| Prev | Next |  |  |  |  |  | atomic_tangent.cpp |

Tan and Tanh as User Atomic Operations: Example and Test
Theory
The code below uses the tan_forward and tan_reverse to implement the tangent and hyperbolic tangent functions as user atomic operations.
sparsity
This atomic operation can use both set and bool sparsity patterns.
Start Class Definition

    # include <cppad/cppad.hpp>
    namespace { // Begin empty namespace
    using CppAD::vector;
    //
    // a utility to compute the union of two sets.
    using CppAD::set_union;
    //
    class atomic_tangent : public CppAD::atomic_base<float> {

Constructor

    private:
         const bool hyperbolic_; // is this hyperbolic tangent
    public:
         // constructor
         atomic_tangent(const char* name, bool hyperbolic)
         : CppAD::atomic_base<float>(name),
         hyperbolic_(hyperbolic)
         { }
    private:

forward

         // forward mode routine called by CppAD
         bool forward(
              size_t                    p ,
              size_t                    q ,
              const vector<bool>&      vx ,
                    vector<bool>&     vzy ,
              const vector<float>&     tx ,
                    vector<float>&    tzy
         )
         {     size_t q1 = q + 1;
    # ifndef NDEBUG
              size_t n  = tx.size()  / q1;
              size_t m  = tzy.size() / q1;
    # endif
              assert( n == 1 );
              assert( m == 2 );
              assert( p <= q );
              size_t j, k;

              // check if this is during the call to old_tan(id, ax, ay)
              if( vx.size() > 0 )
              {     // set variable flag for both y an z
                   vzy[0] = vx[0];
                   vzy[1] = vx[0];
              }

              if( p == 0 )
              {     // z^{(0)} = tan( x^{(0)} ) or tanh( x^{(0)} )
                   if( hyperbolic_ )
                        tzy[0] = float( tanh( tx[0] ) );
                   else     tzy[0] = float( tan( tx[0] ) );

                   // y^{(0)} = z^{(0)} * z^{(0)}
                   tzy[q1 + 0] = tzy[0] * tzy[0];

                   p++;
              }
              for(j = p; j <= q; j++)
              {     float j_inv = 1.f / float(j);
                   if( hyperbolic_ )
                        j_inv = - j_inv;

                   // z^{(j)} = x^{(j)} +- sum_{k=1}^j k x^{(k)} y^{(j-k)} / j
                   tzy[j] = tx[j];
                   for(k = 1; k <= j; k++)
                        tzy[j] += tx[k] * tzy[q1 + j-k] * float(k) * j_inv;

                   // y^{(j)} = sum_{k=0}^j z^{(k)} z^{(j-k)}
                   tzy[q1 + j] = 0.;
                   for(k = 0; k <= j; k++)
                        tzy[q1 + j] += tzy[k] * tzy[j-k];
              }

              // All orders are implemented and there are no possible errors
              return true;
         }

reverse

         // reverse mode routine called by CppAD
         virtual bool reverse(
              size_t                    q ,
              const vector<float>&     tx ,
              const vector<float>&    tzy ,
                    vector<float>&     px ,
              const vector<float>&    pzy
         )
         {     size_t q1 = q + 1;
    # ifndef NDEBUG
              size_t n  = tx.size()  / q1;
              size_t m  = tzy.size() / q1;
    # endif
              assert( px.size()  == n * q1 );
              assert( pzy.size() == m * q1 );
              assert( n == 1 );
              assert( m == 2 );

              size_t j, k;

              // copy because partials w.r.t. y and z need to change
              vector<float> qzy = pzy;

              // initialize accumultion of reverse mode partials
              for(k = 0; k < q1; k++)
                   px[k] = 0.;

              // eliminate positive orders
              for(j = q; j > 0; j--)
              {     float j_inv = 1.f / float(j);
                   if( hyperbolic_ )
                        j_inv = - j_inv;

                   // H_{x^{(k)}} += delta(j-k) +- H_{z^{(j)} y^{(j-k)} * k / j
                   px[j] += qzy[j];
                   for(k = 1; k <= j; k++)
                        px[k] += qzy[j] * tzy[q1 + j-k] * float(k) * j_inv;

                   // H_{y^{j-k)} += +- H_{z^{(j)} x^{(k)} * k / j
                   for(k = 1; k <= j; k++)
                        qzy[q1 + j-k] += qzy[j] * tx[k] * float(k) * j_inv;

                   // H_{z^{(k)}} += H_{y^{(j-1)}} * z^{(j-k-1)} * 2.
                   for(k = 0; k < j; k++)
                        qzy[k] += qzy[q1 + j-1] * tzy[j-k-1] * 2.f;
              }

              // eliminate order zero
              if( hyperbolic_ )
                   px[0] += qzy[0] * (1.f - tzy[q1 + 0]);
              else
                   px[0] += qzy[0] * (1.f + tzy[q1 + 0]);

              return true;
         }

for_sparse_jac

         // forward Jacobian sparsity routine called by CppAD
         virtual bool for_sparse_jac(
              size_t                                p ,
              const vector<bool>&                   r ,
                    vector<bool>&                   s ,
              const vector<float>&                  x )
         {
    # ifndef NDEBUG
              size_t n = r.size() / p;
              size_t m = s.size() / p;
    # endif
              assert( n == x.size() );
              assert( n == 1 );
              assert( m == 2 );

              // sparsity for S(x) = f'(x) * R
              for(size_t j = 0; j < p; j++)
              {     s[0 * p + j] = r[j];
                   s[1 * p + j] = r[j];
              }

              return true;
         }
         // forward Jacobian sparsity routine called by CppAD
         virtual bool for_sparse_jac(
              size_t                                p ,
              const vector< std::set<size_t> >&     r ,
                    vector< std::set<size_t> >&     s ,
              const vector<float>&                  x )
         {
    # ifndef NDEBUG
              size_t n = r.size();
              size_t m = s.size();
    # endif
              assert( n == x.size() );
              assert( n == 1 );
              assert( m == 2 );

              // sparsity for S(x) = f'(x) * R
              s[0] = r[0];
              s[1] = r[0];

              return true;
         }

rev_sparse_jac

         // reverse Jacobian sparsity routine called by CppAD
         virtual bool rev_sparse_jac(
              size_t                                p ,
              const vector<bool>&                  rt ,
                    vector<bool>&                  st ,
              const vector<float>&                  x )
         {
    # ifndef NDEBUG
              size_t n = st.size() / p;
              size_t m = rt.size() / p;
    # endif
              assert( n == 1 );
              assert( m == 2 );
              assert( n == x.size() );

              // sparsity for S(x)^T = f'(x)^T * R^T
              for(size_t j = 0; j < p; j++)
                   st[j] = rt[0 * p + j] | rt[1 * p + j];

              return true;
         }
         // reverse Jacobian sparsity routine called by CppAD
         virtual bool rev_sparse_jac(
              size_t                                p ,
              const vector< std::set<size_t> >&    rt ,
                    vector< std::set<size_t> >&    st ,
              const vector<float>&                  x )
         {
    # ifndef NDEBUG
              size_t n = st.size();
              size_t m = rt.size();
    # endif
              assert( n == 1 );
              assert( m == 2 );
              assert( n == x.size() );

              // sparsity for S(x)^T = f'(x)^T * R^T
              st[0] = set_union(rt[0], rt[1]);
              return true;
         }

rev_sparse_hes

         // reverse Hessian sparsity routine called by CppAD
         virtual bool rev_sparse_hes(
              const vector<bool>&                   vx,
              const vector<bool>&                   s ,
                    vector<bool>&                   t ,
              size_t                                p ,
              const vector<bool>&                   r ,
              const vector<bool>&                   u ,
                    vector<bool>&                   v ,
              const vector<float>&                  x )
         {
    # ifndef NDEBUG
              size_t m = s.size();
              size_t n = t.size();
    # endif
              assert( x.size() == n );
              assert( r.size() == n * p );
              assert( u.size() == m * p );
              assert( v.size() == n * p );
              assert( n == 1 );
              assert( m == 2 );

              // There are no cross term second derivatives for this case,
              // so it is not necessary to vx.

              // sparsity for T(x) = S(x) * f'(x)
              t[0] =  s[0] | s[1];

              // V(x) = f'(x)^T * g''(y) * f'(x) * R  +  g'(y) * f''(x) * R
              // U(x) = g''(y) * f'(x) * R
              // S(x) = g'(y)

              // back propagate the sparsity for U, note both components
              // of f'(x) may be non-zero;
              size_t j;
              for(j = 0; j < p; j++)
                   v[j] = u[ 0 * p + j ] | u[ 1 * p + j ];

              // include forward Jacobian sparsity in Hessian sparsity
              // (note sparsty for f''(x) * R same as for R)
              if( s[0] | s[1] )
              {     for(j = 0; j < p; j++)
                   {     // Visual Studio 2013 generates warning without bool below
                        v[j] |= bool( r[j] );
                   }
              }

              return true;
         }
         // reverse Hessian sparsity routine called by CppAD
         virtual bool rev_sparse_hes(
              const vector<bool>&                   vx,
              const vector<bool>&                   s ,
                    vector<bool>&                   t ,
              size_t                                p ,
              const vector< std::set<size_t> >&     r ,
              const vector< std::set<size_t> >&     u ,
                    vector< std::set<size_t> >&     v ,
              const vector<float>&                  x )
         {
    # ifndef NDEBUG
              size_t m = s.size();
              size_t n = t.size();
    # endif
              assert( x.size() == n );
              assert( r.size() == n );
              assert( u.size() == m );
              assert( v.size() == n );
              assert( n == 1 );
              assert( m == 2 );

              // There are no cross term second derivatives for this case,
              // so it is not necessary to vx.

              // sparsity for T(x) = S(x) * f'(x)
              t[0] =  s[0] | s[1];

              // V(x) = f'(x)^T * g''(y) * f'(x) * R  +  g'(y) * f''(x) * R
              // U(x) = g''(y) * f'(x) * R
              // S(x) = g'(y)

              // back propagate the sparsity for U, note both components
              // of f'(x) may be non-zero;
              v[0] = set_union(u[0], u[1]);

              // include forward Jacobian sparsity in Hessian sparsity
              // (note sparsty for f''(x) * R same as for R)
              if( s[0] | s[1] )
                   v[0] = set_union(v[0], r[0]);

              return true;
         }

End Class Definition

    }; // End of atomic_tangent class
    }  // End empty namespace

Use Atomic Function

    bool tangent(void)
    {     bool ok = true;
         using CppAD::AD;
         using CppAD::NearEqual;
         float eps = 10.f * CppAD::numeric_limits<float>::epsilon();

Constructor

         // --------------------------------------------------------------------
         // Creater a tan and tanh object
         atomic_tangent my_tan("my_tan", false), my_tanh("my_tanh", true);

Recording

         // domain space vector
         size_t n  = 1;
         float  x0 = 0.5;
         CppAD::vector< AD<float> > ax(n);
         ax[0]     = x0;

         // declare independent variables and start tape recording
         CppAD::Independent(ax);

         // range space vector
         size_t m = 3;
         CppAD::vector< AD<float> > af(m);

         // temporary vector for computations
         // (my_tan and my_tanh computes tan or tanh and its square)
         CppAD::vector< AD<float> > az(2);

         // call atomic tan function and store tan(x) in f[0] (ignore tan(x)^2)
         my_tan(ax, az);
         af[0] = az[0];

         // call atomic tanh function and store tanh(x) in f[1] (ignore tanh(x)^2)
         my_tanh(ax, az);
         af[1] = az[0];

         // put a constant in f[2] = tanh(1.) (for sparsity pattern testing)
         CppAD::vector< AD<float> > one(1);
         one[0] = 1.;
         my_tanh(one, az);
         af[2] = az[0];

         // create f: x -> f and stop tape recording
         CppAD::ADFun<float> F;
         F.Dependent(ax, af);

forward

         // check function value
         float tan = std::tan(x0);
         ok &= NearEqual(af[0] , tan,  eps, eps);
         float tanh = std::tanh(x0);
         ok &= NearEqual(af[1] , tanh,  eps, eps);

         // check zero order forward
         CppAD::vector<float> x(n), f(m);
         x[0] = x0;
         f    = F.Forward(0, x);
         ok &= NearEqual(f[0] , tan,  eps, eps);
         ok &= NearEqual(f[1] , tanh,  eps, eps);

         // compute first partial of f w.r.t. x[0] using forward mode
         CppAD::vector<float> dx(n), df(m);
         dx[0] = 1.;
         df    = F.Forward(1, dx);

reverse

         // compute derivative of tan - tanh using reverse mode
         CppAD::vector<float> w(m), dw(n);
         w[0]  = 1.;
         w[1]  = 1.;
         w[2]  = 0.;
         dw    = F.Reverse(1, w);

         // tan'(x)   = 1 + tan(x)  * tan(x)
         // tanh'(x)  = 1 - tanh(x) * tanh(x)
         float tanp  = 1.f + tan * tan;
         float tanhp = 1.f - tanh * tanh;
         ok   &= NearEqual(df[0], tanp, eps, eps);
         ok   &= NearEqual(df[1], tanhp, eps, eps);
         ok   &= NearEqual(dw[0], w[0]*tanp + w[1]*tanhp, eps, eps);

         // compute second partial of f w.r.t. x[0] using forward mode
         CppAD::vector<float> ddx(n), ddf(m);
         ddx[0] = 0.;
         ddf    = F.Forward(2, ddx);

         // compute second derivative of tan - tanh using reverse mode
         CppAD::vector<float> ddw(2);
         ddw   = F.Reverse(2, w);

         // tan''(x)   = 2 *  tan(x) * tan'(x)
         // tanh''(x)  = - 2 * tanh(x) * tanh'(x)
         // Note that second order Taylor coefficient for u half the
         // corresponding second derivative.
         float two    = 2;
         float tanpp  =   two * tan * tanp;
         float tanhpp = - two * tanh * tanhp;
         ok   &= NearEqual(two * ddf[0], tanpp, eps, eps);
         ok   &= NearEqual(two * ddf[1], tanhpp, eps, eps);
         ok   &= NearEqual(ddw[0], w[0]*tanp  + w[1]*tanhp , eps, eps);
         ok   &= NearEqual(ddw[1], w[0]*tanpp + w[1]*tanhpp, eps, eps);

for_sparse_jac

         // Forward mode computation of sparsity pattern for F.
         size_t p = n;
         // user vectorBool because m and n are small
         CppAD::vectorBool r1(p), s1(m * p);
         r1[0] = true;            // propagate sparsity for x[0]
         s1    = F.ForSparseJac(p, r1);
         ok  &= (s1[0] == true);  // f[0] depends on x[0]
         ok  &= (s1[1] == true);  // f[1] depends on x[0]
         ok  &= (s1[2] == false); // f[2] does not depend on x[0]

rev_sparse_jac

         // Reverse mode computation of sparsity pattern for F.
         size_t q = m;
         CppAD::vectorBool s2(q * m), r2(q * n);
         // Sparsity pattern for identity matrix
         size_t i, j;
         for(i = 0; i < q; i++)
         {     for(j = 0; j < m; j++)
                   s2[i * q + j] = (i == j);
         }
         r2   = F.RevSparseJac(q, s2);
         ok  &= (r2[0] == true);  // f[0] depends on x[0]
         ok  &= (r2[1] == true);  // f[1] depends on x[0]
         ok  &= (r2[2] == false); // f[2] does not depend on x[0]

rev_sparse_hes

         // Hessian sparsity for f[0]
         CppAD::vectorBool s3(m), h(p * n);
         s3[0] = true;
         s3[1] = false;
         s3[2] = false;
         h    = F.RevSparseHes(p, s3);
         ok  &= (h[0] == true);  // Hessian is non-zero

         // Hessian sparsity for f[2]
         s3[0] = false;
         s3[2] = true;
         h    = F.RevSparseHes(p, s3);
         ok  &= (h[0] == false);  // Hessian is zero

Large x Values

         // check tanh results for a large value of x
         x[0]  = std::numeric_limits<float>::max() / two;
         f     = F.Forward(0, x);
         tanh  = 1.;
         ok   &= NearEqual(f[1], tanh, eps, eps);
         df    = F.Forward(1, dx);
         tanhp = 0.;
         ok   &= NearEqual(df[1], tanhp, eps, eps);

         return ok;
    }

Input File: example/atomic/tangent.cpp