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Refactor ops to use new Op type instead of separate Transpose flag

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This commit is contained in:
Andreas Longva 2020-12-21 15:09:29 +01:00
parent c6a8fcdee2
commit fe8592fde1
5 changed files with 299 additions and 211 deletions
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14
nalgebra-sparse/src/ops/impl_std_ops.rs
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@ -5,7 +5,7 @@ use crate::ops::serial::{spadd_csr, spadd_pattern, spmm_pattern, spmm_csr};
use nalgebra::{ClosedAdd, ClosedMul, Scalar};
use num_traits::{Zero, One};
use std::sync::Arc;
use crate::ops::Transpose;
use crate::ops::{Op};
impl<'a, T> Add<&'a CsrMatrix<T>> for &'a CsrMatrix<T>
where
@ -21,8 +21,8 @@ where
// 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)
.unwrap();
spadd_csr(&mut result, T::zero(), T::one(), Transpose(false), &self).unwrap();
spadd_csr(&mut result, T::one(), T::one(), Transpose(false), &rhs).unwrap();
spadd_csr(&mut result, T::zero(), T::one(), Op::NoOp(&self)).unwrap();
spadd_csr(&mut result, T::one(), T::one(), Op::NoOp(&rhs)).unwrap();
result
}
}
@ -35,7 +35,7 @@ where
fn add(mut self, rhs: &'a CsrMatrix<T>) -> Self::Output {
if Arc::ptr_eq(self.pattern(), rhs.pattern()) {
spadd_csr(&mut self, T::one(), T::one(), Transpose(false), &rhs).unwrap();
spadd_csr(&mut self, T::one(), T::one(), Op::NoOp(rhs)).unwrap();
self
} else {
&self + rhs
@ -93,10 +93,8 @@ impl_matrix_mul!(<'a>(a: &'a CsrMatrix<T>, b: &'a CsrMatrix<T>) -> CsrMatrix<T>
spmm_csr(&mut result,
T::zero(),
T::one(),
Transpose(false),
a,
Transpose(false),
b)
Op::NoOp(a),
Op::NoOp(b))
.expect("Internal error: spmm failed (please debug).");
result
});

52
nalgebra-sparse/src/ops/mod.rs
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@ -4,14 +4,54 @@ mod impl_std_ops;
pub mod serial;
/// TODO
#[derive(Copy, Clone, Debug, PartialEq, Eq)]
pub struct Transpose(pub bool);
impl Transpose {
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum Op<T> {
/// TODO
pub fn to_bool(&self) -> bool {
self.0
NoOp(T),
/// TODO
Transpose(T),
}
impl<T> Op<T> {
/// TODO
pub fn inner_ref(&self) -> &T {
match self {
Op::NoOp(obj) => &obj,
Op::Transpose(obj) => &obj
}
}
/// TODO
pub fn as_ref(&self) -> Op<&T> {
match self {
Op::NoOp(obj) => Op::NoOp(&obj),
Op::Transpose(obj) => Op::Transpose(&obj)
}
}
/// TODO
pub fn convert<U>(self) -> Op<U>
where T: Into<U>
{
match self {
Op::NoOp(obj) => Op::NoOp(obj.into()),
Op::Transpose(obj) => Op::Transpose(obj.into())
}
}
/// TODO
/// TODO: Rewrite the other functions by leveraging this one
pub fn map_same_op<U, F: FnOnce(T) -> U>(self, f: F) -> Op<U> {
match self {
Op::NoOp(obj) => Op::NoOp(f(obj)),
Op::Transpose(obj) => Op::Transpose(f(obj))
}
}
}
impl<T> From<T> for Op<T> {
fn from(obj: T) -> Self {
Self::NoOp(obj)
}
}

186
nalgebra-sparse/src/ops/serial/csr.rs
View File

@ -1,5 +1,5 @@
use crate::csr::CsrMatrix;
use crate::ops::{Transpose};
use crate::ops::{Op};
use crate::SparseEntryMut;
use crate::ops::serial::{OperationError, OperationErrorType};
use nalgebra::{Scalar, DMatrixSlice, ClosedAdd, ClosedMul, DMatrixSliceMut};
@ -7,65 +7,71 @@ use num_traits::{Zero, One};
use std::sync::Arc;
use std::borrow::Cow;
/// Sparse-dense matrix-matrix multiplication `C <- beta * C + alpha * trans(A) * trans(B)`.
/// Sparse-dense matrix-matrix multiplication `C <- beta * C + alpha * op(A) * op(B)`.
pub fn spmm_csr_dense<'a, T>(c: impl Into<DMatrixSliceMut<'a, T>>,
beta: T,
alpha: T,
trans_a: Transpose,
a: &CsrMatrix<T>,
trans_b: Transpose,
b: impl Into<DMatrixSlice<'a, T>>)
a: Op<&CsrMatrix<T>>,
b: Op<impl Into<DMatrixSlice<'a, T>>>)
where
T: Scalar + ClosedAdd + ClosedMul + Zero + One
{
spmm_csr_dense_(c.into(), beta, alpha, trans_a, a, trans_b, b.into())
let b = b.convert();
spmm_csr_dense_(c.into(), beta, alpha, a, b)
}
fn spmm_csr_dense_<T>(mut c: DMatrixSliceMut<T>,
beta: T,
alpha: T,
trans_a: Transpose,
a: &CsrMatrix<T>,
trans_b: Transpose,
b: DMatrixSlice<T>)
a: Op<&CsrMatrix<T>>,
b: Op<DMatrixSlice<T>>)
where
T: Scalar + ClosedAdd + ClosedMul + Zero + One
{
assert_compatible_spmm_dims!(c, a, b, trans_a, trans_b);
assert_compatible_spmm_dims!(c, a, b);
if trans_a.to_bool() {
// In this case, we have to pre-multiply C by beta
c *= beta;
match a {
Op::Transpose(ref a) => {
// In this case, we have to pre-multiply C by beta
c *= beta;
for k in 0..a.nrows() {
let a_row_k = a.row(k);
for (&i, a_ki) in a_row_k.col_indices().iter().zip(a_row_k.values()) {
let gamma_ki = alpha.inlined_clone() * a_ki.inlined_clone();
let mut c_row_i = c.row_mut(i);
if trans_b.to_bool() {
let b_col_k = b.column(k);
for (c_ij, b_jk) in c_row_i.iter_mut().zip(b_col_k.iter()) {
*c_ij += gamma_ki.inlined_clone() * b_jk.inlined_clone();
}
} else {
let b_row_k = b.row(k);
for (c_ij, b_kj) in c_row_i.iter_mut().zip(b_row_k.iter()) {
*c_ij += gamma_ki.inlined_clone() * b_kj.inlined_clone();
for k in 0..a.nrows() {
let a_row_k = a.row(k);
for (&i, a_ki) in a_row_k.col_indices().iter().zip(a_row_k.values()) {
let gamma_ki = alpha.inlined_clone() * a_ki.inlined_clone();
let mut c_row_i = c.row_mut(i);
match b {
Op::NoOp(ref b) => {
let b_row_k = b.row(k);
for (c_ij, b_kj) in c_row_i.iter_mut().zip(b_row_k.iter()) {
*c_ij += gamma_ki.inlined_clone() * b_kj.inlined_clone();
}
},
Op::Transpose(ref b) => {
let b_col_k = b.column(k);
for (c_ij, b_jk) in c_row_i.iter_mut().zip(b_col_k.iter()) {
*c_ij += gamma_ki.inlined_clone() * b_jk.inlined_clone();
}
},
}
}
}
}
} else {
for j in 0..c.ncols() {
let mut c_col_j = c.column_mut(j);
for (c_ij, a_row_i) in c_col_j.iter_mut().zip(a.row_iter()) {
let mut dot_ij = T::zero();
for (&k, a_ik) in a_row_i.col_indices().iter().zip(a_row_i.values()) {
let b_contrib =
if trans_b.to_bool() { b.index((j, k)) } else { b.index((k, j)) };
dot_ij += a_ik.inlined_clone() * b_contrib.inlined_clone();
},
Op::NoOp(ref a) => {
for j in 0..c.ncols() {
let mut c_col_j = c.column_mut(j);
for (c_ij, a_row_i) in c_col_j.iter_mut().zip(a.row_iter()) {
let mut dot_ij = T::zero();
for (&k, a_ik) in a_row_i.col_indices().iter().zip(a_row_i.values()) {
let b_contrib =
match b {
Op::NoOp(ref b) => b.index((k, j)),
Op::Transpose(ref b) => b.index((j, k))
};
dot_ij += a_ik.inlined_clone() * b_contrib.inlined_clone();
}
*c_ij = beta.inlined_clone() * c_ij.inlined_clone() + alpha.inlined_clone() * dot_ij;
}
*c_ij = beta.inlined_clone() * c_ij.inlined_clone() + alpha.inlined_clone() * dot_ij;
}
}
}
@ -77,32 +83,31 @@ fn spadd_csr_unexpected_entry() -> OperationError {
String::from("Found entry in `a` that is not present in `c`."))
}
/// Sparse matrix addition `C <- beta * C + alpha * trans(A)`.
/// Sparse matrix addition `C <- beta * C + alpha * op(A)`.
///
/// If the pattern of `c` does not accommodate all the non-zero entries in `a`, an error is
/// returned.
pub fn spadd_csr<T>(c: &mut CsrMatrix<T>,
beta: T,
alpha: T,
trans_a: Transpose,
a: &CsrMatrix<T>)
a: Op<&CsrMatrix<T>>)
-> Result<(), OperationError>
where
T: Scalar + ClosedAdd + ClosedMul + Zero + One
{
assert_compatible_spadd_dims!(c, a, trans_a);
assert_compatible_spadd_dims!(c, a);
// TODO: Change CsrMatrix::pattern() to return `&Arc` instead of `Arc`
if Arc::ptr_eq(&c.pattern(), &a.pattern()) {
if Arc::ptr_eq(&c.pattern(), &a.inner_ref().pattern()) {
// Special fast path: The two matrices have *exactly* the same sparsity pattern,
// so we only need to sum the value arrays
for (c_ij, a_ij) in c.values_mut().iter_mut().zip(a.values()) {
for (c_ij, a_ij) in c.values_mut().iter_mut().zip(a.inner_ref().values()) {
let (alpha, beta) = (alpha.inlined_clone(), beta.inlined_clone());
*c_ij = beta * c_ij.inlined_clone() + alpha * a_ij.inlined_clone();
}
Ok(())
} else {
if trans_a.to_bool()
if let Op::Transpose(a) = a
{
if beta != T::one() {
for c_ij in c.values_mut() {
@ -120,7 +125,7 @@ where
}
}
}
} else {
} else if let Op::NoOp(a) = a {
for (mut c_row_i, a_row_i) in c.row_iter_mut().zip(a.row_iter()) {
if beta != T::one() {
for c_ij in c_row_i.values_mut() {
@ -160,56 +165,61 @@ pub fn spmm_csr<'a, T>(
c: &mut CsrMatrix<T>,
beta: T,
alpha: T,
trans_a: Transpose,
a: &CsrMatrix<T>,
trans_b: Transpose,
b: &CsrMatrix<T>)
a: Op<&CsrMatrix<T>>,
b: Op<&CsrMatrix<T>>)
-> Result<(), OperationError>
where
T: Scalar + ClosedAdd + ClosedMul + Zero + One
{
assert_compatible_spmm_dims!(c, a, b, trans_a, trans_b);
assert_compatible_spmm_dims!(c, a, b);
if !trans_a.to_bool() && !trans_b.to_bool() {
for (mut c_row_i, a_row_i) in c.row_iter_mut().zip(a.row_iter()) {
for c_ij in c_row_i.values_mut() {
*c_ij = beta.inlined_clone() * c_ij.inlined_clone();
}
use Op::{NoOp, Transpose};
for (&k, a_ik) in a_row_i.col_indices().iter().zip(a_row_i.values()) {
let b_row_k = b.row(k);
let (mut c_row_i_cols, mut c_row_i_values) = c_row_i.cols_and_values_mut();
let alpha_aik = alpha.inlined_clone() * a_ik.inlined_clone();
for (j, b_kj) in b_row_k.col_indices().iter().zip(b_row_k.values()) {
// Determine the location in C to append the value
let (c_local_idx, _) = c_row_i_cols.iter()
.enumerate()
.find(|(_, c_col)| *c_col == j)
.ok_or_else(spmm_csr_unexpected_entry)?;
match (&a, &b) {
(NoOp(ref a), NoOp(ref b)) => {
for (mut c_row_i, a_row_i) in c.row_iter_mut().zip(a.row_iter()) {
for c_ij in c_row_i.values_mut() {
*c_ij = beta.inlined_clone() * c_ij.inlined_clone();
}
c_row_i_values[c_local_idx] += alpha_aik.inlined_clone() * b_kj.inlined_clone();
c_row_i_cols = &c_row_i_cols[c_local_idx ..];
c_row_i_values = &mut c_row_i_values[c_local_idx ..];
for (&k, a_ik) in a_row_i.col_indices().iter().zip(a_row_i.values()) {
let b_row_k = b.row(k);
let (mut c_row_i_cols, mut c_row_i_values) = c_row_i.cols_and_values_mut();
let alpha_aik = alpha.inlined_clone() * a_ik.inlined_clone();
for (j, b_kj) in b_row_k.col_indices().iter().zip(b_row_k.values()) {
// Determine the location in C to append the value
let (c_local_idx, _) = c_row_i_cols.iter()
.enumerate()
.find(|(_, c_col)| *c_col == j)
.ok_or_else(spmm_csr_unexpected_entry)?;
c_row_i_values[c_local_idx] += alpha_aik.inlined_clone() * b_kj.inlined_clone();
c_row_i_cols = &c_row_i_cols[c_local_idx ..];
c_row_i_values = &mut c_row_i_values[c_local_idx ..];
}
}
}
}
Ok(())
} else {
// Currently we handle transposition by explicitly precomputing transposed matrices
// and calling the operation again without transposition
// TODO: At least use workspaces to allow control of allocations. Maybe
// consider implementing certain patterns (like A^T * B) explicitly
let (a, b) = {
use Cow::*;
match (trans_a, trans_b) {
(Transpose(false), Transpose(false)) => unreachable!(),
(Transpose(true), Transpose(false)) => (Owned(a.transpose()), Borrowed(b)),
(Transpose(false), Transpose(true)) => (Borrowed(a), Owned(b.transpose())),
(Transpose(true), Transpose(true)) => (Owned(a.transpose()), Owned(b.transpose()))
}
};
Ok(())
},
_ => {
// Currently we handle transposition by explicitly precomputing transposed matrices
// and calling the operation again without transposition
// TODO: At least use workspaces to allow control of allocations. Maybe
// consider implementing certain patterns (like A^T * B) explicitly
let a_ref: &CsrMatrix<T> = a.inner_ref();
let b_ref: &CsrMatrix<T> = b.inner_ref();
let (a, b) = {
use Cow::*;
match (&a, &b) {
(NoOp(_), NoOp(_)) => unreachable!(),
(Transpose(ref a), NoOp(_)) => (Owned(a.transpose()), Borrowed(b_ref)),
(NoOp(_), Transpose(ref b)) => (Borrowed(a_ref), Owned(b.transpose())),
(Transpose(ref a), Transpose(ref b)) => (Owned(a.transpose()), Owned(b.transpose()))
}
};
spmm_csr(c, beta, alpha, Transpose(false), a.as_ref(), Transpose(false), b.as_ref())
spmm_csr(c, beta, alpha, NoOp(a.as_ref()), NoOp(b.as_ref()))
}
}
}

65
nalgebra-sparse/src/ops/serial/mod.rs
View File

@ -2,46 +2,47 @@
#[macro_use]
macro_rules! assert_compatible_spmm_dims {
($c:expr, $a:expr, $b:expr, $trans_a:expr, $trans_b:expr) => {
use crate::ops::Transpose;
match ($trans_a, $trans_b) {
(Transpose(false), Transpose(false)) => {
assert_eq!($c.nrows(), $a.nrows(), "C.nrows() != A.nrows()");
assert_eq!($c.ncols(), $b.ncols(), "C.ncols() != B.ncols()");
assert_eq!($a.ncols(), $b.nrows(), "A.ncols() != B.nrows()");
},
(Transpose(true), Transpose(false)) => {
assert_eq!($c.nrows(), $a.ncols(), "C.nrows() != A.ncols()");
assert_eq!($c.ncols(), $b.ncols(), "C.ncols() != B.ncols()");
assert_eq!($a.nrows(), $b.nrows(), "A.nrows() != B.nrows()");
},
(Transpose(false), Transpose(true)) => {
assert_eq!($c.nrows(), $a.nrows(), "C.nrows() != A.nrows()");
assert_eq!($c.ncols(), $b.nrows(), "C.ncols() != B.nrows()");
assert_eq!($a.ncols(), $b.ncols(), "A.ncols() != B.ncols()");
},
(Transpose(true), Transpose(true)) => {
assert_eq!($c.nrows(), $a.ncols(), "C.nrows() != A.ncols()");
assert_eq!($c.ncols(), $b.nrows(), "C.ncols() != B.nrows()");
assert_eq!($a.nrows(), $b.ncols(), "A.nrows() != B.ncols()");
($c:expr, $a:expr, $b:expr) => {
{
use crate::ops::Op::{NoOp, Transpose};
match (&$a, &$b) {
(NoOp(ref a), NoOp(ref b)) => {
assert_eq!($c.nrows(), a.nrows(), "C.nrows() != A.nrows()");
assert_eq!($c.ncols(), b.ncols(), "C.ncols() != B.ncols()");
assert_eq!(a.ncols(), b.nrows(), "A.ncols() != B.nrows()");
},
(Transpose(ref a), NoOp(ref b)) => {
assert_eq!($c.nrows(), a.ncols(), "C.nrows() != A.ncols()");
assert_eq!($c.ncols(), b.ncols(), "C.ncols() != B.ncols()");
assert_eq!(a.nrows(), b.nrows(), "A.nrows() != B.nrows()");
},
(NoOp(ref a), Transpose(ref b)) => {
assert_eq!($c.nrows(), a.nrows(), "C.nrows() != A.nrows()");
assert_eq!($c.ncols(), b.nrows(), "C.ncols() != B.nrows()");
assert_eq!(a.ncols(), b.ncols(), "A.ncols() != B.ncols()");
},
(Transpose(ref a), Transpose(ref b)) => {
assert_eq!($c.nrows(), a.ncols(), "C.nrows() != A.ncols()");
assert_eq!($c.ncols(), b.nrows(), "C.ncols() != B.nrows()");
assert_eq!(a.nrows(), b.ncols(), "A.nrows() != B.ncols()");
}
}
}
}
}
#[macro_use]
macro_rules! assert_compatible_spadd_dims {
($c:expr, $a:expr, $trans_a:expr) => {
use crate::ops::Transpose;
match $trans_a {
Transpose(false) => {
assert_eq!($c.nrows(), $a.nrows(), "C.nrows() != A.nrows()");
assert_eq!($c.ncols(), $a.ncols(), "C.ncols() != A.ncols()");
($c:expr, $a:expr) => {
use crate::ops::Op;
match $a {
Op::NoOp(a) => {
assert_eq!($c.nrows(), a.nrows(), "C.nrows() != A.nrows()");
assert_eq!($c.ncols(), a.ncols(), "C.ncols() != A.ncols()");
},
Transpose(true) => {
assert_eq!($c.nrows(), $a.ncols(), "C.nrows() != A.ncols()");
assert_eq!($c.ncols(), $a.nrows(), "C.ncols() != A.nrows()");
Op::Transpose(a) => {
assert_eq!($c.nrows(), a.ncols(), "C.nrows() != A.ncols()");
assert_eq!($c.ncols(), a.nrows(), "C.ncols() != A.nrows()");
}
}

193
nalgebra-sparse/tests/unit_tests/ops.rs
View File

@ -1,7 +1,7 @@
use crate::common::{csr_strategy, PROPTEST_MATRIX_DIM, PROPTEST_MAX_NNZ,
PROPTEST_I32_VALUE_STRATEGY};
use nalgebra_sparse::ops::serial::{spmm_csr_dense, spadd_pattern, spmm_pattern, spadd_csr, spmm_csr};
use nalgebra_sparse::ops::{Transpose};
use nalgebra_sparse::ops::{Op};
use nalgebra_sparse::csr::CsrMatrix;
use nalgebra_sparse::proptest::{csr, sparsity_pattern};
use nalgebra_sparse::pattern::SparsityPattern;
@ -28,10 +28,8 @@ struct SpmmCsrDenseArgs<T: Scalar> {
c: DMatrix<T>,
beta: T,
alpha: T,
trans_a: Transpose,
a: CsrMatrix<T>,
trans_b: Transpose,
b: DMatrix<T>,
a: Op<CsrMatrix<T>>,
b: Op<DMatrix<T>>,
}
/// Returns matrices C, A and B with compatible dimensions such that it can be used
@ -48,10 +46,10 @@ fn spmm_csr_dense_args_strategy() -> impl Strategy<Value=SpmmCsrDenseArgs<i32>>
(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.to_bool() { (common_dim, c.nrows()) }
if trans_a { (common_dim, c.nrows()) }
else { (c.nrows(), common_dim) };
let b_shape =
if trans_b.to_bool() { (c.ncols(), common_dim) }
if trans_b { (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);
@ -66,10 +64,8 @@ fn spmm_csr_dense_args_strategy() -> impl Strategy<Value=SpmmCsrDenseArgs<i32>>
c,
beta,
alpha,
trans_a,
a,
trans_b,
b,
a: if trans_a { Op::Transpose(a) } else { Op::NoOp(a) },
b: if trans_b { Op::Transpose(b) } else { Op::NoOp(b) },
}
})
}
@ -79,14 +75,13 @@ struct SpaddCsrArgs<T> {
c: CsrMatrix<T>,
beta: T,
alpha: T,
trans_a: Transpose,
a: CsrMatrix<T>,
a: Op<CsrMatrix<T>>,
}
fn spadd_csr_args_strategy() -> impl Strategy<Value=SpaddCsrArgs<i32>> {
let value_strategy = PROPTEST_I32_VALUE_STRATEGY;
spadd_build_pattern_strategy()
spadd_pattern_strategy()
.prop_flat_map(move |(a_pattern, b_pattern)| {
let c_pattern = spadd_pattern(&a_pattern, &b_pattern);
@ -99,8 +94,8 @@ fn spadd_csr_args_strategy() -> impl Strategy<Value=SpaddCsrArgs<i32>> {
let c = CsrMatrix::try_from_pattern_and_values(Arc::new(c_pattern), c_values).unwrap();
let a = CsrMatrix::try_from_pattern_and_values(Arc::new(a_pattern), a_values).unwrap();
let a = if trans_a.to_bool() { a.transpose() } else { a };
SpaddCsrArgs { c, beta, alpha, trans_a, a }
let a = if trans_a { Op::Transpose(a.transpose()) } else { Op::NoOp(a) };
SpaddCsrArgs { c, beta, alpha, a }
})
}
@ -108,8 +103,20 @@ fn dense_strategy() -> impl Strategy<Value=DMatrix<i32>> {
matrix(PROPTEST_I32_VALUE_STRATEGY, PROPTEST_MATRIX_DIM, PROPTEST_MATRIX_DIM)
}
fn trans_strategy() -> impl Strategy<Value=Transpose> + Clone {
proptest::bool::ANY.prop_map(Transpose)
fn trans_strategy() -> impl Strategy<Value=bool> + Clone {
proptest::bool::ANY
}
/// Wraps the values of the given strategy in `Op`, producing both transposed and non-transposed
/// values.
fn op_strategy<S: Strategy>(strategy: S) -> impl Strategy<Value=Op<S::Value>> {
let is_transposed = proptest::bool::ANY;
(strategy, is_transposed)
.prop_map(|(obj, is_trans)| if is_trans {
Op::Transpose(obj)
} else {
Op::NoOp(obj)
})
}
fn pattern_strategy() -> impl Strategy<Value=SparsityPattern> {
@ -117,7 +124,7 @@ fn pattern_strategy() -> impl Strategy<Value=SparsityPattern> {
}
/// Constructs pairs (a, b) where a and b have the same dimensions
fn spadd_build_pattern_strategy() -> impl Strategy<Value=(SparsityPattern, SparsityPattern)> {
fn spadd_pattern_strategy() -> impl Strategy<Value=(SparsityPattern, SparsityPattern)> {
pattern_strategy()
.prop_flat_map(|a| {
let b = sparsity_pattern(Just(a.major_dim()), Just(a.minor_dim()), PROPTEST_MAX_NNZ);
@ -139,10 +146,8 @@ struct SpmmCsrArgs<T> {
c: CsrMatrix<T>,
beta: T,
alpha: T,
trans_a: Transpose,
a: CsrMatrix<T>,
trans_b: Transpose,
b: CsrMatrix<T>
a: Op<CsrMatrix<T>>,
b: Op<CsrMatrix<T>>,
}
fn spmm_csr_args_strategy() -> impl Strategy<Value=SpmmCsrArgs<i32>> {
@ -170,10 +175,8 @@ fn spmm_csr_args_strategy() -> impl Strategy<Value=SpmmCsrArgs<i32>> {
c,
beta,
alpha,
trans_a,
a: if trans_a.to_bool() { a.transpose() } else { a },
trans_b,
b: if trans_b.to_bool() { b.transpose() } else { b }
a: if trans_a { Op::Transpose(a.transpose()) } else { Op::NoOp(a) },
b: if trans_b { Op::Transpose(b.transpose()) } else { Op::NoOp(b) }
}
})
}
@ -182,52 +185,67 @@ fn spmm_csr_args_strategy() -> impl Strategy<Value=SpmmCsrArgs<i32>> {
fn dense_gemm<'a>(c: impl Into<DMatrixSliceMut<'a, i32>>,
beta: i32,
alpha: i32,
trans_a: Transpose,
a: impl Into<DMatrixSlice<'a, i32>>,
trans_b: Transpose,
b: impl Into<DMatrixSlice<'a, i32>>)
a: Op<impl Into<DMatrixSlice<'a, i32>>>,
b: Op<impl Into<DMatrixSlice<'a, i32>>>)
{
let mut c = c.into();
let a = a.into();
let b = b.into();
let a = a.convert();
let b = b.convert();
match (trans_a, trans_b) {
(Transpose(false), Transpose(false)) => c.gemm(alpha, &a, &b, beta),
(Transpose(true), Transpose(false)) => c.gemm(alpha, &a.transpose(), &b, beta),
(Transpose(false), Transpose(true)) => c.gemm(alpha, &a, &b.transpose(), beta),
(Transpose(true), Transpose(true)) => c.gemm(alpha, &a.transpose(), &b.transpose(), beta)
};
use Op::{NoOp, Transpose};
match (a, b) {
(NoOp(a), NoOp(b)) => c.gemm(alpha, &a, &b, beta),
(Transpose(a), NoOp(b)) => c.gemm(alpha, &a.transpose(), &b, beta),
(NoOp(a), Transpose(b)) => c.gemm(alpha, &a, &b.transpose(), beta),
(Transpose(a), Transpose(b)) => 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 }
SpmmCsrDenseArgs { c, beta, alpha, a, 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);
spmm_csr_dense(&mut spmm_result, beta, alpha, a.as_ref(), b.as_ref());
let mut gemm_result = c.clone();
dense_gemm(&mut gemm_result, beta, alpha, trans_a, &DMatrix::from(&a), trans_b, &b);
let a_dense = a.map_same_op(|a| DMatrix::from(&a));
dense_gemm(&mut gemm_result, beta, alpha, a_dense.as_ref(), b.as_ref());
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())
(alpha, beta, c, a, b)
in (PROPTEST_I32_VALUE_STRATEGY,
PROPTEST_I32_VALUE_STRATEGY,
dense_strategy(),
op_strategy(csr_strategy()),
op_strategy(dense_strategy()))
) {
// We refer to `A * B` as the "product"
let product_rows = if trans_a.to_bool() { a.ncols() } else { a.nrows() };
let product_cols = if trans_b.to_bool() { b.nrows() } else { b.ncols() };
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 = if trans_a.to_bool() { a.nrows() } else { a.ncols() };
let product_b_common = if trans_b.to_bool() { b.ncols() } else { b.nrows() };
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()
@ -239,7 +257,7 @@ proptest! {
let result = catch_unwind(|| {
let mut spmm_result = c.clone();
spmm_csr_dense(&mut spmm_result, beta, alpha, trans_a, &a, trans_b, &b);
spmm_csr_dense(&mut spmm_result, beta, alpha, a.as_ref(), b.as_ref());
});
prop_assert!(result.is_err(),
@ -247,7 +265,7 @@ proptest! {
}
#[test]
fn spadd_pattern_test((a, b) in spadd_build_pattern_strategy())
fn spadd_pattern_test((a, b) in spadd_pattern_strategy())
{
// (a, b) are dimensionally compatible patterns
let pattern_result = spadd_pattern(&a, &b);
@ -269,16 +287,18 @@ proptest! {
}
#[test]
fn spadd_csr_test(SpaddCsrArgs { c, beta, alpha, trans_a, a } in spadd_csr_args_strategy()) {
fn spadd_csr_test(SpaddCsrArgs { c, beta, alpha, a } in spadd_csr_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_csr(&mut c_sparse, beta, alpha, trans_a, &a).unwrap();
spadd_csr(&mut c_sparse, beta, alpha, a.as_ref()).unwrap();
let mut c_dense = DMatrix::from(&c);
let op_a_dense = DMatrix::from(&a);
let op_a_dense = if trans_a.to_bool() { op_a_dense.transpose() } else { op_a_dense };
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);
@ -343,19 +363,23 @@ proptest! {
}
#[test]
fn spmm_csr_test(SpmmCsrArgs { c, beta, alpha, trans_a, a, trans_b, b }
fn spmm_csr_test(SpmmCsrArgs { c, beta, alpha, a, b }
in spmm_csr_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(&mut c_sparse, beta, alpha, trans_a, &a, trans_b, &b).unwrap();
spmm_csr(&mut c_sparse, beta, alpha, a.as_ref(), b.as_ref()).unwrap();
let mut c_dense = DMatrix::from(&c);
let op_a_dense = DMatrix::from(&a);
let op_a_dense = if trans_a.to_bool() { op_a_dense.transpose() } else { op_a_dense };
let op_b_dense = DMatrix::from(&b);
let op_b_dense = if trans_b.to_bool() { op_b_dense.transpose() } else { op_b_dense };
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);
@ -363,22 +387,32 @@ proptest! {
#[test]
fn spmm_csr_panics_on_dim_mismatch(
(alpha, beta, c, a, b, trans_a, trans_b)
(alpha, beta, c, a, b)
in (PROPTEST_I32_VALUE_STRATEGY,
PROPTEST_I32_VALUE_STRATEGY,
csr_strategy(),
csr_strategy(),
csr_strategy(),
trans_strategy(),
trans_strategy())
op_strategy(csr_strategy()),
op_strategy(csr_strategy()))
) {
// We refer to `A * B` as the "product"
let product_rows = if trans_a.to_bool() { a.ncols() } else { a.nrows() };
let product_cols = if trans_b.to_bool() { b.nrows() } else { b.ncols() };
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 = if trans_a.to_bool() { a.nrows() } else { a.ncols() };
let product_b_common = if trans_b.to_bool() { b.ncols() } else { b.nrows() };
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()
@ -390,7 +424,7 @@ proptest! {
let result = catch_unwind(|| {
let mut spmm_result = c.clone();
spmm_csr(&mut spmm_result, beta, alpha, trans_a, &a, trans_b, &b).unwrap();
spmm_csr(&mut spmm_result, beta, alpha, a.as_ref(), b.as_ref()).unwrap();
});
prop_assert!(result.is_err(),
@ -399,15 +433,20 @@ proptest! {
#[test]
fn spadd_csr_panics_on_dim_mismatch(
(alpha, beta, c, a, trans_a)
(alpha, beta, c, op_a)
in (PROPTEST_I32_VALUE_STRATEGY,
PROPTEST_I32_VALUE_STRATEGY,
csr_strategy(),
csr_strategy(),
trans_strategy())
op_strategy(csr_strategy()))
) {
let op_a_rows = if trans_a.to_bool() { a.ncols() } else { a.nrows() };
let op_a_cols = if trans_a.to_bool() { a.nrows() } else { a.ncols() };
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;
@ -417,7 +456,7 @@ proptest! {
let result = catch_unwind(|| {
let mut spmm_result = c.clone();
spadd_csr(&mut spmm_result, beta, alpha, trans_a, &a).unwrap();
spadd_csr(&mut spmm_result, beta, alpha, op_a.as_ref()).unwrap();
});
prop_assert!(result.is_err(),