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samuela avatar samuela commented on August 16, 2024

I personally found this incredibly confusing:

julia> ReverseDiff.gradient(sin, 1.5)
ERROR: MethodError: no method matching ReverseDiff.GradientConfig(::Float64)
Closest candidates are:
  ReverseDiff.GradientConfig(::AbstractArray{T,N} where N) where T at /home/samuela/.julia/packages/ReverseDiff/8hp1k/src/api/Config.jl:35
  ReverseDiff.GradientConfig(::AbstractArray{T,N} where N, ::Array{ReverseDiff.AbstractInstruction,1}) where T at /home/samuela/.julia/packages/ReverseDiff/8hp1k/src/api/Config.jl:35
  ReverseDiff.GradientConfig(::Tuple) at /home/samuela/.julia/packages/ReverseDiff/8hp1k/src/api/Config.jl:37
  ...
Stacktrace:
 [1] gradient(::Function, ::Float64) at /home/samuela/.julia/packages/ReverseDiff/8hp1k/src/api/gradients.jl:22
 [2] top-level scope at REPL[13]:1

julia>

And I'm still not sure how to make it actually work:

julia> ReverseDiff.gradient(sin, [1.5])
ERROR: MethodError: no method matching sin(::ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}})
Closest candidates are:
  sin(::Complex{Float16}) at math.jl:1145
  sin(::CUDA.CuKernelContext, ::Any) at /home/samuela/.julia/packages/CUDA/dZvbp/src/gpuarrays.jl:48
  sin(::BigFloat) at mpfr.jl:727
  ...
Stacktrace:
 [1] ReverseDiff.GradientTape(::typeof(sin), ::Array{Float64,1}, ::ReverseDiff.GradientConfig{ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}}}) at /home/samuela/.julia/packages/ReverseDiff/8hp1k/src/api/tape.jl:199
 [2] gradient(::Function, ::Array{Float64,1}, ::ReverseDiff.GradientConfig{ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}}}) at /home/samuela/.julia/packages/ReverseDiff/8hp1k/src/api/gradients.jl:22 (repeats 2 times)
 [3] top-level scope at REPL[14]:1

julia> ReverseDiff.gradient(sin, ([1.5],))
ERROR: MethodError: no method matching sin(::ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}})
Closest candidates are:
  sin(::Complex{Float16}) at math.jl:1145
  sin(::CUDA.CuKernelContext, ::Any) at /home/samuela/.julia/packages/CUDA/dZvbp/src/gpuarrays.jl:48
  sin(::BigFloat) at mpfr.jl:727
  ...
Stacktrace:
 [1] ReverseDiff.GradientTape(::Function, ::Tuple{Array{Float64,1}}, ::ReverseDiff.GradientConfig{Tuple{ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}}}}) at /home/samuela/.julia/packages/ReverseDiff/8hp1k/src/api/tape.jl:207
 [2] gradient(::Function, ::Tuple{Array{Float64,1}}, ::ReverseDiff.GradientConfig{Tuple{ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}}}}) at /home/samuela/.julia/packages/ReverseDiff/8hp1k/src/api/gradients.jl:22 (repeats 2 times)
 [3] top-level scope at REPL[15]:1

Can ReverseDiff.jl take the gradient of a R -> R function?

from reversediff.jl.

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