product function for tensor-times-vector improved.

.github/workflows/windows.yml

@@ -24,7 +24,7 @@ jobs:
 #           - {os: windows-2016, toolset: msvc, version: 14.16, cxxstd: 11} 
 #           - {os: windows-2019, toolset: msvc, version: 14.28, cxxstd: 11}
 #           - {os: windows-2019, toolset: msvc, version: 14.28, cxxstd: 17}
-           - {os: windows-2019, toolset: msvc, version: 14.28, cxxstd: latest}
+           - {os: windows-2019, toolset: msvc, version: 14.29, cxxstd: latest}
 
      steps:
       - uses: actions/checkout@v2

include/boost/numeric/ublas/tensor/extents/extents_functions.hpp

@@ -77,6 +77,37 @@ template <class D>
 //         std::all_of(cbegin(e)+2,cend(e)    , [](auto a){return a==1UL;});
 }
 
+/** @brief Returns true if extents equals (m,[1,1,...,1]) with m>=1 */
+template <class D>
+[[nodiscard]] inline constexpr bool is_row_vector(extents_base<D> const& e)
+{
+  if (empty(e) || size(e) == 1  ) {return false;}
+
+  if(cbegin(e)[0] ==  1ul &&
+      cbegin(e)[1] >  1ul &&
+      std::all_of(cbegin(e)+2ul,cend  (e) , [](auto a){return a==1ul;})){
+    return true;
+  }
+
+  return false;
+}
+
+
+/** @brief Returns true if extents equals (m,[1,1,...,1]) with m>=1 */
+template <class D>
+[[nodiscard]] inline constexpr bool is_col_vector(extents_base<D> const& e)
+{
+  if (empty(e) || size(e) == 1  ) {return false;}
+
+  if(cbegin(e)[0] >  1ul &&
+      cbegin(e)[1] == 1ul &&
+      std::all_of(cbegin(e)+2ul,cend  (e) , [](auto a){return a==1ul;})){
+    return true;
+  }
+
+  return false;
+}
+
 /** @brief Returns true if (m,[n,1,...,1]) with m>=1 or n>=1 */
 template <class D>
 [[nodiscard]] inline constexpr bool is_matrix(extents_base<D> const& e)

include/boost/numeric/ublas/tensor/function/tensor_times_vector.hpp

@@ -15,6 +15,7 @@
 #include <stdexcept>
 #include <type_traits>
 
+#include "../multiplication.hpp"
 #include "../extents.hpp"
 #include "../type_traits.hpp"
 #include "../tags.hpp"
@@ -49,6 +50,190 @@ using enable_ttv_if_extent_has_dynamic_rank = std::enable_if_t<is_dynamic_rank_v
 } // namespace detail
 
 
+namespace detail {
+template <class TC, class TA, class  V, class EC>
+inline auto scalar_scalar_prod(TA const &a, V const &b, EC const& nc_base)
+{
+  assert(ublas::is_scalar(a.extents()));
+  using tensor = TC;
+  using value = typename tensor::value_type;
+  using shape = typename tensor::extents_type;
+  return tensor(shape(nc_base),value(a[0]*b(0)));
+}
+
+template <class TC, class TA, class  V, class EC>
+inline auto vector_vector_prod(TA const &a, V const &b, EC& nc_base, std::size_t m)
+{
+  auto const& na = a.extents();
+
+  assert( ublas::is_vector(na));
+  assert(!ublas::is_scalar(na));
+  assert( ublas::size(na) > 1u);
+  assert(m > 0);
+
+  using tensor = TC;
+  using value = typename tensor::value_type;
+  using shape = typename tensor::extents_type;
+
+  auto const n1 = na[0];
+  auto const n2 = na[1];
+  auto const s = b.size();
+
+  // general
+  // [n1 n2 1 ... 1] xj [s 1] for any 1 <= j <= p with n1==1 or n2==1
+
+
+  // [n1 1 1 ... 1] x1 [n1 1] -> [1 1 1 ... 1]
+  // [1 n2 1 ... 1] x2 [n2 1] -> [1 1 1 ... 1]
+
+
+  assert(n1>1 || n2>1);
+
+  if( (n1>1u && m==1u)  || (n2>1u && m==2u) ){
+    if(m==1u) assert(n2==1u && n1==s);
+    if(m==2u) assert(n1==1u && n2==s);
+    auto cc = std::inner_product( a.begin(), a.end(), b.begin(), value(0) );
+    return tensor(shape(nc_base),value(cc));
+  }
+
+  // [n1 1 1 ... 1] xj [1 1] -> [n1 1 1 ... 1] with j != 1
+  // [1 n2 1 ... 1] xj [1 1] -> [1 n2 1 ... 1] with j != 2
+
+//if( (n1>1u && m!=1u) && (n2>0u && m!=2u) ){
+
+  if(n1>1u) assert(m!=1u);
+  if(n2>1u) assert(m!=2u);
+  assert(s==1u);
+
+  if(n1>1u) assert(n2==1u);
+  if(n2>1u) assert(n1==1u);
+
+  if(n1>1u) nc_base[0] = n1;
+  if(n2>1u) nc_base[1] = n2;
+
+  auto bb = b(0);
+  auto c = tensor(shape(nc_base));
+  std::transform(a.begin(),a.end(),c.begin(),[bb](auto aa){ return aa*bb; });
+  return c;
+//}
+
+
+}
+
+
+/** Computes a matrix-vector product.
+ *
+ *
+ *  @note assume stride 1 for specific dimensions and therefore requires refactoring for subtensor
+ *
+*/
+template <class TC, class TA, class  V, class EC>
+inline auto matrix_vector_prod(TA const &a, V const &b, EC& nc_base, std::size_t m)
+{
+  auto const& na = a.extents();
+
+  assert( ublas::is_matrix(na));
+  assert(!ublas::is_vector(na));
+  assert(!ublas::is_scalar(na));
+  assert( ublas::size(na) > 1u);
+  assert(m > 0);
+
+  using tensor = TC;
+  using shape  = typename tensor::extents_type;
+  using size_t = typename shape::value_type;
+
+  auto const n1 = na[0];
+  auto const n2 = na[1];
+  auto const s = b.size();
+
+  // general
+  // [n1 n2 1 ... 1] xj [s 1] for any 1 <= j <= p with either n1>1 and n2>1
+
+
+  // if [n1 n2 1 ... 1] xj [1 1] -> [n1 n2 1 ... 1] for j > 2
+  if(m > 2){
+    nc_base[0] = n1;
+    nc_base[1] = n2;
+    assert(s == 1);
+    auto c  = tensor(shape(nc_base));
+    auto const bb = b(0);
+    std::transform(a.begin(),a.end(), c.begin(), [bb](auto aa){return aa*bb;});
+    return c;
+  }
+
+
+  // [n1 n2 1 ... 1] x1 [n1 1] -> [n2 1 ... 1] -> vector-times-matrix
+  // [n1 n2 1 ... 1] x2 [n2 1] -> [n1 1 ... 1] -> matrix-times-vector
+
+  nc_base[0] = m==1 ? n2 : n1;
+
+  auto c  = tensor(shape(nc_base));
+  auto const& wa = a.strides();
+  auto const* bdata = &(b(0));
+
+  detail::recursive::mtv(m-1,n1,n2, c.data(), size_t(1), a.data(), wa[0], wa[1], bdata, size_t(1));
+
+  return c;
+}
+
+
+
+template <class TC, class TA, class  V, class EC>
+inline auto tensor_vector_prod(TA const &a, V const &b, EC& nc_base, std::size_t m)
+{
+  auto const& na = a.extents();
+
+  assert( ublas::is_tensor(na));
+  assert( ublas::size(na) > 1u);
+  assert(m > 0);
+
+  using tensor = TC;
+  using shape  = typename tensor::extents_type;
+  using layout = typename tensor::layout_type;
+
+  auto const pa = a.rank();
+  auto const nm = na[m-1];
+  auto const s = b.size();
+
+  auto nb = extents<2>{std::size_t(b.size()),std::size_t(1ul)};
+  auto wb = ublas::to_strides(nb,layout{} );
+
+  //TODO: Include an outer product when legacy vector becomes a new vector.
+
+  for (auto i = 0ul, j = 0ul; i < pa; ++i)
+    if (i != m - 1)
+      nc_base[j++] = na.at(i);
+
+  auto c  = tensor(shape(nc_base));
+
+  // [n1 n2 ... nm ... np] xm [1 1] -> [n1 n2 ... nm-1 nm+1 ... np]
+
+  if(s == 0){
+    assert(nm == 1);
+    auto const bb = b(0);
+    std::transform(a.begin(),a.end(), c.begin(), [bb](auto aa){return aa*bb;});
+    return c;
+  }
+
+
+  // if [n1 n2 n3 ... np] xm [nm 1] -> [n1 n2 ... nm-1 nm+1 ... np]
+
+  auto const& nc = c.extents();
+  auto const& wc = c.strides();
+  auto const& wa = a.strides();
+  auto const* bp = &(b(0));
+
+  ttv(m, pa,
+      c.data(), nc.data(), wc.data(),
+      a.data(), na.data(), wa.data(),
+      bp,       nb.data(), wb.data());
+
+  return c;
+}
+
+}//namespace detail
+
+
 /** @brief Computes the m-mode tensor-times-vector product
      *
      * Implements C[i1,...,im-1,im+1,...,ip] = A[i1,i2,...,ip] * b[im]
@@ -63,45 +248,49 @@ using enable_ttv_if_extent_has_dynamic_rank = std::enable_if_t<is_dynamic_rank_v
     */
 template <class TE, class  A, class  T = typename tensor_core< TE >::value,
           detail::enable_ttv_if_extent_has_dynamic_rank<TE> = true >
-inline decltype(auto) prod( tensor_core< TE > const &a, vector<T, A> const &b, const std::size_t m)
+inline auto prod( tensor_core< TE > const &a, vector<T, A> const &b, const std::size_t m)
 {
 
   using tensor            = tensor_core< TE >;
   using shape             = typename tensor::extents_type;
-  using value             = typename tensor::value_type;
-  using layout            = typename tensor::layout_type;
   using resize_tag        = typename tensor::resizable_tag;
 
-  auto const p = a.rank();
+  auto const pa = a.rank();
 
   static_assert(std::is_same_v<resize_tag,storage_resizable_container_tag>);
   static_assert(is_dynamic_v<shape>);
 
   if (m == 0ul)  throw std::length_error("error in boost::numeric::ublas::prod(ttv): contraction mode must be greater than zero.");
-  if (p < m)     throw std::length_error("error in boost::numeric::ublas::prod(ttv): rank of tensor must be greater than or equal to the contraction mode.");
+  if (pa < m)    throw std::length_error("error in boost::numeric::ublas::prod(ttv): rank of tensor must be greater than or equal to the contraction mode.");
   if (a.empty()) throw std::length_error("error in boost::numeric::ublas::prod(ttv): first argument tensor should not be empty.");
   if (b.empty()) throw std::length_error("error in boost::numeric::ublas::prod(ttv): second argument vector should not be empty.");
 
   auto const& na = a.extents();
-  auto nb = extents<2>{std::size_t(b.size()),std::size_t(1ul)};
-  auto wb = ublas::to_strides(nb,layout{} );
+
+  if(b.size() != na[m-1]) throw std::length_error("error in boost::numeric::ublas::prod(ttv): dimension mismatch of tensor and vector.");
 
   auto const sz = std::max( std::size_t(ublas::size(na)-1u), std::size_t(2) );
   auto nc_base = typename shape::base_type(sz,1);
 
-  for (auto i = 0ul, j = 0ul; i < p; ++i)
-    if (i != m - 1)
-      nc_base[j++] = na.at(i);
+  // output scalar tensor
+  if(ublas::is_scalar(na)){
+    return detail::scalar_scalar_prod<tensor>(a,b,nc_base);
+  }
+
+  // output scalar tensor or vector tensor
+  if (ublas::is_vector(na)){
+    return detail::vector_vector_prod<tensor>(a,b,nc_base,m);
+  }
+
+  // output scalar tensor or vector tensor
+  if (ublas::is_matrix(na)){
+    return detail::matrix_vector_prod<tensor>(a,b,nc_base,m);
+  }
+
+  assert(ublas::is_tensor(na));
+  return detail::tensor_vector_prod<tensor>(a,b,nc_base,m);
 
-  auto nc = shape(nc_base);
-  auto c = tensor( nc, value{} );
 
-  auto const* bb = &(b(0));
-  ttv(m, p,
-      c.data(), c.extents().data(), c.strides().data(),
-      a.data(), a.extents().data(), a.strides().data(),
-      bb,       nb.data(),          wb.data());
-  return c;
 }
 
 
@@ -143,7 +332,6 @@ inline auto prod( tensor_core< TE > const &a, vector<T, A> const &b, const std::
   constexpr auto p  = std::tuple_size_v<shape>;
   constexpr auto sz = std::max(std::size_t(std::tuple_size_v<shape>-1U),std::size_t(2));
 
-  using shape_b   = ublas::extents<2>;
   using shape_c   = ublas::extents<sz>;
   using tensor_c  = tensor_core<tensor_engine<shape_c,layout,container>>;
 
@@ -158,21 +346,25 @@ inline auto prod( tensor_core< TE > const &a, vector<T, A> const &b, const std::
 
   auto nc_base = typename shape_c::base_type{};
   std::fill(nc_base.begin(), nc_base.end(),std::size_t(1));
-  for (auto i = 0ul, j = 0ul; i < p; ++i)
-    if (i != m - 1)
-      nc_base[j++] = na.at(i);
 
-  auto nc = shape_c(std::move(nc_base));
-  auto nb = shape_b{b.size(),1UL};
-  auto wb = ublas::to_strides(nb,layout{});
-  auto c  = tensor_c( std::move(nc) );
-  auto const* bb = &(b(0));
 
-  ttv(m, p,
-      c.data(), c.extents().data(), c.strides().data(),
-      a.data(), a.extents().data(), a.strides().data(),
-      bb,       nb.data(),          wb.data() );
-  return c;
+  // output scalar tensor
+  if(ublas::is_scalar(na)){
+    return detail::scalar_scalar_prod<tensor_c>(a,b,nc_base);
+  }
+
+  // output scalar tensor or vector tensor
+  if (ublas::is_vector(na)){
+    return detail::vector_vector_prod<tensor_c>(a,b,nc_base,m);
+  }
+
+  // output scalar tensor or vector tensor
+  if (ublas::is_matrix(na)){
+    return detail::matrix_vector_prod<tensor_c>(a,b,nc_base,m);
+  }
+
+  assert(ublas::is_tensor(na));
+  return detail::tensor_vector_prod<tensor_c>(a,b,nc_base,m);
 }
 
 
@@ -201,7 +393,7 @@ inline auto prod( tensor_core< TE > const &a, vector<T, A> const &b)
   using shape         = typename tensor::extents;
   using layout        = typename tensor::layout;
   using shape_b       = extents<2>;
-  using shape_c       = remove_element_t<m,shape>;
+  using shape_c       = remove_element_t<m,shape>; // this is wrong
   using container_c   = rebind_storage_size_t<shape_c,container>;
   using tensor_c      = tensor_core<tensor_engine<shape_c,layout,container_c>>;