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atomic_four_mat_mul_rev_depend.hpp |
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@(@\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}} }@)@This is cppad-20221105 documentation. Here is a link to its
current documentation
.
Atomic Matrix Multiply Reverse Dependency Analysis: Example Implementation
Purpose
The rev_depend
routine is used by optimize
to reduce the number of variables in the recording of a function.
Source
# include <cppad/example/atomic_four/mat_mul/mat_mul.hpp>
namespace CppAD { // BEGIN_CPPAD_NAMESPACE
//
// rev_depend override
template <class Base>
bool atomic_mat_mul<Base>::rev_depend(
size_t call_id ,
CppAD::vector<bool>& depend_x ,
const CppAD::vector<bool>& depend_y )
{
//
// n_left, n_middle, n_right
size_t n_left, n_middle, n_right;
get(call_id, n_left, n_middle, n_right);
# ifndef NDEBUG
// n, m
size_t n = depend_x.size();
size_t m = depend_y.size();
//
// check sizes
assert( n == n_left * n_middle + n_middle * n_right );
assert( m == n_left * n_right );
# endif
//
// offset
size_t offset = n_left * n_middle;
//
// type_y
// y[ i * n_right + j] = sum_k
// x[i * n_middle + k] * x[ offset + k * n_right + j]
// type_y
for(size_t i = 0; i < n_left; ++i)
{ for(size_t j = 0; j < n_right; ++j)
{ size_t ij = i * n_right + j;
if( depend_y[ij] )
{ for(size_t k = 0; k < n_middle; ++k)
{ size_t ik = i * n_middle + k;
size_t kj = offset + k * n_right + j;
depend_x[ik] = true;
depend_x[kj] = true;
}
}
}
}
return true;
}
} // END_CPPAD_NAMESPACE
Input File: include/cppad/example/atomic_four/mat_mul/rev_depend.hpp