Package evaluation to test Turing on Julia 1.14.0-DEV.3071 (c76331c927*) started at 2026-08-31T01:34:02.031 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 15.55s ################################################################################ # Installation # Installing Turing... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [fce5fe82] + Turing v0.47.2 Updating `~/.julia/environments/v1.14/Manifest.toml` [47edcb42] + ADTypes v1.24.0 [621f4979] + AbstractFFTs v1.5.0 [80f14c24] + AbstractMCMC v5.16.0 [7a57a42e] + AbstractPPL v0.15.5 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.45 [79e6a3ab] + Adapt v4.7.0 [0bf59076] + AdvancedHMC v0.8.6 [5b7e9947] + AdvancedMH v0.8.10 [b5ca4192] + AdvancedVI v0.7.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [4fba245c] + ArrayInterface v7.30.0 [198e06fe] + BangBang v0.4.9 [76274a88] + Bijectors v0.16.2 [d360d2e6] + ChainRulesCore v1.26.1 [0ca39b1e] + Chairmarks v1.3.1 [9e997f8a] + ChangesOfVariables v0.1.11 [38540f10] + CommonSolve v0.2.14 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [88cd18e8] + ConsoleProgressMonitor v0.1.2 [187b0558] + ConstructionBase v1.6.0 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.6 [e2d170a0] + DataValueInterfaces v1.0.0 [8bb1440f] + DelimitedFiles v1.9.1 [b429d917] + DensityInterface v0.4.0 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.16.0 [a0c0ee7d] + DifferentiationInterface v0.7.21 [0703355e] + DimensionalData v0.30.2 [31c24e10] + Distributions v0.25.131 [ffbed154] + DocStringExtensions v0.9.5 [366bfd00] + DynamicPPL v0.42.7 [cad2338a] + EllipticalSliceSampling v2.0.0 [4e289a0a] + EnumX v1.0.7 [e2ba6199] + ExprTools v0.1.11 [411431e0] + Extents v0.1.6 [9aa1b823] + FastClosures v0.3.2 [1a297f60] + FillArrays v1.17.0 [64ca27bc] + FindFirstFunctions v3.2.1 [6a86dc24] + FiniteDiff v2.33.0 [4a37a8b9] + FlexiChains v0.6.37 [f6369f11] + ForwardDiff v1.4.5 [069b7b12] + FunctionWrappers v1.1.3 [77dc65aa] + FunctionWrappersWrappers v1.13.0 [d9f16b24] + Functors v0.5.3 [46192b85] + GPUArraysCore v0.2.0 ⌃ [a0844989] + Gamma v1.1.0 [34004b35] + HypergeometricFunctions v0.3.30 [22cec73e] + InitialValues v0.3.1 [85a1e053] + Interfaces v0.3.2 [8197267c] + IntervalSets v0.7.14 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.8.0 [682c06a0] + JSON v1.7.1 [1d6d02ad] + LeftChildRightSiblingTrees v0.3.0 [6f1fad26] + Libtask v0.9.18 [d3d80556] + LineSearches v7.7.1 [6fdf6af0] + LogDensityProblems v2.2.0 [996a588d] + LogDensityProblemsAD v1.13.1 ⌅ [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.2.0 [be115224] + MCMCDiagnosticTools v0.3.19 [e80e1ace] + MLJModelInterface v1.12.1 [1914dd2f] + MacroTools v0.5.16 [dbb5928d] + MappedArrays v0.4.3 [e1d29d7a] + Missings v1.2.0 [dbe65cb8] + MistyClosures v2.1.0 [d41bc354] + NLSolversBase v8.0.1 [77ba4419] + NaNMath v1.1.4 [429524aa] + Optim v2.2.2 [3bd65402] + Optimisers v0.4.9 [7f7a1694] + Optimization v5.9.0 [bca83a33] + OptimizationBase v5.5.2 [36348300] + OptimizationOptimJL v0.4.20 ⌅ [bac558e1] + OrderedCollections v1.8.2 [90014a1f] + PDMats v0.11.41 ⌅ [69de0a69] + Parsers v2.8.7 [569bd051] + PartitionedDistributions v0.1.0 [85a6dd25] + PositiveFactorizations v0.2.4 [d236fae5] + PreallocationTools v1.7.1 [aea7be01] + PrecompileTools v1.3.4 [21216c6a] + Preferences v1.5.2 [33c8b6b6] + ProgressLogging v0.1.6 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.4.0 [1fd47b50] + QuadGK v2.11.3 [74087812] + Random123 v1.7.1 [e6cf234a] + RandomNumbers v1.6.0 [3cdcf5f2] + RecipesBase v1.3.4 [731186ca] + RecursiveArrayTools v4.5.1 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.9.0 [f2b01f46] + Roots v3.0.7 [7e49a35a] + RuntimeGeneratedFunctions v0.5.25 [0bca4576] + SciMLBase v3.50.0 [a6db7da4] + SciMLLogging v2.1.0 [c0aeaf25] + SciMLOperators v1.30.0 [431bcebd] + SciMLPublic v1.3.0 [53ae85a6] + SciMLStructures v1.10.5 [30f210dd] + ScientificTypesBase v3.1.0 [efcf1570] + Setfield v1.1.2 [a2af1166] + SortingAlgorithms v1.2.3 [9f842d2f] + SparseConnectivityTracer v1.2.3 [0a514795] + SparseMatrixColorings v0.4.27 [276daf66] + SpecialFunctions v2.9.0 [1e83bf80] + StaticArraysCore v1.4.4 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.4 [82ae8749] + StatsAPI v1.8.0 [2913bbd2] + StatsBase v0.34.13 [4c63d2b9] + StatsFuns v2.2.1 [ec057cc2] + StructUtils v2.8.5 [2efcf032] + SymbolicIndexingInterface v0.3.55 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.14.0 [5d786b92] + TerminalLoggers v0.1.8 [fce5fe82] + Turing v0.47.2 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.2+0 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.12.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.13.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.14.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.13.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.13.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.5.7+0 [4536629a] + OpenBLAS_jll v0.3.34+0 [05823500] + OpenLibm_jll v0.8.7+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [8e850b90] + libblastrampoline_jll v5.15.0+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 5.66s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ TestEnv ~/.julia/packages/TestEnv/sTG3E/src/julia-1.13/activate_set.jl:78 Precompiling package dependencies... Precompiling project... ERROR: LoadError: UndefVarError: `WorldView` not defined in `Compiler` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty(x::Module, f::Symbol) @ Base Base_compiler.jl:51 [2] top-level scope @ ~/.julia/packages/Mooncake/LLB2t/src/interpreter/abstract_interpretation.jl:90 [3] include(mapexpr::Function, mod::Module, _path::String) @ Base Base.jl:335 [4] top-level scope @ ~/.julia/packages/Mooncake/LLB2t/src/Mooncake.jl:183 [5] include(mod::Module, _path::String) @ Base Base.jl:334 [6] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/src/interpreter/abstract_interpretation.jl:90 in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/src/Mooncake.jl:1 in expression starting at stdin:5 ✗ Mooncake 20.2 s ✓ BlackBoxOptim 28.8 s ✓ AdvancedVI → AdvancedVIReverseDiffExt 28.9 s ✓ DynamicPPL → DynamicPPLReverseDiffExt 18.4 s ✓ Turing ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/Mooncake/LLB2t/ext/MooncakeDistancesExt.jl:3 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/ext/MooncakeDistancesExt.jl:1 in expression starting at stdin:5 ✗ Mooncake → MooncakeDistancesExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/Mooncake/LLB2t/ext/MooncakeDistributionsExt.jl:3 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/ext/MooncakeDistributionsExt.jl:1 in expression starting at stdin:5 ✗ Mooncake → MooncakeDistributionsExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/Mooncake/LLB2t/ext/MooncakeLogExpFunctionsExt.jl:6 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/ext/MooncakeLogExpFunctionsExt.jl:1 in expression starting at stdin:5 ✗ Mooncake → MooncakeLogExpFunctionsExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/Mooncake/LLB2t/ext/MooncakeFunctionWrappersExt.jl:5 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/ext/MooncakeFunctionWrappersExt.jl:1 in expression starting at stdin:5 ✗ Mooncake → MooncakeFunctionWrappersExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/Mooncake/LLB2t/ext/MooncakeSpecialFunctionsExt.jl:3 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/ext/MooncakeSpecialFunctionsExt.jl:1 in expression starting at stdin:5 ✗ Mooncake → MooncakeSpecialFunctionsExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/FunctionWrappersWrappers/7CPBX/ext/FunctionWrappersWrappersMooncakeExt.jl:4 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/FunctionWrappersWrappers/7CPBX/ext/FunctionWrappersWrappersMooncakeExt.jl:1 in expression starting at stdin:5 ✗ FunctionWrappersWrappers → FunctionWrappersWrappersMooncakeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/DifferentiationInterface/seMaz/ext/DifferentiationInterfaceMooncakeExt/DifferentiationInterfaceMooncakeExt.jl:5 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/DifferentiationInterface/seMaz/ext/DifferentiationInterfaceMooncakeExt/DifferentiationInterfaceMooncakeExt.jl:1 in expression starting at stdin:5 ✗ DifferentiationInterface → DifferentiationInterfaceMooncakeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/RecursiveArrayTools/9DBPt/ext/RecursiveArrayToolsMooncakeExt.jl:4 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/RecursiveArrayTools/9DBPt/ext/RecursiveArrayToolsMooncakeExt.jl:1 in expression starting at stdin:5 ✗ RecursiveArrayTools → RecursiveArrayToolsMooncakeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/AbstractPPL/Kuf2H/ext/AbstractPPLMooncakeExt.jl:7 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/AbstractPPL/Kuf2H/ext/AbstractPPLMooncakeExt.jl:1 in expression starting at stdin:5 ✗ AbstractPPL → AbstractPPLMooncakeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/SciMLBase/in0OX/ext/SciMLBaseMooncakeExt.jl:3 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/SciMLBase/in0OX/ext/SciMLBaseMooncakeExt.jl:1 in expression starting at stdin:5 ✗ SciMLBase → SciMLBaseMooncakeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/OptimizationBase/Jfw5O/ext/OptimizationMooncakeExt.jl:3 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/OptimizationBase/Jfw5O/ext/OptimizationMooncakeExt.jl:1 in expression starting at stdin:5 ✗ OptimizationBase → OptimizationMooncakeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/Bijectors/0PotT/ext/BijectorsMooncakeExt.jl:3 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Bijectors/0PotT/ext/BijectorsMooncakeExt.jl:1 in expression starting at stdin:5 ✗ Bijectors → BijectorsMooncakeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/AdvancedVI/BGKMH/ext/AdvancedVIMooncakeExt.jl:5 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/AdvancedVI/BGKMH/ext/AdvancedVIMooncakeExt.jl:1 in expression starting at stdin:5 ✗ AdvancedVI → AdvancedVIMooncakeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] require(into::Module, mod::Symbol) @ Base loading.jl:2661 [inlined] [3] top-level scope @ ~/.julia/packages/DynamicPPL/G5x9i/ext/DynamicPPLMooncakeExt.jl:5 [4] include(mod::Module, _path::String) @ Base Base.jl:334 [5] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/DynamicPPL/G5x9i/ext/DynamicPPLMooncakeExt.jl:1 in expression starting at stdin:5 ✗ DynamicPPL → DynamicPPLMooncakeExt 9.5 s ✓ OptimizationBBO 18.8 s ✓ Turing → TuringDynamicHMCExt 18.9 s ✓ Turing → TuringMCMCChainsExt 7 dependencies successfully precompiled in 238 seconds. 338 already precompiled. 15 dependencies had output during precompilation: ┌ SciMLBase → SciMLBaseMooncakeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/SciMLBase/in0OX/ext/SciMLBaseMooncakeExt.jl:3 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/SciMLBase/in0OX/ext/SciMLBaseMooncakeExt.jl:1 │ in expression starting at stdin:5 └ ┌ OptimizationBase → OptimizationMooncakeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/OptimizationBase/Jfw5O/ext/OptimizationMooncakeExt.jl:3 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/OptimizationBase/Jfw5O/ext/OptimizationMooncakeExt.jl:1 │ in expression starting at stdin:5 └ ┌ AdvancedVI → AdvancedVIMooncakeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/AdvancedVI/BGKMH/ext/AdvancedVIMooncakeExt.jl:5 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/AdvancedVI/BGKMH/ext/AdvancedVIMooncakeExt.jl:1 │ in expression starting at stdin:5 └ ┌ Mooncake → MooncakeSpecialFunctionsExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/Mooncake/LLB2t/ext/MooncakeSpecialFunctionsExt.jl:3 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/ext/MooncakeSpecialFunctionsExt.jl:1 │ in expression starting at stdin:5 └ ┌ FunctionWrappersWrappers → FunctionWrappersWrappersMooncakeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/FunctionWrappersWrappers/7CPBX/ext/FunctionWrappersWrappersMooncakeExt.jl:4 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/FunctionWrappersWrappers/7CPBX/ext/FunctionWrappersWrappersMooncakeExt.jl:1 │ in expression starting at stdin:5 └ ┌ RecursiveArrayTools → RecursiveArrayToolsMooncakeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/RecursiveArrayTools/9DBPt/ext/RecursiveArrayToolsMooncakeExt.jl:4 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/RecursiveArrayTools/9DBPt/ext/RecursiveArrayToolsMooncakeExt.jl:1 │ in expression starting at stdin:5 └ ┌ AbstractPPL → AbstractPPLMooncakeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/AbstractPPL/Kuf2H/ext/AbstractPPLMooncakeExt.jl:7 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/AbstractPPL/Kuf2H/ext/AbstractPPLMooncakeExt.jl:1 │ in expression starting at stdin:5 └ ┌ Mooncake → MooncakeLogExpFunctionsExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/Mooncake/LLB2t/ext/MooncakeLogExpFunctionsExt.jl:6 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/ext/MooncakeLogExpFunctionsExt.jl:1 │ in expression starting at stdin:5 └ ┌ DifferentiationInterface → DifferentiationInterfaceMooncakeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/DifferentiationInterface/seMaz/ext/DifferentiationInterfaceMooncakeExt/DifferentiationInterfaceMooncakeExt.jl:5 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/DifferentiationInterface/seMaz/ext/DifferentiationInterfaceMooncakeExt/DifferentiationInterfaceMooncakeExt.jl:1 │ in expression starting at stdin:5 └ ┌ Mooncake → MooncakeDistancesExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/Mooncake/LLB2t/ext/MooncakeDistancesExt.jl:3 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/ext/MooncakeDistancesExt.jl:1 │ in expression starting at stdin:5 └ ┌ DynamicPPL → DynamicPPLMooncakeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/DynamicPPL/G5x9i/ext/DynamicPPLMooncakeExt.jl:5 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/DynamicPPL/G5x9i/ext/DynamicPPLMooncakeExt.jl:1 │ in expression starting at stdin:5 └ ┌ Mooncake → MooncakeFunctionWrappersExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/Mooncake/LLB2t/ext/MooncakeFunctionWrappersExt.jl:5 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/ext/MooncakeFunctionWrappersExt.jl:1 │ in expression starting at stdin:5 └ ┌ Mooncake │ ERROR: LoadError: UndefVarError: `WorldView` not defined in `Compiler` │ Suggestion: check for spelling errors or missing imports. │ Stacktrace: │ [1] getproperty(x::Module, f::Symbol) │ @ Base Base_compiler.jl:51 │ [2] top-level scope │ @ ~/.julia/packages/Mooncake/LLB2t/src/interpreter/abstract_interpretation.jl:90 │ [3] include(mapexpr::Function, mod::Module, _path::String) │ @ Base Base.jl:335 │ [4] top-level scope │ @ ~/.julia/packages/Mooncake/LLB2t/src/Mooncake.jl:183 │ [5] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [6] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/src/interpreter/abstract_interpretation.jl:90 │ in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/src/Mooncake.jl:1 │ in expression starting at stdin:5 └ ┌ Bijectors → BijectorsMooncakeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/Bijectors/0PotT/ext/BijectorsMooncakeExt.jl:3 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Bijectors/0PotT/ext/BijectorsMooncakeExt.jl:1 │ in expression starting at stdin:5 └ ┌ Mooncake → MooncakeDistributionsExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] require(into::Module, mod::Symbol) │ @ Base loading.jl:2661 [inlined] │ [3] top-level scope │ @ ~/.julia/packages/Mooncake/LLB2t/ext/MooncakeDistributionsExt.jl:3 │ [4] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [5] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/ext/MooncakeDistributionsExt.jl:1 │ in expression starting at stdin:5 └ ERROR: LoadError: The following 15 packages failed to precompile: SciMLBase → SciMLBaseMooncakeExt Failed to precompile SciMLBaseMooncakeExt [0dc5e5b9-c8c6-5742-aa3e-ed068812799e] to "/home/pkgeval/.julia/compiled/v1.14/SciMLBaseMooncakeExt/jl_7SPzgY" (ProcessExited(1)). OptimizationBase → OptimizationMooncakeExt Failed to precompile OptimizationMooncakeExt [cd4173cc-9ec5-5ead-8494-4a87bb2827d8] to "/home/pkgeval/.julia/compiled/v1.14/OptimizationMooncakeExt/jl_QgJQlv" (ProcessExited(1)). AdvancedVI → AdvancedVIMooncakeExt Failed to precompile AdvancedVIMooncakeExt [1930e4b2-9ad1-5cc9-876c-5297537dbcd2] to "/home/pkgeval/.julia/compiled/v1.14/AdvancedVIMooncakeExt/jl_ySy9Bz" (ProcessExited(1)). Mooncake → MooncakeSpecialFunctionsExt Failed to precompile MooncakeSpecialFunctionsExt [0ec37179-8e9d-5a9c-aab7-f15cb98de31f] to "/home/pkgeval/.julia/compiled/v1.14/MooncakeSpecialFunctionsExt/jl_9jcnfj" (ProcessExited(1)). FunctionWrappersWrappers → FunctionWrappersWrappersMooncakeExt Failed to precompile FunctionWrappersWrappersMooncakeExt [19ddcfb9-39d4-55a8-a7d4-b64b52fbaa29] to "/home/pkgeval/.julia/compiled/v1.14/FunctionWrappersWrappersMooncakeExt/jl_hrjJH2" (ProcessExited(1)). RecursiveArrayTools → RecursiveArrayToolsMooncakeExt Failed to precompile RecursiveArrayToolsMooncakeExt [822f7f0c-9b40-5518-bb3a-f76131ba87b5] to "/home/pkgeval/.julia/compiled/v1.14/RecursiveArrayToolsMooncakeExt/jl_8jv2bw" (ProcessExited(1)). AbstractPPL → AbstractPPLMooncakeExt Failed to precompile AbstractPPLMooncakeExt [7b09742f-de85-5d09-87d7-be52d6b2c6f4] to "/home/pkgeval/.julia/compiled/v1.14/AbstractPPLMooncakeExt/jl_LkKRLi" (ProcessExited(1)). Mooncake → MooncakeLogExpFunctionsExt Failed to precompile MooncakeLogExpFunctionsExt [7ef3ed66-88e1-50a1-b105-f51979d300cc] to "/home/pkgeval/.julia/compiled/v1.14/MooncakeLogExpFunctionsExt/jl_xbKVcq" (ProcessExited(1)). DifferentiationInterface → DifferentiationInterfaceMooncakeExt Failed to precompile DifferentiationInterfaceMooncakeExt [f60e7217-b702-5749-adb7-f3f2c76df63d] to "/home/pkgeval/.julia/compiled/v1.14/DifferentiationInterfaceMooncakeExt/jl_T4xO31" (ProcessExited(1)). Mooncake → MooncakeDistancesExt Failed to precompile MooncakeDistancesExt [444855e6-11aa-57a8-9239-e21f56196e03] to "/home/pkgeval/.julia/compiled/v1.14/MooncakeDistancesExt/jl_gdVHqf" (ProcessExited(1)). DynamicPPL → DynamicPPLMooncakeExt Failed to precompile DynamicPPLMooncakeExt [5e40a2ea-e1a8-5348-a59d-2e4b6d76b125] to "/home/pkgeval/.julia/compiled/v1.14/DynamicPPLMooncakeExt/jl_Q9KVsR" (ProcessExited(1)). Mooncake → MooncakeFunctionWrappersExt Failed to precompile MooncakeFunctionWrappersExt [5a938603-addc-58b9-81fd-7edcfce5e7b6] to "/home/pkgeval/.julia/compiled/v1.14/MooncakeFunctionWrappersExt/jl_GtZNKI" (ProcessExited(1)). Mooncake Failed to precompile Mooncake [da2b9cff-9c12-43a0-ae48-6db2b0edb7d6] to "/home/pkgeval/.julia/compiled/v1.14/Mooncake/jl_MF8GeD" (ProcessExited(1)). Bijectors → BijectorsMooncakeExt Failed to precompile BijectorsMooncakeExt [092e1185-92ae-533f-8ecc-3dfe7d101bab] to "/home/pkgeval/.julia/compiled/v1.14/BijectorsMooncakeExt/jl_xe9l0i" (ProcessExited(1)). Mooncake → MooncakeDistributionsExt Failed to precompile MooncakeDistributionsExt [6f973b1e-2664-50c5-8d1b-0eeb37954560] to "/home/pkgeval/.julia/compiled/v1.14/MooncakeDistributionsExt/jl_ER3vnh" (ProcessExited(1)). in expression starting at /PkgEval.jl/scripts/precompile.jl:34 Precompilation failed after 265.89s ################################################################################ # Testing # Testing Turing Test Could not use exact versions of packages in manifest, re-resolving. Note: if you do not check your manifest file into source control, then you can probably ignore this message. However, if you do check your manifest file into source control, then you probably want to pass the `allow_reresolve = false` kwarg when calling the `Pkg.test` function. Updating `/tmp/jl_kLGQm6/Project.toml` [4c88cf16] + Aqua v0.8.16 [aaaa29a8] + Clustering v0.15.8 [861a8166] + Combinatorics v1.1.0 [bbc10e6e] + DynamicHMC v3.6.1 [26cc04aa] + FiniteDifferences v0.12.34 [09f84164] + HypothesisTests v0.11.8 [c7f686f2] + MCMCChains v7.7.0 [da2b9cff] + Mooncake v0.5.49 [3e6eede4] + OptimizationBBO v0.4.12 [4e6fcdb7] + OptimizationNLopt v0.3.17 [37e2e3b7] + ReverseDiff v1.17.0 [860ef19b] + StableRNGs v1.0.4 ⌅ [4c63d2b9] ↓ StatsFuns v2.2.1 ⇒ v1.5.2 [a759f4b9] + TimerOutputs v1.2.0 [fce5fe82] + Turing v0.47.2 [44cfe95a] ~ Pkg ⇒ v1.14.0 Updating `/tmp/jl_kLGQm6/Manifest.toml` [0bf59076] + AdvancedHMC v0.8.6 [4c88cf16] + Aqua v0.8.16 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.8 [a134a8b2] + BlackBoxOptim v0.6.12 [fa961155] + CEnum v0.5.0 [aaaa29a8] + Clustering v0.15.8 [861a8166] + Combinatorics v1.1.0 [a8cc5b0e] + Crayons v4.2.0 [8d63f2c5] + DispatchDoctor v0.4.28 [b4f34e82] + Distances v0.10.12 [bbc10e6e] + DynamicHMC v3.6.1 [cad2338a] + EllipticalSliceSampling v2.0.0 [b86e33f2] + FFTA v0.3.1 [26cc04aa] + FiniteDifferences v0.12.34 [09f84164] + HypothesisTests v0.11.8 [18e54dd8] + IntegerMathUtils v0.1.4 [a98d9a8b] + Interpolations v0.16.3 [c8e1da08] + IterTools v1.10.0 [5ab0869b] + KernelDensity v0.6.12 [b964fa9f] + LaTeXStrings v1.4.1 [1fad7336] + LazyStack v0.1.3 [c7f686f2] + MCMCChains v7.7.0 [da2b9cff] + Mooncake v0.5.49 [46d2c3a1] + MuladdMacro v0.2.7 [76087f3c] + NLopt v1.2.1 [c020b1a1] + NaturalSort v1.0.0 [b8a86587] + NearestNeighbors v0.4.29 [6fe1bfb0] + OffsetArrays v1.17.0 [3e6eede4] + OptimizationBBO v0.4.12 [4e6fcdb7] + OptimizationNLopt v0.3.17 [08abe8d2] + PrettyTables v3.4.8 [27ebfcd6] + Primes v0.5.7 [74087812] + Random123 v1.7.1 [e6cf234a] + RandomNumbers v1.6.0 [b3c3ace0] + RangeArrays v0.3.2 [c84ed2f1] + Ratios v0.4.5 [37e2e3b7] + ReverseDiff v1.17.0 [708f8203] + Richardson v1.4.3 [860ef19b] + StableRNGs v1.0.4 [90137ffa] + StaticArrays v1.9.19 ⌅ [4c63d2b9] ↓ StatsFuns v2.2.1 ⇒ v1.5.2 [5e0ebb24] + Strided v2.6.4 [4db3bf67] + StridedViews v0.5.2 ⌅ [892a3eda] + StringManipulation v0.5.0 [02d47bb6] + TensorCast v0.4.9 [a759f4b9] + TimerOutputs v1.2.0 [24ddb15e] + TransmuteDims v0.1.17 [9d95972d] + TupleTools v1.6.0 [fce5fe82] + Turing v0.47.2 [efce3f68] + WoodburyMatrices v1.1.0 [079eb43e] + NLopt_jll v2.11.0+0 [0dad84c5] + ArgTools v1.2.0 [f43a241f] + Downloads v1.7.0 [b27032c2] + LibCURL v1.0.0 [76f85450] + LibGit2 v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] ~ Pkg ⇒ v1.14.0 [1a1011a3] + SharedArrays v1.11.0 [a4e569a6] + Tar v1.10.0 [deac9b47] + LibCURL_jll v8.21.0+0 [e37daf67] + LibGit2_jll v1.9.7+0 [29816b5a] + LibSSH2_jll v1.11.104+0 [14a3606d] + MozillaCACerts_jll v2026.8.13 [458c3c95] + OpenSSL_jll v3.5.8+0 [efcefdf7] + PCRE2_jll v10.47.0+0 [83775a58] + Zlib_jll v1.3.2+0 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850ede] + nghttp2_jll v1.70.0+0 [3f19e933] + p7zip_jll v17.8.2+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Test Successfully re-resolved Status `/tmp/jl_kLGQm6/Project.toml` [47edcb42] ADTypes v1.24.0 [80f14c24] AbstractMCMC v5.16.0 [7a57a42e] AbstractPPL v0.15.5 [5b7e9947] AdvancedMH v0.8.10 [b5ca4192] AdvancedVI v0.7.0 [4c88cf16] Aqua v0.8.16 [198e06fe] BangBang v0.4.9 [76274a88] Bijectors v0.16.2 [aaaa29a8] Clustering v0.15.8 [861a8166] Combinatorics v1.1.0 [31c24e10] Distributions v0.25.131 [bbc10e6e] DynamicHMC v3.6.1 [366bfd00] DynamicPPL v0.42.7 [26cc04aa] FiniteDifferences v0.12.34 [4a37a8b9] FlexiChains v0.6.37 [f6369f11] ForwardDiff v1.4.5 [09f84164] HypothesisTests v0.11.8 [6f1fad26] Libtask v0.9.18 [6fdf6af0] LogDensityProblems v2.2.0 [996a588d] LogDensityProblemsAD v1.13.1 [c7f686f2] MCMCChains v7.7.0 [da2b9cff] Mooncake v0.5.49 [7f7a1694] Optimization v5.9.0 [3e6eede4] OptimizationBBO v0.4.12 [4e6fcdb7] OptimizationNLopt v0.3.17 [36348300] OptimizationOptimJL v0.4.20 [90014a1f] PDMats v0.11.41 [37e2e3b7] ReverseDiff v1.17.0 [276daf66] SpecialFunctions v2.9.0 [860ef19b] StableRNGs v1.0.4 [10745b16] Statistics v1.11.4 [2913bbd2] StatsBase v0.34.13 ⌅ [4c63d2b9] StatsFuns v1.5.2 [a759f4b9] TimerOutputs v1.2.0 [fce5fe82] Turing v0.47.2 [37e2e46d] LinearAlgebra v1.14.0 [56ddb016] Logging v1.11.0 [44cfe95a] Pkg v1.14.0 [9a3f8284] Random v1.11.0 [9e88b42a] Serialization v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_kLGQm6/Manifest.toml` [47edcb42] ADTypes v1.24.0 [621f4979] AbstractFFTs v1.5.0 [80f14c24] AbstractMCMC v5.16.0 [7a57a42e] AbstractPPL v0.15.5 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.45 [79e6a3ab] Adapt v4.7.0 [0bf59076] AdvancedHMC v0.8.6 [5b7e9947] AdvancedMH v0.8.10 [b5ca4192] AdvancedVI v0.7.0 [66dad0bd] AliasTables v1.1.3 [4c88cf16] Aqua v0.8.16 [dce04be8] ArgCheck v2.5.0 [4fba245c] ArrayInterface v7.30.0 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] AxisArrays v0.4.8 [198e06fe] BangBang v0.4.9 [76274a88] Bijectors v0.16.2 [a134a8b2] BlackBoxOptim v0.6.12 [fa961155] CEnum v0.5.0 [d360d2e6] 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LibGit2_jll v1.9.7+0 [29816b5a] LibSSH2_jll v1.11.104+0 [14a3606d] MozillaCACerts_jll v2026.8.13 [4536629a] OpenBLAS_jll v0.3.34+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.8+0 [efcefdf7] PCRE2_jll v10.47.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.2+0 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.70.0+0 [3f19e933] p7zip_jll v17.8.2+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... [ Info: progress logging is disabled globally ERROR: LoadError: UndefVarError: `WorldView` not defined in `Compiler` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty(x::Module, f::Symbol) @ Base Base_compiler.jl:51 [2] top-level scope @ ~/.julia/packages/Mooncake/LLB2t/src/interpreter/abstract_interpretation.jl:90 [3] include(mapexpr::Function, mod::Module, _path::String) @ Base Base.jl:335 [4] top-level scope @ ~/.julia/packages/Mooncake/LLB2t/src/Mooncake.jl:183 [5] include(mod::Module, _path::String) @ Base Base.jl:334 [6] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/src/interpreter/abstract_interpretation.jl:90 in expression starting at /home/pkgeval/.julia/packages/Mooncake/LLB2t/src/Mooncake.jl:1 in expression starting at stdin:5 1 dependency had output during precompilation: ┌ Mooncake │ [Output was shown above] └ AD: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/runtests.jl:38 Got exception outside of a @test LoadError: The following 1 package failed to precompile: Mooncake Failed to precompile Mooncake [da2b9cff-9c12-43a0-ae48-6db2b0edb7d6] to "/home/pkgeval/.julia/compiled/v1.14/Mooncake/jl_yzQPps" (ProcessExited(1)). in expression starting at /home/pkgeval/.julia/packages/Turing/qVOus/test/ad.jl:1 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 6.4 ┌ Info: Found initial step size └ ϵ = 1.6500000000000001 ┌ Warning: There were 1 divergent transitions. Consider reparameterising your model or using a smaller step size. For adaptive samplers such as NUTS and HMCDA, consider increasing `target_accept`. └ @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/hmc.jl:483 ┌ Info: Found initial step size └ ϵ = 1.7000000000000002 ┌ Warning: There were 2 divergent transitions. Consider reparameterising your model or using a smaller step size. For adaptive samplers such as NUTS and HMCDA, consider increasing `target_accept`. └ @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/hmc.jl:483 ┌ Warning: There were 3 divergent transitions. Consider reparameterising your model or using a smaller step size. For adaptive samplers such as NUTS and HMCDA, consider increasing `target_accept`. └ @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/hmc.jl:483 ┌ Warning: Only a single process available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:732 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Warning: failed to find valid initial parameters in 10 tries; consider providing a different initialisation strategy with the `initial_params` keyword └ @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:63 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:549 ┌ Info: Found initial step size └ ϵ = 1.6 PG: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/callbacks.jl:24 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.CallbacksTests.test_normals), DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/callbacks.jl:11 [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [15] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/callbacks.jl:25 [inlined] [16] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [17] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/callbacks.jl:25 [inlined] ┌ Info: Found initial step size └ ϵ = 1.6 [ Info: progress logging is disabled globally ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: It looks like you are using `Threads.@threads` in your model definition. │ │ Note that since version 0.39 of DynamicPPL, threadsafe evaluation of models is disabled by default. If you need it, you will need to explicitly enable it by creating the model, and then running `model = setthreadsafe(model, true)`. │ │ Threadsafe model evaluation is only needed when parallelising tilde-statements (not arbitrary Julia code), and avoiding it can often lead to significant performance improvements. │ │ Please see https://turinglang.org/docs/usage/threadsafe-evaluation/ for more details of when threadsafe evaluation is actually required. └ @ DynamicPPL ~/.julia/packages/DynamicPPL/G5x9i/src/compiler.jl:394 resampling schemes: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:130 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.coinflip), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Int64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Int64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.StratifiedResampler}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.StratifiedResampler}, nparticles::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:689 [14] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:131 [inlined] [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [18] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:137 [inlined] errors when number of observations is not fixed: Test Failed at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:188 Expression: sample(fail_smc(), SMC(), 100) Expected: "number of observations" Message: "MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)\nThe type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.\n\nClosest candidates are:\n Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter\n @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177\n Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange)\n @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180\n Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)\n @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180\n" Stacktrace: [1] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [2] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [3] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:179 [inlined] [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [5] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:188 [inlined] chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:191 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:689 [inlined] [14] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [15] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [16] test_chain_logp_metadata(spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/qVOus/test/test_utils/sampler.jl:22 [17] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [18] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [19] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:192 [inlined] [20] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [21] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:192 [inlined] rng is respected: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:195 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.SamplerTestUtils.var"#f#test_rng_respected##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Float64) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.Xoshiro, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.Xoshiro, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.Xoshiro, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:689 [14] test_rng_respected(spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/qVOus/test/test_utils/sampler.jl:42 [15] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [16] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [17] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:196 [inlined] [18] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [19] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:196 [inlined] log_normalizing_constant: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:199 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.test), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:689 [14] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:200 [inlined] [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [18] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:200 [inlined] MCMCChains preserves ess_per_step: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:213 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.test), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:689 [14] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:214 [inlined] [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [18] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:214 [inlined] multithreaded execution matches serial: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:223 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.coinflip), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Int64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Int64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:689 [14] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:226 [inlined] [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [18] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:227 [inlined] does not resample an already equal-weight population: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:239 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Main.ParticleMCMCTests.var"#25#26"{StableRNGs.LehmerRNG})(::Int64) @ Main.ParticleMCMCTests ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:246 [10] iterate(::Base.Generator{UnitRange{Int64}, Main.ParticleMCMCTests.var"#25#26"{StableRNGs.LehmerRNG}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Main.ParticleMCMCTests.var"#25#26"{StableRNGs.LehmerRNG}}) @ Base array.jl:839 [12] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [14] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:244 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:246 [inlined] reports a population that has died out: Test Failed at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:261 Expression: sample(impossible(), SMC(), 5) Expected: "zero probability at observation 2" Message: "MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)\nThe type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.\n\nClosest candidates are:\n Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter\n @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177\n Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange)\n @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180\n Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)\n @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180\n" Stacktrace: [1] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [2] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [3] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:256 [inlined] [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [5] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:261 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 Test threw exception Expression: sample(normal(), SMC(), 10; initial_params = (; a = 1.0)) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::@NamedTuple{a::Float64}, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{initial_params::@NamedTuple{a::Float64}}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [14] (::Main.ParticleMCMCTests.var"#29#30")() @ Main.ParticleMCMCTests none:-1 [inlined] [15] with_logstate(f::Main.ParticleMCMCTests.var"#29#30", logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [16] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [17] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [18] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [19] match_logs(f::Function, patterns::Tuple{Symbol, Regex}; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [20] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [21] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [22] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [23] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [24] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:268 Test threw exception Expression: sample(normal(), SMC(), 10; initial_params = DynamicPPL.InitFromUniform()) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::DynamicPPL.InitFromUniform{Float64}, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromUniform{Float64}}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [14] (::Main.ParticleMCMCTests.var"#31#32")() @ Main.ParticleMCMCTests none:-1 [inlined] [15] with_logstate(f::Main.ParticleMCMCTests.var"#31#32", logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [16] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [17] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [18] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [19] match_logs(f::Function, patterns::Tuple{Symbol, Regex}; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [20] match_logs(f::Function, patterns::Tuple{Symbol, Regex}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:306 [21] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [22] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [23] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [25] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:268 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:274 Test threw exception Expression: sample(Xoshiro(1), normal(), SMC(), MCMCSerial(), 10, 2; progress = false) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Type, discard_initial::Int64, thinning::Int64, initial_params::DynamicPPL.InitFromPrior, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{progressname::String, chain_number::Int64, progress::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] (::AbstractMCMC.var"#sample_chain#109"{String, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Int64})(i::Int64, seed::UInt64, initial_params::DynamicPPL.InitFromPrior, initial_state::Nothing) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:893 [14] (::Base.Splat{AbstractMCMC.var"#sample_chain#109"{String, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Int64}})(args::Tuple{Int64, UInt64, DynamicPPL.InitFromPrior, Nothing}) @ Base operators.jl:1381 [inlined] [15] iterate(::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}, Vector{DynamicPPL.InitFromPrior}, Vector{Nothing}}}, Base.Splat{AbstractMCMC.var"#sample_chain#109"{String, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Int64}}}) @ Base generator.jl:49 [inlined] [16] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}, Vector{DynamicPPL.InitFromPrior}, Vector{Nothing}}}, Base.Splat{AbstractMCMC.var"#sample_chain#109"{String, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Int64}}}) @ Base array.jl:839 [17] map(::AbstractMCMC.var"#sample_chain#109"{String, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Int64}, ::UnitRange{Int64}, ::Vector{UInt64}, ::Vector{DynamicPPL.InitFromPrior}, ::Vector{Nothing}) @ Base abstractarray.jl:3645 [inlined] [18] mcmcsample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, ::AbstractMCMC.MCMCSerial, N::Int64, nchains::Int64; progressname::String, initial_params::Vector{DynamicPPL.InitFromPrior}, initial_state::Nothing, kwargs::@Kwargs{chain_type::UnionAll, check_model::Bool, verbose::Bool, progress::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:906 [19] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, ensemble::AbstractMCMC.MCMCSerial, N::Int64, n_chains::Int64; chain_type::Core.TypeEgal{FlexiChains.VNChain}, check_model::Bool, verbose::Bool, initial_params::Vector{DynamicPPL.InitFromPrior}, kwargs::@Kwargs{progress::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:151 [20] (::Main.ParticleMCMCTests.var"#33#34")() @ Main.ParticleMCMCTests none:-1 [21] with_logstate(f::Main.ParticleMCMCTests.var"#33#34", logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [22] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [23] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [24] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [25] match_logs(::Function; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [26] match_logs(::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:306 [27] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [28] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [29] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [30] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [31] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:274 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:278 Test threw exception Expression: sample(normal(), SMC(), 10; callback = ((args...,; kwargs...)->begin called = true end)) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Main.ParticleMCMCTests.var"#37#38"{Main.ParticleMCMCTests.var"#39#40"}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{callback::Main.ParticleMCMCTests.var"#37#38"{Main.ParticleMCMCTests.var"#39#40"}}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [14] (::Main.ParticleMCMCTests.var"#35#36")() @ Main.ParticleMCMCTests none:-1 [inlined] [15] with_logstate(f::Main.ParticleMCMCTests.var"#35#36", logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [16] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [17] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [18] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [19] match_logs(f::Function, patterns::Tuple{Symbol, Regex}; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [20] match_logs(f::Function, patterns::Tuple{Symbol, Regex}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:306 [21] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [22] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [23] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [25] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:278 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:285 Test threw exception Expression: sample(normal(), SMC(), 10; save_state = true) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{save_state::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [14] (::Main.ParticleMCMCTests.var"#41#42")() @ Main.ParticleMCMCTests none:-1 [inlined] [15] with_logstate(f::Main.ParticleMCMCTests.var"#41#42", logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [16] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [17] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [18] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [19] match_logs(f::Function, patterns::Tuple{Symbol, Regex}; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [20] match_logs(f::Function, patterns::Tuple{Symbol, Regex}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:306 [21] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [22] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [23] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [25] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:285 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:288 Test threw exception Expression: sample(normal(), SMC(), 10; initial_state = 1) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Int64, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{initial_state::Int64}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [14] (::Main.ParticleMCMCTests.var"#43#44")() @ Main.ParticleMCMCTests none:-1 [inlined] [15] with_logstate(f::Main.ParticleMCMCTests.var"#43#44", logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [16] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [17] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [18] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [19] match_logs(f::Function, patterns::Tuple{Symbol, Regex}; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [20] match_logs(f::Function, patterns::Tuple{Symbol, Regex}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:306 [21] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [22] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [23] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [25] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:288 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:293 Test threw exception Expression: sample(normal(), SMC(), 10; kw...) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{discard_initial::Int64}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [14] (::Main.ParticleMCMCTests.var"#45#46"{@NamedTuple{discard_initial::Int64}})() @ Main.ParticleMCMCTests none:-1 [inlined] [15] with_logstate(f::Main.ParticleMCMCTests.var"#45#46"{@NamedTuple{discard_initial::Int64}}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [16] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [17] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [18] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [19] match_logs(f::Function, patterns::Tuple{Symbol, Regex}; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [20] match_logs(f::Function, patterns::Tuple{Symbol, Regex}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:306 [21] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [22] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [23] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [25] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:293 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:294 Test threw exception Expression: size(chn, 1) == 10 MethodError: no method matching size(::Nothing, ::Int64) The function `size` exists, but no method is defined for this combination of argument types. Closest candidates are: size(!Matched::Type{LinearAlgebra.Diagonal{T, StaticArraysCore.SVector{N, T}} where T}, ::Int64) where N @ StaticArrays ~/.julia/packages/StaticArrays/EnGcj/src/SDiagonal.jl:19 size(!Matched::Type{LinearAlgebra.Diagonal{T, StaticArraysCore.SVector{N, T}}}, ::Int64) where {N, T} @ StaticArrays ~/.julia/packages/StaticArrays/EnGcj/src/SDiagonal.jl:20 size(!Matched::Type{var"#s26"} where var"#s26"<:(Union{LinearAlgebra.Adjoint{T, <:Union{StaticArraysCore.StaticArray{Tuple{var"#s3"}, T, 1} where var"#s3", StaticArraysCore.StaticArray{Tuple{var"#s4", var"#s5"}, T, 2} where {var"#s4", var"#s5"}}}, LinearAlgebra.Diagonal{T, <:StaticArraysCore.StaticArray{Tuple{var"#s14"}, T, 1} where var"#s14"}, LinearAlgebra.Hermitian{T, <:StaticArraysCore.StaticArray{Tuple{var"#s11", var"#s12"}, T, 2} where {var"#s11", var"#s12"}}, LinearAlgebra.LowerTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s19", var"#s20"}, T, 2} where {var"#s19", var"#s20"}}, LinearAlgebra.Symmetric{T, <:StaticArraysCore.StaticArray{Tuple{var"#s8", var"#s9"}, T, 2} where {var"#s8", var"#s9"}}, LinearAlgebra.Transpose{T, <:Union{StaticArraysCore.StaticArray{Tuple{var"#s3"}, T, 1} where var"#s3", StaticArraysCore.StaticArray{Tuple{var"#s4", var"#s5"}, T, 2} where {var"#s4", var"#s5"}}}, LinearAlgebra.UnitLowerTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s25", var"#s26"}, T, 2} where {var"#s25", var"#s26"}}, LinearAlgebra.UnitUpperTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s22", var"#s23"}, T, 2} where {var"#s22", var"#s23"}}, LinearAlgebra.UpperTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s16", var"#s17"}, T, 2} where {var"#s16", var"#s17"}}, StaticArraysCore.StaticArray{Tuple{var"#s26"}, T, 1} where var"#s26", StaticArraysCore.StaticArray{Tuple{var"#s4", var"#s1"}, T, 2} where {var"#s4", var"#s1"}, StaticArraysCore.StaticArray{<:Tuple, T}} where T), ::Int64) @ StaticArrays ~/.julia/packages/StaticArrays/EnGcj/src/abstractarray.jl:5 ... Stacktrace: [1] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [2] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [3] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [5] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:294 [inlined] [6] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] unsupported keywords are reported as ignored: Test Failed at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:295 Expression: chn isa VNChain Evaluated: nothing isa FlexiChains.VNChain Stacktrace: [1] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [2] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [3] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [5] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:295 [inlined] [6] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:784 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:293 Test threw exception Expression: sample(normal(), SMC(), 10; kw...) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{thinning::Int64}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [14] (::Main.ParticleMCMCTests.var"#45#46"{@NamedTuple{thinning::Int64}})() @ Main.ParticleMCMCTests none:-1 [inlined] [15] with_logstate(f::Main.ParticleMCMCTests.var"#45#46"{@NamedTuple{thinning::Int64}}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [16] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [17] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [18] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [19] match_logs(f::Function, patterns::Tuple{Symbol, Regex}; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [20] match_logs(f::Function, patterns::Tuple{Symbol, Regex}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:306 [21] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [22] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [23] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [25] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:293 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:294 Test threw exception Expression: size(chn, 1) == 10 MethodError: no method matching size(::Nothing, ::Int64) The function `size` exists, but no method is defined for this combination of argument types. Closest candidates are: size(!Matched::Type{LinearAlgebra.Diagonal{T, StaticArraysCore.SVector{N, T}} where T}, ::Int64) where N @ StaticArrays ~/.julia/packages/StaticArrays/EnGcj/src/SDiagonal.jl:19 size(!Matched::Type{LinearAlgebra.Diagonal{T, StaticArraysCore.SVector{N, T}}}, ::Int64) where {N, T} @ StaticArrays ~/.julia/packages/StaticArrays/EnGcj/src/SDiagonal.jl:20 size(!Matched::Type{var"#s26"} where var"#s26"<:(Union{LinearAlgebra.Adjoint{T, <:Union{StaticArraysCore.StaticArray{Tuple{var"#s3"}, T, 1} where var"#s3", StaticArraysCore.StaticArray{Tuple{var"#s4", var"#s5"}, T, 2} where {var"#s4", var"#s5"}}}, LinearAlgebra.Diagonal{T, <:StaticArraysCore.StaticArray{Tuple{var"#s14"}, T, 1} where var"#s14"}, LinearAlgebra.Hermitian{T, <:StaticArraysCore.StaticArray{Tuple{var"#s11", var"#s12"}, T, 2} where {var"#s11", var"#s12"}}, LinearAlgebra.LowerTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s19", var"#s20"}, T, 2} where {var"#s19", var"#s20"}}, LinearAlgebra.Symmetric{T, <:StaticArraysCore.StaticArray{Tuple{var"#s8", var"#s9"}, T, 2} where {var"#s8", var"#s9"}}, LinearAlgebra.Transpose{T, <:Union{StaticArraysCore.StaticArray{Tuple{var"#s3"}, T, 1} where var"#s3", StaticArraysCore.StaticArray{Tuple{var"#s4", var"#s5"}, T, 2} where {var"#s4", var"#s5"}}}, LinearAlgebra.UnitLowerTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s25", var"#s26"}, T, 2} where {var"#s25", var"#s26"}}, LinearAlgebra.UnitUpperTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s22", var"#s23"}, T, 2} where {var"#s22", var"#s23"}}, LinearAlgebra.UpperTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s16", var"#s17"}, T, 2} where {var"#s16", var"#s17"}}, StaticArraysCore.StaticArray{Tuple{var"#s26"}, T, 1} where var"#s26", StaticArraysCore.StaticArray{Tuple{var"#s4", var"#s1"}, T, 2} where {var"#s4", var"#s1"}, StaticArraysCore.StaticArray{<:Tuple, T}} where T), ::Int64) @ StaticArrays ~/.julia/packages/StaticArrays/EnGcj/src/abstractarray.jl:5 ... Stacktrace: [1] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [2] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [3] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [5] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:294 [inlined] [6] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] unsupported keywords are reported as ignored: Test Failed at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:295 Expression: chn isa VNChain Evaluated: nothing isa FlexiChains.VNChain Stacktrace: [1] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [2] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [3] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [5] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:295 [inlined] [6] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:784 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:293 Test threw exception Expression: sample(normal(), SMC(), 10; kw...) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{discard_initial::Int64, thinning::Int64}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [14] (::Main.ParticleMCMCTests.var"#45#46"{@NamedTuple{discard_initial::Int64, thinning::Int64}})() @ Main.ParticleMCMCTests none:-1 [inlined] [15] with_logstate(f::Main.ParticleMCMCTests.var"#45#46"{@NamedTuple{discard_initial::Int64, thinning::Int64}}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [16] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [17] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [18] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [19] match_logs(f::Function, patterns::Tuple{Symbol, Regex}; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [20] match_logs(f::Function, patterns::Tuple{Symbol, Regex}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:306 [21] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [22] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [23] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [25] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:293 [inlined] unsupported keywords are reported as ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:294 Test threw exception Expression: size(chn, 1) == 10 MethodError: no method matching size(::Nothing, ::Int64) The function `size` exists, but no method is defined for this combination of argument types. Closest candidates are: size(!Matched::Type{LinearAlgebra.Diagonal{T, StaticArraysCore.SVector{N, T}} where T}, ::Int64) where N @ StaticArrays ~/.julia/packages/StaticArrays/EnGcj/src/SDiagonal.jl:19 size(!Matched::Type{LinearAlgebra.Diagonal{T, StaticArraysCore.SVector{N, T}}}, ::Int64) where {N, T} @ StaticArrays ~/.julia/packages/StaticArrays/EnGcj/src/SDiagonal.jl:20 size(!Matched::Type{var"#s26"} where var"#s26"<:(Union{LinearAlgebra.Adjoint{T, <:Union{StaticArraysCore.StaticArray{Tuple{var"#s3"}, T, 1} where var"#s3", StaticArraysCore.StaticArray{Tuple{var"#s4", var"#s5"}, T, 2} where {var"#s4", var"#s5"}}}, LinearAlgebra.Diagonal{T, <:StaticArraysCore.StaticArray{Tuple{var"#s14"}, T, 1} where var"#s14"}, LinearAlgebra.Hermitian{T, <:StaticArraysCore.StaticArray{Tuple{var"#s11", var"#s12"}, T, 2} where {var"#s11", var"#s12"}}, LinearAlgebra.LowerTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s19", var"#s20"}, T, 2} where {var"#s19", var"#s20"}}, LinearAlgebra.Symmetric{T, <:StaticArraysCore.StaticArray{Tuple{var"#s8", var"#s9"}, T, 2} where {var"#s8", var"#s9"}}, LinearAlgebra.Transpose{T, <:Union{StaticArraysCore.StaticArray{Tuple{var"#s3"}, T, 1} where var"#s3", StaticArraysCore.StaticArray{Tuple{var"#s4", var"#s5"}, T, 2} where {var"#s4", var"#s5"}}}, LinearAlgebra.UnitLowerTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s25", var"#s26"}, T, 2} where {var"#s25", var"#s26"}}, LinearAlgebra.UnitUpperTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s22", var"#s23"}, T, 2} where {var"#s22", var"#s23"}}, LinearAlgebra.UpperTriangular{T, <:StaticArraysCore.StaticArray{Tuple{var"#s16", var"#s17"}, T, 2} where {var"#s16", var"#s17"}}, StaticArraysCore.StaticArray{Tuple{var"#s26"}, T, 1} where var"#s26", StaticArraysCore.StaticArray{Tuple{var"#s4", var"#s1"}, T, 2} where {var"#s4", var"#s1"}, StaticArraysCore.StaticArray{<:Tuple, T}} where T), ::Int64) @ StaticArrays ~/.julia/packages/StaticArrays/EnGcj/src/abstractarray.jl:5 ... Stacktrace: [1] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [2] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [3] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [5] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:294 [inlined] [6] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] unsupported keywords are reported as ignored: Test Failed at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:295 Expression: chn isa VNChain Evaluated: nothing isa FlexiChains.VNChain Stacktrace: [1] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:110 [2] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [3] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:265 [inlined] [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [5] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:295 [inlined] [6] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:784 [inlined] chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:323 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [24] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [25] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [26] test_chain_logp_metadata(spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/qVOus/test/test_utils/sampler.jl:22 [27] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [28] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [29] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:324 [inlined] [30] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [31] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:324 [inlined] rng is respected: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:327 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.SamplerTestUtils.var"#f#test_rng_respected##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Float64) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.Xoshiro, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.Xoshiro, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.Xoshiro, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::Random.Xoshiro, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.Xoshiro, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::Random.Xoshiro, ::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.Xoshiro, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.Xoshiro, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::Random.Xoshiro, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_rng_respected##0", (:z,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [24] test_rng_respected(spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/qVOus/test/test_utils/sampler.jl:42 [25] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [26] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [27] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:328 [inlined] [28] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [29] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:328 [inlined] log_normalizing_constant: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:331 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.test), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [24] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [25] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [26] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:332 [inlined] [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:332 [inlined] log_normalizing_constant is biased upward for conditional sweeps: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:344 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.coinflip), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Int64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Int64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, discard_initial::Int64, thinning::Int64, initial_params::Nothing, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:689 [inlined] [14] (::Main.ParticleMCMCTests.var"#65#66"{Float64, Vector{Int64}})(i::Int64) @ Main.ParticleMCMCTests ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:357 [15] iterate(::Base.Generator{UnitRange{Int64}, Main.ParticleMCMCTests.var"#65#66"{Float64, Vector{Int64}}}) @ Base generator.jl:49 [inlined] [16] _collect(c::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, Main.ParticleMCMCTests.var"#65#66"{Float64, Vector{Int64}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base array.jl:859 [17] collect_similar(cont::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, Main.ParticleMCMCTests.var"#65#66"{Float64, Vector{Int64}}}) @ Base array.jl:774 [18] map(f::Function, A::UnitRange{Int64}) @ Base abstractarray.jl:3491 [19] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [20] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [21] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:351 [inlined] [22] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [23] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:356 [inlined] multithreaded execution matches serial: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:368 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.coinflip), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Int64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Int64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [24] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [25] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [26] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:372 [inlined] [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:373 [inlined] conditional sweeps ignore the named resampling scheme: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:378 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#drifting#drifting##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#drifting#drifting##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Float64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#drifting#drifting##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#drifting#drifting##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#drifting#drifting##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Main.ParticleMCMCTests.var"#conditional_sweep#conditional_sweep##0"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#drifting#drifting##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}})(scheme::Turing.Inference.MultinomialResampler) @ Main.ParticleMCMCTests ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:391 [10] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [12] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:382 [inlined] [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [14] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:399 [inlined] reference particle: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:405 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [24] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [25] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [26] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:406 [inlined] [29] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [30] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:406 [inlined] initial_params is ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:413 Test threw exception Expression: sample(normal(), PG(5), 10; initial_params = (; a = 1.0)) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromParams{DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.InitFromPrior}, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromParams{DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.InitFromPrior}, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromParams{DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.InitFromPrior}, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromParams{DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.InitFromPrior}, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromParams{DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.InitFromPrior}, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{TestLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::TestLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromParams{DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.InitFromPrior}, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::@NamedTuple{a::Float64}, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [23] sample(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{initial_params::@NamedTuple{a::Float64}}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [24] (::Main.ParticleMCMCTests.var"#67#68")() @ Main.ParticleMCMCTests none:-1 [inlined] [25] with_logstate(f::Main.ParticleMCMCTests.var"#67#68", logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [26] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [27] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [28] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [29] match_logs(f::Function, patterns::Tuple{Symbol, Regex}; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [30] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:413 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:413 [inlined] initial_params is ignored: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:417 Test threw exception Expression: sample(Xoshiro(1), normal(), PG(5), MCMCSerial(), 10, 2; progress = false) MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool, chain_number::Int64}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool, chain_number::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::Random.Xoshiro, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool, chain_number::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool, chain_number::Int64}, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool, chain_number::Int64}, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{TestLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::TestLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool, chain_number::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::UnionAll, verbose::Bool, kwargs::@Kwargs{progressname::String, initial_state::Nothing, chain_number::Int64, progress::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] (::AbstractMCMC.var"#sample_chain#109"{String, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Int64})(i::Int64, seed::UInt64, initial_params::DynamicPPL.InitFromPrior, initial_state::Nothing) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:893 [24] (::Base.Splat{AbstractMCMC.var"#sample_chain#109"{String, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Int64}})(args::Tuple{Int64, UInt64, DynamicPPL.InitFromPrior, Nothing}) @ Base operators.jl:1381 [inlined] [25] iterate(::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}, Vector{DynamicPPL.InitFromPrior}, Vector{Nothing}}}, Base.Splat{AbstractMCMC.var"#sample_chain#109"{String, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Int64}}}) @ Base generator.jl:49 [inlined] [26] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}, Vector{DynamicPPL.InitFromPrior}, Vector{Nothing}}}, Base.Splat{AbstractMCMC.var"#sample_chain#109"{String, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Int64}}}) @ Base array.jl:839 [27] map(::AbstractMCMC.var"#sample_chain#109"{String, Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Int64}, ::UnitRange{Int64}, ::Vector{UInt64}, ::Vector{DynamicPPL.InitFromPrior}, ::Vector{Nothing}) @ Base abstractarray.jl:3645 [inlined] [28] mcmcsample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, ::AbstractMCMC.MCMCSerial, N::Int64, nchains::Int64; progressname::String, initial_params::Vector{DynamicPPL.InitFromPrior}, initial_state::Nothing, kwargs::@Kwargs{chain_type::UnionAll, check_model::Bool, verbose::Bool, progress::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:906 [29] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, ensemble::AbstractMCMC.MCMCSerial, N::Int64, n_chains::Int64; chain_type::Core.TypeEgal{FlexiChains.VNChain}, check_model::Bool, verbose::Bool, initial_params::Vector{DynamicPPL.InitFromPrior}, kwargs::@Kwargs{progress::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:151 [30] (::Main.ParticleMCMCTests.var"#69#70")() @ Main.ParticleMCMCTests none:-1 [31] with_logstate(f::Main.ParticleMCMCTests.var"#69#70", logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [32] with_logger(f::Function, logger::TestLogger) @ Base.CoreLogging logging/logging.jl:653 [33] collect_test_logs(f::Function; kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:126 [inlined] [34] collect_test_logs(f::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:124 [inlined] [35] match_logs(::Function; match_mode::Symbol, kwargs::@Kwargs{}) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:307 [inlined] [36] match_logs(::Function) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/logging.jl:306 [37] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:413 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:417 [inlined] the saved state survives serialisation: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:420 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool, save_state::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool, save_state::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool, save_state::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool, save_state::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool, save_state::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool, save_state::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{save_state::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [25] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:421 [inlined] [26] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [27] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:421 [inlined] ensuring reference consistency: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:432 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#state_space_model#state_space_model##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#state_space_model#state_space_model##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Float64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#state_space_model#state_space_model##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#state_space_model#state_space_model##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#state_space_model#state_space_model##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Main.ParticleMCMCTests.var"#run_csmc##2#run_csmc##3"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#state_space_model#state_space_model##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, StableRNGs.LehmerRNG})(::Int64) @ Main.ParticleMCMCTests ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:454 [10] iterate(::Base.Generator{UnitRange{Int64}, Main.ParticleMCMCTests.var"#run_csmc##2#run_csmc##3"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#state_space_model#state_space_model##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, StableRNGs.LehmerRNG}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Main.ParticleMCMCTests.var"#run_csmc##2#run_csmc##3"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#state_space_model#state_space_model##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, StableRNGs.LehmerRNG}}) @ Base array.jl:839 [12] (::Main.ParticleMCMCTests.var"#run_csmc#run_csmc##0")(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#state_space_model#state_space_model##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, N::Int64, nsteps::Int64, rng::StableRNGs.LehmerRNG) @ Main.ParticleMCMCTests ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:454 [13] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [15] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:437 [inlined] [16] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [17] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:475 [inlined] reference is pinned to retained values under re-conditioning: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:480 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#reconditioned#reconditioned##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#reconditioned#reconditioned##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Condition, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#reconditioned#reconditioned##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Condition, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Float64) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#reconditioned#reconditioned##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Condition, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#reconditioned#reconditioned##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Condition, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [11] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:485 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [13] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:491 [inlined] value replay detects a changed latent trace: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:503 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#branch_changes#branch_changes##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#branch_changes#branch_changes##0", (:flag, :y), (), (), Tuple{Bool, Float64}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Bool, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#branch_changes#branch_changes##0", (:flag, :y), (), (), Tuple{Bool, Float64}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Bool, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#branch_changes#branch_changes##0", (:flag, :y), (), (), Tuple{Bool, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#branch_changes#branch_changes##0", (:flag, :y), (), (), Tuple{Bool, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [11] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:504 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [13] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:515 [inlined] value replay handles a slice assume: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:539 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Float64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#slice_assume#slice_assume##0", (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [24] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [25] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [26] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:543 [inlined] [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:549 [inlined] latents whose dimension varies between executions: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:553 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Float64}, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Float64}, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#random_dimension#random_dimension##0", (:y, :c), (), (), Tuple{Vector{Float64}, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [24] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [25] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [26] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:565 [inlined] [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:584 [inlined] conditional sweeps target the exact posterior: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:601 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Float64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}, states::Tuple{DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{6, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(:i,), Tuple{Int64}}}, MHLinkedValues::DynamicPPL.VNTAccumulator{:MHLinkedValues, typeof(Turing.Inference.store_linked_values), DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}, MHUnspecifiedPriors::DynamicPPL.VNTAccumulator{:MHUnspecifiedPriors, Turing.Inference.StoreUnspecifiedPriors, DynamicPPL.VarNamedTuples.VarNamedTuple{(:i,), Tuple{Distributions.Categorical{Float64, Vector{Float64}}}}}}}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:i, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:i, :z), Tuple{Int64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:571 [16] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:i, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [17] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:i, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [18] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [19] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:i, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:i, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [24] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:i, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [25] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:i, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [26] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#switching#switching##0"{Tuple{Float64, Float64}, Tuple{Float64, Float64}, Float64}, (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:i, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [27] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [28] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [29] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:614 [inlined] [30] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [31] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:638 [inlined] addlogprob leads to reweighting: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:644 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [24] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [25] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [26] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:647 [inlined] [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:657 [inlined] keyword argument handling: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:666 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Core.kwcall), @NamedTuple{n::Float64}, Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Float64; kwargs::@Kwargs{n::Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [8] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [9] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [10] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [11] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [12] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [13] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [14] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [15] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [16] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [17] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [18] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [19] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [20] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [21] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#61#85"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [23] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [25] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:667 [inlined] [26] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [27] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:672 [inlined] submodels without kwargs: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:681 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#nested#nested##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Float64) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#nested#nested##0", (:y,), (), (), Tuple{Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [24] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [25] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [26] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:682 [inlined] [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:694 [inlined] submodels with kwargs: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:698 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#outer_kwarg1#outer_kwarg1##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [24] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:305 [25] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [26] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:699 [inlined] [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:708 [inlined] ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 parallel chains (MCMCThreads): Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:731 Got exception outside of a @test TaskFailedException nested task error: TaskFailedException nested task error: MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.coinflip), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Int64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Int64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#63#64"{Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#63#64"{Random.Xoshiro, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, nparticles::Int64; check_model::Bool, chain_type::Type, discard_initial::Int64, thinning::Int64, initial_params::DynamicPPL.InitFromPrior, initial_state::Nothing, save_state::Bool, callback::Nothing, verbose::Bool, kwargs::@Kwargs{progress::Bool, chain_number::Int64}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:722 [13] (::AbstractMCMC.var"#62#63"{Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Random.Xoshiro, UnitRange{Int64}, Vector{DynamicPPL.InitFromPrior}, Nothing, Int64, Vector{Any}, Vector{UInt64}})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:684 Stacktrace: [1] sync_end(c::Channel{Any}, src::Core.CancellationTokenSource) @ Base task.jl:830 [2] macro expansion @ task.jl:925 [inlined] [3] (::AbstractMCMC.var"#60#61"{Vector{DynamicPPL.InitFromPrior}, Nothing, Int64, Vector{Any}, Vector{UInt64}, Int64, Int64, Vector{Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Vector{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}, Vector{Random.Xoshiro}, UnitRange{Int64}})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:641 Stacktrace: [1] sync_end(c::Channel{Any}, src::Core.CancellationTokenSource) @ Base task.jl:830 [2] macro expansion @ task.jl:925 [inlined] [3] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:616 [inlined] [4] (::AbstractMCMC.var"#54#55"{String, Vector{DynamicPPL.InitFromPrior}, Nothing, Int64, Int64, Vector{Any}, Vector{UInt64}, Int64, Int64, Vector{Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Vector{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}, Vector{Random.Xoshiro}, UnitRange{Int64}})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [5] with_logstate(f::AbstractMCMC.var"#54#55"{String, Vector{DynamicPPL.InitFromPrior}, Nothing, Int64, Int64, Vector{Any}, Vector{UInt64}, Int64, Int64, Vector{Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Vector{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}}, Vector{Random.Xoshiro}, UnitRange{Int64}}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [6] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [7] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [8] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [9] mcmcsample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, ::AbstractMCMC.MCMCThreads, N::Int64, nchains::Int64; progress::Bool, progressname::String, initial_params::Vector{DynamicPPL.InitFromPrior}, initial_state::Nothing, kwargs::@Kwargs{chain_type::UnionAll, check_model::Bool, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:586 [10] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, ensemble::AbstractMCMC.MCMCThreads, N::Int64, n_chains::Int64; chain_type::Core.TypeEgal{FlexiChains.VNChain}, check_model::Bool, verbose::Bool, initial_params::Vector{DynamicPPL.InitFromPrior}, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:151 [11] sample(rng::Random.Xoshiro, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.coinflip), (:y,), (), (), Tuple{Vector{Int64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, ensemble::AbstractMCMC.MCMCThreads, N::Int64, n_chains::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:133 [12] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:736 [inlined] [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [14] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:732 advance!: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:747 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.test), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:747 [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [11] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:749 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [13] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:749 [inlined] matches a direct evaluation: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:755 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.test), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:747 [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [11] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:759 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [13] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:759 [inlined] fork: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:777 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.test), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.test), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:747 [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [11] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:778 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [13] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:778 [inlined] both fields of a named addlogprob are weighted: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:787 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.ParticleMCMCTests.var"#both_terms#both_terms##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#both_terms#both_terms##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.ParticleMCMCTests.var"#both_terms#both_terms##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#both_terms#both_terms##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#both_terms#both_terms##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:747 [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [11] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:791 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [13] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:795 [inlined] reference consumes no randomness: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:803 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.centred_normal), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.centred_normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.centred_normal), (), (), (), Tuple{}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.centred_normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.centred_normal), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:747 [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [11] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:808 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [13] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:808 [inlined] PG recovers the exact smoothing marginals: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:906 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.lgssm), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Float64}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Float64}, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [24] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:891 [25] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [26] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:907 [inlined] [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:908 [inlined] ┌ Info: Found initial step size └ ϵ = 0.8 Gibbs(q => NUTS, x => CSMC) recovers the exact posterior: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:918 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.lgssm), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:x, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Float64}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:x, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Float64}, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:x, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:x, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:x, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:x, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:x, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:x, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:x, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:x, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}, states::Tuple{Turing.Inference.HMCState{AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}, DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:q,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:x, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:q, :x), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:571 [16] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:x, AbstractPPL.Iden}}}, Tuple{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [17] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:x, AbstractPPL.Iden}}}, Tuple{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [18] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [19] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:x, AbstractPPL.Iden}}}, Tuple{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:x, AbstractPPL.Iden}}}, Tuple{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [24] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:x, AbstractPPL.Iden}}}, Tuple{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [25] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:x, AbstractPPL.Iden}}}, Tuple{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [26] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.lgssm), (:y, :a, :r), (), (), Tuple{Vector{Float64}, Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:x, AbstractPPL.Iden}}}, Tuple{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [27] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:891 [28] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [29] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:919 [inlined] [30] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [31] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:936 [inlined] PG recovers the exact state marginals: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:963 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.hmm), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Float64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.CondFixContext{DynamicPPL.Fix, DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{Float64}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [24] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:950 [25] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [26] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:966 [inlined] [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:967 [inlined] Gibbs(sd => HMC, z => CSMC) recovers the exact posterior: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:977 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.ParticleMCMCTests.hmm), DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Vector{Float64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Vector{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}, states::Tuple{Turing.Inference.HMCState{AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:sd, :z), Tuple{Float64, DynamicPPL.VarNamedTuples.PartialArray{Int64, 1, Vector{Int64}, Vector{Bool}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:571 [16] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [17] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [18] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [19] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [24] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [25] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [26] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.ParticleMCMCTests.hmm), (:y,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:sd, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:z, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [27] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:950 [28] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [29] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:978 [inlined] [30] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [31] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/particle_mcmc.jl:989 [inlined] [ Info: (varname = s, exact = 2.0416666666666665, evaluated = 2.0703723323764933) [ Info: (varname = m, exact = 1.1666666666666667, evaluated = 1.159950127444649) [ Info: Testing emcee with large number of iterations [ Info: (varname = s, exact = 2.0416666666666665, evaluated = 2.041349397195655) [ Info: (varname = m, exact = 1.1666666666666667, evaluated = 1.1787532039447761) [ Info: Starting ESS tests [ Info: Starting ESS inference tests [ Info: (varname = m, exact = 0.8, evaluated = 0.8172942592919593) [ Info: (varname = m[1], exact = 0.0, evaluated = -0.02456171083886478) [ Info: (varname = m[2], exact = 0.8, evaluated = 0.8075869528540663) gdemo with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/ess.jl:61 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Float64, ::Vararg{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [16] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [17] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [18] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [19] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [20] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [21] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [22] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [23] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [24] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [25] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [26] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/ess.jl:14 [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/ess.jl:48 [inlined] [29] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [30] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/ess.jl:62 [inlined] [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/ess.jl:65 [inlined] MoGtest_default with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/ess.jl:69 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Matrix{Float64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Matrix{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS, Turing.Inference.ESS}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS, Turing.Inference.ESS}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [16] _step_or_step_warmup(::Int64, ::Int64, ::StableRNGs.LehmerRNG, ::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS, Turing.Inference.ESS}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [17] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [18] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS, Turing.Inference.ESS}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [19] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS, Turing.Inference.ESS}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [20] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [21] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [22] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [23] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS, Turing.Inference.ESS}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [24] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS, Turing.Inference.ESS}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [25] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS, Turing.Inference.ESS}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [26] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/ess.jl:14 [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/ess.jl:48 [inlined] [29] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [30] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/ess.jl:70 [inlined] [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/ess.jl:75 [inlined] [ Info: Skipping test_sampler_analytical for demo_nested_colons due to MCMCChains limitations. ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 WARNING: Method definition (::GibbsTests.Wrapper{T})(T) where {T<:Real} in module GibbsTests at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:36 overwritten at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:199. WARNING: Method definition (::GibbsTests.Wrapper{T<:Real})(Any) in module GibbsTests at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:36 overwritten at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:199. Sampler call order: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:149 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{Main.GibbsTests.var"#test_model#test_model##1", DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Int64, DynamicPPL.TypeWrap{Vector{Float64}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Int64, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step(::Random.TaskLocalRNG, ::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}}, DynamicPPL.DefaultContext}, false}, ::Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ Main.GibbsTests ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:195 [14] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}}, DynamicPPL.DefaultContext}, false}, sampler::Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [15] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}, states::Tuple{DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{6, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :n), Tuple{Float64, Int64}}}, MHLinkedValues::DynamicPPL.VNTAccumulator{:MHLinkedValues, typeof(Turing.Inference.store_linked_values), DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}, MHUnspecifiedPriors::DynamicPPL.VNTAccumulator{:MHUnspecifiedPriors, Turing.Inference.StoreUnspecifiedPriors, DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{Distributions.Normal{Float64}}}}}}}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{6, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :n), Tuple{Float64, Int64, Int64}}}, MHLinkedValues::DynamicPPL.VNTAccumulator{:MHLinkedValues, typeof(Turing.Inference.store_linked_values), DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}, MHUnspecifiedPriors::DynamicPPL.VNTAccumulator{:MHUnspecifiedPriors, Turing.Inference.StoreUnspecifiedPriors, DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Distributions.Normal{Float64}, Distributions.Poisson{Float64}}}}}}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [16] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r, :n), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, Int64}}, states::Tuple{DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{6, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :n), Tuple{Float64, Int64}}}, MHLinkedValues::DynamicPPL.VNTAccumulator{:MHLinkedValues, typeof(Turing.Inference.store_linked_values), DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}, MHUnspecifiedPriors::DynamicPPL.VNTAccumulator{:MHUnspecifiedPriors, Turing.Inference.StoreUnspecifiedPriors, DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{Distributions.Normal{Float64}}}}}}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:571 [17] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m, :xs, :ys, :q, :r), Tuple{Float64, Int64, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}, DynamicPPL.VarNamedTuples.VarNamedTuple{(:a,), Tuple{Float64}}, DynamicPPL.VarNamedTuples.PartialArray{Float64, 1, Vector{Float64}, Vector{Bool}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:571 [18] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [19] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [26] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [28] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [29] sample(model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [30] sample(model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#1394")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:xs, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:ys, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:q, AbstractPPL.Property{:a, AbstractPPL.Iden}}}, Vector{AbstractPPL.VarName{:r, AbstractPPL.Index{Tuple{Int64}, @NamedTuple{}, AbstractPPL.Iden}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{Returns{DynamicPPL.InitFromPrior}, DynamicPPL.UnlinkAll}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [31] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:152 [32] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [33] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:245 [inlined] [ Info: Starting Gibbs tests Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:379 Test threw exception Expression: sample(gdemo_default, s2, N) isa VNChain MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}, AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}, AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}, AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}, AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}, AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}, AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}, AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}, AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}, AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [16] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [17] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [18] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [19] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [20] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [21] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [22] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [23] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [24] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [25] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [26] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [27] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [28] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:352 [29] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [30] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:357 [inlined] [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:379 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:380 Test threw exception Expression: sample(gdemo_default, s3, N) isa VNChain MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [16] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [17] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [18] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [19] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [20] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [21] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [22] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [23] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [24] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [25] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [26] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [27] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [28] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:352 [29] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [30] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:357 [inlined] [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:380 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:382 Test threw exception Expression: sample(gdemo_default, s5, N) isa VNChain MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{Turing.Inference.HMCState{AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), 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AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), 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Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), 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DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Turing.Inference.HMCState{AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{Turing.Inference.HMCState{AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Turing.Inference.HMCState{AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:571 [16] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{Turing.Inference.HMCState{AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:571 [17] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:571 [18] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [19] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [26] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [28] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [29] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [30] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [31] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:352 [32] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [33] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:357 [inlined] [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:382 [inlined] [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:383 Test threw exception Expression: sample(gdemo_default, s6, N) isa VNChain MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/repeat_sampler.jl:80 [inlined] [15] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{Turing.Inference.HMCState{AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, AbstractPPL.Evaluators.Prepared{ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, AbstractPPL.Evaluators.VectorEvaluator{false, typeof(DynamicPPL.logdensity_internal), Tuple{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarNamedTuples.VarNamedTuple{(:s,), Tuple{DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, AbstractPPLForwardDiffExt.FDCache{:scalar, DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, Nothing, Nothing}, 1}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}, false}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [16] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:571 [17] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [18] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [19] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [20] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [21] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [22] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [24] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [28] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [29] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [30] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:352 [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:357 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:383 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] CSMC and HMC on gdemo: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:389 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Float64, ::Vararg{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [16] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [17] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [18] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [19] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [20] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [21] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [22] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [23] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [24] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [25] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [26] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [27] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [28] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:352 [29] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [30] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:389 [inlined] [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:390 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:391 [inlined] ┌ Info: Found initial step size └ ϵ = 1.6 [ Info: (varname = s, exact = 2.0416666666666665, evaluated = 2.1066878216673537) [ Info: (varname = m, exact = 1.1666666666666667, evaluated = 1.1887493336099382) CSMC and ESS on gdemo: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:403 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Float64, ::Vararg{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:s, :m), Tuple{Float64, Float64}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [16] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [17] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [18] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [19] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [20] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [21] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [22] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [23] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [24] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [25] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [26] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [27] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:m, AbstractPPL.Iden}}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.ESS}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [28] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:352 [29] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [30] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:389 [inlined] [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:404 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:405 [inlined] CSMC on gdemo: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:410 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.SMCContext, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.SMCContext, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Float64, ::Vararg{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [15] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [16] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [17] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [18] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [19] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [20] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [21] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [22] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [23] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [24] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [25] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [26] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:352 [27] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [28] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:389 [inlined] [29] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [30] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:411 [inlined] [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:412 [inlined] PG and HMC on MoGtest_default: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:416 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Matrix{Float64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Matrix{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [16] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [17] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [18] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [19] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [20] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [21] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [22] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [23] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [24] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [25] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [26] sample(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [27] sample(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [28] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:352 [29] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [30] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:389 [inlined] [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:417 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:421 [inlined] [ Info: (varname = s, exact = 2.0416666666666665, evaluated = 2.1021788421430716) [ Info: (varname = m, exact = 1.1666666666666667, evaluated = 1.1760477860471288) Multiple overlapping samplers on MoGtest_default: Error During Test at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:439 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::I, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) where I<:Compiler.AbstractInterpreter @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1177 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any, !Matched::Compiler.WorldRange) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:1180 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/utils.jl:40 [4] build_callable(sig::Core.TypeEgal{Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, Turing.Inference.SMCContext}, false}, DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, Matrix{Float64}}}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:196 [5] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:422 [6] Libtask.TapedTask(::Turing.Inference.Particle{Random123.Philox2x{UInt64, 10}, Float64}, ::Function, ::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, Turing.Inference.SMCContext}, false}, ::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{4, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}, RawValues::DynamicPPL.VNTAccumulator{:RawValues, DynamicPPL.GetRawValues, DynamicPPL.VarNamedTuples.VarNamedTuple{(), Tuple{}}}}}}, ::Matrix{Float64}) @ Libtask ~/.julia/packages/Libtask/xWKdS/src/copyable_task.jl:413 [7] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}, reference::Nothing) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:113 [inlined] [8] Turing.Inference.Particle(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, rng::Random123.Philox2x{UInt64, 10}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:109 [9] (::Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}})(::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [10] iterate(::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base generator.jl:49 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#74#75"{Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}}}) @ Base array.jl:839 [12] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/particle_mcmc.jl:837 [13] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, AbstractPPL.Iden}, AbstractPPL.VarName{:z2, AbstractPPL.Iden}, AbstractPPL.VarName{:z3, AbstractPPL.Iden}, AbstractPPL.VarName{:z4, AbstractPPL.Iden}}, Base.RefValue{DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, verbose::Bool, discard_sample::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/interface.jl:118 [inlined] [14] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, samplers::Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, vnt::DynamicPPL.VarNamedTuples.VarNamedTuple{(:mu1, :mu2, :z1, :z2, :z3, :z4), Tuple{Float64, Float64, Vararg{Int64, 4}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553 [15] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64, verbose::Bool}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:504 [16] _step_or_step_warmup(::Int64, ::Int64, ::Random.TaskLocalRNG, ::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, ::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}; kwargs::@Kwargs{discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:126 [inlined] [17] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:223 [inlined] [18] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:134 [19] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Core.TypeEgal{FlexiChains.VNChain}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging logging/logging.jl:542 [20] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging logging/logging.jl:653 [21] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:157 [22] macro expansion @ ~/.julia/packages/AbstractMCMC/NK6XN/src/logging.jl:133 [inlined] [23] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Core.TypeEgal{FlexiChains.VNChain}, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, verbose::Bool}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:204 [24] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Core.TypeEgal{FlexiChains.VNChain}, verbose::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:106 [inlined] [25] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:94 [inlined] [26] sample(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64; kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:91 [inlined] [27] sample(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}, Vector{AbstractPPL.VarName{:mu1, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{:mu2, AbstractPPL.Iden}}, Vector{AbstractPPL.VarName{sym, AbstractPPL.Iden} where sym}}, Tuple{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{Turing.Inference.ESSThresholdResampler{Float64, Turing.Inference.StratifiedResampler}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/abstractmcmc.jl:88 [28] top-level scope @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:352 [29] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [30] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:389 [inlined] [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:440 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:449 [inlined] ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 6.4 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Warning: There were 1 divergent transitions. Consider reparameterising your model or using a smaller step size. For adaptive samplers such as NUTS and HMCDA, consider increasing `target_accept`. └ @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/hmc.jl:483 ┌ Warning: There were 1 divergent transitions. Consider reparameterising your model or using a smaller step size. For adaptive samplers such as NUTS and HMCDA, consider increasing `target_accept`. └ @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/hmc.jl:483 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 6.4 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Warning: There were 1 divergent transitions. Consider reparameterising your model or using a smaller step size. For adaptive samplers such as NUTS and HMCDA, consider increasing `target_accept`. └ @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/hmc.jl:483 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: There were 1 divergent transitions. Consider reparameterising your model or using a smaller step size. For adaptive samplers such as NUTS and HMCDA, consider increasing `target_accept`. └ @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/hmc.jl:483 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Warning: There were 2 divergent transitions. Consider reparameterising your model or using a smaller step size. For adaptive samplers such as NUTS and HMCDA, consider increasing `target_accept`. └ @ Turing.Inference ~/.julia/packages/Turing/qVOus/src/mcmc/hmc.jl:483 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 6.4 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 6.4 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/NK6XN/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile. --trace-compile is enabled during profile collection. ====================================================================================== cmd: /opt/julia/bin/julia 103 running 1 of 1 signal (10): User defined signal 1 egal_types at /source/src/builtins.c:202:13 ijl_types_equal at /source/src/subtype.c:3927:41 jl_specializations_get_linfo_ at /source/src/gf.c:275:17 cache_result at /source/src/gf.c:1959:15 ml_matches at /source/src/gf.c:5694:13 ijl_matching_methods at /source/src/gf.c:3564:12 [inlined] ijl_matching_methods at /source/src/gf.c:3551:26 _methods_by_ftype at ./runtime_internals.jl:1777:0 [inlined] _findall at ./../usr/share/julia/Compiler/src/methodtable.jl:105:0 [inlined] #findall#5 at ./../usr/share/julia/Compiler/src/methodtable.jl:70:0 [inlined] findall at ./../usr/share/julia/Compiler/src/methodtable.jl:70:0 [inlined] #findall#13 at ./../usr/share/julia/Compiler/src/methodtable.jl:113:0 (pc: 27) findall at ./../usr/share/julia/Compiler/src/methodtable.jl:110:0 [inlined] find_simple_method_matches at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:421:0 (pc: 6) #find_method_matches#154 at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:380:0 [inlined] find_method_matches at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:373:0 (pc: 25) jfptr_find_method_matches_4.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 abstract_call_gf_by_type at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:136:0 (pc: 11) abstract_call_known at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3082:0 (pc: 1830) abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3233:0 (pc: 345) abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3226:0 [inlined] abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3372:0 [inlined] abstract_eval_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3390:0 (pc: 119) abstract_eval_statement_expr at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3817:0 (pc: 4) abstract_eval_basic_statement at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4284:0 [inlined] abstract_eval_basic_statement at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4246:0 [inlined] typeinf_local at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4827:0 (pc: 3370) jfptr_typeinf_local_1.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 typeinf at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:5100:0 (pc: 690) typeinf_ext at ./../usr/share/julia/Compiler/src/typeinfer.jl:1790:0 (pc: 106) typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:2063:0 [inlined] typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:2072:0 (pc: 16) jfptr_typeinf_ext_toplevel_5.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 jl_apply at /source/src/julia.h:2533:12 [inlined] jl_type_infer at /source/src/gf.c:482:35 jl_compile_method_very_internal at /source/src/gf.c:4076:20 _jl_invoke at /source/src/gf.c:4576:16 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 #gibbs_initialstep_recursive#115 at /home/pkgeval/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:553:0 (pc: 7) unknown function (ip: 0x7d359c2c7dc8) at (unknown file) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:530:0 (pc: 3) unknown function (ip: 0x7d359c2c73ce) at (unknown file) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 #gibbs_initialstep_recursive#115 at /home/pkgeval/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:571:0 (pc: 24) gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:530:0 [inlined] gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:530:0 [inlined] #step#113 at /home/pkgeval/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:471:0 (pc: 12) step at /home/pkgeval/.julia/packages/Turing/qVOus/src/mcmc/gibbs.jl:457:0 (pc: 1) unknown function (ip: 0x7d359c2b5861) at (unknown file) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 jl_apply at /source/src/julia.h:2533:12 [inlined] do_call at /source/src/interpreter.c:123:26 eval_value at /source/src/interpreter.c:259:16 eval_stmt_value at /source/src/interpreter.c:194:23 [inlined] eval_body at /source/src/interpreter.c:829:21 eval_body at /source/src/interpreter.c:704:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:704:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:704:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:704:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 jl_interpret_toplevel_thunk at /source/src/interpreter.c:1052:21 ijl_eval_thunk at /source/src/toplevel.c:772:18 jl_toplevel_eval_flex at /source/src/toplevel.c:716:26 jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 jl_eval_module_expr at /source/src/toplevel.c:266:5 [inlined] jl_toplevel_eval_flex at /source/src/toplevel.c:669:27 jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 jl_toplevel_eval_flex at /source/src/toplevel.c:688:27 ijl_toplevel_eval at /source/src/toplevel.c:786:12 ijl_toplevel_eval_in at /source/src/toplevel.c:831:13 eval at ./boot.jl:618:0 (pc: 1) include_string at ./loading.jl:3258:0 (pc: 140) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 _include at ./loading.jl:3320:0 (pc: 123) include at ./Base.jl:335:0 (pc: 1) IncludeInto at ./Base.jl:336:0 (pc: 2) jfptr_IncludeInto_1.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 jl_apply at /source/src/julia.h:2533:12 [inlined] do_call at /source/src/interpreter.c:123:26 eval_value at /source/src/interpreter.c:259:16 eval_stmt_value at /source/src/interpreter.c:194:23 [inlined] eval_body at /source/src/interpreter.c:829:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:704:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:704:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 eval_body at /source/src/interpreter.c:712:21 jl_interpret_toplevel_thunk at /source/src/interpreter.c:1052:21 ijl_eval_thunk at /source/src/toplevel.c:772:18 jl_toplevel_eval_flex at /source/src/toplevel.c:716:26 jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 jl_toplevel_eval_flex at /source/src/toplevel.c:688:27 ijl_toplevel_eval at /source/src/toplevel.c:786:12 ijl_toplevel_eval_in at /source/src/toplevel.c:831:13 eval at ./boot.jl:618:0 (pc: 1) include_string at ./loading.jl:3258:0 (pc: 140) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 _include at ./loading.jl:3320:0 (pc: 123) include at ./Base.jl:335:0 (pc: 1) IncludeInto at ./Base.jl:336:0 (pc: 2) jfptr_IncludeInto_1.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 jl_apply at /source/src/julia.h:2533:12 [inlined] do_call at /source/src/interpreter.c:123:26 eval_value at /source/src/interpreter.c:259:16 eval_stmt_value at /source/src/interpreter.c:194:23 [inlined] eval_body at /source/src/interpreter.c:829:21 jl_interpret_toplevel_thunk at /source/src/interpreter.c:1052:21 ijl_eval_thunk at /source/src/toplevel.c:772:18 jl_toplevel_eval_flex at /source/src/toplevel.c:716:26 jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 jl_toplevel_eval_flex at /source/src/toplevel.c:688:27 ijl_toplevel_eval at /source/src/toplevel.c:786:12 ijl_toplevel_eval_in at /source/src/toplevel.c:831:13 eval at ./boot.jl:618:0 (pc: 1) __script_entry_eval at ./client.jl:106:0 [inlined] exec_options at ./client.jl:350:0 (pc: 426) _start at ./client.jl:695:0 (pc: 217) jfptr__start_0.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 jl_apply at /source/src/julia.h:2533:12 [inlined] true_main at /source/src/jlapi.c:989:29 jl_repl_entrypoint at /source/src/jlapi.c:1156:15 main at /source/cli/loader_exe.c:117:15 unknown function (ip: 0x7d3624aea249) at /lib/x86_64-linux-gnu/libc.so.6 __libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point. Disabling --trace-compile ============================================================== ┌ Info: Found initial step size └ ϵ = 1.6 ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile. --trace-compile is enabled during profile collection. ====================================================================================== cmd: /opt/julia/bin/julia 1 running 0 of 1 signal (10): User defined signal 1 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404:0 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430:0 ijl_task_get_next at /source/src/scheduler.c:573:34 wait at ./task.jl:1652:0 (pc: 108) wait_forever at ./task.jl:1528:0 (pc: 4) jfptr_wait_forever_0.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4584:23 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 jl_apply at /source/src/julia.h:2533:12 [inlined] start_task at /source/src/task.c:1278:23 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point. Disabling --trace-compile ============================================================== Overhead ╎ [+additional indent] Count File:Line Function ========================================================= Thread 1 (default) Task 0x00007dbfbec5cb50 Total snapshots: 349. Utilization: 0% ╎349 @Base/task.jl:1528 wait_forever() 348╎ 349 @Base/task.jl:? wait() ┌ Info: Found initial step size └ ϵ = 0.8 [1] signal 15: Terminated in expression starting at /PkgEval.jl/scripts/evaluate.jl:214 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404:0 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430:0 ijl_task_get_next at /source/src/scheduler.c:573:34 wait at ./task.jl:1652:0 (pc: 108) [103] signal 15: Terminated in expression starting at /home/pkgeval/.julia/packages/Turing/qVOus/test/mcmc/gibbs.jl:351 malloc at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) operator new at /workspace/srcdir/gcc-15.2.0/libstdc++-v3/libsupc++/new_op.cc:50:22 wait_safe_interrupt at ./park.jl:231:0 (pc: 6) #wait#428 at ./condition.jl:390:0 (pc: 117) wait at ./condition.jl:325:0 [inlined] _trywait at ./asyncevent.jl:204:0 (pc: 38) #_trywait#722 at ./asyncevent.jl:175:0 [inlined] _trywait at ./asyncevent.jl:175:0 [inlined] profile_printing_listener at ./Base.jl:366:0 (pc: 23) allocate at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/ext/new_allocator.h:114:41 [inlined] allocate at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/alloc_traits.h:444:32 [inlined] _M_get_node at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:580:39 [inlined] _M_create_node at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:630:15 [inlined] operator() at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:548:62 [inlined] _M_insert_, std::less, std::allocator >::_Alloc_node> at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1806:29 [inlined] _M_insert_unique at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:2149:11 insert at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/stl_set.h:511:48 [inlined] insertImpl at /source/usr/include/llvm/ADT/SmallSet.h:242:12 insert at /source/usr/include/llvm/ADT/SmallSet.h:183:75 [inlined] linkOutput at /source/src/jitlayers.cpp:2541:31 materialize at /source/src/jitlayers.cpp:1100:28 _ZN4llvm3orc19MaterializationTask3runEv at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line) dispatch at /source/src/julia-task-dispatcher.h:377:13 [inlined] dispatch at /source/src/julia-task-dispatcher.h:366:6 #start_profile_listener##0 at ./Base.jl:386:0 (pc: 2) jfptr_YY.start_profile_listenerYY.YY.0_0.1 at /opt/julia/lib/julia/sys.so (unknown line) start_task at /source/src/task.c:1275:23 unknown function (ip: (nil)) at (unknown file) Allocations: 17666296 (Pool: 17665495; Big: 801); GC: 17 _ZN4llvm3orc16ExecutionSession12dispatchTaskESt10unique_ptrINS0_4TaskESt14default_deleteIS3_EE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line) _ZN4llvm3orc16ExecutionSession22dispatchOutstandingMUsEv at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line) _ZN4llvm3orc16ExecutionSession17OL_completeLookupESt10unique_ptrINS0_21InProgressLookupStateESt14default_deleteIS3_EESt10shared_ptrINS0_23AsynchronousSymbolQueryEESt8functionIFvRKNS_8DenseMapIPNS0_8JITDylibENS_8DenseSetINS0_15SymbolStringPtrENS_12DenseMapInfoISF_vEEEENSG_ISD_vEENS_6detail12DenseMapPairISD_SI_EEEEEE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line) _ZN4llvm3orc25InProgressFullLookupState8completeESt10unique_ptrINS0_21InProgressLookupStateESt14default_deleteIS3_EE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line) _ZN4llvm3orc16ExecutionSession19OL_applyQueryPhase1ESt10unique_ptrINS0_21InProgressLookupStateESt14default_deleteIS3_EENS_5ErrorE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line) _ZN4llvm3orc16ExecutionSession6lookupENS0_10LookupKindERKSt6vectorISt4pairIPNS0_8JITDylibENS0_19JITDylibLookupFlagsEESaIS8_EENS0_15SymbolLookupSetENS0_11SymbolStateENS_15unique_functionIFvNS_8ExpectedINS_8DenseMapINS0_15SymbolStringPtrENS0_17ExecutorSymbolDefENS_12DenseMapInfoISI_vEENS_6detail12DenseMapPairISI_SJ_EEEEEEEEESt8functionIFvRKNSH_IS6_NS_8DenseSetISI_SL_EENSK_IS6_vEENSN_IS6_SV_EEEEEE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line) publishCIs at /source/src/jitlayers.cpp:2247:14 jl_compile_codeinst_impl at /source/src/jitlayers.cpp:521:39 jl_compile_method_very_internal at /source/src/gf.c:4099:27 _jl_invoke at /source/src/gf.c:4576:16 [inlined] ijl_apply_generic at /source/src/gf.c:4832:12 profile_printing_listener at ./Base.jl:368:0 (pc: 73) #start_profile_listener##0 at ./Base.jl:386:0 (pc: 2) jfptr_YY.start_profile_listenerYY.YY.0_0.1 at /opt/julia/lib/julia/sys.so (unknown line) start_task at /source/src/task.c:1275:23 unknown function (ip: (nil)) at (unknown file) Allocations: 1850182632 (Pool: 1850173008; Big: 9624); GC: 456 PkgEval terminated after 2730.21s: test duration exceeded the time limit