1189 lines
45 KiB
Rust
1189 lines
45 KiB
Rust
use crate::common::{csc_strategy, csr_strategy, PROPTEST_MATRIX_DIM, PROPTEST_MAX_NNZ, PROPTEST_I32_VALUE_STRATEGY, non_zero_i32_value_strategy, value_strategy};
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use nalgebra_sparse::ops::serial::{spmm_csr_dense, spmm_csc_dense, spadd_pattern, spadd_csr_prealloc, spadd_csc_prealloc, spmm_csr_prealloc, spmm_csc_prealloc, spsolve_csc_lower_triangular, spmm_csr_pattern};
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use nalgebra_sparse::ops::{Op};
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use nalgebra_sparse::csr::CsrMatrix;
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use nalgebra_sparse::csc::CscMatrix;
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use nalgebra_sparse::proptest::{csc, csr, sparsity_pattern};
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use nalgebra_sparse::pattern::SparsityPattern;
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use nalgebra::{DMatrix, Scalar, DMatrixSliceMut, DMatrixSlice};
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use nalgebra::proptest::{matrix, vector};
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use proptest::prelude::*;
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use matrixcompare::prop_assert_matrix_eq;
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use std::panic::catch_unwind;
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/// Represents the sparsity pattern of a CSR matrix as a dense matrix with 0/1
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fn dense_csr_pattern(pattern: &SparsityPattern) -> DMatrix<i32> {
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let boolean_csr = CsrMatrix::try_from_pattern_and_values(
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pattern.clone(),
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vec![1; pattern.nnz()])
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.unwrap();
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DMatrix::from(&boolean_csr)
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}
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/// Represents the sparsity pattern of a CSC matrix as a dense matrix with 0/1
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fn dense_csc_pattern(pattern: &SparsityPattern) -> DMatrix<i32> {
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let boolean_csc = CscMatrix::try_from_pattern_and_values(
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pattern.clone(),
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vec![1; pattern.nnz()])
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.unwrap();
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DMatrix::from(&boolean_csc)
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}
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#[derive(Debug)]
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struct SpmmCsrDenseArgs<T: Scalar> {
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c: DMatrix<T>,
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beta: T,
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alpha: T,
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a: Op<CsrMatrix<T>>,
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b: Op<DMatrix<T>>,
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}
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#[derive(Debug)]
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struct SpmmCscDenseArgs<T: Scalar> {
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c: DMatrix<T>,
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beta: T,
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alpha: T,
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a: Op<CscMatrix<T>>,
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b: Op<DMatrix<T>>,
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}
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/// Returns matrices C, A and B with compatible dimensions such that it can be used
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/// in an `spmm` operation `C = beta * C + alpha * trans(A) * trans(B)`.
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fn spmm_csr_dense_args_strategy() -> impl Strategy<Value=SpmmCsrDenseArgs<i32>> {
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let max_nnz = PROPTEST_MAX_NNZ;
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let value_strategy = PROPTEST_I32_VALUE_STRATEGY;
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let c_rows = PROPTEST_MATRIX_DIM;
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let c_cols = PROPTEST_MATRIX_DIM;
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let common_dim = PROPTEST_MATRIX_DIM;
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let trans_strategy = trans_strategy();
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let c_matrix_strategy = matrix(value_strategy.clone(), c_rows, c_cols);
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(c_matrix_strategy, common_dim, trans_strategy.clone(), trans_strategy.clone())
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.prop_flat_map(move |(c, common_dim, trans_a, trans_b)| {
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let a_shape =
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if trans_a { (common_dim, c.nrows()) }
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else { (c.nrows(), common_dim) };
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let b_shape =
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if trans_b { (c.ncols(), common_dim) }
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else { (common_dim, c.ncols()) };
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let a = csr(value_strategy.clone(), a_shape.0, a_shape.1, max_nnz);
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let b = matrix(value_strategy.clone(), b_shape.0, b_shape.1);
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// We use the same values for alpha, beta parameters as for matrix elements
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let alpha = value_strategy.clone();
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let beta = value_strategy.clone();
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(Just(c), beta, alpha, Just(trans_a), a, Just(trans_b), b)
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}).prop_map(|(c, beta, alpha, trans_a, a, trans_b, b)| {
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SpmmCsrDenseArgs {
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c,
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beta,
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alpha,
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a: if trans_a { Op::Transpose(a) } else { Op::NoOp(a) },
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b: if trans_b { Op::Transpose(b) } else { Op::NoOp(b) },
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}
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})
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}
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/// Returns matrices C, A and B with compatible dimensions such that it can be used
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/// in an `spmm` operation `C = beta * C + alpha * trans(A) * trans(B)`.
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fn spmm_csc_dense_args_strategy() -> impl Strategy<Value=SpmmCscDenseArgs<i32>> {
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spmm_csr_dense_args_strategy()
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.prop_map(|args| {
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SpmmCscDenseArgs {
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c: args.c,
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beta: args.beta,
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alpha: args.alpha,
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a: args.a.map_same_op(|a| CscMatrix::from(&a)),
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b: args.b
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}
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})
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}
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#[derive(Debug)]
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struct SpaddCsrArgs<T> {
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c: CsrMatrix<T>,
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beta: T,
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alpha: T,
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a: Op<CsrMatrix<T>>,
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}
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#[derive(Debug)]
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struct SpaddCscArgs<T> {
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c: CscMatrix<T>,
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beta: T,
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alpha: T,
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a: Op<CscMatrix<T>>,
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}
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fn spadd_csr_prealloc_args_strategy() -> impl Strategy<Value=SpaddCsrArgs<i32>> {
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let value_strategy = PROPTEST_I32_VALUE_STRATEGY;
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spadd_pattern_strategy()
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.prop_flat_map(move |(a_pattern, b_pattern)| {
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let c_pattern = spadd_pattern(&a_pattern, &b_pattern);
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let a_values = vec![value_strategy.clone(); a_pattern.nnz()];
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let c_values = vec![value_strategy.clone(); c_pattern.nnz()];
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let alpha = value_strategy.clone();
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let beta = value_strategy.clone();
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(Just(c_pattern), Just(a_pattern), c_values, a_values, alpha, beta, trans_strategy())
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}).prop_map(|(c_pattern, a_pattern, c_values, a_values, alpha, beta, trans_a)| {
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let c = CsrMatrix::try_from_pattern_and_values(c_pattern, c_values).unwrap();
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let a = CsrMatrix::try_from_pattern_and_values(a_pattern, a_values).unwrap();
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let a = if trans_a { Op::Transpose(a.transpose()) } else { Op::NoOp(a) };
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SpaddCsrArgs { c, beta, alpha, a }
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})
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}
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fn spadd_csc_prealloc_args_strategy() -> impl Strategy<Value=SpaddCscArgs<i32>> {
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spadd_csr_prealloc_args_strategy()
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.prop_map(|args| SpaddCscArgs {
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c: CscMatrix::from(&args.c),
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beta: args.beta,
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alpha: args.alpha,
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a: args.a.map_same_op(|a| CscMatrix::from(&a))
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})
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}
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fn dense_strategy() -> impl Strategy<Value=DMatrix<i32>> {
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matrix(PROPTEST_I32_VALUE_STRATEGY, PROPTEST_MATRIX_DIM, PROPTEST_MATRIX_DIM)
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}
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fn trans_strategy() -> impl Strategy<Value=bool> + Clone {
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proptest::bool::ANY
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}
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/// Wraps the values of the given strategy in `Op`, producing both transposed and non-transposed
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/// values.
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fn op_strategy<S: Strategy>(strategy: S) -> impl Strategy<Value=Op<S::Value>> {
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let is_transposed = proptest::bool::ANY;
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(strategy, is_transposed)
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.prop_map(|(obj, is_trans)| if is_trans {
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Op::Transpose(obj)
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} else {
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Op::NoOp(obj)
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})
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}
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fn pattern_strategy() -> impl Strategy<Value=SparsityPattern> {
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sparsity_pattern(PROPTEST_MATRIX_DIM, PROPTEST_MATRIX_DIM, PROPTEST_MAX_NNZ)
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}
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/// Constructs pairs (a, b) where a and b have the same dimensions
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fn spadd_pattern_strategy() -> impl Strategy<Value=(SparsityPattern, SparsityPattern)> {
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pattern_strategy()
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.prop_flat_map(|a| {
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let b = sparsity_pattern(a.major_dim(), a.minor_dim(), PROPTEST_MAX_NNZ);
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(Just(a), b)
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})
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}
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/// Constructs pairs (a, b) where a and b have compatible dimensions for a matrix product
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fn spmm_csr_pattern_strategy() -> impl Strategy<Value=(SparsityPattern, SparsityPattern)> {
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pattern_strategy()
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.prop_flat_map(|a| {
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let b = sparsity_pattern(a.minor_dim(), PROPTEST_MATRIX_DIM, PROPTEST_MAX_NNZ);
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(Just(a), b)
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})
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}
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#[derive(Debug)]
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struct SpmmCsrArgs<T> {
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c: CsrMatrix<T>,
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beta: T,
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alpha: T,
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a: Op<CsrMatrix<T>>,
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b: Op<CsrMatrix<T>>,
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}
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#[derive(Debug)]
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struct SpmmCscArgs<T> {
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c: CscMatrix<T>,
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beta: T,
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alpha: T,
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a: Op<CscMatrix<T>>,
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b: Op<CscMatrix<T>>,
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}
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fn spmm_csr_prealloc_args_strategy() -> impl Strategy<Value=SpmmCsrArgs<i32>> {
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spmm_csr_pattern_strategy()
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.prop_flat_map(|(a_pattern, b_pattern)| {
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let a_values = vec![PROPTEST_I32_VALUE_STRATEGY; a_pattern.nnz()];
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let b_values = vec![PROPTEST_I32_VALUE_STRATEGY; b_pattern.nnz()];
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let c_pattern = spmm_csr_pattern(&a_pattern, &b_pattern);
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let c_values = vec![PROPTEST_I32_VALUE_STRATEGY; c_pattern.nnz()];
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let a = a_values.prop_map(move |values|
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CsrMatrix::try_from_pattern_and_values(a_pattern.clone(), values).unwrap());
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let b = b_values.prop_map(move |values|
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CsrMatrix::try_from_pattern_and_values(b_pattern.clone(), values).unwrap());
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let c = c_values.prop_map(move |values|
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CsrMatrix::try_from_pattern_and_values(c_pattern.clone(), values).unwrap());
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let alpha = PROPTEST_I32_VALUE_STRATEGY;
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let beta = PROPTEST_I32_VALUE_STRATEGY;
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(c, beta, alpha, trans_strategy(), a, trans_strategy(), b)
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})
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.prop_map(|(c, beta, alpha, trans_a, a, trans_b, b)| {
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SpmmCsrArgs::<i32> {
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c,
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beta,
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alpha,
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a: if trans_a { Op::Transpose(a.transpose()) } else { Op::NoOp(a) },
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b: if trans_b { Op::Transpose(b.transpose()) } else { Op::NoOp(b) }
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}
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})
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}
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fn spmm_csc_prealloc_args_strategy() -> impl Strategy<Value=SpmmCscArgs<i32>> {
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// Note: Converting from CSR is simple, but might be significantly slower than
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// writing a common implementation that can be shared between CSR and CSC args
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spmm_csr_prealloc_args_strategy()
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.prop_map(|args| {
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SpmmCscArgs {
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c: CscMatrix::from(&args.c),
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beta: args.beta,
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alpha: args.alpha,
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a: args.a.map_same_op(|a| CscMatrix::from(&a)),
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b: args.b.map_same_op(|b| CscMatrix::from(&b))
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}
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})
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}
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fn csc_invertible_diagonal() -> impl Strategy<Value=CscMatrix<f64>> {
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let non_zero_values = value_strategy::<f64>()
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.prop_filter("Only non-zeros values accepted", |x| x != &0.0);
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vector(non_zero_values, PROPTEST_MATRIX_DIM)
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.prop_map(|d| {
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let mut matrix = CscMatrix::identity(d.len());
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matrix.values_mut().clone_from_slice(&d.as_slice());
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matrix
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})
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}
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fn csc_square_with_non_zero_diagonals() -> impl Strategy<Value=CscMatrix<f64>> {
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csc_invertible_diagonal()
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.prop_flat_map(|d| {
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csc(value_strategy::<f64>(), d.nrows(), d.nrows(), PROPTEST_MAX_NNZ)
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.prop_map(move |mut c| {
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for (i, j, v) in c.triplet_iter_mut() {
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if i == j {
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*v = 0.0;
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}
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}
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// Return the sum of a matrix with zero diagonals and an invertible diagonal
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// matrix
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c + &d
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})
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})
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}
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/// Helper function to help us call dense GEMM with our `Op` type
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fn dense_gemm<'a>(beta: i32,
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c: impl Into<DMatrixSliceMut<'a, i32>>,
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alpha: i32,
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a: Op<impl Into<DMatrixSlice<'a, i32>>>,
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b: Op<impl Into<DMatrixSlice<'a, i32>>>)
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{
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let mut c = c.into();
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let a = a.convert();
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let b = b.convert();
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use Op::{NoOp, Transpose};
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match (a, b) {
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(NoOp(a), NoOp(b)) => c.gemm(alpha, &a, &b, beta),
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(Transpose(a), NoOp(b)) => c.gemm(alpha, &a.transpose(), &b, beta),
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(NoOp(a), Transpose(b)) => c.gemm(alpha, &a, &b.transpose(), beta),
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(Transpose(a), Transpose(b)) => c.gemm(alpha, &a.transpose(), &b.transpose(), beta)
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}
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}
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proptest! {
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#[test]
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fn spmm_csr_dense_agrees_with_dense_result(
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SpmmCsrDenseArgs { c, beta, alpha, a, b }
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in spmm_csr_dense_args_strategy()
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) {
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let mut spmm_result = c.clone();
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spmm_csr_dense(beta, &mut spmm_result, alpha, a.as_ref(), b.as_ref());
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let mut gemm_result = c.clone();
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let a_dense = a.map_same_op(|a| DMatrix::from(&a));
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dense_gemm(beta, &mut gemm_result, alpha, a_dense.as_ref(), b.as_ref());
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prop_assert_eq!(spmm_result, gemm_result);
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}
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#[test]
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fn spmm_csr_dense_panics_on_dim_mismatch(
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(alpha, beta, c, a, b)
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in (PROPTEST_I32_VALUE_STRATEGY,
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PROPTEST_I32_VALUE_STRATEGY,
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dense_strategy(),
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op_strategy(csr_strategy()),
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op_strategy(dense_strategy()))
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) {
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// We refer to `A * B` as the "product"
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let product_rows = match &a {
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Op::NoOp(ref a) => a.nrows(),
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Op::Transpose(ref a) => a.ncols(),
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};
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let product_cols = match &b {
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Op::NoOp(ref b) => b.ncols(),
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Op::Transpose(ref b) => b.nrows(),
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};
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// Determine the common dimension in the product
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// from the perspective of a and b, respectively
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let product_a_common = match &a {
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Op::NoOp(ref a) => a.ncols(),
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Op::Transpose(ref a) => a.nrows(),
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};
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let product_b_common = match &b {
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Op::NoOp(ref b) => b.nrows(),
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Op::Transpose(ref b) => b.ncols()
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};
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let dims_are_compatible = product_rows == c.nrows()
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&& product_cols == c.ncols()
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&& product_a_common == product_b_common;
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// If the dimensions randomly happen to be compatible, then of course we need to
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// skip the test, so we assume that they are not.
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prop_assume!(!dims_are_compatible);
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let result = catch_unwind(|| {
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let mut spmm_result = c.clone();
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spmm_csr_dense(beta, &mut spmm_result, alpha, a.as_ref(), b.as_ref());
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});
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prop_assert!(result.is_err(),
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"The SPMM kernel executed successfully despite mismatch dimensions");
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}
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#[test]
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fn spadd_pattern_test((a, b) in spadd_pattern_strategy())
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{
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// (a, b) are dimensionally compatible patterns
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let pattern_result = spadd_pattern(&a, &b);
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// To verify the pattern, we construct CSR matrices with positive integer entries
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// corresponding to a and b, and convert them to dense matrices.
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// The sum of these dense matrices will then have non-zeros in exactly the same locations
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// as the result of "adding" the sparsity patterns
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let a_csr = CsrMatrix::try_from_pattern_and_values(a.clone(), vec![1; a.nnz()])
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.unwrap();
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let a_dense = DMatrix::from(&a_csr);
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let b_csr = CsrMatrix::try_from_pattern_and_values(b.clone(), vec![1; b.nnz()])
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.unwrap();
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let b_dense = DMatrix::from(&b_csr);
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let c_dense = a_dense + b_dense;
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let c_csr = CsrMatrix::from(&c_dense);
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prop_assert_eq!(&pattern_result, c_csr.pattern());
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}
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#[test]
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fn spadd_csr_prealloc_test(SpaddCsrArgs { c, beta, alpha, a } in spadd_csr_prealloc_args_strategy()) {
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// Test that we get the expected result by comparing to an equivalent dense operation
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// (here we give in the C matrix, so the sparsity pattern is essentially fixed)
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let mut c_sparse = c.clone();
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spadd_csr_prealloc(beta, &mut c_sparse, alpha, a.as_ref()).unwrap();
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let mut c_dense = DMatrix::from(&c);
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let op_a_dense = match a {
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Op::NoOp(a) => DMatrix::from(&a),
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Op::Transpose(a) => DMatrix::from(&a).transpose(),
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};
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c_dense = beta * c_dense + alpha * &op_a_dense;
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prop_assert_eq!(&DMatrix::from(&c_sparse), &c_dense);
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}
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#[test]
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fn csr_add_csr(
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// a and b have the same dimensions
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(a, b)
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in csr_strategy()
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.prop_flat_map(|a| {
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let b = csr(PROPTEST_I32_VALUE_STRATEGY, a.nrows(), a.ncols(), PROPTEST_MAX_NNZ);
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(Just(a), b)
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}))
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{
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// We use the dense result as the ground truth for the arithmetic result
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let c_dense = DMatrix::from(&a) + DMatrix::from(&b);
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// However, it's not enough only to cover the dense result, we also need to verify the
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// sparsity pattern. We can determine the exact sparsity pattern by using
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// dense arithmetic with positive integer values and extracting positive entries.
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let c_dense_pattern = dense_csr_pattern(a.pattern()) + dense_csr_pattern(b.pattern());
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let c_pattern = CsrMatrix::from(&c_dense_pattern).pattern().clone();
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// Check each combination of owned matrices and references
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let c_owned_owned = a.clone() + b.clone();
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prop_assert_eq!(&DMatrix::from(&c_owned_owned), &c_dense);
|
|
prop_assert_eq!(c_owned_owned.pattern(), &c_pattern);
|
|
|
|
let c_owned_ref = a.clone() + &b;
|
|
prop_assert_eq!(&DMatrix::from(&c_owned_ref), &c_dense);
|
|
prop_assert_eq!(c_owned_ref.pattern(), &c_pattern);
|
|
|
|
let c_ref_owned = &a + b.clone();
|
|
prop_assert_eq!(&DMatrix::from(&c_ref_owned), &c_dense);
|
|
prop_assert_eq!(c_ref_owned.pattern(), &c_pattern);
|
|
|
|
let c_ref_ref = &a + &b;
|
|
prop_assert_eq!(&DMatrix::from(&c_ref_ref), &c_dense);
|
|
prop_assert_eq!(c_ref_ref.pattern(), &c_pattern);
|
|
}
|
|
|
|
#[test]
|
|
fn csr_sub_csr(
|
|
// a and b have the same dimensions
|
|
(a, b)
|
|
in csr_strategy()
|
|
.prop_flat_map(|a| {
|
|
let b = csr(PROPTEST_I32_VALUE_STRATEGY, a.nrows(), a.ncols(), PROPTEST_MAX_NNZ);
|
|
(Just(a), b)
|
|
}))
|
|
{
|
|
// See comments in csr_add_csr for rationale for checking the pattern this way
|
|
let c_dense = DMatrix::from(&a) - DMatrix::from(&b);
|
|
let c_dense_pattern = dense_csr_pattern(a.pattern()) + dense_csr_pattern(b.pattern());
|
|
let c_pattern = CsrMatrix::from(&c_dense_pattern).pattern().clone();
|
|
|
|
// Check each combination of owned matrices and references
|
|
let c_owned_owned = a.clone() - b.clone();
|
|
prop_assert_eq!(&DMatrix::from(&c_owned_owned), &c_dense);
|
|
prop_assert_eq!(c_owned_owned.pattern(), &c_pattern);
|
|
|
|
let c_owned_ref = a.clone() - &b;
|
|
prop_assert_eq!(&DMatrix::from(&c_owned_ref), &c_dense);
|
|
prop_assert_eq!(c_owned_ref.pattern(), &c_pattern);
|
|
|
|
let c_ref_owned = &a - b.clone();
|
|
prop_assert_eq!(&DMatrix::from(&c_ref_owned), &c_dense);
|
|
prop_assert_eq!(c_ref_owned.pattern(), &c_pattern);
|
|
|
|
let c_ref_ref = &a - &b;
|
|
prop_assert_eq!(&DMatrix::from(&c_ref_ref), &c_dense);
|
|
prop_assert_eq!(c_ref_ref.pattern(), &c_pattern);
|
|
}
|
|
|
|
#[test]
|
|
fn spmm_csr_pattern_test((a, b) in spmm_csr_pattern_strategy())
|
|
{
|
|
// (a, b) are multiplication-wise dimensionally compatible patterns
|
|
let c_pattern = spmm_csr_pattern(&a, &b);
|
|
|
|
// To verify the pattern, we construct CSR matrices with positive integer entries
|
|
// corresponding to a and b, and convert them to dense matrices.
|
|
// The product of these dense matrices will then have non-zeros in exactly the same locations
|
|
// as the result of "multiplying" the sparsity patterns
|
|
let a_csr = CsrMatrix::try_from_pattern_and_values(a.clone(), vec![1; a.nnz()])
|
|
.unwrap();
|
|
let a_dense = DMatrix::from(&a_csr);
|
|
let b_csr = CsrMatrix::try_from_pattern_and_values(b.clone(), vec![1; b.nnz()])
|
|
.unwrap();
|
|
let b_dense = DMatrix::from(&b_csr);
|
|
let c_dense = a_dense * b_dense;
|
|
let c_csr = CsrMatrix::from(&c_dense);
|
|
|
|
prop_assert_eq!(&c_pattern, c_csr.pattern());
|
|
}
|
|
|
|
#[test]
|
|
fn spmm_csr_prealloc_test(SpmmCsrArgs { c, beta, alpha, a, b }
|
|
in spmm_csr_prealloc_args_strategy()
|
|
) {
|
|
// Test that we get the expected result by comparing to an equivalent dense operation
|
|
// (here we give in the C matrix, so the sparsity pattern is essentially fixed)
|
|
let mut c_sparse = c.clone();
|
|
spmm_csr_prealloc(beta, &mut c_sparse, alpha, a.as_ref(), b.as_ref()).unwrap();
|
|
|
|
let mut c_dense = DMatrix::from(&c);
|
|
let op_a_dense = match a {
|
|
Op::NoOp(ref a) => DMatrix::from(a),
|
|
Op::Transpose(ref a) => DMatrix::from(a).transpose(),
|
|
};
|
|
let op_b_dense = match b {
|
|
Op::NoOp(ref b) => DMatrix::from(b),
|
|
Op::Transpose(ref b) => DMatrix::from(b).transpose(),
|
|
};
|
|
c_dense = beta * c_dense + alpha * &op_a_dense * op_b_dense;
|
|
|
|
prop_assert_eq!(&DMatrix::from(&c_sparse), &c_dense);
|
|
}
|
|
|
|
#[test]
|
|
fn spmm_csr_prealloc_panics_on_dim_mismatch(
|
|
(alpha, beta, c, a, b)
|
|
in (PROPTEST_I32_VALUE_STRATEGY,
|
|
PROPTEST_I32_VALUE_STRATEGY,
|
|
csr_strategy(),
|
|
op_strategy(csr_strategy()),
|
|
op_strategy(csr_strategy()))
|
|
) {
|
|
// We refer to `A * B` as the "product"
|
|
let product_rows = match &a {
|
|
Op::NoOp(ref a) => a.nrows(),
|
|
Op::Transpose(ref a) => a.ncols(),
|
|
};
|
|
let product_cols = match &b {
|
|
Op::NoOp(ref b) => b.ncols(),
|
|
Op::Transpose(ref b) => b.nrows(),
|
|
};
|
|
// Determine the common dimension in the product
|
|
// from the perspective of a and b, respectively
|
|
let product_a_common = match &a {
|
|
Op::NoOp(ref a) => a.ncols(),
|
|
Op::Transpose(ref a) => a.nrows(),
|
|
};
|
|
let product_b_common = match &b {
|
|
Op::NoOp(ref b) => b.nrows(),
|
|
Op::Transpose(ref b) => b.ncols(),
|
|
};
|
|
|
|
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_prealloc(beta, &mut spmm_result, alpha, a.as_ref(), b.as_ref()).unwrap();
|
|
});
|
|
|
|
prop_assert!(result.is_err(),
|
|
"The SPMM kernel executed successfully despite mismatch dimensions");
|
|
}
|
|
|
|
#[test]
|
|
fn spadd_csr_prealloc_panics_on_dim_mismatch(
|
|
(alpha, beta, c, op_a)
|
|
in (PROPTEST_I32_VALUE_STRATEGY,
|
|
PROPTEST_I32_VALUE_STRATEGY,
|
|
csr_strategy(),
|
|
op_strategy(csr_strategy()))
|
|
) {
|
|
let op_a_rows = match &op_a {
|
|
&Op::NoOp(ref a) => a.nrows(),
|
|
&Op::Transpose(ref a) => a.ncols()
|
|
};
|
|
let op_a_cols = match &op_a {
|
|
&Op::NoOp(ref a) => a.ncols(),
|
|
&Op::Transpose(ref a) => a.nrows()
|
|
};
|
|
|
|
let dims_are_compatible = c.nrows() == op_a_rows && c.ncols() == op_a_cols;
|
|
|
|
// 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();
|
|
spadd_csr_prealloc(beta, &mut spmm_result, alpha, op_a.as_ref()).unwrap();
|
|
});
|
|
|
|
prop_assert!(result.is_err(),
|
|
"The SPMM kernel executed successfully despite mismatch dimensions");
|
|
}
|
|
|
|
#[test]
|
|
fn csr_mul_csr(
|
|
// a and b have dimensions compatible for multiplication
|
|
(a, b)
|
|
in csr_strategy()
|
|
.prop_flat_map(|a| {
|
|
let max_nnz = PROPTEST_MAX_NNZ;
|
|
let cols = PROPTEST_MATRIX_DIM;
|
|
let b = csr(PROPTEST_I32_VALUE_STRATEGY, a.ncols(), cols, max_nnz);
|
|
(Just(a), b)
|
|
}))
|
|
{
|
|
// We use the dense result as the ground truth for the arithmetic result
|
|
let c_dense = DMatrix::from(&a) * DMatrix::from(&b);
|
|
// However, it's not enough only to cover the dense result, we also need to verify the
|
|
// sparsity pattern. We can determine the exact sparsity pattern by using
|
|
// dense arithmetic with positive integer values and extracting positive entries.
|
|
let c_dense_pattern = dense_csr_pattern(a.pattern()) * dense_csr_pattern(b.pattern());
|
|
let c_pattern = CsrMatrix::from(&c_dense_pattern).pattern().clone();
|
|
|
|
// Check each combination of owned matrices and references
|
|
let c_owned_owned = a.clone() * b.clone();
|
|
prop_assert_eq!(&DMatrix::from(&c_owned_owned), &c_dense);
|
|
prop_assert_eq!(c_owned_owned.pattern(), &c_pattern);
|
|
|
|
let c_owned_ref = a.clone() * &b;
|
|
prop_assert_eq!(&DMatrix::from(&c_owned_ref), &c_dense);
|
|
prop_assert_eq!(c_owned_ref.pattern(), &c_pattern);
|
|
|
|
let c_ref_owned = &a * b.clone();
|
|
prop_assert_eq!(&DMatrix::from(&c_ref_owned), &c_dense);
|
|
prop_assert_eq!(c_ref_owned.pattern(), &c_pattern);
|
|
|
|
let c_ref_ref = &a * &b;
|
|
prop_assert_eq!(&DMatrix::from(&c_ref_ref), &c_dense);
|
|
prop_assert_eq!(c_ref_ref.pattern(), &c_pattern);
|
|
}
|
|
|
|
#[test]
|
|
fn spmm_csc_prealloc_test(SpmmCscArgs { c, beta, alpha, a, b }
|
|
in spmm_csc_prealloc_args_strategy()
|
|
) {
|
|
// Test that we get the expected result by comparing to an equivalent dense operation
|
|
// (here we give in the C matrix, so the sparsity pattern is essentially fixed)
|
|
let mut c_sparse = c.clone();
|
|
spmm_csc_prealloc(beta, &mut c_sparse, alpha, a.as_ref(), b.as_ref()).unwrap();
|
|
|
|
let mut c_dense = DMatrix::from(&c);
|
|
let op_a_dense = match a {
|
|
Op::NoOp(ref a) => DMatrix::from(a),
|
|
Op::Transpose(ref a) => DMatrix::from(a).transpose(),
|
|
};
|
|
let op_b_dense = match b {
|
|
Op::NoOp(ref b) => DMatrix::from(b),
|
|
Op::Transpose(ref b) => DMatrix::from(b).transpose(),
|
|
};
|
|
c_dense = beta * c_dense + alpha * &op_a_dense * op_b_dense;
|
|
|
|
prop_assert_eq!(&DMatrix::from(&c_sparse), &c_dense);
|
|
}
|
|
|
|
#[test]
|
|
fn spmm_csc_prealloc_panics_on_dim_mismatch(
|
|
(alpha, beta, c, a, b)
|
|
in (PROPTEST_I32_VALUE_STRATEGY,
|
|
PROPTEST_I32_VALUE_STRATEGY,
|
|
csc_strategy(),
|
|
op_strategy(csc_strategy()),
|
|
op_strategy(csc_strategy()))
|
|
) {
|
|
// We refer to `A * B` as the "product"
|
|
let product_rows = match &a {
|
|
Op::NoOp(ref a) => a.nrows(),
|
|
Op::Transpose(ref a) => a.ncols(),
|
|
};
|
|
let product_cols = match &b {
|
|
Op::NoOp(ref b) => b.ncols(),
|
|
Op::Transpose(ref b) => b.nrows(),
|
|
};
|
|
// Determine the common dimension in the product
|
|
// from the perspective of a and b, respectively
|
|
let product_a_common = match &a {
|
|
Op::NoOp(ref a) => a.ncols(),
|
|
Op::Transpose(ref a) => a.nrows(),
|
|
};
|
|
let product_b_common = match &b {
|
|
Op::NoOp(ref b) => b.nrows(),
|
|
Op::Transpose(ref b) => b.ncols(),
|
|
};
|
|
|
|
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_csc_prealloc(beta, &mut spmm_result, alpha, a.as_ref(), b.as_ref()).unwrap();
|
|
});
|
|
|
|
prop_assert!(result.is_err(),
|
|
"The SPMM kernel executed successfully despite mismatch dimensions");
|
|
}
|
|
|
|
#[test]
|
|
fn csc_mul_csc(
|
|
// a and b have dimensions compatible for multiplication
|
|
(a, b)
|
|
in csc_strategy()
|
|
.prop_flat_map(|a| {
|
|
let max_nnz = PROPTEST_MAX_NNZ;
|
|
let cols = PROPTEST_MATRIX_DIM;
|
|
let b = csc(PROPTEST_I32_VALUE_STRATEGY, a.ncols(), cols, max_nnz);
|
|
(Just(a), b)
|
|
})
|
|
.prop_map(|(a, b)| {
|
|
println!("a: {} x {}, b: {} x {}", a.nrows(), a.ncols(), b.nrows(), b.ncols());
|
|
(a, b)
|
|
}))
|
|
{
|
|
assert_eq!(a.ncols(), b.nrows());
|
|
// We use the dense result as the ground truth for the arithmetic result
|
|
let c_dense = DMatrix::from(&a) * DMatrix::from(&b);
|
|
// However, it's not enough only to cover the dense result, we also need to verify the
|
|
// sparsity pattern. We can determine the exact sparsity pattern by using
|
|
// dense arithmetic with positive integer values and extracting positive entries.
|
|
let c_dense_pattern = dense_csc_pattern(a.pattern()) * dense_csc_pattern(b.pattern());
|
|
let c_pattern = CscMatrix::from(&c_dense_pattern).pattern().clone();
|
|
|
|
// Check each combination of owned matrices and references
|
|
let c_owned_owned = a.clone() * b.clone();
|
|
prop_assert_eq!(&DMatrix::from(&c_owned_owned), &c_dense);
|
|
prop_assert_eq!(c_owned_owned.pattern(), &c_pattern);
|
|
|
|
let c_owned_ref = a.clone() * &b;
|
|
prop_assert_eq!(&DMatrix::from(&c_owned_ref), &c_dense);
|
|
prop_assert_eq!(c_owned_ref.pattern(), &c_pattern);
|
|
|
|
let c_ref_owned = &a * b.clone();
|
|
prop_assert_eq!(&DMatrix::from(&c_ref_owned), &c_dense);
|
|
prop_assert_eq!(c_ref_owned.pattern(), &c_pattern);
|
|
|
|
let c_ref_ref = &a * &b;
|
|
prop_assert_eq!(&DMatrix::from(&c_ref_ref), &c_dense);
|
|
prop_assert_eq!(c_ref_ref.pattern(), &c_pattern);
|
|
}
|
|
|
|
#[test]
|
|
fn spmm_csc_dense_agrees_with_dense_result(
|
|
SpmmCscDenseArgs { c, beta, alpha, a, b }
|
|
in spmm_csc_dense_args_strategy()
|
|
) {
|
|
let mut spmm_result = c.clone();
|
|
spmm_csc_dense(beta, &mut spmm_result, alpha, a.as_ref(), b.as_ref());
|
|
|
|
let mut gemm_result = c.clone();
|
|
let a_dense = a.map_same_op(|a| DMatrix::from(&a));
|
|
dense_gemm(beta, &mut gemm_result, alpha, a_dense.as_ref(), b.as_ref());
|
|
|
|
prop_assert_eq!(spmm_result, gemm_result);
|
|
}
|
|
|
|
#[test]
|
|
fn spmm_csc_dense_panics_on_dim_mismatch(
|
|
(alpha, beta, c, a, b)
|
|
in (PROPTEST_I32_VALUE_STRATEGY,
|
|
PROPTEST_I32_VALUE_STRATEGY,
|
|
dense_strategy(),
|
|
op_strategy(csc_strategy()),
|
|
op_strategy(dense_strategy()))
|
|
) {
|
|
// We refer to `A * B` as the "product"
|
|
let product_rows = match &a {
|
|
Op::NoOp(ref a) => a.nrows(),
|
|
Op::Transpose(ref a) => a.ncols(),
|
|
};
|
|
let product_cols = match &b {
|
|
Op::NoOp(ref b) => b.ncols(),
|
|
Op::Transpose(ref b) => b.nrows(),
|
|
};
|
|
// Determine the common dimension in the product
|
|
// from the perspective of a and b, respectively
|
|
let product_a_common = match &a {
|
|
Op::NoOp(ref a) => a.ncols(),
|
|
Op::Transpose(ref a) => a.nrows(),
|
|
};
|
|
let product_b_common = match &b {
|
|
Op::NoOp(ref b) => b.nrows(),
|
|
Op::Transpose(ref b) => b.ncols()
|
|
};
|
|
|
|
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_csc_dense(beta, &mut spmm_result, alpha, a.as_ref(), b.as_ref());
|
|
});
|
|
|
|
prop_assert!(result.is_err(),
|
|
"The SPMM kernel executed successfully despite mismatch dimensions");
|
|
}
|
|
|
|
#[test]
|
|
fn spadd_csc_prealloc_test(SpaddCscArgs { c, beta, alpha, a } in spadd_csc_prealloc_args_strategy()) {
|
|
// Test that we get the expected result by comparing to an equivalent dense operation
|
|
// (here we give in the C matrix, so the sparsity pattern is essentially fixed)
|
|
|
|
let mut c_sparse = c.clone();
|
|
spadd_csc_prealloc(beta, &mut c_sparse, alpha, a.as_ref()).unwrap();
|
|
|
|
let mut c_dense = DMatrix::from(&c);
|
|
let op_a_dense = match a {
|
|
Op::NoOp(a) => DMatrix::from(&a),
|
|
Op::Transpose(a) => DMatrix::from(&a).transpose(),
|
|
};
|
|
c_dense = beta * c_dense + alpha * &op_a_dense;
|
|
|
|
prop_assert_eq!(&DMatrix::from(&c_sparse), &c_dense);
|
|
}
|
|
|
|
#[test]
|
|
fn spadd_csc_prealloc_panics_on_dim_mismatch(
|
|
(alpha, beta, c, op_a)
|
|
in (PROPTEST_I32_VALUE_STRATEGY,
|
|
PROPTEST_I32_VALUE_STRATEGY,
|
|
csc_strategy(),
|
|
op_strategy(csc_strategy()))
|
|
) {
|
|
let op_a_rows = match &op_a {
|
|
&Op::NoOp(ref a) => a.nrows(),
|
|
&Op::Transpose(ref a) => a.ncols()
|
|
};
|
|
let op_a_cols = match &op_a {
|
|
&Op::NoOp(ref a) => a.ncols(),
|
|
&Op::Transpose(ref a) => a.nrows()
|
|
};
|
|
|
|
let dims_are_compatible = c.nrows() == op_a_rows && c.ncols() == op_a_cols;
|
|
|
|
// 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();
|
|
spadd_csc_prealloc(beta, &mut spmm_result, alpha, op_a.as_ref()).unwrap();
|
|
});
|
|
|
|
prop_assert!(result.is_err(),
|
|
"The SPMM kernel executed successfully despite mismatch dimensions");
|
|
}
|
|
|
|
#[test]
|
|
fn csc_add_csc(
|
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// a and b have the same dimensions
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(a, b)
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in csc_strategy()
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.prop_flat_map(|a| {
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let b = csc(PROPTEST_I32_VALUE_STRATEGY, a.nrows(), a.ncols(), PROPTEST_MAX_NNZ);
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(Just(a), b)
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}))
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{
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// We use the dense result as the ground truth for the arithmetic result
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let c_dense = DMatrix::from(&a) + DMatrix::from(&b);
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// However, it's not enough only to cover the dense result, we also need to verify the
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// sparsity pattern. We can determine the exact sparsity pattern by using
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// dense arithmetic with positive integer values and extracting positive entries.
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let c_dense_pattern = dense_csc_pattern(a.pattern()) + dense_csc_pattern(b.pattern());
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let c_pattern = CscMatrix::from(&c_dense_pattern).pattern().clone();
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// Check each combination of owned matrices and references
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let c_owned_owned = a.clone() + b.clone();
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prop_assert_eq!(&DMatrix::from(&c_owned_owned), &c_dense);
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prop_assert_eq!(c_owned_owned.pattern(), &c_pattern);
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let c_owned_ref = a.clone() + &b;
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prop_assert_eq!(&DMatrix::from(&c_owned_ref), &c_dense);
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prop_assert_eq!(c_owned_ref.pattern(), &c_pattern);
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let c_ref_owned = &a + b.clone();
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prop_assert_eq!(&DMatrix::from(&c_ref_owned), &c_dense);
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prop_assert_eq!(c_ref_owned.pattern(), &c_pattern);
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let c_ref_ref = &a + &b;
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prop_assert_eq!(&DMatrix::from(&c_ref_ref), &c_dense);
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prop_assert_eq!(c_ref_ref.pattern(), &c_pattern);
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}
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#[test]
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fn csc_sub_csc(
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// a and b have the same dimensions
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(a, b)
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in csc_strategy()
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.prop_flat_map(|a| {
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let b = csc(PROPTEST_I32_VALUE_STRATEGY, a.nrows(), a.ncols(), PROPTEST_MAX_NNZ);
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(Just(a), b)
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}))
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{
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// See comments in csc_add_csc for rationale for checking the pattern this way
|
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let c_dense = DMatrix::from(&a) - DMatrix::from(&b);
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let c_dense_pattern = dense_csc_pattern(a.pattern()) + dense_csc_pattern(b.pattern());
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let c_pattern = CscMatrix::from(&c_dense_pattern).pattern().clone();
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|
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// Check each combination of owned matrices and references
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let c_owned_owned = a.clone() - b.clone();
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prop_assert_eq!(&DMatrix::from(&c_owned_owned), &c_dense);
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prop_assert_eq!(c_owned_owned.pattern(), &c_pattern);
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let c_owned_ref = a.clone() - &b;
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prop_assert_eq!(&DMatrix::from(&c_owned_ref), &c_dense);
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prop_assert_eq!(c_owned_ref.pattern(), &c_pattern);
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let c_ref_owned = &a - b.clone();
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prop_assert_eq!(&DMatrix::from(&c_ref_owned), &c_dense);
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prop_assert_eq!(c_ref_owned.pattern(), &c_pattern);
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let c_ref_ref = &a - &b;
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prop_assert_eq!(&DMatrix::from(&c_ref_ref), &c_dense);
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prop_assert_eq!(c_ref_ref.pattern(), &c_pattern);
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}
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|
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#[test]
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fn csr_mul_scalar((scalar, matrix) in (PROPTEST_I32_VALUE_STRATEGY, csr_strategy())) {
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let dense = DMatrix::from(&matrix);
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let dense_result = dense * scalar;
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|
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let result_owned_owned = matrix.clone() * scalar;
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let result_owned_ref = matrix.clone() * &scalar;
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let result_ref_owned = &matrix * scalar;
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let result_ref_ref = &matrix * &scalar;
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|
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// Check that all the combinations of reference and owned variables return the same
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// result
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prop_assert_eq!(&result_owned_ref, &result_owned_owned);
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prop_assert_eq!(&result_ref_owned, &result_owned_owned);
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prop_assert_eq!(&result_ref_ref, &result_owned_owned);
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|
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// Check that this result is consistent with the dense result, and that the
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// NNZ is the same as before
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prop_assert_eq!(result_owned_owned.nnz(), matrix.nnz());
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prop_assert_eq!(DMatrix::from(&result_owned_owned), dense_result);
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|
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// Finally, check mul-assign
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let mut result_assign_owned = matrix.clone();
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result_assign_owned *= scalar;
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let mut result_assign_ref = matrix.clone();
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result_assign_ref *= &scalar;
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|
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prop_assert_eq!(&result_assign_owned, &result_owned_owned);
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prop_assert_eq!(&result_assign_ref, &result_owned_owned);
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}
|
|
|
|
#[test]
|
|
fn csc_mul_scalar((scalar, matrix) in (PROPTEST_I32_VALUE_STRATEGY, csc_strategy())) {
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let dense = DMatrix::from(&matrix);
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let dense_result = dense * scalar;
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|
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let result_owned_owned = matrix.clone() * scalar;
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let result_owned_ref = matrix.clone() * &scalar;
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let result_ref_owned = &matrix * scalar;
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let result_ref_ref = &matrix * &scalar;
|
|
|
|
// Check that all the combinations of reference and owned variables return the same
|
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// result
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prop_assert_eq!(&result_owned_ref, &result_owned_owned);
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prop_assert_eq!(&result_ref_owned, &result_owned_owned);
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prop_assert_eq!(&result_ref_ref, &result_owned_owned);
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|
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// Check that this result is consistent with the dense result, and that the
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// NNZ is the same as before
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prop_assert_eq!(result_owned_owned.nnz(), matrix.nnz());
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prop_assert_eq!(DMatrix::from(&result_owned_owned), dense_result);
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|
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// Finally, check mul-assign
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let mut result_assign_owned = matrix.clone();
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result_assign_owned *= scalar;
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let mut result_assign_ref = matrix.clone();
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result_assign_ref *= &scalar;
|
|
|
|
prop_assert_eq!(&result_assign_owned, &result_owned_owned);
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prop_assert_eq!(&result_assign_ref, &result_owned_owned);
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}
|
|
|
|
#[test]
|
|
fn scalar_mul_csr((scalar, matrix) in (PROPTEST_I32_VALUE_STRATEGY, csr_strategy())) {
|
|
// For scalar * matrix, we cannot generally implement this for any type T,
|
|
// so we have implemented this for the built in types separately. This requires
|
|
// us to also test these types separately. For validation, we check that
|
|
// scalar * matrix == matrix * scalar,
|
|
// which is sufficient for correctness if matrix * scalar is correctly implemented
|
|
// (which is tested separately).
|
|
// We only test for i32 here, because with our current implementation, the implementations
|
|
// for different types are completely identical and only rely on basic arithmetic
|
|
// operations
|
|
let result = &matrix * scalar;
|
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prop_assert_eq!(&(scalar * matrix.clone()), &result);
|
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prop_assert_eq!(&(scalar * &matrix), &result);
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|
prop_assert_eq!(&(&scalar * matrix.clone()), &result);
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|
prop_assert_eq!(&(&scalar * &matrix), &result);
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|
}
|
|
|
|
#[test]
|
|
fn scalar_mul_csc((scalar, matrix) in (PROPTEST_I32_VALUE_STRATEGY, csc_strategy())) {
|
|
// See comments for scalar_mul_csr
|
|
let result = &matrix * scalar;
|
|
prop_assert_eq!(&(scalar * matrix.clone()), &result);
|
|
prop_assert_eq!(&(scalar * &matrix), &result);
|
|
prop_assert_eq!(&(&scalar * matrix.clone()), &result);
|
|
prop_assert_eq!(&(&scalar * &matrix), &result);
|
|
}
|
|
|
|
#[test]
|
|
fn csr_neg(csr in csr_strategy()) {
|
|
let result = &csr - 2 * &csr;
|
|
prop_assert_eq!(-&csr, result.clone());
|
|
prop_assert_eq!(-csr, result);
|
|
}
|
|
|
|
#[test]
|
|
fn csc_neg(csc in csc_strategy()) {
|
|
let result = &csc - 2 * &csc;
|
|
prop_assert_eq!(-&csc, result.clone());
|
|
prop_assert_eq!(-csc, result);
|
|
}
|
|
|
|
#[test]
|
|
fn csr_div((csr, divisor) in (csr_strategy(), non_zero_i32_value_strategy())) {
|
|
let result_owned_owned = csr.clone() / divisor;
|
|
let result_owned_ref = csr.clone() / &divisor;
|
|
let result_ref_owned = &csr / divisor;
|
|
let result_ref_ref = &csr / &divisor;
|
|
|
|
// Verify that all results are the same
|
|
prop_assert_eq!(&result_owned_ref, &result_owned_owned);
|
|
prop_assert_eq!(&result_ref_owned, &result_owned_owned);
|
|
prop_assert_eq!(&result_ref_ref, &result_owned_owned);
|
|
|
|
// Check that NNZ was left unchanged
|
|
prop_assert_eq!(result_owned_owned.nnz(), csr.nnz());
|
|
|
|
// Then compare against the equivalent dense result
|
|
let dense_result = DMatrix::from(&csr) / divisor;
|
|
prop_assert_eq!(DMatrix::from(&result_owned_owned), dense_result);
|
|
}
|
|
|
|
#[test]
|
|
fn csc_div((csc, divisor) in (csc_strategy(), non_zero_i32_value_strategy())) {
|
|
let result_owned_owned = csc.clone() / divisor;
|
|
let result_owned_ref = csc.clone() / &divisor;
|
|
let result_ref_owned = &csc / divisor;
|
|
let result_ref_ref = &csc / &divisor;
|
|
|
|
// Verify that all results are the same
|
|
prop_assert_eq!(&result_owned_ref, &result_owned_owned);
|
|
prop_assert_eq!(&result_ref_owned, &result_owned_owned);
|
|
prop_assert_eq!(&result_ref_ref, &result_owned_owned);
|
|
|
|
// Check that NNZ was left unchanged
|
|
prop_assert_eq!(result_owned_owned.nnz(), csc.nnz());
|
|
|
|
// Then compare against the equivalent dense result
|
|
let dense_result = DMatrix::from(&csc) / divisor;
|
|
prop_assert_eq!(DMatrix::from(&result_owned_owned), dense_result);
|
|
}
|
|
|
|
#[test]
|
|
fn csr_div_assign((csr, divisor) in (csr_strategy(), non_zero_i32_value_strategy())) {
|
|
let result_owned = {
|
|
let mut csr = csr.clone();
|
|
csr /= divisor;
|
|
csr
|
|
};
|
|
|
|
let result_ref = {
|
|
let mut csr = csr.clone();
|
|
csr /= &divisor;
|
|
csr
|
|
};
|
|
|
|
let expected_result = csr / divisor;
|
|
|
|
prop_assert_eq!(&result_owned, &expected_result);
|
|
prop_assert_eq!(&result_ref, &expected_result);
|
|
}
|
|
|
|
#[test]
|
|
fn csc_div_assign((csc, divisor) in (csc_strategy(), non_zero_i32_value_strategy())) {
|
|
let result_owned = {
|
|
let mut csc = csc.clone();
|
|
csc /= divisor;
|
|
csc
|
|
};
|
|
|
|
let result_ref = {
|
|
let mut csc = csc.clone();
|
|
csc /= &divisor;
|
|
csc
|
|
};
|
|
|
|
let expected_result = csc / divisor;
|
|
|
|
prop_assert_eq!(&result_owned, &expected_result);
|
|
prop_assert_eq!(&result_ref, &expected_result);
|
|
}
|
|
|
|
#[test]
|
|
fn csr_mul_dense(
|
|
// a and b have dimensions compatible for multiplication
|
|
(a, b)
|
|
in csr_strategy()
|
|
.prop_flat_map(|a| {
|
|
let cols = PROPTEST_MATRIX_DIM;
|
|
let b = matrix(PROPTEST_I32_VALUE_STRATEGY, a.ncols(), cols);
|
|
(Just(a), b)
|
|
}))
|
|
{
|
|
let expected = DMatrix::from(&a) * &b;
|
|
prop_assert_eq!(&a * &b, expected.clone());
|
|
prop_assert_eq!(&a * b.clone(), expected.clone());
|
|
prop_assert_eq!(a.clone() * &b, expected.clone());
|
|
prop_assert_eq!(a.clone() * b.clone(), expected.clone());
|
|
}
|
|
|
|
#[test]
|
|
fn csc_mul_dense(
|
|
// a and b have dimensions compatible for multiplication
|
|
(a, b)
|
|
in csc_strategy()
|
|
.prop_flat_map(|a| {
|
|
let cols = PROPTEST_MATRIX_DIM;
|
|
let b = matrix(PROPTEST_I32_VALUE_STRATEGY, a.ncols(), cols);
|
|
(Just(a), b)
|
|
}))
|
|
{
|
|
let expected = DMatrix::from(&a) * &b;
|
|
prop_assert_eq!(&a * &b, expected.clone());
|
|
prop_assert_eq!(&a * b.clone(), expected.clone());
|
|
prop_assert_eq!(a.clone() * &b, expected.clone());
|
|
prop_assert_eq!(a.clone() * b.clone(), expected.clone());
|
|
}
|
|
|
|
#[test]
|
|
fn csc_solve_lower_triangular_no_transpose(
|
|
// A CSC matrix `a` and a dimensionally compatible dense matrix `b`
|
|
(a, b)
|
|
in csc_square_with_non_zero_diagonals()
|
|
.prop_flat_map(|a| {
|
|
let nrows = a.nrows();
|
|
(Just(a), matrix(value_strategy::<f64>(), nrows, PROPTEST_MATRIX_DIM))
|
|
}))
|
|
{
|
|
let mut x = b.clone();
|
|
spsolve_csc_lower_triangular(Op::NoOp(&a), &mut x).unwrap();
|
|
|
|
let a_lower = a.lower_triangle();
|
|
// We're using a high tolerance here because there are some "bad" inputs that can give
|
|
// severe loss of precision.
|
|
prop_assert_matrix_eq!(&a_lower * &x, &b, comp = abs, tol = 1e-4);
|
|
}
|
|
|
|
#[test]
|
|
fn csc_solve_lower_triangular_transpose(
|
|
// A CSC matrix `a` and a dimensionally compatible dense matrix `b` (with a transposed)
|
|
(a, b)
|
|
in csc_square_with_non_zero_diagonals()
|
|
.prop_flat_map(|a| {
|
|
let ncols = a.ncols();
|
|
(Just(a), matrix(value_strategy::<f64>(), ncols, PROPTEST_MATRIX_DIM))
|
|
}))
|
|
{
|
|
let mut x = b.clone();
|
|
spsolve_csc_lower_triangular(Op::Transpose(&a), &mut x).unwrap();
|
|
|
|
let a_lower = a.lower_triangle();
|
|
// We're using a high tolerance here because there are some "bad" inputs that can give
|
|
// severe loss of precision.
|
|
prop_assert_matrix_eq!(&a_lower.transpose() * &x, &b, comp = abs, tol = 1e-4);
|
|
}
|
|
|
|
} |