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Old Tan and Tanh as User Atomic Operations: Example and Test
Deprecated 2013-05-27
This example has not deprecated; see atomic_tangent.cpp instead.
Theory
The code below uses the tan_forward and tan_reverse to implement the tangent ( `id == 0` ) and hyperbolic tangent ( `id == 1` ) functions as user atomic operations. `

    # include <cppad/cppad.hpp>

    namespace { // Begin empty namespace
         using CppAD::vector;

         // a utility to compute the union of two sets.
         using CppAD::set_union;

         // ----------------------------------------------------------------------
         // forward mode routine called by CppAD
         bool old_tan_forward(
              size_t                   id ,
              size_t                order ,
              size_t                    n ,
              size_t                    m ,
              const vector<bool>&      vx ,
              vector<bool>&           vzy ,
              const vector<float>&     tx ,
              vector<float>&          tzy
         )
         {
              assert( id == 0 || id == 1 );
              assert( n == 1 );
              assert( m == 2 );
              assert( tx.size() >= (order+1) * n );
              assert( tzy.size() >= (order+1) * m );

              size_t n_order = order + 1;
              size_t j = order;
              size_t k;

              // check if this is during the call to old_tan(id, ax, ay)
              if( vx.size() > 0 )
              {     assert( vx.size() >= n );
                   assert( vzy.size() >= m );

                   // now setvzy
                   vzy[0] = vx[0];
                   vzy[1] = vx[0];
              }

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

                   // y^{(0)} = z^{(0)} * z^{(0)}
                   tzy[n_order + 0] = tzy[0] * tzy[0];
              }
              else
              {     float j_inv = 1.f / float(j);
                   if( id == 1 )
                        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[n_order + j-k] * float(k) * j_inv;

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

              // All orders are implemented and there are no possible errors
              return true;
         }
         // ----------------------------------------------------------------------
         // reverse mode routine called by CppAD
         bool old_tan_reverse(
              size_t                   id ,
              size_t                order ,
              size_t                    n ,
              size_t                    m ,
              const vector<float>&     tx ,
              const vector<float>&    tzy ,
              vector<float>&           px ,
              const vector<float>&    pzy
         )
         {
              assert( id == 0 || id == 1 );
              assert( n == 1 );
              assert( m == 2 );
              assert( tx.size() >= (order+1) * n );
              assert( tzy.size() >= (order+1) * m );
              assert( px.size() >= (order+1) * n );
              assert( pzy.size() >= (order+1) * m );

              size_t n_order = order + 1;
              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 < n_order; k++)
                   px[k] = 0.;

              // eliminate positive orders
              for(j = order; j > 0; j--)
              {     float j_inv = 1.f / float(j);
                   if( id == 1 )
                        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[n_order + j-k] * float(k) * j_inv;

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

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

              return true;
         }
         // ----------------------------------------------------------------------
         // forward Jacobian sparsity routine called by CppAD
         bool old_tan_for_jac_sparse(
              size_t                               id ,
              size_t                                n ,
              size_t                                m ,
              size_t                                p ,
              const vector< std::set<size_t> >&     r ,
              vector< std::set<size_t> >&           s )
         {
              assert( n == 1 );
              assert( m == 2 );
              assert( id == 0 || id == 1 );
              assert( r.size() >= n );
              assert( s.size() >= m );

              // sparsity for z and y are the same as for x
              s[0] = r[0];
              s[1] = r[0];

              return true;
         }
         // ----------------------------------------------------------------------
         // reverse Jacobian sparsity routine called by CppAD
         bool old_tan_rev_jac_sparse(
              size_t                               id ,
              size_t                                n ,
              size_t                                m ,
              size_t                                p ,
              vector< std::set<size_t> >&           r ,
              const vector< std::set<size_t> >&     s )
         {
              assert( n == 1 );
              assert( m == 2 );
              assert( id == 0 || id == 1 );
              assert( r.size() >= n );
              assert( s.size() >= m );

              // note that, if the users code only uses z, and not y,
              // we could just set r[0] = s[0]
              r[0] = set_union(s[0], s[1]);
              return true;
         }
         // ----------------------------------------------------------------------
         // reverse Hessian sparsity routine called by CppAD
         bool old_tan_rev_hes_sparse(
              size_t                               id ,
              size_t                                n ,
              size_t                                m ,
              size_t                                p ,
              const vector< std::set<size_t> >&     r ,
              const vector<bool>&                   s ,
              vector<bool>&                         t ,
              const vector< std::set<size_t> >&     u ,
              vector< std::set<size_t> >&           v )
         {
              assert( n == 1 );
              assert( m == 2 );
              assert( id == 0 || id == 1 );
              assert( r.size() >= n );
              assert( s.size() >= m );
              assert( t.size() >= n );
              assert( u.size() >= m );
              assert( v.size() >= n );

              // back propagate Jacobian sparsity. If users code only uses z,
              // we could just set t[0] = s[0];
              t[0] =  s[0] | s[1];

              // back propagate Hessian sparsity, ...
              v[0] = set_union(u[0], u[1]);

              // convert forward Jacobian sparsity to Hessian sparsity
              // because tan and tanh are nonlinear
              if( t[0] )
                   v[0] = set_union(v[0], r[0]);

              return true;
         }
         // ---------------------------------------------------------------------
         // Declare the AD<float> routine old_tan(id, ax, ay)
         CPPAD_USER_ATOMIC(
              old_tan                 ,
              CppAD::vector           ,
              float                   ,
              old_tan_forward         ,
              old_tan_reverse         ,
              old_tan_for_jac_sparse  ,
              old_tan_rev_jac_sparse  ,
              old_tan_rev_hes_sparse
         )
    } // End empty namespace

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

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

         // call user tan function and store tan(x) in f[0] (ignore tan(x)^2)
         size_t id = 0;
         old_tan(id, ax, az);
         af[0] = az[0];

         // call user tanh function and store tanh(x) in f[1] (ignore tanh(x)^2)
         id = 1;
         old_tan(id, 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.;
         old_tan(id, one, az);
         af[2] = az[0];

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

         // 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);

         // 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);

         // 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]

         // 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]

         // 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

         // 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);

         // --------------------------------------------------------------------
         // Free all temporary work space associated with old_atomic objects.
         // (If there are future calls to user atomic functions, they will
         // create new temporary work space.)
         CppAD::user_atomic<float>::clear();

         return ok;
    }

`

Input File: example/deprecated/old_tan.cpp