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

fadbad_mat_mul.cpp

View page source

Fadbad Speed: Matrix Multiplication

Specifications

See link_mat_mul .

Implementation

// suppress conversion warnings before other includes
# include <cppad/wno_conversion.hpp>
//
# include <FADBAD++/badiff.h>
# include <cppad/speed/mat_sum_sq.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/utility/vector.hpp>

// list of possible options
# 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       )
{
    // speed test global option values
    if( global_option["memory"] || global_option["onetape"] || global_option["atomic"] || global_option["optimize"] )
        return false;
    // The correctness check for this test is failing, so abort (for now).
    return false;

    // -----------------------------------------------------
    // setup

    // object for computing determinant
    typedef fadbad::B<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

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

        // set independent variable values
        for(j = 0; j < n; j++)
            X[j] = x[j];

        // do the computation
        mat_sum_sq(size, X, Y, Z);

        // create function object f : X -> Z
        Z[0].diff(0, m);  // index 0 of m dependent variables

        // evaluate and return gradient using reverse mode
        for(j = 0; j < n; j++)
            dz[j] = X[j].d(0); // partial Z[0] w.r.t X[j]
    }
    // return function value
    z[0] = Z[0].x();

    // ---------------------------------------------------------
    return true;
}