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| 1 | +## Pushforward |
| 2 | + |
| 3 | +struct SymbolicsOneArgPushforwardExtras{E1,E2} <: PushforwardExtras |
| 4 | + pf_exe::E1 |
| 5 | + pf_exe!::E2 |
| 6 | +end |
| 7 | + |
| 8 | +function DI.prepare_pushforward(f, ::AnyAutoSymbolics, x, dx) |
| 9 | + x_var = if x isa Number |
| 10 | + variable(:x) |
| 11 | + else |
| 12 | + variables(:x, axes(x)...) |
| 13 | + end |
| 14 | + dx_var = if dx isa Number |
| 15 | + variable(:dx) |
| 16 | + else |
| 17 | + variables(:dx, axes(dx)...) |
| 18 | + end |
| 19 | + t_var = variable(:t) |
| 20 | + step_der_var = derivative(f(x_var + t_var * dx_var), t_var) |
| 21 | + pf_var = substitute(step_der_var, Dict(t_var => zero(eltype(x)))) |
| 22 | + |
| 23 | + res = build_function(pf_var, vcat(myvec(x_var), myvec(dx_var)); expression=Val(false)) |
| 24 | + (pf_exe, pf_exe!) = if res isa Tuple |
| 25 | + res |
| 26 | + elseif res isa RuntimeGeneratedFunction |
| 27 | + res, nothing |
| 28 | + end |
| 29 | + return SymbolicsOneArgPushforwardExtras(pf_exe, pf_exe!) |
| 30 | +end |
| 31 | + |
| 32 | +function DI.pushforward( |
| 33 | + f, ::AnyAutoSymbolics, x, dx, extras::SymbolicsOneArgPushforwardExtras |
| 34 | +) |
| 35 | + v_vec = vcat(myvec(x), myvec(dx)) |
| 36 | + dy = extras.pf_exe(v_vec) |
| 37 | + return dy |
| 38 | +end |
| 39 | + |
| 40 | +function DI.pushforward!( |
| 41 | + f, dy, ::AnyAutoSymbolics, x, dx, extras::SymbolicsOneArgPushforwardExtras |
| 42 | +) |
| 43 | + v_vec = vcat(myvec(x), myvec(dx)) |
| 44 | + extras.pf_exe!(dy, v_vec) |
| 45 | + return dy |
| 46 | +end |
| 47 | + |
| 48 | +function DI.value_and_pushforward( |
| 49 | + f, backend::AnyAutoSymbolics, x, dx, extras::SymbolicsOneArgPushforwardExtras |
| 50 | +) |
| 51 | + return f(x), DI.pushforward(f, backend, x, dx, extras) |
| 52 | +end |
| 53 | + |
| 54 | +function DI.value_and_pushforward!( |
| 55 | + f, dy, backend::AnyAutoSymbolics, x, dx, extras::SymbolicsOneArgPushforwardExtras |
| 56 | +) |
| 57 | + return f(x), DI.pushforward!(f, dy, backend, x, dx, extras) |
| 58 | +end |
| 59 | + |
| 60 | +## Derivative |
| 61 | + |
| 62 | +struct SymbolicsOneArgDerivativeExtras{E1,E2} <: DerivativeExtras |
| 63 | + der_exe::E1 |
| 64 | + der_exe!::E2 |
| 65 | +end |
| 66 | + |
| 67 | +function DI.prepare_derivative(f, ::AnyAutoSymbolics, x) |
| 68 | + x_var = variable(:x) |
| 69 | + der_var = derivative(f(x_var), x_var) |
| 70 | + |
| 71 | + res = build_function(der_var, x_var; expression=Val(false)) |
| 72 | + (der_exe, der_exe!) = if res isa Tuple |
| 73 | + res |
| 74 | + elseif res isa RuntimeGeneratedFunction |
| 75 | + res, nothing |
| 76 | + end |
| 77 | + return SymbolicsOneArgDerivativeExtras(der_exe, der_exe!) |
| 78 | +end |
| 79 | + |
| 80 | +function DI.derivative(f, ::AnyAutoSymbolics, x, extras::SymbolicsOneArgDerivativeExtras) |
| 81 | + return extras.der_exe(x) |
| 82 | +end |
| 83 | + |
| 84 | +function DI.derivative!( |
| 85 | + f, der, ::AnyAutoSymbolics, x, extras::SymbolicsOneArgDerivativeExtras |
| 86 | +) |
| 87 | + extras.der_exe!(der, x) |
| 88 | + return der |
| 89 | +end |
| 90 | + |
| 91 | +function DI.value_and_derivative( |
| 92 | + f, backend::AnyAutoSymbolics, x, extras::SymbolicsOneArgDerivativeExtras |
| 93 | +) |
| 94 | + return f(x), DI.derivative(f, backend, x, extras) |
| 95 | +end |
| 96 | + |
| 97 | +function DI.value_and_derivative!( |
| 98 | + f, der, backend::AnyAutoSymbolics, x, extras::SymbolicsOneArgDerivativeExtras |
| 99 | +) |
| 100 | + return f(x), DI.derivative!(f, der, backend, x, extras) |
| 101 | +end |
| 102 | + |
| 103 | +## Gradient |
| 104 | + |
| 105 | +struct SymbolicsOneArgGradientExtras{E1,E2} <: GradientExtras |
| 106 | + grad_exe::E1 |
| 107 | + grad_exe!::E2 |
| 108 | +end |
| 109 | + |
| 110 | +function DI.prepare_gradient(f, ::AnyAutoSymbolics, x) |
| 111 | + x_var = variables(:x, axes(x)...) |
| 112 | + # Symbolic.gradient only accepts vectors |
| 113 | + grad_var = gradient(f(x_var), vec(x_var)) |
| 114 | + |
| 115 | + res = build_function(grad_var, vec(x_var); expression=Val(false)) |
| 116 | + (grad_exe, grad_exe!) = if res isa Tuple |
| 117 | + res |
| 118 | + elseif res isa RuntimeGeneratedFunction |
| 119 | + res, nothing |
| 120 | + end |
| 121 | + return SymbolicsOneArgGradientExtras(grad_exe, grad_exe!) |
| 122 | +end |
| 123 | + |
| 124 | +function DI.gradient(f, ::AnyAutoSymbolics, x, extras::SymbolicsOneArgGradientExtras) |
| 125 | + return reshape(extras.grad_exe(vec(x)), size(x)) |
| 126 | +end |
| 127 | + |
| 128 | +function DI.gradient!(f, grad, ::AnyAutoSymbolics, x, extras::SymbolicsOneArgGradientExtras) |
| 129 | + extras.grad_exe!(vec(grad), vec(x)) |
| 130 | + return grad |
| 131 | +end |
| 132 | + |
| 133 | +function DI.value_and_gradient( |
| 134 | + f, backend::AnyAutoSymbolics, x, extras::SymbolicsOneArgGradientExtras |
| 135 | +) |
| 136 | + return f(x), DI.gradient(f, backend, x, extras) |
| 137 | +end |
| 138 | + |
| 139 | +function DI.value_and_gradient!( |
| 140 | + f, grad, backend::AnyAutoSymbolics, x, extras::SymbolicsOneArgGradientExtras |
| 141 | +) |
| 142 | + return f(x), DI.gradient!(f, grad, backend, x, extras) |
| 143 | +end |
| 144 | + |
| 145 | +## Jacobian |
| 146 | + |
| 147 | +struct SymbolicsOneArgJacobianExtras{E1,E2} <: JacobianExtras |
| 148 | + jac_exe::E1 |
| 149 | + jac_exe!::E2 |
| 150 | +end |
| 151 | + |
| 152 | +function DI.prepare_jacobian(f, backend::AnyAutoSymbolics, x) |
| 153 | + x_var = variables(:x, axes(x)...) |
| 154 | + jac_var = if issparse(backend) |
| 155 | + sparsejacobian(f(x_var), x_var) |
| 156 | + else |
| 157 | + jacobian(f(x_var), x_var) |
| 158 | + end |
| 159 | + |
| 160 | + res = build_function(jac_var, x_var; expression=Val(false)) |
| 161 | + (jac_exe, jac_exe!) = if res isa Tuple |
| 162 | + res |
| 163 | + elseif res isa RuntimeGeneratedFunction |
| 164 | + res, nothing |
| 165 | + end |
| 166 | + return SymbolicsOneArgJacobianExtras(jac_exe, jac_exe!) |
| 167 | +end |
| 168 | + |
| 169 | +function DI.jacobian(f, ::AnyAutoSymbolics, x, extras::SymbolicsOneArgJacobianExtras) |
| 170 | + return extras.jac_exe(x) |
| 171 | +end |
| 172 | + |
| 173 | +function DI.jacobian!(f, jac, ::AnyAutoSymbolics, x, extras::SymbolicsOneArgJacobianExtras) |
| 174 | + extras.jac_exe!(jac, x) |
| 175 | + return jac |
| 176 | +end |
| 177 | + |
| 178 | +function DI.value_and_jacobian( |
| 179 | + f, backend::AnyAutoSymbolics, x, extras::SymbolicsOneArgJacobianExtras |
| 180 | +) |
| 181 | + return f(x), DI.jacobian(f, backend, x, extras) |
| 182 | +end |
| 183 | + |
| 184 | +function DI.value_and_jacobian!( |
| 185 | + f, jac, backend::AnyAutoSymbolics, x, extras::SymbolicsOneArgJacobianExtras |
| 186 | +) |
| 187 | + return f(x), DI.jacobian!(f, jac, backend, x, extras) |
| 188 | +end |
| 189 | + |
| 190 | +## Hessian |
| 191 | + |
| 192 | +struct SymbolicsOneArgHessianExtras{E1,E2} <: HessianExtras |
| 193 | + hess_exe::E1 |
| 194 | + hess_exe!::E2 |
| 195 | +end |
| 196 | + |
| 197 | +function DI.prepare_hessian(f, backend::AnyAutoSymbolics, x) |
| 198 | + x_var = variables(:x, axes(x)...) |
| 199 | + # Symbolic.gradient only accepts vectors |
| 200 | + hess_var = if issparse(backend) |
| 201 | + sparsehessian(f(x_var), vec(x_var)) |
| 202 | + else |
| 203 | + hessian(f(x_var), vec(x_var)) |
| 204 | + end |
| 205 | + |
| 206 | + res = build_function(hess_var, vec(x_var); expression=Val(false)) |
| 207 | + (hess_exe, hess_exe!) = if res isa Tuple |
| 208 | + res |
| 209 | + elseif res isa RuntimeGeneratedFunction |
| 210 | + res, nothing |
| 211 | + end |
| 212 | + return SymbolicsOneArgHessianExtras(hess_exe, hess_exe!) |
| 213 | +end |
| 214 | + |
| 215 | +function DI.hessian(f, ::AnyAutoSymbolics, x, extras::SymbolicsOneArgHessianExtras) |
| 216 | + return extras.hess_exe(vec(x)) |
| 217 | +end |
| 218 | + |
| 219 | +function DI.hessian!(f, hess, ::AnyAutoSymbolics, x, extras::SymbolicsOneArgHessianExtras) |
| 220 | + extras.hess_exe!(hess, vec(x)) |
| 221 | + return hess |
| 222 | +end |
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