Prev Next cppad_mat_mul.cpp

@(@\newcommand{\W}[1]{ \; #1 \; } \newcommand{\R}[1]{ {\rm #1} } \newcommand{\B}[1]{ {\bf #1} } \newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} } \newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} } \newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} } \newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }@)@
CppAD Speed, Matrix Multiplication

Specifications
See link_mat_mul .

Implementation
# include <cppad/cppad.hpp>
# include <cppad/speed/mat_sum_sq.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/example/mat_mul.hpp>

// Note that CppAD uses global_option["memory"] at the main program level
# include <map>
extern std::map<std::string, bool> global_option;

bool link_mat_mul(
     size_t                           size     ,
     size_t                           repeat   ,
     CppAD::vector<double>&           x        ,
     CppAD::vector<double>&           z        ,
     CppAD::vector<double>&           dz
)
{
     // --------------------------------------------------------------------
     // check global options
     const char* valid[] = { "memory", "onetape", "optimize", "atomic"};
     size_t n_valid = sizeof(valid) / sizeof(valid[0]);
     typedef std::map<std::string, bool>::iterator iterator;
     //
     for(iterator itr=global_option.begin(); itr!=global_option.end(); ++itr)
     {     if( itr->second )
          {     bool ok = false;
               for(size_t i = 0; i < n_valid; i++)
                    ok |= itr->first == valid[i];
               if( ! ok )
                    return false;
          }
     }
     // --------------------------------------------------------------------
     // optimization options: no conditional skips or compare operators
     std::string options="no_compare_op";
     // -----------------------------------------------------
     // setup
     typedef CppAD::AD<double>           ADScalar;
     typedef CppAD::vector<ADScalar>     ADVector;

     size_t j;               // temporary index
     size_t m = 1;           // number of dependent variables
     size_t n = size * size; // number of independent variables
     ADVector   X(n);        // AD domain space vector
     ADVector   Y(n);        // Store product matrix
     ADVector   Z(m);        // AD range space vector
     CppAD::ADFun<double> f; // AD function object

     // vectors of reverse mode weights
     CppAD::vector<double> w(1);
     w[0] = 1.;

     // user atomic information
     CppAD::vector<ADScalar> ax(3 + 2 * n), ay(n);
     atomic_mat_mul atom_mul;
     //
     if( global_option["boolsparsity"] )
          atom_mul.option( CppAD::atomic_base<double>::pack_sparsity_enum );
     else
          atom_mul.option( CppAD::atomic_base<double>::set_sparsity_enum );
     // ------------------------------------------------------
     if( ! global_option["onetape"] ) while(repeat--)
     {     // get the next matrix
          CppAD::uniform_01(n, x);
          for( j = 0; j < n; j++)
               X[j] = x[j];

          // declare independent variables
          Independent(X);

          // do computations
          if( ! global_option["atomic"] )
               mat_sum_sq(size, X, Y, Z);
          else
          {     ax[0] = ADScalar( size ); // number of rows in left matrix
               ax[1] = ADScalar( size ); // rows in left and columns in right
               ax[2] = ADScalar( size ); // number of columns in right matrix
               for(j = 0; j < n; j++)
               {     ax[3 + j]     = X[j];
                    ax[3 + n + j] = X[j];
               }
               // Y = X * X
               atom_mul(ax, ay);
               Z[0] = 0.;
               for(j = 0; j < n; j++)
                    Z[0] += ay[j];
          }
          // create function object f : X -> Z
          f.Dependent(X, Z);

          if( global_option["optimize"] )
               f.optimize(options);

          // skip comparison operators
          f.compare_change_count(0);

          // evaluate and return gradient using reverse mode
          z  = f.Forward(0, x);
          dz = f.Reverse(1, w);
     }
     else
     {     // get a next matrix
          CppAD::uniform_01(n, x);
          for(j = 0; j < n; j++)
               X[j] = x[j];

          // declare independent variables
          Independent(X);

          // do computations
          if( ! global_option["atomic"] )
               mat_sum_sq(size, X, Y, Z);
          else
          {     for(j = 0; j < n; j++)
               {     ax[j]   = X[j];
                    ax[j+n] = X[j];
               }
               // Y = X * X
               atom_mul(ax, ay);
               Z[0] = 0.;
               for(j = 0; j < n; j++)
                    Z[0] += ay[j];
          }

          // create function object f : X -> Z
          f.Dependent(X, Z);

          if( global_option["optimize"] )
               f.optimize(options);

          // skip comparison operators
          f.compare_change_count(0);

          while(repeat--)
          {     // get a next matrix
               CppAD::uniform_01(n, x);

               // evaluate and return gradient using reverse mode
               z  = f.Forward(0, x);
               dz = f.Reverse(1, w);
          }
     }
     // --------------------------------------------------------------------
     // Free temporary work space (any future atomic_mat_mul constructors
     // would create new temporary work space.)
     CppAD::user_atomic<double>::clear();

     return true;
}

Input File: speed/cppad/mat_mul.cpp