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Added macro for asserting that tensors are equal. #523

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Added macro for asserting that tensors are equal. #523

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42 changes: 42 additions & 0 deletions src/tensor/mod.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -289,5 +289,47 @@ impl Tensor {
}
}

#[macro_export]
macro_rules! assert_tensor_eq {
($generic:ty, $a:expr, $b:expr, $eps:expr) => {{
fn unravel_index(index: usize, shape: &[i64]) -> Vec<i64> {
let mut result = Vec::with_capacity(shape.len());
let mut index = index as i64;
for dim in shape.iter().rev() {
result.push(index % dim);
index /= dim;
}
result.reverse();
result
}

let (a, b): (&Tensor, &Tensor) = (&$a, &$b);
let eps = $eps;
assert_eq!(a.size(), b.size(), "Tensor size mismatch");
let shape = a.size();

for (i, (&a, &b)) in
Vec::<$generic>::from(a).iter().zip(Vec::<$generic>::from(b).iter()).enumerate()
{
assert!(
(a - b).abs() < eps,
"Tensor mismatch at index {:?}: {} != {}",
unravel_index(i, &shape),
a,
b
);
}
}};
(f64, $a:expr, $b:expr) => {{
assert_tensor_eq!(f64, $a, $b, 1e-5);
}};
(f32, $a:expr, $b:expr) => {{
assert_tensor_eq!(f32, $a, $b, 1e-5);
}};
($generic:ty, $a:expr, $b:expr) => {{
assert_tensor_eq!($generic, $a, $b, <$generic>::default());
}};
}

#[used]
static INIT_ARRAY: [unsafe extern "C" fn(); 1] = [dummy_cuda_dependency];
125 changes: 118 additions & 7 deletions tests/tensor_tests.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@ use anyhow::Result;
use half::f16;
use std::convert::{TryFrom, TryInto};
use std::f32;
use tch::{Device, Tensor};
use tch::{assert_tensor_eq, Device, Tensor};

#[test]
#[cfg(feature = "cuda-tests")]
Expand Down Expand Up @@ -351,15 +351,18 @@ fn sparse() {
fn einsum() {
// Element-wise squaring of a vector.
let t = Tensor::of_slice(&[1.0, 2.0, 3.0]);
let t = Tensor::einsum("i, i -> i", &[&t, &t]);
assert_eq!(Vec::<f64>::from(&t), [1.0, 4.0, 9.0]);
let e = Tensor::einsum("i, i -> i", &[&t, &t]);
assert_eq!(Vec::<f64>::from(&e), [1.0, 4.0, 9.0]);
assert_tensor_eq!(f64, &t.multiply(&t), e);
// Matrix transpose
let t = Tensor::of_slice(&[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).reshape(&[2, 3]);
let t = Tensor::einsum("ij -> ji", &[t]);
assert_eq!(Vec::<f64>::from(&t), [1.0, 4.0, 2.0, 5.0, 3.0, 6.0]);
let e = Tensor::einsum("ij -> ji", &[&t]);
assert_eq!(Vec::<f64>::from(&e), [1.0, 4.0, 2.0, 5.0, 3.0, 6.0]);
assert_tensor_eq!(f64, &t.transpose(0, 1), e);
// Sum all elements
let t = Tensor::einsum("ij -> ", &[t]);
assert_eq!(Vec::<f64>::from(&t), [21.0]);
let e = Tensor::einsum("ij -> ", &[&t]);
assert_eq!(Vec::<f64>::from(&e), [21.0]);
assert_tensor_eq!(f64, &t.sum(tch::Kind::Float), e);
}

#[test]
Expand Down Expand Up @@ -458,3 +461,111 @@ fn set_data() {
t.set_data(&t.to_kind(tch::Kind::BFloat16));
assert_eq!(t.kind(), tch::Kind::BFloat16);
}

#[test]
fn einsum_matrix_multiply() -> Result<()> {
let v = Tensor::rand(&[1, 4], (tch::Kind::Float, Device::Cpu));
let a = Tensor::rand(&[3, 4], (tch::Kind::Float, Device::Cpu));
let b = Tensor::rand(&[4, 3], (tch::Kind::Float, Device::Cpu));
let ab = a.mm(&b);
let av = a.mm(&v.transpose(0, 1));

let einsum_ab = Tensor::einsum("ij,jk->ik", &[&a, &b]);
let einsum_av = Tensor::einsum("ij,kj->ik", &[&a, &v]);
let einsum_hadamard = Tensor::einsum("ij,ij->ij", &[&a, &a]);

assert_tensor_eq!(f64, &ab, &einsum_ab);
assert_tensor_eq!(f64, &av, &einsum_av);
assert_tensor_eq!(f64, &a.multiply(&a), &einsum_hadamard);

Ok(())
}

#[test]
fn einsum_batch_matrix_multiply() -> Result<()> {
let a = Tensor::rand(&[3, 2, 5], (tch::Kind::Float, Device::Cpu));
let b = Tensor::rand(&[3, 5, 2], (tch::Kind::Float, Device::Cpu));
let ab = a.bmm(&b);

let einsum_ab = Tensor::einsum("ijk,ikl->ijl", &[&a, &b]);

assert_tensor_eq!(f64, &ab, &einsum_ab);

Ok(())
}

#[test]
fn einsum_outer_product() -> Result<()> {
let a = Tensor::rand(&[3], (tch::Kind::Float, Device::Cpu));
let b = Tensor::rand(&[3], (tch::Kind::Float, Device::Cpu));
let ab = a.outer(&b);
let einsum_ab = Tensor::einsum("i,j->ij", &[&a, &b]);

assert_tensor_eq!(f64, &ab, &einsum_ab);

Ok(())
}

#[test]
fn einsum_diagonal() -> Result<()> {
let a = Tensor::rand(&[3, 3], (tch::Kind::Float, Device::Cpu));
let diag = a.diag(0);
let einsum_diag = Tensor::einsum("ii->i", &[&a]);

assert_tensor_eq!(f64, &diag, &einsum_diag);

Ok(())
}

#[test]
fn einsum_trace() -> Result<()> {
let a = Tensor::rand(&[3, 3], (tch::Kind::Float, Device::Cpu));
let trace = a.trace();
let einsum_trace = Tensor::einsum("ii->", &[&a]);

assert_tensor_eq!(f64, &trace, &einsum_trace);

Ok(())
}

#[test]
fn einsum_dot_product() -> Result<()> {
let a = Tensor::rand(&[3], (tch::Kind::Float, Device::Cpu));
let b = Tensor::rand(&[3], (tch::Kind::Float, Device::Cpu));
let m = Tensor::rand(&[3, 4], (tch::Kind::Float, Device::Cpu));

let einsum_m = Tensor::einsum("ij,ij->", &[&m, &m]);
let einsum_dot = Tensor::einsum("i,i->", &[&a, &b]);

assert_tensor_eq!(f64, &a.dot(&b), &einsum_dot);
assert_tensor_eq!(f64, &m.multiply(&m).sum(tch::Kind::Float), &einsum_m);

Ok(())
}

#[test]
fn einsum_permute() -> Result<()> {
let a = Tensor::rand(&[5, 4, 3], (tch::Kind::Float, Device::Cpu));
let einsum_a = Tensor::einsum("ijk->kji", &[&a]);
assert_eq!(vec![3, 4, 5], einsum_a.size());

Ok(())
}

#[test]
fn einsum_sum() -> Result<()> {
let a = Tensor::rand(&[3], (tch::Kind::Float, Device::Cpu));
let b = Tensor::rand(&[3, 3], (tch::Kind::Float, Device::Cpu));

let einsum_a = Tensor::einsum("i->", &[&a]);
let einsum_b = Tensor::einsum("ij->", &[&b]);
let column_einsum_b = Tensor::einsum("ij->j", &[&b]);
let row_einsum_b = Tensor::einsum("ij->i", &[&b]);

assert_tensor_eq!(f64, &a.sum(tch::Kind::Float), &einsum_a);
assert_tensor_eq!(f64, &b.sum(tch::Kind::Float), &einsum_b);
assert_tensor_eq!(f64, &b.sum_dim_intlist(&[0], false, tch::Kind::Float), &column_einsum_b);
assert_tensor_eq!(f64, &b.sum_dim_intlist(&[1], false, tch::Kind::Float), &row_einsum_b);

Ok(())
}