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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.
This commit is contained in:
Andreas Longva 2020-12-21 16:05:38 +01:00
parent 4af3fcbdd3
commit 66cbd26702
3 changed files with 24 additions and 24 deletions
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10
nalgebra-sparse/src/ops/impl_std_ops.rs
View File

@ -1,7 +1,7 @@
use crate::csr::CsrMatrix; use crate::csr::CsrMatrix;
use std::ops::{Add, Mul}; use std::ops::{Add, Mul};
use crate::ops::serial::{spadd_csr, spadd_pattern, spmm_pattern, spmm_csr}; use crate::ops::serial::{spadd_csr_prealloc, spadd_pattern, spmm_pattern, spmm_csr_prealloc};
use nalgebra::{ClosedAdd, ClosedMul, Scalar}; use nalgebra::{ClosedAdd, ClosedMul, Scalar};
use num_traits::{Zero, One}; use num_traits::{Zero, One};
use std::sync::Arc; use std::sync::Arc;
@ -21,8 +21,8 @@ where
// We are giving data that is valid by definition, so it is safe to unwrap below // We are giving data that is valid by definition, so it is safe to unwrap below
let mut result = CsrMatrix::try_from_pattern_and_values(Arc::new(pattern), values) let mut result = CsrMatrix::try_from_pattern_and_values(Arc::new(pattern), values)
.unwrap(); .unwrap();
spadd_csr(T::zero(), &mut result, T::one(), Op::NoOp(&self)).unwrap(); spadd_csr_prealloc(T::zero(), &mut result, T::one(), Op::NoOp(&self)).unwrap();
spadd_csr(T::one(), &mut result, T::one(), Op::NoOp(&rhs)).unwrap(); spadd_csr_prealloc(T::one(), &mut result, T::one(), Op::NoOp(&rhs)).unwrap();
result result
} }
} }
@ -35,7 +35,7 @@ where
fn add(mut self, rhs: &'a CsrMatrix<T>) -> Self::Output { fn add(mut self, rhs: &'a CsrMatrix<T>) -> Self::Output {
if Arc::ptr_eq(self.pattern(), rhs.pattern()) { if Arc::ptr_eq(self.pattern(), rhs.pattern()) {
spadd_csr(T::one(), &mut self, T::one(), Op::NoOp(rhs)).unwrap(); spadd_csr_prealloc(T::one(), &mut self, T::one(), Op::NoOp(rhs)).unwrap();
self self
} else { } else {
&self + rhs &self + rhs
@ -90,7 +90,7 @@ impl_matrix_mul!(<'a>(a: &'a CsrMatrix<T>, b: &'a CsrMatrix<T>) -> CsrMatrix<T>
let values = vec![T::zero(); pattern.nnz()]; let values = vec![T::zero(); pattern.nnz()];
let mut result = CsrMatrix::try_from_pattern_and_values(Arc::new(pattern), values) let mut result = CsrMatrix::try_from_pattern_and_values(Arc::new(pattern), values)
.unwrap(); .unwrap();
spmm_csr(T::zero(), spmm_csr_prealloc(T::zero(),
&mut result, &mut result,
T::one(), T::one(),
Op::NoOp(a), Op::NoOp(a),

6
nalgebra-sparse/src/ops/serial/csr.rs
View File

@ -87,7 +87,7 @@ fn spadd_csr_unexpected_entry() -> OperationError {
/// ///
/// If the pattern of `c` does not accommodate all the non-zero entries in `a`, an error is /// If the pattern of `c` does not accommodate all the non-zero entries in `a`, an error is
/// returned. /// returned.
pub fn spadd_csr<T>(beta: T, pub fn spadd_csr_prealloc<T>(beta: T,
c: &mut CsrMatrix<T>, c: &mut CsrMatrix<T>,
alpha: T, alpha: T,
a: Op<&CsrMatrix<T>>) a: Op<&CsrMatrix<T>>)
@ -161,7 +161,7 @@ fn spmm_csr_unexpected_entry() -> OperationError {
} }
/// Sparse-sparse matrix multiplication, `C <- beta * C + alpha * op(A) * op(B)`. /// Sparse-sparse matrix multiplication, `C <- beta * C + alpha * op(A) * op(B)`.
pub fn spmm_csr<T>( pub fn spmm_csr_prealloc<T>(
beta: T, beta: T,
c: &mut CsrMatrix<T>, c: &mut CsrMatrix<T>,
alpha: T, alpha: T,
@ -218,7 +218,7 @@ where
} }
}; };
spmm_csr(beta, c, alpha, NoOp(a.as_ref()), NoOp(b.as_ref())) spmm_csr_prealloc(beta, c, alpha, NoOp(a.as_ref()), NoOp(b.as_ref()))
} }
} }
} }

24
nalgebra-sparse/tests/unit_tests/ops.rs
View File

@ -1,6 +1,6 @@
use crate::common::{csr_strategy, PROPTEST_MATRIX_DIM, PROPTEST_MAX_NNZ, use crate::common::{csr_strategy, PROPTEST_MATRIX_DIM, PROPTEST_MAX_NNZ,
PROPTEST_I32_VALUE_STRATEGY}; PROPTEST_I32_VALUE_STRATEGY};
use nalgebra_sparse::ops::serial::{spmm_csr_dense, spadd_pattern, spmm_pattern, spadd_csr, spmm_csr}; use nalgebra_sparse::ops::serial::{spmm_csr_dense, spadd_pattern, spmm_pattern, spadd_csr_prealloc, spmm_csr_prealloc};
use nalgebra_sparse::ops::{Op}; use nalgebra_sparse::ops::{Op};
use nalgebra_sparse::csr::CsrMatrix; use nalgebra_sparse::csr::CsrMatrix;
use nalgebra_sparse::proptest::{csr, sparsity_pattern}; use nalgebra_sparse::proptest::{csr, sparsity_pattern};
@ -78,7 +78,7 @@ struct SpaddCsrArgs<T> {
a: Op<CsrMatrix<T>>, a: Op<CsrMatrix<T>>,
} }
fn spadd_csr_args_strategy() -> impl Strategy<Value=SpaddCsrArgs<i32>> { fn spadd_csr_prealloc_args_strategy() -> impl Strategy<Value=SpaddCsrArgs<i32>> {
let value_strategy = PROPTEST_I32_VALUE_STRATEGY; let value_strategy = PROPTEST_I32_VALUE_STRATEGY;
spadd_pattern_strategy() spadd_pattern_strategy()
@ -150,7 +150,7 @@ struct SpmmCsrArgs<T> {
b: Op<CsrMatrix<T>>, b: Op<CsrMatrix<T>>,
} }
fn spmm_csr_args_strategy() -> impl Strategy<Value=SpmmCsrArgs<i32>> { fn spmm_csr_prealloc_args_strategy() -> impl Strategy<Value=SpmmCsrArgs<i32>> {
spmm_pattern_strategy() spmm_pattern_strategy()
.prop_flat_map(|(a_pattern, b_pattern)| { .prop_flat_map(|(a_pattern, b_pattern)| {
let a_values = vec![PROPTEST_I32_VALUE_STRATEGY; a_pattern.nnz()]; let a_values = vec![PROPTEST_I32_VALUE_STRATEGY; a_pattern.nnz()];
@ -287,12 +287,12 @@ proptest! {
} }
#[test] #[test]
fn spadd_csr_test(SpaddCsrArgs { c, beta, alpha, a } in spadd_csr_args_strategy()) { fn spadd_csr_prealloc_test(SpaddCsrArgs { c, beta, alpha, a } in spadd_csr_prealloc_args_strategy()) {
// Test that we get the expected result by comparing to an equivalent dense operation // 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) // (here we give in the C matrix, so the sparsity pattern is essentially fixed)
let mut c_sparse = c.clone(); let mut c_sparse = c.clone();
spadd_csr(beta, &mut c_sparse, alpha, a.as_ref()).unwrap(); spadd_csr_prealloc(beta, &mut c_sparse, alpha, a.as_ref()).unwrap();
let mut c_dense = DMatrix::from(&c); let mut c_dense = DMatrix::from(&c);
let op_a_dense = match a { let op_a_dense = match a {
@ -363,13 +363,13 @@ proptest! {
} }
#[test] #[test]
fn spmm_csr_test(SpmmCsrArgs { c, beta, alpha, a, b } fn spmm_csr_prealloc_test(SpmmCsrArgs { c, beta, alpha, a, b }
in spmm_csr_args_strategy() in spmm_csr_prealloc_args_strategy()
) { ) {
// Test that we get the expected result by comparing to an equivalent dense operation // 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) // (here we give in the C matrix, so the sparsity pattern is essentially fixed)
let mut c_sparse = c.clone(); let mut c_sparse = c.clone();
spmm_csr(beta, &mut c_sparse, alpha, a.as_ref(), b.as_ref()).unwrap(); spmm_csr_prealloc(beta, &mut c_sparse, alpha, a.as_ref(), b.as_ref()).unwrap();
let mut c_dense = DMatrix::from(&c); let mut c_dense = DMatrix::from(&c);
let op_a_dense = match a { let op_a_dense = match a {
@ -386,7 +386,7 @@ proptest! {
} }
#[test] #[test]
fn spmm_csr_panics_on_dim_mismatch( fn spmm_csr_prealloc_panics_on_dim_mismatch(
(alpha, beta, c, a, b) (alpha, beta, c, a, b)
in (PROPTEST_I32_VALUE_STRATEGY, in (PROPTEST_I32_VALUE_STRATEGY,
PROPTEST_I32_VALUE_STRATEGY, PROPTEST_I32_VALUE_STRATEGY,
@ -424,7 +424,7 @@ proptest! {
let result = catch_unwind(|| { let result = catch_unwind(|| {
let mut spmm_result = c.clone(); let mut spmm_result = c.clone();
spmm_csr(beta, &mut spmm_result, alpha, a.as_ref(), b.as_ref()).unwrap(); spmm_csr_prealloc(beta, &mut spmm_result, alpha, a.as_ref(), b.as_ref()).unwrap();
}); });
prop_assert!(result.is_err(), prop_assert!(result.is_err(),
@ -432,7 +432,7 @@ proptest! {
} }
#[test] #[test]
fn spadd_csr_panics_on_dim_mismatch( fn spadd_csr_prealloc_panics_on_dim_mismatch(
(alpha, beta, c, op_a) (alpha, beta, c, op_a)
in (PROPTEST_I32_VALUE_STRATEGY, in (PROPTEST_I32_VALUE_STRATEGY,
PROPTEST_I32_VALUE_STRATEGY, PROPTEST_I32_VALUE_STRATEGY,
@ -456,7 +456,7 @@ proptest! {
let result = catch_unwind(|| { let result = catch_unwind(|| {
let mut spmm_result = c.clone(); let mut spmm_result = c.clone();
spadd_csr(beta, &mut spmm_result, alpha, op_a.as_ref()).unwrap(); spadd_csr_prealloc(beta, &mut spmm_result, alpha, op_a.as_ref()).unwrap();
}); });
prop_assert!(result.is_err(), prop_assert!(result.is_err(),