|
| 1 | +for (ADGradient, fbackend) in ((:EnzymeADGradient , :AutoEnzyme ), |
| 2 | + (:ZygoteADGradient , :AutoZygote ), |
| 3 | + # (:ForwardDiffADGradient , :AutoForwardDiff ), |
| 4 | + # (:ReverseDiffADGradient , :AutoReverseDiff ), |
| 5 | + (:MooncakeADGradient , :AutoMooncake ), |
| 6 | + (:DiffractorADGradient , :AutoDiffractor ), |
| 7 | + (:TrackerADGradient , :AutoTracker ), |
| 8 | + (:SymbolicsADGradient , :AutoSymbolics ), |
| 9 | + (:ChainRulesADGradient , :AutoChainRules ), |
| 10 | + (:FastDifferentiationADGradient , :AutoFastDifferentiation ), |
| 11 | + (:FiniteDiffADGradient , :AutoFiniteDiff ), |
| 12 | + (:FiniteDifferencesADGradient , :AutoFiniteDifferences ), |
| 13 | + (:PolyesterForwardDiffADGradient, :AutoPolyesterForwardDiff)) |
| 14 | + @eval begin |
| 15 | + |
| 16 | + struct $ADGradient{B, E} <: ADBackend |
| 17 | + backend::B |
| 18 | + prep::E |
| 19 | + end |
| 20 | + |
| 21 | + function $ADGradient( |
| 22 | + nvar::Integer, |
| 23 | + f, |
| 24 | + ncon::Integer = 0, |
| 25 | + c::Function = (args...) -> []; |
| 26 | + x0::AbstractVector = rand(nvar), |
| 27 | + kwargs..., |
| 28 | + ) |
| 29 | + backend = $fbackend() |
| 30 | + prep = DifferentiationInterface.prepare_gradient(f, backend, x0) |
| 31 | + return $ADGradient(backend, prep) |
| 32 | + end |
| 33 | + |
| 34 | + function gradient(b::$ADGradient, f, x) |
| 35 | + g = DifferentiationInterface.gradient(f, b.prep, b.backend, x) |
| 36 | + return g |
| 37 | + end |
| 38 | + |
| 39 | + function gradient!(b::$ADGradient, g, f, x) |
| 40 | + DifferentiationInterface.gradient!(f, g, b.prep, b.backend, x) |
| 41 | + return g |
| 42 | + end |
| 43 | + |
| 44 | + end |
| 45 | +end |
| 46 | + |
| 47 | +for (ADJprod, fbackend) in ((:EnzymeADJprod , :AutoEnzyme ), |
| 48 | + (:ZygoteADJprod , :AutoZygote ), |
| 49 | + # (:ForwardDiffADJprod , :AutoForwardDiff ), |
| 50 | + # (:ReverseDiffADJprod , :AutoReverseDiff ), |
| 51 | + (:MooncakeADJprod , :AutoMooncake ), |
| 52 | + (:DiffractorADJprod , :AutoDiffractor ), |
| 53 | + (:TrackerADJprod , :AutoTracker ), |
| 54 | + (:SymbolicsADJprod , :AutoSymbolics ), |
| 55 | + (:ChainRulesADJprod , :AutoChainRules ), |
| 56 | + (:FastDifferentiationADJprod , :AutoFastDifferentiation ), |
| 57 | + (:FiniteDiffADJprod , :AutoFiniteDiff ), |
| 58 | + (:FiniteDifferencesADJprod , :AutoFiniteDifferences ), |
| 59 | + (:PolyesterForwardDiffADJprod, :AutoPolyesterForwardDiff)) |
| 60 | + @eval begin |
| 61 | + |
| 62 | + struct $ADJprod{B, E} <: ADBackend |
| 63 | + backend::B |
| 64 | + prep::E |
| 65 | + end |
| 66 | + |
| 67 | + function $ADJprod( |
| 68 | + nvar::Integer, |
| 69 | + f, |
| 70 | + ncon::Integer = 0, |
| 71 | + c::Function = (args...) -> []; |
| 72 | + x0::AbstractVector = rand(nvar), |
| 73 | + kwargs..., |
| 74 | + ) |
| 75 | + backend = $fbackend() |
| 76 | + dx = similar(x0, nvar) |
| 77 | + prep = DifferentiationInterface.prepare_pushforward(f, backend, x0, dx) |
| 78 | + return $ADJprod(backend, prep) |
| 79 | + end |
| 80 | + |
| 81 | + function Jprod!(b::$ADJprod, Jv, f, x, v, ::Val) |
| 82 | + DifferentiationInterface.pushforward!(f, Jv, b.prep, b.backend, x, v) |
| 83 | + return Jv |
| 84 | + end |
| 85 | + |
| 86 | + end |
| 87 | +end |
| 88 | + |
| 89 | +for (ADJtprod, fbackend) in ((:EnzymeADJtprod , :AutoEnzyme ), |
| 90 | + (:ZygoteADJtprod , :AutoZygote ), |
| 91 | + # (:ForwardDiffADJtprod , :AutoForwardDiff ), |
| 92 | + # (:ReverseDiffADJtprod , :AutoReverseDiff ), |
| 93 | + (:MooncakeADJtprod , :AutoMooncake ), |
| 94 | + (:DiffractorADJtprod , :AutoDiffractor ), |
| 95 | + (:TrackerADJtprod , :AutoTracker ), |
| 96 | + (:SymbolicsADJtprod , :AutoSymbolics ), |
| 97 | + (:ChainRulesADJtprod , :AutoChainRules ), |
| 98 | + (:FastDifferentiationADJtprod , :AutoFastDifferentiation ), |
| 99 | + (:FiniteDiffADJtprod , :AutoFiniteDiff ), |
| 100 | + (:FiniteDifferencesADJtprod , :AutoFiniteDifferences ), |
| 101 | + (:PolyesterForwardDiffADJtprod, :AutoPolyesterForwardDiff)) |
| 102 | + @eval begin |
| 103 | + |
| 104 | + struct $ADJtprod{B, E} <: ADBackend |
| 105 | + backend::B |
| 106 | + prep::E |
| 107 | + end |
| 108 | + |
| 109 | + function $ADJtprod( |
| 110 | + nvar::Integer, |
| 111 | + f, |
| 112 | + ncon::Integer = 0, |
| 113 | + c::Function = (args...) -> []; |
| 114 | + x0::AbstractVector = rand(nvar), |
| 115 | + kwargs..., |
| 116 | + ) |
| 117 | + backend = $fbackend() |
| 118 | + dy = similar(x0, ncon) |
| 119 | + prep = DifferentiationInterface.prepare_pullback(f, backend, x0, dy) |
| 120 | + return $ADJtprod(backend, prep) |
| 121 | + end |
| 122 | + |
| 123 | + function Jtprod!(b::$ADJtprod, Jtv, f, x, v, ::Val) |
| 124 | + DifferentiationInterface.pullback!(f, Jtv, b.prep, b.backend, x, v) |
| 125 | + return Jtv |
| 126 | + end |
| 127 | + |
| 128 | + end |
| 129 | +end |
| 130 | + |
| 131 | +for (ADJacobian, fbackend) in ((:EnzymeADJacobian , :AutoEnzyme ), |
| 132 | + (:ZygoteADJacobian , :AutoZygote ), |
| 133 | + # (:ForwardDiffADJacobian , :AutoForwardDiff ), |
| 134 | + # (:ReverseDiffADJacobian , :AutoReverseDiff ), |
| 135 | + (:MooncakeADJacobian , :AutoMooncake ), |
| 136 | + (:DiffractorADJacobian , :AutoDiffractor ), |
| 137 | + (:TrackerADJacobian , :AutoTracker ), |
| 138 | + (:SymbolicsADJacobian , :AutoSymbolics ), |
| 139 | + (:ChainRulesADJacobian , :AutoChainRules ), |
| 140 | + (:FastDifferentiationADJacobian , :AutoFastDifferentiation ), |
| 141 | + (:FiniteDiffADJacobian , :AutoFiniteDiff ), |
| 142 | + (:FiniteDifferencesADJacobian , :AutoFiniteDifferences ), |
| 143 | + (:PolyesterForwardDiffADJacobian, :AutoPolyesterForwardDiff)) |
| 144 | + @eval begin |
| 145 | + |
| 146 | + struct $ADJacobian{B, E} <: ADBackend |
| 147 | + backend::B |
| 148 | + prep::E |
| 149 | + end |
| 150 | + |
| 151 | + function $ADJacobian( |
| 152 | + nvar::Integer, |
| 153 | + f, |
| 154 | + ncon::Integer = 0, |
| 155 | + c::Function = (args...) -> []; |
| 156 | + x0::AbstractVector = rand(nvar), |
| 157 | + kwargs..., |
| 158 | + ) |
| 159 | + backend = $fbackend() |
| 160 | + y = similar(x0, ncon) |
| 161 | + prep = DifferentiationInterface.prepare_jacobian(f, y, backend, x0) |
| 162 | + return $ADJacobian(backend, prep) |
| 163 | + end |
| 164 | + |
| 165 | + function jacobian(b::$ADJacobian, f, x) |
| 166 | + J = DifferentiationInterface.jacobian(f, b.prep, b.backend, x) |
| 167 | + return J |
| 168 | + end |
| 169 | + |
| 170 | + end |
| 171 | +end |
| 172 | + |
| 173 | +for (ADHvprod, fbackend) in ((:EnzymeADHvprod , :AutoEnzyme ), |
| 174 | + (:ZygoteADHvprod , :AutoZygote ), |
| 175 | + # (:ForwardDiffADHvprod , :AutoForwardDiff ), |
| 176 | + # (:ReverseDiffADHvprod , :AutoReverseDiff ), |
| 177 | + (:MooncakeADHvprod , :AutoMooncake ), |
| 178 | + (:DiffractorADHvprod , :AutoDiffractor ), |
| 179 | + (:TrackerADHvprod , :AutoTracker ), |
| 180 | + (:SymbolicsADHvprod , :AutoSymbolics ), |
| 181 | + (:ChainRulesADHvprod , :AutoChainRules ), |
| 182 | + (:FastDifferentiationADHvprod , :AutoFastDifferentiation ), |
| 183 | + (:FiniteDiffADHvprod , :AutoFiniteDiff ), |
| 184 | + (:FiniteDifferencesADHvprod , :AutoFiniteDifferences ), |
| 185 | + (:PolyesterForwardDiffADHvprod, :AutoPolyesterForwardDiff)) |
| 186 | + @eval begin |
| 187 | + |
| 188 | + struct $ADHvprod{B, E} <: ADBackend |
| 189 | + backend::B |
| 190 | + prep::E |
| 191 | + end |
| 192 | + |
| 193 | + function $ADHvprod( |
| 194 | + nvar::Integer, |
| 195 | + f, |
| 196 | + ncon::Integer = 0, |
| 197 | + c::Function = (args...) -> []; |
| 198 | + x0::AbstractVector = rand(nvar), |
| 199 | + kwargs..., |
| 200 | + ) |
| 201 | + backend = $fbackend() |
| 202 | + tx = similar(x0) |
| 203 | + prep = DifferentiationInterface.prepare_hvp(f, backend, x0, tx) |
| 204 | + return $ADHvprod(backend, prep) |
| 205 | + end |
| 206 | + |
| 207 | + function Hvprod!(b::$ADHvprod, Hv, f, x, v, ::Val) |
| 208 | + DifferentiationInterface.hvp!(f, Hv, b.prep, b.backend, x, v) |
| 209 | + return Hv |
| 210 | + end |
| 211 | + |
| 212 | + end |
| 213 | +end |
| 214 | + |
| 215 | +for (ADHessian, fbackend) in ((:EnzymeADHessian , :AutoEnzyme ), |
| 216 | + (:ZygoteADHessian , :AutoZygote ), |
| 217 | + # (:ForwardDiffADHessian , :AutoForwardDiff ), |
| 218 | + # (:ReverseDiffADHessian , :AutoReverseDiff ), |
| 219 | + (:MooncakeADHessian , :AutoMooncake ), |
| 220 | + (:DiffractorADHessian , :AutoDiffractor ), |
| 221 | + (:TrackerADHessian , :AutoTracker ), |
| 222 | + (:SymbolicsADHessian , :AutoSymbolics ), |
| 223 | + (:ChainRulesADHessian , :AutoChainRules ), |
| 224 | + (:FastDifferentiationADHessian , :AutoFastDifferentiation ), |
| 225 | + (:FiniteDiffADHessian , :AutoFiniteDiff ), |
| 226 | + (:FiniteDifferencesADHessian , :AutoFiniteDifferences ), |
| 227 | + (:PolyesterForwardDiffADHessian, :AutoPolyesterForwardDiff)) |
| 228 | + @eval begin |
| 229 | + |
| 230 | + struct $ADHessian{B, E} <: ADBackend |
| 231 | + backend::B |
| 232 | + prep::E |
| 233 | + end |
| 234 | + |
| 235 | + function $ADHessian( |
| 236 | + nvar::Integer, |
| 237 | + f, |
| 238 | + ncon::Integer = 0, |
| 239 | + c::Function = (args...) -> []; |
| 240 | + x0::AbstractVector = rand(nvar), |
| 241 | + kwargs..., |
| 242 | + ) |
| 243 | + backend = $fbackend() |
| 244 | + prep = DifferentiationInterface.prepare_hessian(f, backend, x0) |
| 245 | + return $ADHessian(backend, prep) |
| 246 | + end |
| 247 | + |
| 248 | + function hessian(b::$ADHessian, f, x) |
| 249 | + H = DifferentiationInterface.hessian(f, b.prep, b.backend, x) |
| 250 | + return H |
| 251 | + end |
| 252 | + |
| 253 | + end |
| 254 | +end |
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