nalgebra/nalgebra-sparse/tests/unit_tests/ops.rs
2021-01-26 10:11:24 +01:00

171 lines
5.9 KiB
Rust

use nalgebra_sparse::coo::CooMatrix;
use nalgebra_sparse::ops::serial::{spmv_coo, spmm_csr_dense};
use nalgebra_sparse::ops::{no_transpose, Transposition};
use nalgebra_sparse::csr::CsrMatrix;
use nalgebra_sparse::proptest::csr;
use nalgebra::{DVector, DMatrix, Scalar, DMatrixSliceMut, DMatrixSlice};
use nalgebra::proptest::matrix;
use proptest::prelude::*;
use std::panic::catch_unwind;
#[test]
fn spmv_coo_agrees_with_dense_gemv() {
let x = DVector::from_column_slice(&[2, 3, 4, 5]);
let i = vec![0, 0, 1, 1, 2, 2];
let j = vec![0, 3, 0, 1, 1, 3];
let v = vec![3, 2, 1, 2, 3, 1];
let a = CooMatrix::try_from_triplets(3, 4, i, j, v).unwrap();
let betas = [0, 1, 2];
let alphas = [0, 1, 2];
for &beta in &betas {
for &alpha in &alphas {
let mut y = DVector::from_column_slice(&[2, 5, 3]);
let mut y_dense = y.clone();
spmv_coo(beta, &mut y, alpha, &a, &x);
y_dense.gemv(alpha, &DMatrix::from(&a), &x, beta);
assert_eq!(y, y_dense);
}
}
}
#[derive(Debug)]
struct SpmmCsrDenseArgs<T: Scalar> {
c: DMatrix<T>,
beta: T,
alpha: T,
trans_a: Transposition,
a: CsrMatrix<T>,
trans_b: Transposition,
b: DMatrix<T>,
}
/// Returns matrices C, A and B with compatible dimensions such that it can be used
/// in an `spmm` operation `C = beta * C + alpha * trans(A) * trans(B)`.
fn spmm_csr_dense_args_strategy() -> impl Strategy<Value=SpmmCsrDenseArgs<i32>> {
let max_nnz = 40;
let value_strategy = -5 ..= 5;
let c_rows = 0 ..= 6usize;
let c_cols = 0 ..= 6usize;
let common_dim = 0 ..= 6usize;
let trans_strategy = trans_strategy();
let c_matrix_strategy = matrix(value_strategy.clone(), c_rows, c_cols);
(c_matrix_strategy, common_dim, trans_strategy.clone(), trans_strategy.clone())
.prop_flat_map(move |(c, common_dim, trans_a, trans_b)| {
let a_shape =
if trans_a.is_transpose() { (common_dim, c.nrows()) }
else { (c.nrows(), common_dim) };
let b_shape =
if trans_b.is_transpose() { (c.ncols(), common_dim) }
else { (common_dim, c.ncols()) };
let a = csr(value_strategy.clone(), Just(a_shape.0), Just(a_shape.1), max_nnz);
let b = matrix(value_strategy.clone(), b_shape.0, b_shape.1);
// We use the same values for alpha, beta parameters as for matrix elements
let alpha = value_strategy.clone();
let beta = value_strategy.clone();
(Just(c), beta, alpha, Just(trans_a), a, Just(trans_b), b)
}).prop_map(|(c, beta, alpha, trans_a, a, trans_b, b)| {
SpmmCsrDenseArgs {
c,
beta,
alpha,
trans_a,
a,
trans_b,
b,
}
})
}
fn csr_strategy() -> impl Strategy<Value=CsrMatrix<i32>> {
csr(-5 ..= 5, 0 ..= 6usize, 0 ..= 6usize, 40)
}
fn dense_strategy() -> impl Strategy<Value=DMatrix<i32>> {
matrix(-5 ..= 5, 0 ..= 6, 0 ..= 6)
}
fn trans_strategy() -> impl Strategy<Value=Transposition> + Clone {
proptest::bool::ANY.prop_map(Transposition::from_bool)
}
/// Helper function to help us call dense GEMM with our transposition parameters
fn dense_gemm<'a>(c: impl Into<DMatrixSliceMut<'a, i32>>,
beta: i32,
alpha: i32,
trans_a: Transposition,
a: impl Into<DMatrixSlice<'a, i32>>,
trans_b: Transposition,
b: impl Into<DMatrixSlice<'a, i32>>)
{
let mut c = c.into();
let a = a.into();
let b = b.into();
use Transposition::{Transpose, NoTranspose};
match (trans_a, trans_b) {
(NoTranspose, NoTranspose) => c.gemm(alpha, &a, &b, beta),
(Transpose, NoTranspose) => c.gemm(alpha, &a.transpose(), &b, beta),
(NoTranspose, Transpose) => c.gemm(alpha, &a, &b.transpose(), beta),
(Transpose, Transpose) => c.gemm(alpha, &a.transpose(), &b.transpose(), beta)
};
}
proptest! {
#[test]
fn spmm_csr_dense_agrees_with_dense_result(
SpmmCsrDenseArgs { c, beta, alpha, trans_a, a, trans_b, b }
in spmm_csr_dense_args_strategy()
) {
let mut spmm_result = c.clone();
spmm_csr_dense(&mut spmm_result, beta, alpha, trans_a, &a, trans_b, &b);
let mut gemm_result = c.clone();
dense_gemm(&mut gemm_result, beta, alpha, trans_a, &DMatrix::from(&a), trans_b, &b);
prop_assert_eq!(spmm_result, gemm_result);
}
#[test]
fn spmm_csr_dense_panics_on_dim_mismatch(
(alpha, beta, c, a, b, trans_a, trans_b)
in (-5 ..= 5, -5 ..= 5, dense_strategy(), csr_strategy(),
dense_strategy(), trans_strategy(), trans_strategy())
) {
// We refer to `A * B` as the "product"
let product_rows = if trans_a.is_transpose() { a.ncols() } else { a.nrows() };
let product_cols = if trans_b.is_transpose() { b.nrows() } else { b.ncols() };
// Determine the common dimension in the product
// from the perspective of a and b, respectively
let product_a_common = if trans_a.is_transpose() { a.nrows() } else { a.ncols() };
let product_b_common = if trans_b.is_transpose() { b.ncols() } else { b.nrows() };
let dims_are_compatible = product_rows == c.nrows()
&& product_cols == c.ncols()
&& product_a_common == product_b_common;
// If the dimensions randomly happen to be compatible, then of course we need to
// skip the test, so we assume that they are not.
prop_assume!(!dims_are_compatible);
let result = catch_unwind(|| {
let mut spmm_result = c.clone();
spmm_csr_dense(&mut spmm_result, beta, alpha, trans_a, &a, trans_b, &b);
});
prop_assert!(result.is_err(),
"The SPMM kernel executed successfully despite mismatch dimensions");
}
}