor([
1, 2, 3, 4,
5, 6, 7, 8,
9, 10, 11, 12,
13, 14, 15, 16,
], [4, 4]);
expect(t.mul(o).data).toEqual([
1, 2, 3, 4,
5, 6, 7, 8,
9, 10, 11, 12,
13, 14, 15, 16,
]);
});
test("Tensor add with a tensor", () => {
let t = new Tensor([2, 3, 4], [3]);
let o = new Tensor([5, 6, 7], [3]);
expect(t.add(o).data).toEqual([7, 9, 11]);
});
})
describe("CPU TensorXScalar Operations", () => {
test("Tensor mul with a scalar", () => {
let t = new Tensor([2, 3, 4], [3]);
expect(t.mul(3).data).toEqual([6, 9, 12]);
});
test("Tensor add with a scalar", () => {
let t = new Tensor([2, 3, 4], [3]);
expect(t.add(3).data).toEqual([5, 6, 7]);
});
})
describe("CPU Grad Tensor Operations", () => {
test("Tensor add Tensor backward", () => {
let x = new Tensor([1, 2, 3, 4], [2, 2]);
let y = new Tensor([5, 6, 7, 8], [2, 2]);
let z = x.add(y);
z.backward();
expect(x.grad.data).toEqual([1, 1, 1, 1]);
});
test("Tensor add Scalar backward", () => {
let x = new Tensor([1, 2, 3, 4], [2, 2]);
let z = x.add(3);
z.backward();
expect(x.grad.data).toEqual([1, 1, 1, 1]);
});
test("Tensor mul Tensor backward", () => {
let x = new Tensor([1, 2, 3, 4], [2, 2]);
let y = new Tensor([5, 6, 7, 8], [2, 2]);
let z = x.mul(y);
z.backward();
expect(x.grad.data).toEqual([11, 15, 11, 15]);
expect(y.grad.data).toEqual([4, 4, 6, 6]);
});
test("Tensor mul Scalar backward", () => {
let x = new Tensor([1, 2, 3, 4], [2, 2]);
let z = x.mul(3);
z.backward();
expect(x.grad.data).toEqual([3, 3, 3, 3]);
});
test("Tensor sub Tensor backward", () => {
let x = new Tensor([1, 2, 3, 4], [2, 2]);
let y = new Tensor([5, 6, 7, 8], [2, 2]);
let z = x.sub(y);
z.backward();
expect(x.grad.data).toEqual([1, 1, 1, 1]);
expect(y.grad.data).toEqual([1, 1, 1, 1]);
});
test("Tensor sub Scalar backward", () => {
let x = new Tensor([1, 2, 3, 4], [2, 2]);
let z = x.sub(3);
z.backward();
expect(x.grad.data).toEqual([1, 1, 1, 1]);
});
test("Tensor ReLU Tensor backward", () => {
let x = new Tensor([1, -2, 3, -4], [2, 2]);
let z = x.ReLU();
z.backward();
expect(x.grad.data).toEqual([1, 0, 1, 0]);
});
test("Tensor leakyReLU Tensor backward", () => {
let x = new Tensor([1, -2, 3, -4], [2, 2]);
let z = x.leakyReLU(0.1);
z.backward();
expect(x.grad.data).toEqual([1, 0.1, 1, 0.1]);
});
test("Test power Tensor backward", () => {
let x = new Tensor([1, 2, 3, 4], [2, 2]);
let z = x.pow(2);
z.backward();
expect(x.grad.data).toEqual([2, 4, 6, 8]);
});
test("Test elemWiseMul Tensor backward", () => {
let x = new Tensor([1, 2, 3, 4], [2, 2]);
let y = new Tensor([5, 6, 7, 8], [2, 2]);
let z = x.elemWiseMul(y);
z.backward();
expect(x.grad.data).toEqual([5, 6, 7, 8]);
expect(y.grad.data).toEqual([1, 2, 3, 4]);
});
})
describe("Tensor deep backward", () => {
// verified against JAX
test("Test mul mul backward", () => {
let x = new Tensor([5, 7], [1, 2]);
let y = new Tensor([5, 6, 7, 8], [2, 2]);
let z = new Tensor([1, 2], [2, 1]);
let w = x.mul(y).mul(z);
expect(w.data).toEqual([246]);
w.backward();
expect(x.grad.data).toEqual([17, 23]);
expect(y.grad.data).toEqual([5, 10, 7, 14]);
expect(z.grad.data).toEqual([74, 86]);
});
test("Test mul mul sub backward", () => {
let x = new Tensor([5, 7], [1, 2]);
let y = new Tensor([5, 6, 7, 8], [2, 2]);
let z = new Tensor([1, 2], [2, 1]);
let w = x.mul(y).mul(z).sub(new Tensor([14], [1]));
expect(w.data).toEqual([232]);
w.backward();
expect(x.grad.data).toEqual([17, 23]);
expect(y.grad.data).toEqual([5, 10, 7, 14]);
expect(z.grad.data).toEqual([74, 86]);
});
test("Test mul mul sub pow backward", () => {
let x = new Tensor([5, 7], [1, 2]);
let y = new Tensor([5, 6, 7, 8], [2, 2]);
let z = new Tensor([1, 2], [2, 1]);
let w = x.mul(y).mul(z).sub(new Tensor([14], [1])).pow(2);
expect(w.data).toEqual([53824]);
w.backward();
expect(x.grad.data).toEqual([7888, 10672]);
expect(y.grad.data).toEqual([2320, 4640, 3248, 6496]);
expect(z.grad.data).toEqual([34336, 39904]);
});
test("Test mul mul add sub pow backward", () => {
let x = new Tensor([5, 7], [1, 2]);
let y = new Tensor([5, 6, 7, 8], [2, 2]);
let z = new Tensor([1, 2], [2, 1]);
let b = new Tensor([4], [1, 1]);
let w = x.mul(y).mul(z).add(b).sub(new Tensor([14], [1])).pow(2);
expect(w.data).toEqual([55696]);
w.backward();
expect(x.grad.data).toEqual([8024, 10856]);
expect(y.grad.data).toEqual([2360, 4720, 3304, 6608]);
expect(z.grad.data).toEqual([34928, 40592]);
});
test("Test mul mul add ReLU sub pow backward", () => {
let x = new Tensor([5, 7], [1, 2]);
let y = new Tensor([5, 6, 7, 8], [2, 2]);
let z = new Tensor([1, 2], [2, 1]);
let b = new Tensor([4], [1, 1]);
let w = x.mul(y).mul(z).add(b).ReLU().sub(new Tensor([14], [1])).pow(2);
expect(w.data).toEqual([55696]);
w.backward();
expect(x.grad.data).toEqual([8024, 10856]);
expect(y.grad.data).toEqual([2360, 4720, 3304, 6608]);
expect(z.grad.data).toEqual([34928, 40592]);
});
test("Test learn via backprop, single step", () => {
let x = new Tensor(2, [1, 2]);
let y = new Tensor(4, [2, 2]);
let z = new Tensor(2, [2, 1]);
let b = new Tensor(1, [1, 1]);
let desired = new Tensor([14], [1]);
x.randomize();
y.randomize();
z.randomize();
b.randomize();
let loss1 = x.mul(y).mul(z).add(b).ReLU().sub(desired).pow(2);
loss1.backward();
x = x.sub(x.grad.mul(0.01));
y = y.sub(y.grad.mul(0.01));
z = z.sub(z.grad.mul(0.01));
b = b.sub(b.grad.mul(0.01));
let loss2 = x.mul(y).mul(z).add(b).ReLU().sub(desired).pow(2);
expect(loss2.data[0]).toBeLessThan(loss1.data[0]);
});
test("Test learn via backprop, 10 steps", () => {
let x = new Tensor(2, [1, 2]);
let y = new Tensor(4, [2, 2]);
let z = new Tensor(2, [2, 1]);
let b = new Tensor(1, [1, 1]);
let desired = new Tensor([14], [1]);
x.randomize();
y.randomize();
z.randomize();
b.randomize();
for (let i = 0; i < 10; ++i) {
x.zeroGrad();
y.zeroGrad();
z.zeroGrad();
b.zeroGrad();
let loss1 = x.mul(y).mul(z).add(b).ReLU().sub(desired).pow(2);
loss1.backward();
x = x.sub(x.grad.mul(0.01));
y = y.sub(y.grad.mul(0.01));
z = z.sub(z.grad.mul(0.01));
b = b.sub(b.grad.mul(0.01));
let loss2 = x.mul(y).mul(z).add(b).ReLU().sub(desired).pow(2);
expect(loss2.data[0]).toBeLessThan(loss1.data[0]);
}
});
});