Implement spmm_csr_dense

nalgebra-sparse/src/ops/mod.rs

@@ -0,0 +1,34 @@
+//! TODO
+
+pub mod serial;
+
+/// TODO
+#[derive(Copy, Clone, Debug, PartialEq, Eq)]
+pub enum Transposition {
+    /// TODO
+    Transpose,
+    /// TODO
+    NoTranspose,
+}
+
+impl Transposition {
+    /// TODO
+    pub fn is_transpose(&self) -> bool {
+        self == &Self::Transpose
+    }
+
+    /// TODO
+    pub fn from_bool(transpose: bool) -> Self {
+        if transpose { Self::Transpose } else { Self::NoTranspose }
+    }
+}
+
+/// TODO
+pub fn transpose() -> Transposition {
+    Transposition::Transpose
+}
+
+/// TODO
+pub fn no_transpose() -> Transposition {
+    Transposition::NoTranspose
+}

nalgebra-sparse/src/ops/serial/coo.rs

@@ -12,6 +12,8 @@ use num_traits::{One, Zero};
 ///
 /// If `beta == 0`, the elements in `y` are never read.
 ///
+/// TODO: Rethink this function
+///
 /// Panics
 /// ------
 ///

nalgebra-sparse/src/ops/serial/csr.rs

@@ -0,0 +1,68 @@
+use crate::csr::CsrMatrix;
+use crate::ops::Transposition;
+use nalgebra::{DVectorSlice, Scalar, DMatrixSlice, DVectorSliceMut, ClosedAdd, ClosedMul, DMatrixSliceMut};
+use num_traits::{Zero, One};
+
+/// Sparse-dense matrix-matrix multiplication `C = beta * C + alpha * trans(A) * trans(B)`.
+pub fn spmm_csr_dense<'a, T>(c: impl Into<DMatrixSliceMut<'a, T>>,
+                             beta: T,
+                             alpha: T,
+                             trans_a: Transposition,
+                             a: &CsrMatrix<T>,
+                             trans_b: Transposition,
+                             b: 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())
+}
+
+fn spmm_csr_dense_<T>(mut c: DMatrixSliceMut<T>,
+                      beta: T,
+                      alpha: T,
+                      trans_a: Transposition,
+                      a: &CsrMatrix<T>,
+                      trans_b: Transposition,
+                      b: DMatrixSlice<T>)
+where
+    T: Scalar + ClosedAdd + ClosedMul + Zero + One
+{
+    assert_compatible_spmm_dims!(c, a, b, trans_a, trans_b);
+
+    if trans_a.is_transpose() {
+        // 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.is_transpose() {
+                    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();
+                    }
+                }
+            }
+        }
+    } 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.is_transpose() { b.index((j, k)) } else { b.index((k, j)) };
+                    dot_ij += a_ik.inlined_clone() * b_contrib.inlined_clone();
+                }
+                *c_ij = beta.inlined_clone() * c_ij.inlined_clone() + alpha.inlined_clone() * dot_ij;
+            }
+        }
+    }
+}

nalgebra-sparse/src/ops/serial/mod.rs

@@ -0,0 +1,37 @@
+//! TODO
+
+#[macro_use]
+macro_rules! assert_compatible_spmm_dims {
+    ($c:expr, $a:expr, $b:expr, $trans_a:expr, $trans_b:expr) => {
+        use crate::ops::Transposition::{Transpose, NoTranspose};
+        match ($trans_a, $trans_b) {
+            (NoTranspose, NoTranspose) => {
+                assert_eq!($c.nrows(), $a.nrows(), "C.nrows() != A.nrows()");
+                assert_eq!($c.ncols(), $b.ncols(), "C.ncols() != B.ncols()");
+                assert_eq!($a.ncols(), $b.nrows(), "A.ncols() != B.nrows()");
+            },
+            (Transpose, NoTranspose) => {
+                assert_eq!($c.nrows(), $a.ncols(), "C.nrows() != A.ncols()");
+                assert_eq!($c.ncols(), $b.ncols(), "C.ncols() != B.ncols()");
+                assert_eq!($a.nrows(), $b.nrows(), "A.nrows() != B.nrows()");
+            },
+            (NoTranspose, Transpose) => {
+                assert_eq!($c.nrows(), $a.nrows(), "C.nrows() != A.nrows()");
+                assert_eq!($c.ncols(), $b.nrows(), "C.ncols() != B.nrows()");
+                assert_eq!($a.ncols(), $b.ncols(), "A.ncols() != B.ncols()");
+            },
+            (Transpose, Transpose) => {
+                assert_eq!($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()");
+            }
+        }
+
+    }
+}
+
+mod coo;
+mod csr;
+
+pub use coo::*;
+pub use csr::*;

nalgebra-sparse/tests/unit_tests/ops.rs

@@ -1,6 +1,15 @@
 use nalgebra_sparse::coo::CooMatrix;
-use nalgebra_sparse::ops::spmv_coo;
+use nalgebra_sparse::ops::serial::{spmv_coo, spmm_csr_dense};
-use nalgebra::{DVector, DMatrix};
+use nalgebra_sparse::ops::{no_transpose, Transposition};
+use nalgebra_sparse::csr::CsrMatrix;
+use nalgebra_sparse::proptest::csr;
+
+use nalgebra::{DVector, DMatrix, Scalar, DMatrixSliceMut, DMatrixSlice};
+use nalgebra::proptest::matrix;
+
+use proptest::prelude::*;
+
+use std::panic::catch_unwind;
 
 #[test]
 fn spmv_coo_agrees_with_dense_gemv() {
@@ -25,4 +34,138 @@ fn spmv_coo_agrees_with_dense_gemv() {
             assert_eq!(y, y_dense);
         }
     }
+}
+
+#[derive(Debug)]
+struct SpmmCsrDenseArgs<T: Scalar> {
+    c: DMatrix<T>,
+    beta: T,
+    alpha: T,
+    trans_a: Transposition,
+    a: CsrMatrix<T>,
+    trans_b: Transposition,
+    b: DMatrix<T>,
+}
+
+/// Returns matrices C, A and B with compatible dimensions such that it can be used
+/// in an `spmm` operation `C = beta * C + alpha * trans(A) * trans(B)`.
+fn spmm_csr_dense_args_strategy() -> impl Strategy<Value=SpmmCsrDenseArgs<i32>> {
+    let max_nnz = 40;
+    let value_strategy = -5 ..= 5;
+    let c_rows = 0 ..= 6usize;
+    let c_cols = 0 ..= 6usize;
+    let common_dim = 0 ..= 6usize;
+    let trans_strategy = trans_strategy();
+    let c_matrix_strategy = matrix(value_strategy.clone(), c_rows, c_cols);
+
+    (c_matrix_strategy, common_dim, trans_strategy.clone(), trans_strategy.clone())
+        .prop_flat_map(move |(c, common_dim, trans_a, trans_b)| {
+            let a_shape =
+                if trans_a.is_transpose() { (common_dim, c.nrows()) }
+                else { (c.nrows(), common_dim) };
+            let b_shape =
+                if trans_b.is_transpose() { (c.ncols(), common_dim) }
+                else { (common_dim, c.ncols()) };
+            let a = csr(value_strategy.clone(), Just(a_shape.0), Just(a_shape.1), max_nnz);
+            let b = matrix(value_strategy.clone(), b_shape.0, b_shape.1);
+
+            // We use the same values for alpha, beta parameters as for matrix elements
+            let alpha = value_strategy.clone();
+            let beta = value_strategy.clone();
+
+            (Just(c), beta, alpha, Just(trans_a), a, Just(trans_b), b)
+        }).prop_map(|(c, beta, alpha, trans_a, a, trans_b, b)| {
+            SpmmCsrDenseArgs {
+                c,
+                beta,
+                alpha,
+                trans_a,
+                a,
+                trans_b,
+                b,
+            }
+        })
+}
+
+fn csr_strategy() -> impl Strategy<Value=CsrMatrix<i32>> {
+    csr(-5 ..= 5, 0 ..= 6usize, 0 ..= 6usize, 40)
+}
+
+fn dense_strategy() -> impl Strategy<Value=DMatrix<i32>> {
+    matrix(-5 ..= 5, 0 ..= 6, 0 ..= 6)
+}
+
+fn trans_strategy() -> impl Strategy<Value=Transposition> + Clone {
+    proptest::bool::ANY.prop_map(Transposition::from_bool)
+}
+
+/// Helper function to help us call dense GEMM with our transposition parameters
+fn dense_gemm<'a>(c: impl Into<DMatrixSliceMut<'a, i32>>,
+                  beta: i32,
+                  alpha: i32,
+                  trans_a: Transposition,
+                  a: impl Into<DMatrixSlice<'a, i32>>,
+                  trans_b: Transposition,
+                  b: impl Into<DMatrixSlice<'a, i32>>)
+{
+    let mut c = c.into();
+    let a = a.into();
+    let b = b.into();
+
+    use Transposition::{Transpose, NoTranspose};
+    match (trans_a, trans_b) {
+        (NoTranspose, NoTranspose) => c.gemm(alpha, &a, &b, beta),
+        (Transpose, NoTranspose) => c.gemm(alpha, &a.transpose(), &b, beta),
+        (NoTranspose, Transpose) => c.gemm(alpha, &a, &b.transpose(), beta),
+        (Transpose, Transpose) => c.gemm(alpha, &a.transpose(), &b.transpose(), beta)
+    };
+}
+
+proptest! {
+
+    #[test]
+    fn spmm_csr_dense_agrees_with_dense_result(
+        SpmmCsrDenseArgs { c, beta, alpha, trans_a, a, trans_b, b }
+         in spmm_csr_dense_args_strategy()
+    ) {
+        let mut spmm_result = c.clone();
+        spmm_csr_dense(&mut spmm_result, beta, alpha, trans_a, &a, trans_b, &b);
+
+        let mut gemm_result = c.clone();
+        dense_gemm(&mut gemm_result, beta, alpha, trans_a, &DMatrix::from(&a), trans_b, &b);
+
+        prop_assert_eq!(spmm_result, gemm_result);
+    }
+
+    #[test]
+    fn spmm_csr_dense_panics_on_dim_mismatch(
+        (alpha, beta, c, a, b, trans_a, trans_b)
+        in (-5 ..= 5, -5 ..= 5, dense_strategy(), csr_strategy(),
+            dense_strategy(), trans_strategy(), trans_strategy())
+    ) {
+        // We refer to `A * B` as the "product"
+        let product_rows = if trans_a.is_transpose() { a.ncols() } else { a.nrows() };
+        let product_cols = if trans_b.is_transpose() { b.nrows() } else { b.ncols() };
+        // Determine the common dimension in the product
+        // from the perspective of a and b, respectively
+        let product_a_common = if trans_a.is_transpose() { a.nrows() } else { a.ncols() };
+        let product_b_common = if trans_b.is_transpose() { b.ncols() } else { b.nrows() };
+
+        let dims_are_compatible = product_rows == c.nrows()
+            && product_cols == c.ncols()
+            && product_a_common == product_b_common;
+
+        // If the dimensions randomly happen to be compatible, then of course we need to
+        // skip the test, so we assume that they are not.
+        prop_assume!(!dims_are_compatible);
+
+        let result = catch_unwind(|| {
+            let mut spmm_result = c.clone();
+            spmm_csr_dense(&mut spmm_result, beta, alpha, trans_a, &a, trans_b, &b);
+        });
+
+        prop_assert!(result.is_err(),
+            "The SPMM kernel executed successfully despite mismatch dimensions");
+    }
+
 }