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Neuralpde #65

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merged 2 commits into from
Nov 8, 2023
Merged

Neuralpde #65

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0ff0853
derivative for matrix
zhujch1 Nov 7, 2023
15b98c3
remove dependency SlicedMap
zhujch1 Nov 8, 2023
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2 changes: 0 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@ ChainRules = "082447d4-558c-5d27-93f4-14fc19e9eca2"
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
ChainRulesOverloadGeneration = "f51149dc-2911-5acf-81fc-2076a2a81d4f"
IrrationalConstants = "92d709cd-6900-40b7-9082-c6be49f344b6"
SliceMap = "82cb661a-3f19-5665-9e27-df437c7e54c8"
SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
Expand All @@ -19,7 +18,6 @@ ChainRules = "1"
ChainRulesCore = "1"
ChainRulesOverloadGeneration = "0.1"
IrrationalConstants = "0.2"
SliceMap = "0.2"
SpecialFunctions = "2"
SymbolicUtils = "1"
Zygote = "0.6.55"
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11 changes: 11 additions & 0 deletions src/chainrules.jl
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Expand Up @@ -41,6 +41,17 @@ function rrule(::typeof(*), A::AbstractMatrix{S},
return A * t, gemv_pullback
end

function rrule(::typeof(*), A::AbstractMatrix{S},
B::AbstractMatrix{TaylorScalar{T, N}}) where {N, S, T}
project_A = ProjectTo(A)
project_B = ProjectTo(B)
function gemm_pullback(x̄)
X̄ = unthunk(x̄)
NoTangent(), @thunk(project_A(X̄ * transpose(B))), @thunk(project_B(transpose(A) * X̄))
end
return A * B, gemm_pullback
end

@adjoint function +(t::Vector{TaylorScalar{T, N}}, v::Vector{T}) where {N, T}
project_v = ProjectTo(v)
t + v, x̄ -> (x̄, project_v(x̄))
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7 changes: 4 additions & 3 deletions src/derivative.jl
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@@ -1,5 +1,4 @@

using SliceMap
export derivative

"""
Expand Down Expand Up @@ -56,10 +55,12 @@ end

@inline function derivative(f, x::AbstractMatrix{T}, vN::Val{N}) where {T <: TN, N}
size(x)[1] != 1 && @warn "x is not a row vector."
mapcols(u -> derivative(f, u[1], vN), x)
t = make_taylor.(x, one(N), vN)
return extract_derivative.(f(t), N)
end

@inline function derivative(f, x::AbstractMatrix{T}, l::AbstractVector{S},
vN::Val{N}) where {T <: TN, S <: TN, N}
mapcols(u -> derivative(f, u, l, vN), x)
t = make_taylor.(x, l, vN)
return extract_derivative.(f(t), N)
end
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