Package evaluation of Turing on Julia 1.13.0-DEV.1099 (5c93bf20fd*) started at 2025-09-10T01:35:11.935 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.27s ################################################################################ # Installation # Installing Turing... Resolving package versions... Updating `~/.julia/environments/v1.13/Project.toml` [fce5fe82] + Turing v0.40.3 Updating `~/.julia/environments/v1.13/Manifest.toml` [47edcb42] + ADTypes v1.17.0 [621f4979] + AbstractFFTs v1.5.0 [80f14c24] + AbstractMCMC v5.7.2 [7a57a42e] + AbstractPPL v0.13.1 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.3.0 [0bf59076] + AdvancedHMC v0.8.1 [5b7e9947] + AdvancedMH v0.8.8 [576499cb] + AdvancedPS v0.7.0 [b5ca4192] + AdvancedVI v0.4.1 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [4fba245c] + ArrayInterface v7.20.0 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.7 [198e06fe] + BangBang v0.4.4 [9718e550] + Baselet v0.1.1 [76274a88] + Bijectors v0.15.10 [082447d4] + ChainRules v1.72.5 [d360d2e6] + ChainRulesCore v1.26.0 [0ca39b1e] + Chairmarks v1.3.1 [9e997f8a] + ChangesOfVariables v0.1.10 [861a8166] + Combinatorics v1.0.3 [38540f10] + CommonSolve v0.2.4 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.18.0 [a33af91c] + CompositionsBase v0.1.2 [88cd18e8] + ConsoleProgressMonitor v0.1.2 [187b0558] + ConstructionBase v1.6.0 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 ⌅ [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [b429d917] + DensityInterface v0.4.0 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [a0c0ee7d] + DifferentiationInterface v0.7.7 [31c24e10] + Distributions v0.25.120 [ced4e74d] + DistributionsAD v0.6.58 [ffbed154] + DocStringExtensions v0.9.5 [366bfd00] + DynamicPPL v0.37.2 [cad2338a] + EllipticalSliceSampling v2.0.0 [4e289a0a] + EnumX v1.0.5 [e2ba6199] + ExprTools v0.1.10 [55351af7] + ExproniconLite v0.10.14 [7a1cc6ca] + FFTW v1.9.0 [9aa1b823] + FastClosures v0.3.2 [1a297f60] + FillArrays v1.13.0 [6a86dc24] + FiniteDiff v2.28.1 [f6369f11] + ForwardDiff v1.1.0 [069b7b12] + FunctionWrappers v1.1.3 [77dc65aa] + FunctionWrappersWrappers v0.1.3 [d9f16b24] + Functors v0.5.2 [46192b85] + GPUArraysCore v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [22cec73e] + InitialValues v0.3.1 [a98d9a8b] + Interpolations v0.16.2 [8197267c] + IntervalSets v0.7.11 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.4 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.1 [682c06a0] + JSON v0.21.4 [ae98c720] + Jieko v0.2.1 [5ab0869b] + KernelDensity v0.6.10 [5be7bae1] + LBFGSB v0.4.1 [b964fa9f] + LaTeXStrings v1.4.0 [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1 [6f1fad26] + Libtask v0.9.4 [d3d80556] + LineSearches v7.4.0 [6fdf6af0] + LogDensityProblems v2.1.2 [996a588d] + LogDensityProblemsAD v1.13.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.1.0 ⌃ [c7f686f2] + MCMCChains v7.2.1 [be115224] + MCMCDiagnosticTools v0.3.15 [e80e1ace] + MLJModelInterface v1.12.0 [1914dd2f] + MacroTools v0.5.16 [dbb5928d] + MappedArrays v0.4.2 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [dbe65cb8] + MistyClosures v2.1.0 [2e0e35c7] + Moshi v0.3.7 [d41bc354] + NLSolversBase v7.10.0 [77ba4419] + NaNMath v1.1.3 [86f7a689] + NamedArrays v0.10.4 [c020b1a1] + NaturalSort v1.0.0 [6fe1bfb0] + OffsetArrays v1.17.0 [429524aa] + Optim v1.13.2 [3bd65402] + Optimisers v0.4.6 [7f7a1694] + Optimization v4.6.0 [bca83a33] + OptimizationBase v2.10.0 [36348300] + OptimizationOptimJL v0.4.3 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.35 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.3 [85a6dd25] + PositiveFactorizations v0.2.4 [d236fae5] + PreallocationTools v0.4.34 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.0 [08abe8d2] + PrettyTables v3.0.8 [33c8b6b6] + ProgressLogging v0.1.5 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [74087812] + Random123 v1.7.1 [e6cf234a] + RandomNumbers v1.6.0 [b3c3ace0] + RangeArrays v0.3.2 [c84ed2f1] + Ratios v0.4.5 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 ⌃ [731186ca] + RecursiveArrayTools v3.36.0 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.8.0 [f2b01f46] + Roots v2.2.10 [7e49a35a] + RuntimeGeneratedFunctions v0.5.15 ⌅ [26aad666] + SSMProblems v0.5.2 [0bca4576] + SciMLBase v2.117.1 [c0aeaf25] + SciMLOperators v1.7.1 [53ae85a6] + SciMLStructures v1.7.0 [30f210dd] + ScientificTypesBase v3.0.0 [efcf1570] + Setfield v1.1.2 [a2af1166] + SortingAlgorithms v1.2.2 [9f842d2f] + SparseConnectivityTracer v1.0.1 [dc90abb0] + SparseInverseSubset v0.1.2 [0a514795] + SparseMatrixColorings v0.4.21 [276daf66] + SpecialFunctions v2.5.1 [171d559e] + SplittablesBase v0.1.15 [90137ffa] + StaticArrays v1.9.15 [1e83bf80] + StaticArraysCore v1.4.3 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.6 [4c63d2b9] + StatsFuns v1.5.0 [892a3eda] + StringManipulation v0.4.1 [09ab397b] + StructArrays v0.7.1 ⌃ [2efcf032] + SymbolicIndexingInterface v0.3.38 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [5d786b92] + TerminalLoggers v0.1.7 [28d57a85] + Transducers v0.4.84 [fce5fe82] + Turing v0.40.3 [3a884ed6] + UnPack v1.0.2 [efce3f68] + WoodburyMatrices v1.0.0 [700de1a5] + ZygoteRules v0.2.7 [f5851436] + FFTW_jll v3.3.11+0 [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0 [81d17ec3] + L_BFGS_B_jll v3.0.1+0 [856f044c] + MKL_jll v2025.2.0+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [1317d2d5] + oneTBB_jll v2022.0.0+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.13.0 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [1a1011a3] + SharedArrays v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] + LibCURL_jll v8.15.0+1 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.8.12 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.2+0 [efcefdf7] + PCRE2_jll v10.46.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.1+2 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.13.1+0 [8e850ede] + nghttp2_jll v1.67.0+0 [3f19e933] + p7zip_jll v17.6.0+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 7.62s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:76 Precompiling package dependencies... Precompilation completed after 792.44s ################################################################################ # 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_KSOLMD/Project.toml` [4c88cf16] + Aqua v0.8.14 [aaaa29a8] + Clustering v0.15.8 [bbc10e6e] + DynamicHMC v3.5.1 [26cc04aa] + FiniteDifferences v0.12.32 [09f84164] + HypothesisTests v0.11.5 [c7f686f2] ↑ MCMCChains v7.2.1 ⇒ v7.3.0 [3e6eede4] + OptimizationBBO v0.4.1 [4e6fcdb7] + OptimizationNLopt v0.3.2 [37e2e3b7] + ReverseDiff v1.16.1 [860ef19b] + StableRNGs v1.0.3 [a759f4b9] + TimerOutputs v0.5.29 [fce5fe82] + Turing v0.40.3 Updating `/tmp/jl_KSOLMD/Manifest.toml` [0bf59076] + AdvancedHMC v0.8.1 [4c88cf16] + Aqua v0.8.14 [a134a8b2] + BlackBoxOptim v0.6.3 [fa961155] + CEnum v0.5.0 [a9c8d775] + CPUTime v1.0.0 [aaaa29a8] + Clustering v0.15.8 [b4f34e82] + Distances v0.10.12 [bbc10e6e] + DynamicHMC v3.5.1 [cad2338a] + EllipticalSliceSampling v2.0.0 [26cc04aa] + FiniteDifferences v0.12.32 [09f84164] + HypothesisTests v0.11.5 [1fad7336] + LazyStack v0.1.3 [6f1fad26] + Libtask v0.9.4 [c7f686f2] ↑ MCMCChains v7.2.1 ⇒ v7.3.0 [dbe65cb8] + MistyClosures v2.1.0 [76087f3c] + NLopt v1.2.1 [b8a86587] + NearestNeighbors v0.4.22 [3e6eede4] + OptimizationBBO v0.4.1 [4e6fcdb7] + OptimizationNLopt v0.3.2 [65ce6f38] + PackageExtensionCompat v1.0.2 ⌅ [08abe8d2] ↓ PrettyTables v3.0.8 ⇒ v2.4.0 [731186ca] ↑ RecursiveArrayTools v3.36.0 ⇒ v3.37.1 [37e2e3b7] + ReverseDiff v1.16.1 [708f8203] + Richardson v1.4.2 [d4ead438] + SpatialIndexing v0.1.6 [860ef19b] + StableRNGs v1.0.3 [5e0ebb24] + Strided v2.3.2 [4db3bf67] + StridedViews v0.4.1 [2efcf032] ↑ SymbolicIndexingInterface v0.3.38 ⇒ v0.3.43 [02d47bb6] + TensorCast v0.4.9 [a759f4b9] + TimerOutputs v0.5.29 [24ddb15e] + TransmuteDims v0.1.17 [9d95972d] + TupleTools v1.6.0 [fce5fe82] + Turing v0.40.3 [079eb43e] + NLopt_jll v2.10.0+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_KSOLMD/Project.toml` [47edcb42] ADTypes v1.17.0 [80f14c24] AbstractMCMC v5.7.2 [7a57a42e] AbstractPPL v0.13.1 [5b7e9947] AdvancedMH v0.8.8 [576499cb] AdvancedPS v0.7.0 [b5ca4192] AdvancedVI v0.4.1 [4c88cf16] Aqua v0.8.14 [198e06fe] BangBang v0.4.4 [76274a88] Bijectors v0.15.10 [aaaa29a8] Clustering v0.15.8 [861a8166] Combinatorics v1.0.3 [31c24e10] Distributions v0.25.120 [ced4e74d] DistributionsAD v0.6.58 [bbc10e6e] DynamicHMC v3.5.1 [366bfd00] DynamicPPL v0.37.2 [26cc04aa] FiniteDifferences v0.12.32 [f6369f11] ForwardDiff v1.1.0 [09f84164] HypothesisTests v0.11.5 [6fdf6af0] LogDensityProblems v2.1.2 [996a588d] LogDensityProblemsAD v1.13.1 [c7f686f2] MCMCChains v7.3.0 [86f7a689] NamedArrays v0.10.4 [429524aa] Optim v1.13.2 [7f7a1694] Optimization v4.6.0 [3e6eede4] OptimizationBBO v0.4.1 [4e6fcdb7] OptimizationNLopt v0.3.2 [36348300] OptimizationOptimJL v0.4.3 [90014a1f] PDMats v0.11.35 [37e2e3b7] ReverseDiff v1.16.1 [276daf66] SpecialFunctions v2.5.1 [860ef19b] StableRNGs v1.0.3 [2913bbd2] StatsBase v0.34.6 [4c63d2b9] StatsFuns v1.5.0 [a759f4b9] TimerOutputs v0.5.29 [fce5fe82] Turing v0.40.3 [37e2e46d] LinearAlgebra v1.13.0 [44cfe95a] Pkg v1.13.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_KSOLMD/Manifest.toml` [47edcb42] ADTypes v1.17.0 [621f4979] AbstractFFTs v1.5.0 [80f14c24] AbstractMCMC v5.7.2 [7a57a42e] AbstractPPL v0.13.1 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.3.0 [0bf59076] AdvancedHMC v0.8.1 [5b7e9947] AdvancedMH v0.8.8 [576499cb] AdvancedPS v0.7.0 [b5ca4192] AdvancedVI v0.4.1 [66dad0bd] AliasTables v1.1.3 [4c88cf16] Aqua v0.8.14 [dce04be8] ArgCheck v2.5.0 [4fba245c] ArrayInterface v7.20.0 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] AxisArrays v0.4.7 [198e06fe] BangBang v0.4.4 [9718e550] Baselet v0.1.1 [76274a88] Bijectors v0.15.10 [a134a8b2] BlackBoxOptim v0.6.3 [fa961155] CEnum v0.5.0 [a9c8d775] CPUTime v1.0.0 [082447d4] ChainRules v1.72.5 [d360d2e6] ChainRulesCore v1.26.0 [0ca39b1e] Chairmarks v1.3.1 [9e997f8a] ChangesOfVariables v0.1.10 [aaaa29a8] Clustering v0.15.8 [861a8166] Combinatorics v1.0.3 [38540f10] CommonSolve v0.2.4 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.18.0 [a33af91c] CompositionsBase v0.1.2 [88cd18e8] ConsoleProgressMonitor v0.1.2 [187b0558] ConstructionBase v1.6.0 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 ⌅ [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [b429d917] DensityInterface v0.4.0 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 [a0c0ee7d] DifferentiationInterface v0.7.7 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.120 [ced4e74d] DistributionsAD v0.6.58 [ffbed154] DocStringExtensions v0.9.5 [bbc10e6e] DynamicHMC v3.5.1 [366bfd00] DynamicPPL v0.37.2 [cad2338a] EllipticalSliceSampling v2.0.0 [4e289a0a] EnumX v1.0.5 [e2ba6199] ExprTools v0.1.10 [55351af7] ExproniconLite v0.10.14 [7a1cc6ca] FFTW v1.9.0 [9aa1b823] FastClosures v0.3.2 [1a297f60] FillArrays v1.13.0 [6a86dc24] FiniteDiff v2.28.1 [26cc04aa] FiniteDifferences v0.12.32 [f6369f11] ForwardDiff v1.1.0 [069b7b12] FunctionWrappers v1.1.3 [77dc65aa] FunctionWrappersWrappers v0.1.3 [d9f16b24] Functors v0.5.2 [46192b85] GPUArraysCore v0.2.0 [34004b35] HypergeometricFunctions v0.3.28 [09f84164] HypothesisTests v0.11.5 [22cec73e] InitialValues v0.3.1 [a98d9a8b] Interpolations v0.16.2 [8197267c] IntervalSets v0.7.11 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.1 [682c06a0] JSON v0.21.4 [ae98c720] Jieko v0.2.1 [5ab0869b] KernelDensity v0.6.10 [5be7bae1] LBFGSB v0.4.1 [b964fa9f] LaTeXStrings v1.4.0 [1fad7336] LazyStack v0.1.3 [1d6d02ad] LeftChildRightSiblingTrees v0.2.1 [6f1fad26] Libtask v0.9.4 [d3d80556] LineSearches v7.4.0 [6fdf6af0] LogDensityProblems v2.1.2 [996a588d] LogDensityProblemsAD v1.13.1 [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.1.0 [c7f686f2] MCMCChains v7.3.0 [be115224] MCMCDiagnosticTools v0.3.15 [e80e1ace] MLJModelInterface v1.12.0 [1914dd2f] MacroTools v0.5.16 [dbb5928d] MappedArrays v0.4.2 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [dbe65cb8] MistyClosures v2.1.0 [2e0e35c7] Moshi v0.3.7 [d41bc354] NLSolversBase v7.10.0 [76087f3c] NLopt v1.2.1 [77ba4419] NaNMath v1.1.3 [86f7a689] NamedArrays v0.10.4 [c020b1a1] NaturalSort v1.0.0 [b8a86587] NearestNeighbors v0.4.22 [6fe1bfb0] OffsetArrays v1.17.0 [429524aa] Optim v1.13.2 [3bd65402] Optimisers v0.4.6 [7f7a1694] Optimization v4.6.0 [3e6eede4] OptimizationBBO v0.4.1 [bca83a33] OptimizationBase v2.10.0 [4e6fcdb7] OptimizationNLopt v0.3.2 [36348300] OptimizationOptimJL v0.4.3 [bac558e1] OrderedCollections v1.8.1 [90014a1f] PDMats v0.11.35 [65ce6f38] PackageExtensionCompat v1.0.2 [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.3 [85a6dd25] PositiveFactorizations v0.2.4 [d236fae5] PreallocationTools v0.4.34 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.0 ⌅ [08abe8d2] PrettyTables v2.4.0 [33c8b6b6] ProgressLogging v0.1.5 [92933f4c] ProgressMeter v1.11.0 [43287f4e] PtrArrays v1.3.0 [1fd47b50] QuadGK v2.11.2 [74087812] Random123 v1.7.1 [e6cf234a] RandomNumbers v1.6.0 [b3c3ace0] RangeArrays v0.3.2 [c84ed2f1] Ratios v0.4.5 [c1ae055f] RealDot v0.1.0 [3cdcf5f2] RecipesBase v1.3.4 [731186ca] RecursiveArrayTools v3.37.1 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [37e2e3b7] ReverseDiff v1.16.1 [708f8203] Richardson v1.4.2 [79098fc4] Rmath v0.8.0 [f2b01f46] Roots v2.2.10 [7e49a35a] RuntimeGeneratedFunctions v0.5.15 ⌅ [26aad666] SSMProblems v0.5.2 [0bca4576] SciMLBase v2.117.1 [c0aeaf25] SciMLOperators v1.7.1 [53ae85a6] SciMLStructures v1.7.0 [30f210dd] ScientificTypesBase v3.0.0 [efcf1570] Setfield v1.1.2 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Serialization v1.11.0 [1a1011a3] SharedArrays v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.13.0 [f489334b] StyledStrings v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] LibCURL_jll v8.15.0+1 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.8.12 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.2+0 [efcefdf7] PCRE2_jll v10.46.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.1+2 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.13.1+0 [8e850ede] nghttp2_jll v1.67.0+0 [3f19e933] p7zip_jll v17.6.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... Precompiling packages... 11466.1 ms ✓ HypothesisTests 1 dependency successfully precompiled in 16 seconds. 61 already precompiled. [ Info: [Turing]: progress logging is disabled globally Persistent tasks: Test Failed at /home/pkgeval/.julia/packages/Aqua/MCcFg/src/persistent_tasks.jl:38 Expression: !(has_persistent_tasks(package; kwargs...)) Evaluated: !(has_persistent_tasks(Base.PkgId(Base.UUID("fce5fe82-541a-59a6-adf8-730c64b5f9a0"), "Turing"))) Stacktrace: [1] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:751 [inlined] [2] test_persistent_tasks(package::Base.PkgId; broken::Bool, kwargs::@Kwargs{}) @ Aqua ~/.julia/packages/Aqua/MCcFg/src/persistent_tasks.jl:38 [3] test_persistent_tasks @ ~/.julia/packages/Aqua/MCcFg/src/persistent_tasks.jl:34 [inlined] [4] #test_persistent_tasks#57 @ ~/.julia/packages/Aqua/MCcFg/src/persistent_tasks.jl:43 [inlined] [5] test_persistent_tasks @ ~/.julia/packages/Aqua/MCcFg/src/persistent_tasks.jl:42 [inlined] [6] macro expansion @ ~/.julia/packages/Aqua/MCcFg/src/Aqua.jl:109 [inlined] [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [8] test_all(testtarget::Module; ambiguities::Bool, unbound_args::Bool, undefined_exports::Bool, project_extras::Bool, stale_deps::Bool, deps_compat::Bool, piracies::Bool, persistent_tasks::Bool, undocumented_names::Bool) @ Aqua ~/.julia/packages/Aqua/MCcFg/src/Aqua.jl:109 Precompiling packages... 41324.6 ms ✓ ReverseDiff 1 dependency successfully precompiled in 42 seconds. 32 already precompiled. Precompiling packages... 23727.0 ms ✓ DistributionsAD → DistributionsADReverseDiffExt 1 dependency successfully precompiled in 30 seconds. 91 already precompiled. Precompiling packages... 20562.1 ms ✓ Bijectors → BijectorsReverseDiffExt 1 dependency successfully precompiled in 24 seconds. 91 already precompiled. Precompiling packages... 11143.0 ms ✓ LogDensityProblemsAD → LogDensityProblemsADReverseDiffExt 1 dependency successfully precompiled in 12 seconds. 37 already precompiled. Precompiling packages... 10968.1 ms ✓ ArrayInterface → ArrayInterfaceReverseDiffExt 1 dependency successfully precompiled in 12 seconds. 39 already precompiled. Precompiling packages... 9761.3 ms ✓ PreallocationTools → PreallocationToolsReverseDiffExt 1 dependency successfully precompiled in 11 seconds. 42 already precompiled. Precompiling packages... 13081.3 ms ✓ SciMLBase → SciMLBaseReverseDiffExt 1 dependency successfully precompiled in 16 seconds. 86 already precompiled. Precompiling packages... 10729.9 ms ✓ DifferentiationInterface → DifferentiationInterfaceReverseDiffExt 9673.7 ms ✓ OptimizationBase → OptimizationReverseDiffExt 2 dependencies successfully precompiled in 22 seconds. 118 already precompiled. Precompiling packages... 20046.5 ms ✓ Bijectors → BijectorsReverseDiffChainRulesExt 1 dependency successfully precompiled in 24 seconds. 111 already precompiled. [ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[s] [ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[m] [ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[s] [ Info: Running AD on demo_assume_index_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[m] [ Info: Running AD on demo_assume_index_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[s] [ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[m] [ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[s] [ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[m] [ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[s] [ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[m] [ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[s] [ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[m] [ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[s] [ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[m] [ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[s] [ Info: Running AD on demo_assume_index_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[m] [ Info: Running AD on demo_assume_index_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[s] [ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[m] [ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[s] [ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[m] [ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[s] [ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[m] [ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[s] [ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[m] [ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) constructor: Error During Test at /home/pkgeval/.julia/packages/Turing/ArWoY/test/essential/container.jl:20 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:510 [12] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/essential/container.jl:10 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [14] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/essential/container.jl:21 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/essential/container.jl:25 [inlined] [17] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [18] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:33 [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [20] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:42 [inlined] [21] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [22] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:25 [inlined] [23] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [24] top-level scope @ none:6 [25] eval(m::Module, e::Any) @ Core ./boot.jl:489 [26] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [27] _start() @ Base ./client.jl:563 fork: Error During Test at /home/pkgeval/.julia/packages/Turing/ArWoY/test/essential/container.jl:38 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:510 [12] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/essential/container.jl:10 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [14] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/essential/container.jl:39 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/essential/container.jl:51 [inlined] [17] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [18] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:33 [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [20] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:42 [inlined] [21] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [22] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:25 [inlined] [23] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [24] top-level scope @ none:6 [25] eval(m::Module, e::Any) @ Core ./boot.jl:489 [26] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [27] _start() @ Base ./client.jl:563 models: Error During Test at /home/pkgeval/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:27 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}) @ Base ./array.jl:835 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; nparticles::Int64, kwargs::@Kwargs{initial_params::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:200 [16] kwcall(::@NamedTuple{initial_params::Nothing, nparticles::Int64}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:186 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{nparticles::Int64}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:133 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [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:651 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nparticles::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:161 [27] sample @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:149 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:16 [32] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:13 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:28 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:36 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [38] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [44] top-level scope @ none:6 [45] eval(m::Module, e::Any) @ Core ./boot.jl:489 [46] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [47] _start() @ Base ./client.jl:563 chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:53 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}) @ Base ./array.jl:835 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; nparticles::Int64, kwargs::@Kwargs{initial_params::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:200 [16] kwcall(::@NamedTuple{initial_params::Nothing, nparticles::Int64}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:186 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{nparticles::Int64}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:133 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [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:651 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nparticles::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:161 [27] sample @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:149 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:16 [32] test_chain_logp_metadata(spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/ArWoY/test/test_utils/sampler.jl:18 [33] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:13 [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:54 [inlined] [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:54 [inlined] [38] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [39] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:33 [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:46 [inlined] [42] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [43] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:25 [inlined] [44] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [45] top-level scope @ none:6 [46] eval(m::Module, e::Any) @ Core ./boot.jl:489 [47] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [48] _start() @ Base ./client.jl:563 logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:57 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}) @ Base ./array.jl:835 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; nparticles::Int64, kwargs::@Kwargs{initial_params::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:200 [16] kwcall(::@NamedTuple{initial_params::Nothing, nparticles::Int64}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:186 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{nparticles::Int64}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:133 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [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:651 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nparticles::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:161 [27] sample @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:149 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:16 [32] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:13 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:58 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:70 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [38] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [44] top-level scope @ none:6 [45] eval(m::Module, e::Any) @ Core ./boot.jl:489 [46] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [47] _start() @ Base ./client.jl:563 chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:99 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:835 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:312 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:133 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [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:651 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:93 [27] sample @ ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:83 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:16 [32] test_chain_logp_metadata(spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/ArWoY/test/test_utils/sampler.jl:18 [33] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:81 [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:100 [inlined] [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:100 [inlined] [38] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [39] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:33 [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:46 [inlined] [42] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [43] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:25 [inlined] [44] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [45] top-level scope @ none:6 [46] eval(m::Module, e::Any) @ Core ./boot.jl:489 [47] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [48] _start() @ Base ./client.jl:563 logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:103 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:835 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:312 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:133 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [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:651 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:93 [27] sample @ ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:83 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:16 [32] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:81 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:104 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:116 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [38] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [44] top-level scope @ none:6 [45] eval(m::Module, e::Any) @ Core ./boot.jl:489 [46] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [47] _start() @ Base ./client.jl:563 reference particle: Error During Test at /home/pkgeval/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:124 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:835 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:312 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:133 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [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:651 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:93 [27] sample @ ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:83 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:16 [32] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:81 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:125 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:125 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [38] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [44] top-level scope @ none:6 [45] eval(m::Module, e::Any) @ Core ./boot.jl:489 [46] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [47] _start() @ Base ./client.jl:563 addlogprob leads to reweighting: Error During Test at /home/pkgeval/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:130 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:835 [15] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:312 [17] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:133 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [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:651 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:93 [27] sample @ ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:83 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:22 [30] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:81 [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:133 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/particle_mcmc.jl:143 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [36] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:33 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:46 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:25 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [45] _start() @ Base ./client.jl:563 ┌ Warning: The model does not contain any parameters. └ @ DynamicPPL.DebugUtils ~/.julia/packages/DynamicPPL/cUed6/src/debug_utils.jl:305 [ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.0598561011023344) [ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.158304117994203) [ Info: Testing emcee with large number of iterations [ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 1.9758334846581072) [ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.176829569918974) [ Info: Starting ESS tests [ Info: Starting ESS inference tests [ Info: (symbol = :m, exact = 0.8, evaluated = 0.8172942592919593) [ Info: (symbol = "m[1]", exact = 0.0, evaluated = -0.02456171083886478) [ Info: (symbol = "m[2]", exact = 0.8, evaluated = 0.8075869528540663) gdemo with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/ArWoY/test/mcmc/ess.jl:55 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:835 [15] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/particle_mcmc.jl:312 [17] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:133 [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:116 [19] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:459 [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:429 [21] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:383 [22] step @ ~/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:371 [inlined] [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined] [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [26] 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:651 [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [28] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [29] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [31] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:93 [32] sample @ ~/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:83 [inlined] [33] #sample#112 @ ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:31 [inlined] [34] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:22 [35] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/mcmc/ess.jl:14 [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/ess.jl:42 [inlined] [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/ess.jl:56 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/mcmc/ess.jl:57 [inlined] [42] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [43] top-level scope @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:33 [44] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [45] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:46 [inlined] [46] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [47] macro expansion @ ~/.julia/packages/Turing/ArWoY/test/runtests.jl:25 [inlined] [48] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [49] top-level scope @ none:6 [50] eval(m::Module, e::Any) @ Core ./boot.jl:489 [51] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [52] _start() @ Base ./client.jl:563 ====================================================================================== 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 107 running 1 of 1 signal (10): User defined signal 1 _ZN12_GLOBAL__N_18Verifier11visitMDNodeERKN4llvm6MDNodeENS0_19AreDebugLocsAllowedE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN12_GLOBAL__N_18Verifier16visitInstructionERN4llvm11InstructionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN12_GLOBAL__N_18Verifier6verifyERKN4llvm8FunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm14verifyFunctionERKNS_8FunctionEPNS_11raw_ostreamE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) verifyLLVMIR at /source/src/pipeline.cpp:898 runOnLoop at /source/src/llvm-julia-licm.cpp:356 run at /source/src/llvm-julia-licm.cpp:381 run at /source/usr/include/llvm/IR/PassManagerInternal.h:91 _ZN4llvm11PassManagerINS_4LoopENS_15AnalysisManagerIS1_JRNS_27LoopStandardAnalysisResultsEEEEJS4_RNS_10LPMUpdaterEEE13runSinglePassIS1_St10unique_ptrINS_6detail11PassConceptIS1_S5_JS4_S7_EEESt14default_deleteISD_EEEESt8optionalINS_17PreservedAnalysesEERT_RT0_RS5_S4_S7_RNS_19PassInstrumentationE.isra.0 at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm11PassManagerINS_4LoopENS_15AnalysisManagerIS1_JRNS_27LoopStandardAnalysisResultsEEEEJS4_RNS_10LPMUpdaterEEE24runWithoutLoopNestPassesERS1_RS5_S4_S7_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm11PassManagerINS_4LoopENS_15AnalysisManagerIS1_JRNS_27LoopStandardAnalysisResultsEEEEJS4_RNS_10LPMUpdaterEEE3runERS1_RS5_S4_S7_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/usr/include/llvm/IR/PassManagerInternal.h:91 _ZN4llvm25FunctionToLoopPassAdaptor3runERNS_8FunctionERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/usr/include/llvm/IR/PassManagerInternal.h:91 _ZN4llvm11PassManagerINS_8FunctionENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/usr/include/llvm/IR/PassManagerInternal.h:91 _ZN4llvm27ModuleToFunctionPassAdaptor3runERNS_6ModuleERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/usr/include/llvm/IR/PassManagerInternal.h:91 _ZN4llvm11PassManagerINS_6ModuleENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/src/pipeline.cpp:791 operator() at /source/src/jitlayers.cpp:1510 withModuleDo<(anonymous namespace)::sizedOptimizerT::operator()(llvm::orc::ThreadSafeModule) [with long unsigned int N = 4]:: > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:136 [inlined] operator() at /source/src/jitlayers.cpp:1471 [inlined] operator() at /source/src/jitlayers.cpp:1623 [inlined] addModule at /source/src/jitlayers.cpp:2080 jl_compile_codeinst_now at /source/src/jitlayers.cpp:682 jl_compile_codeinst_impl at /source/src/jitlayers.cpp:873 jl_compile_method_internal at /source/src/gf.c:3595 _jl_invoke at /source/src/gf.c:4053 [inlined] ijl_apply_generic at /source/src/gf.c:4258 typed_varinfo at /home/pkgeval/.julia/packages/DynamicPPL/cUed6/src/varinfo.jl:238 unknown function (ip: 0x7e5accdf9ddf) at (unknown file) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 typed_varinfo at /home/pkgeval/.julia/packages/DynamicPPL/cUed6/src/varinfo.jl:286 default_varinfo at /home/pkgeval/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:80 [inlined] initial_varinfo at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:356 [inlined] #step#74 at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:381 step at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:371 [inlined] macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined] #25 at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 with_logstate at ./logging/logging.jl:540 unknown function (ip: 0x7e5ac3019ddc) at (unknown file) _jl_invoke at /source/src/gf.c:4061 [inlined] ijl_apply_generic at /source/src/gf.c:4258 with_logger at ./logging/logging.jl:651 with_progresslogger at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 unknown function (ip: 0x7e5acaf05cce) at (unknown file) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] #mcmcsample#23 at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 mcmcsample at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 unknown function (ip: 0x7e5ac3005f40) at (unknown file) _jl_invoke at /source/src/gf.c:4061 [inlined] ijl_apply_generic at /source/src/gf.c:4258 #sample#56 at /home/pkgeval/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:93 sample at /home/pkgeval/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:83 [inlined] #sample#112 at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:31 [inlined] sample at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:22 unknown function (ip: 0x7e5ac31d39cb) at (unknown file) _jl_invoke at /source/src/gf.c:4061 [inlined] ijl_apply_generic at /source/src/gf.c:4258 jl_apply at /source/src/julia.h:2382 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_body at /source/src/interpreter.c:581 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:772 jl_eval_module_expr at /source/src/toplevel.c:195 [inlined] jl_toplevel_eval_flex at /source/src/toplevel.c:657 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 include_string at ./loading.jl:2918 _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 _include at ./loading.jl:2978 include at ./Base.jl:309 IncludeInto at ./Base.jl:310 unknown function (ip: 0x7e5b00912052) at (unknown file) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 jl_apply at /source/src/julia.h:2382 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:690 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:772 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 include_string at ./loading.jl:2918 _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 _include at ./loading.jl:2978 include at ./Base.jl:309 IncludeInto at ./Base.jl:310 jfptr_IncludeInto_55996.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 jl_apply at /source/src/julia.h:2382 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:690 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:772 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 exec_options at ./client.jl:296 _start at ./client.jl:563 jfptr__start_51596.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 jl_apply at /source/src/julia.h:2382 [inlined] true_main at /source/src/jlapi.c:971 jl_repl_entrypoint at /source/src/jlapi.c:1138 main at /source/cli/loader_exe.c:58 unknown function (ip: 0x7e5b0226c249) 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 ============================================================== ====================================================================================== 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 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457 wait at ./task.jl:1200 wait_forever at ./task.jl:1137 jfptr_wait_forever_61890.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 jl_apply at /source/src/julia.h:2382 [inlined] start_task at /source/src/task.c:1253 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point. Disabling --trace-compile ============================================================== ┌ Warning: There were no samples collected in one or more groups. │ This may be due to idle threads, or you may need to run your │ program longer (perhaps by running it multiple times), │ or adjust the delay between samples with `Profile.init()`. └ @ Profile /opt/julia/share/julia/stdlib/v1.13/Profile/src/Profile.jl:1362 Overhead ╎ [+additional indent] Count File:Line Function ========================================================= Thread 1 (default) Task 0x00007b5d221890f0 Total snapshots: 382. Utilization: 0% ╎382 @Base/task.jl:1137 wait_forever() 381╎ 382 @Base/task.jl:1200 wait() [1] signal 15: Terminated in expression starting at /PkgEval.jl/scripts/evaluate.jl:210 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 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457 wait at ./task.jl:1200 wait_forever at ./task.jl:1137 jfptr_wait_forever_61890.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 jl_apply at /source/src/julia.h:2382 [inlined] start_task at /source/src/task.c:1253 unknown function (ip: (nil)) at (unknown file) Allocations: 30964724 (Pool: 30964046; Big: 678); GC: 25 [107] signal 15: Terminated in expression starting at /home/pkgeval/.julia/packages/Turing/ArWoY/test/mcmc/ess.jl:13 _ZNK4llvm10DataLayout17getTypeSizeInBitsEPNS_4TypeE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm14MemoryLocation9getOrNoneEPKNS_11InstructionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm23MemoryDependenceResults28getNonLocalPointerDependencyEPNS_11InstructionERNS_15SmallVectorImplINS_17NonLocalDepResultEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm7GVNPass19processNonLocalLoadEPNS_8LoadInstE.part.0 at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm7GVNPass18processInstructionEPNS_11InstructionE.part.0 at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm7GVNPass12processBlockEPNS_10BasicBlockE.part.0 at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm7GVNPass17iterateOnFunctionERNS_8FunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm7GVNPass7runImplERNS_8FunctionERNS_15AssumptionCacheERNS_13DominatorTreeERKNS_17TargetLibraryInfoERNS_9AAResultsEPNS_23MemoryDependenceResultsERNS_8LoopInfoEPNS_25OptimizationRemarkEmitterEPNS_9MemorySSAE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm7GVNPass3runERNS_8FunctionERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/usr/include/llvm/IR/PassManagerInternal.h:91 _ZN4llvm11PassManagerINS_8FunctionENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/usr/include/llvm/IR/PassManagerInternal.h:91 _ZN4llvm27ModuleToFunctionPassAdaptor3runERNS_6ModuleERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/usr/include/llvm/IR/PassManagerInternal.h:91 _ZN4llvm11PassManagerINS_6ModuleENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/src/pipeline.cpp:791 operator() at /source/src/jitlayers.cpp:1510 withModuleDo<(anonymous namespace)::sizedOptimizerT::operator()(llvm::orc::ThreadSafeModule) [with long unsigned int N = 4]:: > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:136 [inlined] operator() at /source/src/jitlayers.cpp:1471 [inlined] operator() at /source/src/jitlayers.cpp:1623 [inlined] addModule at /source/src/jitlayers.cpp:2080 jl_compile_codeinst_now at /source/src/jitlayers.cpp:682 jl_compile_codeinst_impl at /source/src/jitlayers.cpp:873 jl_compile_method_internal at /source/src/gf.c:3595 _jl_invoke at /source/src/gf.c:4053 [inlined] ijl_apply_generic at /source/src/gf.c:4258 #gibbs_initialstep_recursive#76 at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:459 unknown function (ip: 0x7e5ac30389e8) at (unknown file) _jl_invoke at /source/src/gf.c:4061 [inlined] ijl_apply_generic at /source/src/gf.c:4258 gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:429 gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:429 unknown function (ip: 0x7e5ac30368ce) at (unknown file) _jl_invoke at /source/src/gf.c:4061 [inlined] ijl_apply_generic at /source/src/gf.c:4258 #step#74 at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:383 step at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/gibbs.jl:371 [inlined] macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined] #25 at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 with_logstate at ./logging/logging.jl:540 unknown function (ip: 0x7e5ac3019ddc) at (unknown file) _jl_invoke at /source/src/gf.c:4061 [inlined] ijl_apply_generic at /source/src/gf.c:4258 with_logger at ./logging/logging.jl:651 with_progresslogger at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 unknown function (ip: 0x7e5acaf05cce) at (unknown file) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] #mcmcsample#23 at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 mcmcsample at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 unknown function (ip: 0x7e5ac3005f40) at (unknown file) _jl_invoke at /source/src/gf.c:4061 [inlined] ijl_apply_generic at /source/src/gf.c:4258 #sample#56 at /home/pkgeval/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:93 sample at /home/pkgeval/.julia/packages/DynamicPPL/cUed6/src/sampler.jl:83 [inlined] #sample#112 at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:31 [inlined] sample at /home/pkgeval/.julia/packages/Turing/ArWoY/src/mcmc/abstractmcmc.jl:22 unknown function (ip: 0x7e5ac31d39cb) at (unknown file) _jl_invoke at /source/src/gf.c:4061 [inlined] ijl_apply_generic at /source/src/gf.c:4258 jl_apply at /source/src/julia.h:2382 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_body at /source/src/interpreter.c:581 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:772 jl_eval_module_expr at /source/src/toplevel.c:195 [inlined] jl_toplevel_eval_flex at /source/src/toplevel.c:657 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 include_string at ./loading.jl:2918 _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 _include at ./loading.jl:2978 include at ./Base.jl:309 IncludeInto at ./Base.jl:310 unknown function (ip: 0x7e5b00912052) at (unknown file) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 jl_apply at /source/src/julia.h:2382 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:690 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:772 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 include_string at ./loading.jl:2918 _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 _include at ./loading.jl:2978 include at ./Base.jl:309 IncludeInto at ./Base.jl:310 jfptr_IncludeInto_55996.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 jl_apply at /source/src/julia.h:2382 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:690 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:772 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 exec_options at ./client.jl:296 _start at ./client.jl:563 jfptr__start_51596.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4045 [inlined] ijl_apply_generic at /source/src/gf.c:4258 jl_apply at /source/src/julia.h:2382 [inlined] true_main at /source/src/jlapi.c:971 jl_repl_entrypoint at /source/src/jlapi.c:1138 main at /source/cli/loader_exe.c:58 unknown function (ip: 0x7e5b0226c249) 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) Allocations: 924608617 (Pool: 924603919; Big: 4698); GC: 226 PkgEval terminated after 2736.32s: test duration exceeded the time limit