Bayesian Models are not running with Julia 1.9.2

[sourish-cmi]

Describe the bug
Following code is not running with Julia 1.9.2

julia> model = fit(@formula(MPG ~ HP + WT+Gear), df, LinearRegression(),Prior_Ridge())

Error

Sampling 100%|█████████████████████████████████████████████████████████████████████| Time: 0:00:01
ERROR: MethodError: no method matching ADgradient(::Val{:ForwardDiff}, ::WARNING: both Bijectors and Base export "stack"; uses of it in module Turing must be qualified
Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:v, :σ, :β), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:v, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:v, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:β, Setfield.IdentityLens}, Int64}, Vector{DistributionsAD.TuringScalMvNormal{Vector{Float64}, Float64}}, Vector{AbstractPPL.VarName{:β, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{CRRao.var"#LinearRegression#1"{Float64}, (:X, :y), (), (), Tuple{Matrix{Float64}, Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}; gradientconfig::ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}})

Closest candidates are:
  ADgradient(::Val{:ForwardDiff}, ::Any; chunk, tag, x) got unsupported keyword argument "gradientconfig"
   @ LogDensityProblemsADForwardDiffExt ~/.julia/packages/LogDensityProblemsAD/tXC2y/ext/LogDensityProblemsADForwardDiffExt.jl:98
  ADgradient(::Val{kind}, ::Any; kwargs...) where kind
   @ LogDensityProblemsAD ~/.julia/packages/LogDensityProblemsAD/tXC2y/src/LogDensityProblemsAD.jl:68
  ADgradient(::ADTypes.AutoForwardDiff{C}, ::Any) where C got unsupported keyword argument "gradientconfig"
   @ LogDensityProblemsADADTypesExt ~/.julia/packages/LogDensityProblemsAD/tXC2y/ext/LogDensityProblemsADADTypesExt.jl:26
  ...

Stacktrace:
  [1] kwerr(::NamedTuple{(:gradientconfig,), Tuple{ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}}}}, ::Function, ::Val{:ForwardDiff}, ::Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:v, :σ, :β), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:v, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:v, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:β, Setfield.IdentityLens}, Int64}, Vector{DistributionsAD.TuringScalMvNormal{Vector{Float64}, Float64}}, Vector{AbstractPPL.VarName{:β, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{CRRao.var"#LinearRegression#1"{Float64}, (:X, :y), (), (), Tuple{Matrix{Float64}, Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext})
    @ Base ./error.jl:165
  [2] ADgradient(ad::Turing.Essential.ForwardDiffAD{0, true}, ℓ::Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:v, :σ, :β), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:v, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:v, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:β, Setfield.IdentityLens}, Int64}, Vector{DistributionsAD.TuringScalMvNormal{Vector{Float64}, Float64}}, Vector{AbstractPPL.VarName{:β, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{CRRao.var"#LinearRegression#1"{Float64}, (:X, :y), (), (), Tuple{Matrix{Float64}, Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext})
    @ Turing.Essential ~/.julia/packages/Turing/UsWJl/src/essential/ad.jl:102
  [3] ADgradient(ℓ::Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:v, :σ, :β), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:v, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:v, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:β, Setfield.IdentityLens}, Int64}, Vector{DistributionsAD.TuringScalMvNormal{Vector{Float64}, Float64}}, Vector{AbstractPPL.VarName{:β, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{CRRao.var"#LinearRegression#1"{Float64}, (:X, :y), (), (), Tuple{Matrix{Float64}, Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext})
    @ Turing.Essential ~/.julia/packages/Turing/UsWJl/src/essential/ad.jl:82
  [4] initialstep(rng::Random.MersenneTwister, model::DynamicPPL.Model{CRRao.var"#LinearRegression#1"{Float64}, (:X, :y), (), (), Tuple{Matrix{Float64}, Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, vi::DynamicPPL.TypedVarInfo{NamedTuple{(:v, :σ, :β), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:v, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:v, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:β, Setfield.IdentityLens}, Int64}, Vector{DistributionsAD.TuringScalMvNormal{Vector{Float64}, Float64}}, Vector{AbstractPPL.VarName{:β, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}; init_params::Nothing, nadapts::Int64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing.Inference ~/.julia/packages/Turing/UsWJl/src/inference/hmc.jl:161
  [5] step(rng::Random.MersenneTwister, model::DynamicPPL.Model{CRRao.var"#LinearRegression#1"{Float64}, (:X, :y), (), (), Tuple{Matrix{Float64}, Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}; resume_from::Nothing, init_params::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}})
    @ DynamicPPL ~/.julia/packages/DynamicPPL/UFajj/src/sampler.jl:111
  [6] macro expansion
    @ ~/.julia/packages/AbstractMCMC/fWWW0/src/sample.jl:125 [inlined]
  [7] macro expansion
    @ ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:328 [inlined]
  [8] (::AbstractMCMC.var"#21#22"{Bool, String, Nothing, Int64, Int64, Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}}, Random.MersenneTwister, DynamicPPL.Model{CRRao.var"#LinearRegression#1"{Float64}, (:X, :y), (), (), Tuple{Matrix{Float64}, Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64})()
    @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fWWW0/src/logging.jl:12
  [9] with_logstate(f::Function, logstate::Any)
    @ Base.CoreLogging ./logging.jl:514
 [10] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#1#3"{Module}}, LoggingExtras.EarlyFilteredLogger{Logging.ConsoleLogger, AbstractMCMC.var"#2#4"{Module}}}})
    @ Base.CoreLogging ./logging.jl:626
 [11] with_progresslogger(f::Function, _module::Module, logger::Logging.ConsoleLogger)
    @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fWWW0/src/logging.jl:36
 [12] macro expansion
    @ ~/.julia/packages/AbstractMCMC/fWWW0/src/logging.jl:11 [inlined]
 [13] mcmcsample(rng::Random.MersenneTwister, model::DynamicPPL.Model{CRRao.var"#LinearRegression#1"{Float64}, (:X, :y), (), (), Tuple{Matrix{Float64}, Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, discard_initial::Int64, thinning::Int64, chain_type::Type, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}})
    @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fWWW0/src/sample.jl:116
 [14] sample(rng::Random.MersenneTwister, model::DynamicPPL.Model{CRRao.var"#LinearRegression#1"{Float64}, (:X, :y), (), (), Tuple{Matrix{Float64}, Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, N::Int64; chain_type::Type, resume_from::Nothing, progress::Bool, nadapts::Int64, discard_adapt::Bool, discard_initial::Int64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing.Inference ~/.julia/packages/Turing/UsWJl/src/inference/hmc.jl:133
 [15] sample
    @ ~/.julia/packages/Turing/UsWJl/src/inference/hmc.jl:103 [inlined]
 [16] #sample#2
    @ ~/.julia/packages/Turing/UsWJl/src/inference/Inference.jl:146 [inlined]
 [17] sample(rng::Random.MersenneTwister, model::DynamicPPL.Model{CRRao.var"#LinearRegression#1"{Float64}, (:X, :y), (), (), Tuple{Matrix{Float64}, Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, alg::NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}, N::Int64)
    @ Turing.Inference ~/.julia/packages/Turing/UsWJl/src/inference/Inference.jl:139
 [18] linear_reg(formula::FormulaTerm{Term, Tuple{Term, Term, Term}}, data::DataFrame, turingModel::CRRao.var"#LinearRegression#1"{Float64}, sim_size::Int64)
    @ CRRao ~/.julia/packages/CRRao/wXk3z/src/bayesian/linear_regression.jl:8
 [19] fit(formula::FormulaTerm{Term, Tuple{Term, Term, Term}}, data::DataFrame, modelClass::LinearRegression, prior::Prior_Ridge, h::Float64, sim_size::Int64)
    @ CRRao ~/.julia/packages/CRRao/wXk3z/src/bayesian/linear_regression.jl:100
 [20] fit(formula::FormulaTerm{Term, Tuple{Term, Term, Term}}, data::DataFrame, modelClass::LinearRegression, prior::Prior_Ridge)
    @ CRRao ~/.julia/packages/CRRao/wXk3z/src/bayesian/linear_regression.jl:83
 [21] top-level scope
    @ REPL[18]:1

Looks like some packages are missing - not sure which caused the error?

@ShouvikGhosh2048 @ayushpatnaikgit @codetalker7

[ShouvikGhosh2048]

Similar issue occurs in #123

[sourish-cmi]

Yes - seems like same problem. May be Turing is not synced yet with 1.9

[sourish-cmi]

In the linear_regression.jl - in line number 8, we are calling the turingModel(X,y) - but the function is not defined anywhere. Please check urgently. I think this is breaking the entire Bayesian module.

https://github.com/xKDR/CRRao.jl/blob/main/src/bayesian/linear_regression.jl

@ShouvikGhosh2048 @ayushpatnaikgit @codetalker7 @ajaynshah @SusanXKDR @mousum-github

[ShouvikGhosh2048]

turingModel is passed as a parameter to the linear_reg function:

CRRao.jl/src/bayesian/linear_regression.jl

Line 1 in b0d3984

function linear_reg(formula::FormulaTerm, data::DataFrame, turingModel::Function, sim_size::Int64)

When we use linear_reg we create the model and pass it to the function:

CRRao.jl/src/bayesian/linear_regression.jl

Lines 75 to 101 in b0d3984

	function fit(
	formula::FormulaTerm,
	data::DataFrame,
	modelClass::LinearRegression,
	prior::Prior_Ridge,
	h::Float64 = 0.01,
	sim_size::Int64 = 1000
	)
	@model LinearRegression(X, y) = begin
	p = size(X, 2)
	#priors
	a0 = 0.1
	b0 = 0.1
	v ~ InverseGamma(h, h)
	σ ~ InverseGamma(a0, b0)
	#α ~ Normal(0, v * σ)
	β ~ filldist(Normal(0, v * σ), p)
	#likelihood
	#y ~ MvNormal(α .+ X * β, σ)
	y ~ MvNormal(X * β, σ)
	end
	return linear_reg(formula, data, LinearRegression, sim_size)
	end

[sourish-cmi]

I understand that turingModel is passed as a parameter to the linear_reg function. But I am not sure if it is passing the LinearRegression(X,y) as a function. I think it is not defined as a function. It is defined as macro @model LinearRegression(X,y) =...end

LinearRegression is a function and as well as it is defined as struct in CRRao.jl -- it is better to use different name

[sourish-cmi]

@ShouvikGhosh2048 @ayushpatnaikgit

I think CRRao is using a really old version of Turing "0.23.2" -- Turing no more supports "0.23.2" - we should bump up CRRao to the Turing latest version of "0.30.7"

@ShouvikGhosh2048 can you please try it?

[sourish-cmi]

It is being fixed and new release 0.1.1 is being done