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MethodError on length(::DiffResults.ImmutableDiffResult{1, Float64, Tuple{Float64}}) #265
Description
Hello, here to report another possible bug found upstream in Turing. In the following example, f differentiates just fine but g doesn't.
import ReverseDiff
f(x) = exp.(x[])
f([1.0])
ReverseDiff.gradient(f, [1.0])
g(x) = exp.(reshape(vec(x), ()))
g([1.0])
ReverseDiff.gradient(g, [1.0])Edit: h(x) = exp.(fill(x[], ())) works fine too, but j(x) = exp.(reshape(x, ())) doesn't.
Stack trace
julia> ReverseDiff.gradient(g, [1.0])
ERROR: MethodError: no method matching length(::DiffResults.ImmutableDiffResult{1, Float64, Tuple{Float64}})
The function `length` exists, but no method is defined for this combination of argument types.
Closest candidates are:
length(::Cmd)
@ Base process.jl:716
length(::Base.MethodSpecializations)
@ Base reflection.jl:1317
length(::Core.SimpleVector)
@ Base essentials.jl:933
...
Stacktrace:
[1] _similar_shape(itr::DiffResults.ImmutableDiffResult{1, Float64, Tuple{Float64}}, ::Base.HasLength)
@ Base ./array.jl:652
[2] _collect(cont::UnitRange{…}, itr::DiffResults.ImmutableDiffResult{…}, ::Base.HasEltype, isz::Base.HasLength)
@ Base ./array.jl:711
[3] collect(itr::DiffResults.ImmutableDiffResult{1, Float64, Tuple{Float64}})
@ Base ./array.jl:705
[4] broadcastable(x::DiffResults.ImmutableDiffResult{1, Float64, Tuple{Float64}})
@ Base.Broadcast ./broadcast.jl:707
[5] broadcasted
@ ./broadcast.jl:1318 [inlined]
[6] broadcast(f::ReverseDiff.ForwardOptimize{…}, x::ReverseDiff.TrackedArray{…})
@ ReverseDiff ~/.julia/packages/ReverseDiff/p1MzG/src/derivatives/elementwise.jl:237
[7] broadcast
@ ~/.julia/packages/ReverseDiff/p1MzG/src/derivatives/elementwise.jl:198 [inlined]
[8] _materialize
@ ~/.julia/packages/ReverseDiff/p1MzG/src/derivatives/broadcast.jl:265 [inlined]
[9] materialize
@ ~/.julia/packages/ReverseDiff/p1MzG/src/derivatives/broadcast.jl:273 [inlined]
[10] g(x::ReverseDiff.TrackedArray{Float64, Float64, 1, Vector{Float64}, Vector{Float64}})
@ Main ./REPL[6]:1
[11] ReverseDiff.GradientTape(f::typeof(g), input::Vector{…}, cfg::ReverseDiff.GradientConfig{…})
@ ReverseDiff ~/.julia/packages/ReverseDiff/p1MzG/src/api/tape.jl:199
[12] gradient(f::Function, input::Vector{Float64}, cfg::ReverseDiff.GradientConfig{ReverseDiff.TrackedArray{…}})
@ ReverseDiff ~/.julia/packages/ReverseDiff/p1MzG/src/api/gradients.jl:22
[13] top-level scope
@ REPL[8]:1
Some type information was truncated. Use `show(err)` to see complete types.
Version info
(ppl) pkg> st
Status `~/ppl/Project.toml`
[37e2e3b7] ReverseDiff v1.15.3
julia> versioninfo()
Julia Version 1.11.1
Commit 8f5b7ca12ad (2024-10-16 10:53 UTC)
Build Info:
Official https://julialang.org/ release
Platform Info:
OS: macOS (arm64-apple-darwin22.4.0)
CPU: 10 × Apple M1 Pro
WORD_SIZE: 64
LLVM: libLLVM-16.0.6 (ORCJIT, apple-m1)
Threads: 1 default, 0 interactive, 1 GC (on 8 virtual cores)
Activity
Put broadcasting on Julia's documented custom-broadcast interface