[ONEMKL_SYCL][REF] Add operation_info_t and matrix_opt to more cases and add two examples to cover it

examples/CMakeLists.txt

@@ -11,6 +11,11 @@ if (SPBLAS_CPU_BACKEND)
   add_example(simple_sptrsv)
   add_example(spmm_csc)
   add_example(matrix_opt_example)
+  if (ENABLE_ONEMKL_SYCL OR SPBLAS_REFERENCE_BACKEND)
+    # needs CPU + matrix_opt + operation_info_t to run
+    add_example(sptrsv_csr) # needs triangular_solve{_inspect} to run
+    add_example(spmm_csr) # needs multiply{_inspect} to run
+  endif()
 endif()
 
 # GPU examples

examples/spmm_csr.cpp

@@ -0,0 +1,53 @@
+#include <spblas/spblas.hpp>
+
+#include <fmt/core.h>
+#include <fmt/ranges.h>
+
+int main(int argc, char** argv) {
+  using namespace spblas;
+  namespace md = spblas::__mdspan;
+
+  using T = float;
+
+  spblas::index_t m = 10;
+  spblas::index_t n = 10;
+  spblas::index_t k = 10;
+  spblas::index_t nnz_in = 20;
+
+  fmt::print("\n\t###########################################################"
+             "######################");
+  fmt::print("\n\t### Running Advanced SpMM Example:");
+  fmt::print("\n\t###");
+  fmt::print("\n\t###");
+  fmt::print("\n\t### with ");
+  fmt::print("\n\t### A, in CSR format, of size ({}, {}) with nnz = {}", m, k,
+             nnz_in);
+  fmt::print("\n\t### x, a dense matrix, of size ({}, {})", k, n);
+  fmt::print("\n\t### y, a dense vector, of size ({}, {})", m, n);
+  fmt::print("\n\t### using float and spblas::index_t (size = {} bytes)",
+             sizeof(spblas::index_t));
+  fmt::print("\n\t###########################################################"
+             "######################");
+  fmt::print("\n");
+
+  auto&& [values, rowptr, colind, shape, nnz] = generate_csr<T>(m, k, nnz_in);
+
+  csr_view<T> a(values, rowptr, colind, shape, nnz);
+  matrix_opt a_opt(a);
+
+  std::vector<T> x_values(k * n, 1);
+  std::vector<T> y_values(m * n, 0);
+
+  md::mdspan x(x_values.data(), k, n);
+  md::mdspan y(y_values.data(), m, n);
+
+  // Y = A * X
+  auto state = multiply_inspect(a_opt, x, y);
+  multiply(state, a_opt, x, y);
+
+  fmt::print("{}\n", spblas::__backend::values(y));
+
+  fmt::print("\tExample is completed!\n");
+
+  return 0;
+}

examples/sptrsv_csr.cpp

@@ -0,0 +1,63 @@
+#include <spblas/spblas.hpp>
+
+#include <fmt/core.h>
+#include <fmt/ranges.h>
+
+int main(int argc, char** argv) {
+  using namespace spblas;
+
+  using T = float;
+
+  spblas::index_t m = 100;
+  spblas::index_t nnz_in = 20;
+
+  fmt::print("\n\t###########################################################"
+             "######################");
+  fmt::print("\n\t### Running Full SpTRSV Example:");
+  fmt::print("\n\t###");
+  fmt::print("\n\t###   solve for x:  A * x = alpha * b");
+  fmt::print("\n\t###");
+  fmt::print("\n\t### with ");
+  fmt::print("\n\t### A, in CSR format, of size ({}, {}) with nnz = {}", m, m,
+             nnz_in);
+  fmt::print("\n\t### x, a dense vector, of size ({}, {})", m, 1);
+  fmt::print("\n\t### b, a dense vector, of size ({}, {})", m, 1);
+  fmt::print("\n\t### using float and spblas::index_t (size = {} bytes)",
+             sizeof(spblas::index_t));
+  fmt::print("\n\t###########################################################"
+             "######################");
+  fmt::print("\n");
+
+  auto&& [values, rowptr, colind, shape, nnz] =
+      generate_csr<T, spblas::index_t>(m, m, nnz_in);
+
+  // scale values of matrix to make the implicit unit diagonal matrix
+  // be diagonally dominant, so it is solveable
+  T scale_factor = 1e-3f;
+  std::transform(values.begin(), values.end(), values.begin(),
+                 [scale_factor](T val) { return scale_factor * val; });
+
+  csr_view<T, spblas::index_t> a(values, rowptr, colind, shape, nnz);
+
+  matrix_opt a_opt(a);
+
+  // Scale every value of `a` by 5 in place.
+  // scale(5.f, a);
+
+  std::vector<T> x(m, 0);
+  std::vector<T> b(m, 1);
+
+  T alpha = 1.2f;
+  auto b_scaled = scaled(alpha, b);
+
+  // solve for x:  lower(A) * x = alpha * b
+  triangular_solve_inspect(a_opt, spblas::upper_triangle_t{},
+                           spblas::implicit_unit_diagonal_t{}, b_scaled, x);
+
+  triangular_solve(a_opt, spblas::upper_triangle_t{},
+                   spblas::implicit_unit_diagonal_t{}, b_scaled, x);
+
+  fmt::print("\tExample is completed!\n");
+
+  return 0;
+}

include/spblas/algorithms/multiply.hpp

@@ -5,18 +5,46 @@
 
 namespace spblas {
 
+// SpMV variants
+template <matrix A, vector B, vector C>
+operation_info_t multiply_inspect(A&& a, B&& b, C&& c);
+
+template <matrix A, vector B, vector C>
+void multiply_inspect(operation_info_t& info, A&& a, B&& b, C&& c);
+
 template <matrix A, vector B, vector C>
 void multiply(A&& a, B&& b, C&& c);
 
+template <matrix A, vector B, vector C>
+void multiply(operation_into_t& info, A&& a, B&& b, C&& c);
+
+// SpMM variants
 template <matrix A, matrix B, matrix C>
 void multiply(A&& a, B&& b, C&& c);
 
+template <matrix A, matrix B, matrix C>
+void multiply(operation_info_t& info, A&& a, B&& b, C&& c);
+
+// SpMM and SpGEMM multiply_inspect variants
 template <matrix A, matrix B, matrix C>
 operation_info_t multiply_inspect(A&& a, B&& b, C&& c);
 
 template <matrix A, matrix B, matrix C>
 void multiply_inspect(operation_info_t& info, A&& a, B&& b, C&& c);
 
+// SpGEMM variants
+template <typename ExecutionPolicy, matrix A, matrix B, matrix C>
+operation_info_t multiply_compute(ExecutionPolicy&& policy, A&& a, B&& b,
+                                  C&& c);
+
+template <typename ExecutionPolicy, matrix A, matrix B, matrix C>
+void multiply_compute(ExecutionPolicy&& policy, operation_info_t& info, A&& a,
+                      B&& b, C&& c);
+
+template <typename ExecutionPolicy, matrix A, matrix B, matrix C>
+void multiply_fill(ExecutionPolicy&& policy, operation_info_t& info, A&& a,
+                   B&& b, C&& c);
+
 template <matrix A, matrix B, matrix C>
 operation_info_t multiply_compute(A&& a, B&& b, C&& c);
 

include/spblas/algorithms/multiply_impl.hpp

@@ -15,6 +15,19 @@
 
 namespace spblas {
 
+// SpMV inspect
+template <matrix A, vector B, vector C>
+operation_info_t multiply_inspect(A&& a, B&& b, C&& c) {
+  log_trace("");
+  return operation_info_t{};
+}
+
+// SpMV inspect
+template <matrix A, vector B, vector C>
+operation_info_t multiply_inspect(operation_info_t& info, A&& a, B&& b, C&& c) {
+  log_trace("");
+}
+
 // C = AB
 // SpMV
 template <matrix A, vector B, vector C>
@@ -39,6 +52,15 @@ void multiply(A&& a, B&& b, C&& c) {
   });
 }
 
+// C = AB
+// SpMV with info input
+template <matrix A, vector B, vector C>
+  requires(__backend::lookupable<B> && __backend::lookupable<C>)
+void multiply(operation_info_t& info, A&& a, B&& b, C&& c) {
+  log_trace("");
+  multiply(std::forward<A>(a), std::forward<B>(b), std::forward<C>(c));
+}
+
 // C = AB
 // SpMM
 template <matrix A, matrix B, matrix C>
@@ -52,11 +74,14 @@ void multiply(A&& a, B&& b, C&& c) {
         "multiply: matrix dimensions are incompatible.");
   }
 
+  // initializes c to zero so we can use += everywhere
   __backend::for_each(c, [](auto&& e) {
     auto&& [_, v] = e;
     v = 0;
   });
 
+  // traverses elements of a and performs appropriate
+  // multiplication with B rows
   __backend::for_each(a, [&](auto&& e) {
     auto&& [idx, a_v] = e;
     auto&& [i, k] = idx;
@@ -66,23 +91,44 @@ void multiply(A&& a, B&& b, C&& c) {
   });
 }
 
+// C = AB
+// SpMM with info
+template <matrix A, matrix B, matrix C>
+  requires(__backend::lookupable<B> && __backend::lookupable<C>)
+void multiply(operation_info_t& info, A&& a, B&& b, C&& c) {
+  log_trace("");
+  multiply(std::forward<A>(a), std::forward<B>(b), std::forward<C>(c));
+}
+
+// C = AB
+// SpMM or SpGEMM multiply_inspect variants end up here
 template <matrix A, matrix B, matrix C>
 operation_info_t multiply_inspect(A&& a, B&& b, C&& c) {
+  log_trace("");
   return operation_info_t{};
 }
 
+// C = AB
+// SpMM or SpGEMM multiply_inspect variants end up here
 template <matrix A, matrix B, matrix C>
-void multiply_inspect(operation_info_t& info, A&& a, B&& b, C&& c){};
+void multiply_inspect(operation_info_t& info, A&& a, B&& b, C&& c) {
+  log_trace("");
+};
 
+// C = AB
+// SpGEMM compute stage with CSR output
 template <matrix A, matrix B, matrix C>
   requires(__backend::row_iterable<A> && __backend::row_iterable<B> &&
            __detail::is_csr_view_v<C>)
 void multiply_compute(operation_info_t& info, A&& a, B&& b, C&& c) {
+  log_trace("");
   auto new_info = multiply_compute(std::forward<A>(a), std::forward<B>(b),
                                    std::forward<C>(c));
   info.update_impl_(new_info.result_shape(), new_info.result_nnz());
 }
 
+// C = AB
+// SpGEMM compute stage with CSC output
 template <matrix A, matrix B, matrix C>
   requires(__backend::column_iterable<A> && __backend::column_iterable<B> &&
            __detail::is_csc_view_v<C>)
@@ -93,6 +139,7 @@ void multiply_compute(operation_info_t& info, A&& a, B&& b, C&& c) {
 }
 
 // C = AB
+// SpGEMM fill stage with CSR or CSC output
 template <matrix A, matrix B, matrix C>
 void multiply_fill(operation_info_t info, A&& a, B&& b, C&& c) {
   log_trace("");

include/spblas/algorithms/triangular_solve.hpp

@@ -3,13 +3,16 @@
 #include <spblas/concepts.hpp>
 #include <spblas/detail/operation_info_t.hpp>
 
-template <class ExecutionPolicy, in - matrix InMat, class Triangle,
-          class DiagonalStorage, in - vector InVec, out - vector OutVec>
-void triangular_matrix_vector_solve(ExecutionPolicy&& exec, InMat A, Triangle t,
-                                    DiagonalStorage d, InVec b, OutVec x);
-
 namespace spblas {
 
+template <matrix A, class Triangle, class DiagonalStorage, vector B, vector X>
+void triangular_solve_inspect(operation_info_t& info, A&& a, Triangle uplo,
+                              DiagonalStorage diag, B&& b, X&& x);
+
+template <matrix A, class Triangle, class DiagonalStorage, vector B, vector X>
+operation_info_t triangular_solve_inspect(A&& a, Triangle uplo,
+                                          DiagonalStorage diag, B&& b, X&& x);
+
 template <matrix A, class Triangle, class DiagonalStorage, vector B, vector X>
 void triangular_solve(A&& a, Triangle uplo, DiagonalStorage diag, B&& b, X&& x);
 

include/spblas/algorithms/triangular_solve_impl.hpp

@@ -8,10 +8,41 @@
 
 namespace spblas {
 
+// X = inv(A) B
+// SpTRSV inspect stage
+template <matrix A, class Triangle, class DiagonalStorage, vector B, vector X>
+  requires(__backend::row_iterable<A> && __backend::lookupable<B> &&
+           __backend::lookupable<X>)
+operation_info_t triangular_solve_inspect(A&& a, Triangle t, DiagonalStorage d,
+                                          B&& b, X&& x) {
+  log_trace("");
+  static_assert(std::is_same_v<Triangle, upper_triangle_t> ||
+                std::is_same_v<Triangle, lower_triangle_t>);
+  assert(__backend::shape(a)[0] == __backend::shape(a)[1]);
+
+  return operation_info_t{};
+}
+
+// X = inv(A) B
+// SpTRSV inspect stage
+template <matrix A, class Triangle, class DiagonalStorage, vector B, vector X>
+  requires(__backend::row_iterable<A> && __backend::lookupable<B> &&
+           __backend::lookupable<X>)
+void triangular_solve_inspect(operation_info_t& info, A&& a, Triangle t,
+                              DiagonalStorage d, B&& b, X&& x) {
+  log_trace("");
+  static_assert(std::is_same_v<Triangle, upper_triangle_t> ||
+                std::is_same_v<Triangle, lower_triangle_t>);
+  assert(__backend::shape(a)[0] == __backend::shape(a)[1]);
+}
+
+// X = inv(A) B
+// SpTRSV solve stage
 template <matrix A, class Triangle, class DiagonalStorage, vector B, vector X>
   requires(__backend::row_iterable<A> && __backend::lookupable<B> &&
            __backend::lookupable<X>)
 void triangular_solve(A&& a, Triangle t, DiagonalStorage d, B&& b, X&& x) {
+  log_trace("");
   static_assert(std::is_same_v<Triangle, upper_triangle_t> ||
                 std::is_same_v<Triangle, lower_triangle_t>);
   assert(__backend::shape(a)[0] == __backend::shape(a)[1]);
@@ -62,4 +93,17 @@ void triangular_solve(A&& a, Triangle t, DiagonalStorage d, B&& b, X&& x) {
   }
 }
 
+// X = inv(A) B
+// SpTRSV solve stage with info
+template <matrix A, class Triangle, class DiagonalStorage, vector B, vector X>
+  requires(__backend::row_iterable<A> && __backend::lookupable<B> &&
+           __backend::lookupable<X>)
+void triangular_solve(operation_info_t& info, A&& a, Triangle t,
+                      DiagonalStorage d, B&& b, X&& x) {
+  log_trace("");
+  triangular_solve(std::forward<A>(a), std::forward<Triangle>(t),
+                   std::forward<DiagonalStorage>(d), std::forward<B>(b),
+                   std::forward<X>(x));
+}
+
 } // namespace spblas

include/spblas/vendor/onemkl_sycl/detail/create_matrix_handle.hpp

@@ -18,8 +18,14 @@ oneapi::mkl::sparse::matrix_handle_t create_matrix_handle(sycl::queue& q,
   oneapi::mkl::sparse::init_matrix_handle(&handle);
 
   oneapi::mkl::sparse::set_csr_data(
-      q, handle, m.shape()[0], m.shape()[1], oneapi::mkl::index_base::zero,
-      m.rowptr().data(), m.colind().data(), m.values().data())
+      q, handle, m.shape()[0], m.shape()[1],
+#if defined(__INTEL_MKL__) &&                                                  \
+    ((__INTEL_MKL__ == 2025) && (__INTEL_MKL_MINOR__ == 3) ||                  \
+     (__INTEL_MKL__ > 2025))
+      m.size(), // nnz added in 2025.3, and without deprecated
+#endif
+      oneapi::mkl::index_base::zero, m.rowptr().data(), m.colind().data(),
+      m.values().data())
       .wait();
 
   return handle;
@@ -33,8 +39,14 @@ oneapi::mkl::sparse::matrix_handle_t create_matrix_handle(sycl::queue& q,
   oneapi::mkl::sparse::init_matrix_handle(&handle);
 
   oneapi::mkl::sparse::set_csr_data(
-      q, handle, m.shape()[1], m.shape()[0], oneapi::mkl::index_base::zero,
-      m.colptr().data(), m.rowind().data(), m.values().data())
+      q, handle, m.shape()[1], m.shape()[0],
+#if defined(__INTEL_MKL__) &&                                                  \
+    ((__INTEL_MKL__ == 2025) && (__INTEL_MKL_MINOR__ == 3) ||                  \
+     (__INTEL_MKL__ > 2025))
+      m.size(), // nnz added in 2025.3, and without deprecated
+#endif
+      oneapi::mkl::index_base::zero, m.colptr().data(), m.rowind().data(),
+      m.values().data())
       .wait();
 
   return handle;