julia> Flux._match_eltype("justforprinting", Float32, [1, 2f0])  # does nothing
2-element Vector{Float32}:
 1.0
 2.0
 
julia> Flux._match_eltype("justforprinting", Float32, [1, 2])  # convert integers, silently
2-element Vector{Float32}:
 1.0
 2.0

julia> Flux._match_eltype("justforprinting", Float32, [1, 2.0])  # convert Float64, print warning once
┌ Warning: Layer with Float32 parameters got Float64 input.
│   The input will be converted, but any earlier layers may be very slow.
│   layer = "justforprinting"
│   summary(x) = "2-element Vector{Float64}"
└ @ Flux ~/.julia/packages/Flux/5vIRy/src/layers/stateless.jl:60
2-element Vector{Float32}:
 1.0
 2.0

julia> Enzyme.gradient(Reverse, sum∘Flux._match_eltype, "str", Float32, [1, 2f0])
(nothing, nothing, Float32[1.0, 1.0])

julia> Enzyme.gradient(Reverse, sum∘Flux._match_eltype, "str", Float32, [1, 2.0])
(nothing, nothing, [1.0, 1.0])

julia> Enzyme.gradient(Reverse, sum∘Flux._match_eltype, "str", Float32, [1, 2f0])
(nothing, nothing, Float32[1.0, 1.0])

julia> Enzyme.gradient(Reverse, sum∘Flux._match_eltype, "str", Float32, [1, 2.0])
┌ Warning: Layer with Float32 parameters got Float64 input.
│   The input will be converted, but any earlier layers may be very slow.
│   layer = "str"
│   summary(x) = "2-element Vector{Float64}"
└ @ Flux ~/.julia/packages/Flux/HBF2N/src/layers/stateless.jl:60
(nothing, nothing, [1.0, 1.0])

(jl_vUrrev) pkg> st Enzyme
Status `/private/var/folders/yq/4p2zwd614y59gszh7y9ypyhh0000gn/T/jl_vUrrev/Project.toml`
  [7da242da] Enzyme v0.13.21

julia> VERSION
v"1.10.4"

julia> versioninfo()
Julia Version 1.11.2
Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 12 × Intel(R) Xeon(R) CPU E5-2603 v4 @ 1.70GHz
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, broadwell)
Threads: 4 default, 0 interactive, 2 GC (on 12 virtual cores)
Environment:
  JULIA_NUM_THREADS = 4

(jl_7brNeg) pkg> st
Status `/tmp/jl_7brNeg/Project.toml`
  [7da242da] Enzyme v0.13.22
  [587475ba] Flux v0.16.0
  
julia> reproducer()
ERROR: Constant memory is stored (or returned) to a differentiable variable.
As a result, Enzyme cannot provably ensure correctness and throws this error.
This might be due to the use of a constant variable as temporary storage for active memory (https://enzyme.mit.edu/julia/stable/faq/#Runtime-Activity).
If Enzyme should be able to prove this use non-differentable, open an issue!
To work around this issue, either:
 a) rewrite this variable to not be conditionally active (fastest, but requires a code change), or
 b) set the Enzyme mode to turn on runtime activity (e.g. autodiff(set_runtime_activity(Reverse), ...) ). This will maintain correctness, but may slightly reduce performance.
Mismatched activity for:   store {} addrspace(10)* %61, {} addrspace(10)* addrspace(10)* %.repack32, align 8, !dbg !262, !tbaa !264, !alias.scope !202, !noalias !266 const val:   %61 = call fastcc nonnull {} addrspace(10)* @julia_summary_15589({} addrspace(10)* nocapture noundef nonnull readonly align 8 dereferenceable(32) %1) #213, !dbg !252
Type tree: {[-1]:Pointer}
 llvalue=  %61 = call fastcc nonnull {} addrspace(10)* @julia_summary_15589({} addrspace(10)* nocapture noundef nonnull readonly align 8 dereferenceable(32) %1) #213, !dbg !252

Stacktrace:
 [1] #invokelatest#2
   @ ./essentials.jl:1057
 [2] invokelatest
   @ ./essentials.jl:1052
 [3] macro expansion
   @ ./logging/logging.jl:388
 [4] _match_eltype
   @ ~/.julia/packages/Flux/Mhg1r/src/layers/stateless.jl:60

Stacktrace:
  [1] #invokelatest#2
    @ ./essentials.jl:1057 [inlined]
  [2] invokelatest
    @ ./essentials.jl:1052 [inlined]
  [3] macro expansion
    @ ./logging/logging.jl:388 [inlined]
  [4] _match_eltype
    @ ~/.julia/packages/Flux/Mhg1r/src/layers/stateless.jl:60
  [5] _match_eltype
    @ ~/.julia/packages/Flux/Mhg1r/src/layers/stateless.jl:85 [inlined]
  [6] Dense
    @ ~/.julia/packages/Flux/Mhg1r/src/layers/basic.jl:198
  [7] macro expansion
    @ ~/.julia/packages/Flux/Mhg1r/src/layers/basic.jl:68 [inlined]
  [8] _applychain
    @ ~/.julia/packages/Flux/Mhg1r/src/layers/basic.jl:68
  [9] Chain
    @ ~/.julia/packages/Flux/Mhg1r/src/layers/basic.jl:65 [inlined]
 [10] #2
    @ ./REPL[5]:8 [inlined]
 [11] diffejulia__2_15066_inner_184wrap
    @ ./REPL[5]:0
 [12] macro expansion
    @ ~/.julia/packages/Enzyme/haqjK/src/compiler.jl:5204 [inlined]
 [13] enzyme_call
    @ ~/.julia/packages/Enzyme/haqjK/src/compiler.jl:4750 [inlined]
 [14] CombinedAdjointThunk
    @ ~/.julia/packages/Enzyme/haqjK/src/compiler.jl:4622 [inlined]
 [15] autodiff(::EnzymeCore.ReverseMode{…}, ::EnzymeCore.Const{…}, ::Type{…}, ::EnzymeCore.Duplicated{…}, ::EnzymeCore.Const{…}, ::EnzymeCore.Const{…})
    @ Enzyme ~/.julia/packages/Enzyme/haqjK/src/Enzyme.jl:503
 [16] _enzyme_gradient(::Function, ::EnzymeCore.Duplicated{Flux.Chain{…}}, ::Vararg{Union{…}}; zero::Bool)
    @ FluxEnzymeExt ~/.julia/packages/Flux/Mhg1r/ext/FluxEnzymeExt/FluxEnzymeExt.jl:49
 [17] gradient(::Function, ::EnzymeCore.Duplicated{Flux.Chain{…}}, ::Vararg{Union{…}}; zero::Bool)
    @ Flux ~/.julia/packages/Flux/Mhg1r/src/gradient.jl:122
 [18] reproducer()
    @ Main ./REPL[5]:7
 [19] top-level scope
    @ REPL[6]:1
Some type information was truncated. Use `show(err)` to see complete types.