MooncakeInterpreter Inference Results Discrepancy w.r.t. NativeInterpreter

[willtebbutt]

using Cthulhu, Tapir

# Specify function + args.
fargs = (Base._mapreduce_dim, Base.Fix1(view, [5.0, 4.0]), vcat, Float64[], [1:1, 2:2], :)
tt = typeof(fargs)

# Construct the relevant interpreters.
native_interp = Core.Compiler.NativeInterpreter();
cthulhu_interp = Cthulhu.CthulhuInterpreter();
tapir_interp = Tapir.TapirInterpreter();

# Both of these correctly infer the return type, Vector{Float64}.
Base.code_typed_by_type(tt; optimize=true, interp=native_interp)
Base.code_ircode_by_type(tt; optimize_until=nothing, interp=native_interp)

# Inference fails.
Base.code_typed_by_type(tt; optimize=true, interp=cthulhu_interp)
Base.code_ircode_by_type(tt; optimize_until=nothing, interp=cthulhu_interp)

# Inference fails.
Base.code_typed_by_type(tt; optimize=true, interp=tapir_interp)
Base.code_ircode_by_type(tt; optimize_until=nothing, interp=tapir_interp)

The same thing happens for Enzyme. For example, the code_ircode_by_type result for NativeInterpreter is

julia> Base.code_ircode_by_type(tt; optimize_until=nothing, interp=native_interp)
1-element Vector{Any}:
362 1 ─ %1 = %new(Base.MappingRF{Base.Fix1{typeof(view), Vector{Float64}}, Base.BottomRF{typeof(vcat)}}, _2, $(QuoteNode(Base.BottomRF{typeof(vcat)}(vcat))))::Base.MappingRF{Base.Fix1{typeof(view), Vector{Float64}}, Base.BottomRF{typeof(vcat)}}
    │   %2 = invoke Base._foldl_impl(%1::Base.MappingRF{Base.Fix1{typeof(view), Vector{Float64}}, Base.BottomRF{typeof(vcat)}}, _4::Vector{Float64}, _5::Vector{UnitRange{Int64}})::Vector{Float64}
    └──      return %2                                │        
     => Vector{Float64}

while the result for TapirInterpreter is

julia> Base.code_ircode_by_type(tt; optimize_until=nothing, interp=tapir_interp)
1-element Vector{Any}:
362 1 ─ %1 = %new(Base.MappingRF{Base.Fix1{typeof(view), Vector{Float64}}, Base.BottomRF{typeof(vcat)}}, _2, $(QuoteNode(Base.BottomRF{typeof(vcat)}(vcat))))::Base.MappingRF{Base.Fix1{typeof(view), Vector{Float64}}, Base.BottomRF{typeof(vcat)}}
    │   %2 = invoke Base.foldl_impl(%1::Base.MappingRF{Base.Fix1{typeof(view), Vector{Float64}}, Base.BottomRF{typeof(vcat)}}, _4::Vector{Float64}, _5::Vector{UnitRange{Int64}})::AbstractVector
    └──      return %2                                │        
     => AbstractVector

julia> versioninfo()
Julia Version 1.10.5
Commit 6f3fdf7b362 (2024-08-27 14:19 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: macOS (x86_64-apple-darwin22.4.0)
  CPU: 12 × Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, skylake)
Threads: 6 default, 0 interactive, 3 GC (on 12 virtual cores)
Environment:
  JULIA_NUM_THREADS = 6

julia> Pkg.status()
Status `/private/var/folders/z7/0fkyw8ms795b7znc_3vbvrsw0000gn/T/jl_9hQ5av/Project.toml`
  [f68482b8] Cthulhu v2.14.0
  [07d77754] Tapir v0.2.42

[yebai]

It might take a while before the Julia compiler team fixes this; can we work around it?