Assertion `!ijl_types_equal(mi->specTypes, type)` fails when testing FourierFlows

[KristofferC]

When testing FourierFlows the following assertion is hit:

julia: /source/src/gf.c:221: jl_specializations_get_linfo_: Assertion `!ijl_types_equal(mi->specTypes, type)' failed.

[57] signal (6.-6): Aborted
in expression starting at /home/pkgeval/.julia/packages/FourierFlows/WMSMN/test/runtests.jl:41
gsignal at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
abort at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
unknown function (ip: 0x7f2e3ac1440e)
__assert_fail at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
jl_specializations_get_linfo_ at /source/src/gf.c:221
#specialize_method#289 at ./compiler/utilities.jl:226 [inlined]
specialize_method at ./compiler/utilities.jl:214 [inlined]
typeinf_edge at ./compiler/typeinfer.jl:871
abstract_call_method at ./compiler/abstractinterpretation.jl:629
abstract_call_gf_by_type at ./compiler/abstractinterpretation.jl:95
abstract_call_known at ./compiler/abstractinterpretation.jl:2052
abstract_call at ./compiler/abstractinterpretation.jl:2134
abstract_call at ./compiler/abstractinterpretation.jl:2127
...

PkgEval log: https://s3.amazonaws.com/julialang-reports/nanosoldier/pkgeval/by_hash/c9a32f4_vs_e4ee485/FourierFlows.primary.log

[inkydragon]

A short test code. It could be further simplified.

• test with Version 1.10.0-alpha1

using FourierFlows
using FourierFlows.FFTW


# include("test/test_grid.jl")
function test_plan_flows_fftrfft(::CPU; T=Float64)
    A = device_array(CPU())
    return typeof(FourierFlows.plan_flows_fft(A(rand(Complex{T}, (4,))))) <: FFTW.cFFTWPlan{Complex{T},-1,false,1} &&
            FourierFlows.plan_flows_rfft(A(rand(T, (4, 6, 8))), [1, 2]).region == [1, 2]
end

"This func contains many errors!"
function bad_function(g::ThreeDGrid)
    rand() > 0 || error("[bad_function] in func")
    ret1 = complex_func(g)
    ret2 = complex_func2(ret1)
    ret3 = complex_func3(ret2)
    return __undef_var__
end

# include("test/test_ifft.jl")
function test_irfft_cosmxcosny(g::ThreeDGrid)
    @show "[test_irfft_cosmxcosny] in func"
    bad = bad_function(g)
    irfft(bad, g.nx)
end

dev = CPU();
nx, ny, nz = 2, 2, 2;
Lx, Ly, Lz = 0, 0, 0;
g3 = ThreeDGrid(dev; nx, Lx, ny, Ly, nz, Lz);

@show "test_plan_flows_fftrfft"
test_plan_flows_fftrfft(dev)  # nedd this call

@show "--> test_irfft_cosmxcosny"
test_irfft_cosmxcosny(g3)
# if you run `test_irfft_cosmxcosny` before `test_plan_flows_fftrfft`
#   You will got: "ERROR: UndefVarError"

[maleadt]

I'm looking into reducing/bisecting this.

[maleadt]

Partially reduced to:

using CUDA, FFTW

plan_flows_rfft(argskwargs) = plan_rfft
plan_flows_rfft(a::CuArray, args... ) = plan_rfft(a, args... )
function ThreeDGrid(dev; nx, Lx, ny, Ly, nz)
    dev(dev)
end
device_array(CPU) = Array
function test_plan_flows_fftrfft( T)
  A = device_array(0)
  typeof(plan_flows_rfft(A(rand(T, 0)))) <: FFTW.rFFTWPlan &&
    plan_flows_rfft(A0, [])
end
dev = function (:)
  irfft(deepcopy0, gnx)
end
test_plan_flows_fftrfft(dev)
nx, ny, nz = 2, 0, 6
Lx, Ly= π, 4.0
ThreeDGrid(dev; nx, Lx, ny, Ly, nz )

Bisected to #49189, which doesn't make much sense...

[maleadt]

Further reduced:

struct HandleCache
    active_handles::Set{Pair{Tuple{Any,Int,Dims},Cint}}
    function HandleCache()
        new()
    end
end
function pop!(cache, key)
    push!(cache.active_handles, key => handle)
end
struct CuArray
    dims::Dims
end
size(x::CuArray) = x.dims
const idle_handles = HandleCache()
function cufftGetPlan(args...)
    pop!(idle_handles, (ctx, args...))
end
function plan_brfft()
  cufftGetPlan(1, (size(X)...,))
end
plan_brfft()

This also bisects to a better culprit: #49017 (cc @vtjnash).

[vtjnash]

Further reduces to this set of facts, where we end up confused about there an NTuple is supposed to end up in the hash table:

julia> A = Val{Tuple{Vararg{Int}}}
Val{Tuple{Vararg{Int64}}}

julia> B = Val{Tuple{Vararg{Int64,N}} where N}
Val{Tuple{Vararg{Int64, N}} where N}

julia> A == B
false

julia> A <: B && B <: A
true

julia> A = Val{Tuple{Vararg{Int}}}
Val{Tuple{Vararg{Int64}}}

julia> B = Val{Tuple{Vararg{Int64,N}} where N}
Val{Tuple{Vararg{Int64, N}} where N}

julia> A.hash
-56068198

julia> B.hash
-545448230

julia> Tuple{Vararg{Int64}}.hash
1495893083

julia> (Tuple{Vararg{Int64,N}} where N).body.hash
0