Add prealloc suffix to spmm_csr and spadd_csr
The suffix is intended to communicate that these methods assume `preallocated` storage, i.e. they try to store the result in a matrix which already has the correct sparsity pattern for the operation.
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@ -1,7 +1,7 @@
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use crate::csr::CsrMatrix;
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use std::ops::{Add, Mul};
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use crate::ops::serial::{spadd_csr, spadd_pattern, spmm_pattern, spmm_csr};
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use crate::ops::serial::{spadd_csr_prealloc, spadd_pattern, spmm_pattern, spmm_csr_prealloc};
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use nalgebra::{ClosedAdd, ClosedMul, Scalar};
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use num_traits::{Zero, One};
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use std::sync::Arc;
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@ -21,8 +21,8 @@ where
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// We are giving data that is valid by definition, so it is safe to unwrap below
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let mut result = CsrMatrix::try_from_pattern_and_values(Arc::new(pattern), values)
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.unwrap();
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spadd_csr(T::zero(), &mut result, T::one(), Op::NoOp(&self)).unwrap();
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spadd_csr(T::one(), &mut result, T::one(), Op::NoOp(&rhs)).unwrap();
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spadd_csr_prealloc(T::zero(), &mut result, T::one(), Op::NoOp(&self)).unwrap();
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spadd_csr_prealloc(T::one(), &mut result, T::one(), Op::NoOp(&rhs)).unwrap();
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result
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}
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}
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@ -35,7 +35,7 @@ where
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fn add(mut self, rhs: &'a CsrMatrix<T>) -> Self::Output {
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if Arc::ptr_eq(self.pattern(), rhs.pattern()) {
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spadd_csr(T::one(), &mut self, T::one(), Op::NoOp(rhs)).unwrap();
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spadd_csr_prealloc(T::one(), &mut self, T::one(), Op::NoOp(rhs)).unwrap();
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self
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} else {
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&self + rhs
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@ -90,7 +90,7 @@ impl_matrix_mul!(<'a>(a: &'a CsrMatrix<T>, b: &'a CsrMatrix<T>) -> CsrMatrix<T>
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let values = vec![T::zero(); pattern.nnz()];
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let mut result = CsrMatrix::try_from_pattern_and_values(Arc::new(pattern), values)
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.unwrap();
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spmm_csr(T::zero(),
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spmm_csr_prealloc(T::zero(),
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&mut result,
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T::one(),
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Op::NoOp(a),
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@ -87,7 +87,7 @@ fn spadd_csr_unexpected_entry() -> OperationError {
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///
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/// If the pattern of `c` does not accommodate all the non-zero entries in `a`, an error is
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/// returned.
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pub fn spadd_csr<T>(beta: T,
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pub fn spadd_csr_prealloc<T>(beta: T,
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c: &mut CsrMatrix<T>,
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alpha: T,
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a: Op<&CsrMatrix<T>>)
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@ -161,7 +161,7 @@ fn spmm_csr_unexpected_entry() -> OperationError {
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}
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/// Sparse-sparse matrix multiplication, `C <- beta * C + alpha * op(A) * op(B)`.
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pub fn spmm_csr<T>(
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pub fn spmm_csr_prealloc<T>(
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beta: T,
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c: &mut CsrMatrix<T>,
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alpha: T,
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@ -218,7 +218,7 @@ where
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}
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};
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spmm_csr(beta, c, alpha, NoOp(a.as_ref()), NoOp(b.as_ref()))
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spmm_csr_prealloc(beta, c, alpha, NoOp(a.as_ref()), NoOp(b.as_ref()))
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}
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}
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}
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@ -1,6 +1,6 @@
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use crate::common::{csr_strategy, PROPTEST_MATRIX_DIM, PROPTEST_MAX_NNZ,
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PROPTEST_I32_VALUE_STRATEGY};
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use nalgebra_sparse::ops::serial::{spmm_csr_dense, spadd_pattern, spmm_pattern, spadd_csr, spmm_csr};
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use nalgebra_sparse::ops::serial::{spmm_csr_dense, spadd_pattern, spmm_pattern, spadd_csr_prealloc, spmm_csr_prealloc};
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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::proptest::{csr, sparsity_pattern};
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@ -78,7 +78,7 @@ struct SpaddCsrArgs<T> {
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a: Op<CsrMatrix<T>>,
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}
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fn spadd_csr_args_strategy() -> impl Strategy<Value=SpaddCsrArgs<i32>> {
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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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@ -150,7 +150,7 @@ struct SpmmCsrArgs<T> {
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b: Op<CsrMatrix<T>>,
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}
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fn spmm_csr_args_strategy() -> impl Strategy<Value=SpmmCsrArgs<i32>> {
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fn spmm_csr_prealloc_args_strategy() -> impl Strategy<Value=SpmmCsrArgs<i32>> {
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spmm_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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@ -287,12 +287,12 @@ proptest! {
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}
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#[test]
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fn spadd_csr_test(SpaddCsrArgs { c, beta, alpha, a } in spadd_csr_args_strategy()) {
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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(beta, &mut c_sparse, alpha, a.as_ref()).unwrap();
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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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@ -363,13 +363,13 @@ proptest! {
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}
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#[test]
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fn spmm_csr_test(SpmmCsrArgs { c, beta, alpha, a, b }
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in spmm_csr_args_strategy()
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fn spmm_csr_prealloc_test(SpmmCsrArgs { c, beta, alpha, a, b }
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in spmm_csr_prealloc_args_strategy()
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) {
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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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spmm_csr(beta, &mut c_sparse, alpha, a.as_ref(), b.as_ref()).unwrap();
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spmm_csr_prealloc(beta, &mut c_sparse, alpha, a.as_ref(), b.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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@ -386,7 +386,7 @@ proptest! {
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}
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#[test]
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fn spmm_csr_panics_on_dim_mismatch(
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fn spmm_csr_prealloc_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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@ -424,7 +424,7 @@ proptest! {
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let result = catch_unwind(|| {
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let mut spmm_result = c.clone();
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spmm_csr(beta, &mut spmm_result, alpha, a.as_ref(), b.as_ref()).unwrap();
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spmm_csr_prealloc(beta, &mut spmm_result, alpha, a.as_ref(), b.as_ref()).unwrap();
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});
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prop_assert!(result.is_err(),
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@ -432,7 +432,7 @@ proptest! {
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}
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#[test]
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fn spadd_csr_panics_on_dim_mismatch(
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fn spadd_csr_prealloc_panics_on_dim_mismatch(
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(alpha, beta, c, op_a)
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in (PROPTEST_I32_VALUE_STRATEGY,
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PROPTEST_I32_VALUE_STRATEGY,
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@ -456,7 +456,7 @@ proptest! {
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let result = catch_unwind(|| {
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let mut spmm_result = c.clone();
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spadd_csr(beta, &mut spmm_result, alpha, op_a.as_ref()).unwrap();
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spadd_csr_prealloc(beta, &mut spmm_result, alpha, op_a.as_ref()).unwrap();
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});
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prop_assert!(result.is_err(),
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