# Turing GLM error when trying to use ReverseDiff as the AD backend

**URL:** <https://discourse.julialang.org/t/turing-glm-error-when-trying-to-use-reversediff-as-the-ad-backend/83803>\
**Category:** Probabilistic Programming\
**Created:** [July 5, 2022, 10:07pm UTC](https://discourse.julialang.org/t/turing-glm-error-when-trying-to-use-reversediff-as-the-ad-backend/83803 "2022-07-05T22:07:20Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![sree\_datta](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sree_datta/32/32723_2.png) [@sree\_datta](https://discourse.julialang.org/u/sree_datta)\
**Post date:** [July 5, 2022, 10:07pm UTC](https://discourse.julialang.org/t/turing-glm-error-when-trying-to-use-reversediff-as-the-ad-backend/83803/1 "2022-07-05T22:07:20Z")

</div>

@Storopoli @devmotion @torfjelde  
Working with the same data set where we were discussing the use of `ReverseDiff` for a more efficient and faster sampling, I tried to run the same model using `TuringGLM` and its’ formulaic approach. However, I get the following error, which I do not understand. Any help to debug would be great. Here is the code I ran:

```julia
using CSV, DataFrames, Turing, TuringGLM, StatsPlots, ArviZ
using ReverseDiff, Memoization
Turing.setadbackend(:reversediff)
Turing.setrdcache(true)

hcp = CSV.read("/home/sreedta/Documents/bayes/hcp_julia_1000.csv", DataFrame)

hcpdata = hcp[:, ["mdmidc", "nrx", "calls80", "samples10", "lle50", "spe80", "copay10", "fto10"]]

jtgmod1 = @formula(nrx ~ calls80 + samples10 + lle50 + spe80 + copay10 + fto10 + (calls80 | mdmidc))

model = turing_model(jtgmod1, hcpdata);

chn = sample(model, NUTS(1_000, 0.98), 4_000, progress=true); 

here the error occurs. I did not see this before

Error Output:
UndefVarError: τ not defined

Stacktrace:
  [1] macro expansion
    @ ~/.julia/packages/DynamicPPL/R7VK9/src/compiler.jl:539 [inlined]
  [2] (::TuringGLM.var"#normal_model_ranef#16"{Int64, Int64, CustomPrior, Vector{String}, Int64, Vector{Int64}})( __model__ ::DynamicPPL.Model{TuringGLM.var"#normal_model_ranef#16"{Int64, Int64, CustomPrior, Vector{String}, Int64, Vector{Int64}}, (:y, :X, :predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (:predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (), Tuple{Vector{Float64}, Matrix{Float64}, Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, Tuple{Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, DynamicPPL.DefaultContext}, __varinfo__ ::DynamicPPL.UntypedVarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}, Vector{Set{DynamicPPL.Selector}}}, Float64}, __context__ ::DynamicPPL.SamplingContext{DynamicPPL.SampleFromUniform, DynamicPPL.DefaultContext, Random._GLOBAL_RNG}, y::Vector{Float64}, X::Matrix{Float64}, predictors::Int64, idxs::Vector{Int64}, n_gr::Int64, intercept_ranef::Vector{String}, μ_X::Int64, σ_X::Int64, prior::CustomPrior, residual::Float64)
    @ TuringGLM ~/.julia/packages/TuringGLM/s2Pou/src/turing_model.jl:190
  [3] macro expansion
    @ ~/.julia/packages/DynamicPPL/R7VK9/src/model.jl:493 [inlined]
  [4] _evaluate!!
    @ ~/.julia/packages/DynamicPPL/R7VK9/src/model.jl:476 [inlined]
  [5] evaluate_threadunsafe!!
    @ ~/.julia/packages/DynamicPPL/R7VK9/src/model.jl:451 [inlined]
  [6] evaluate!!
    @ ~/.julia/packages/DynamicPPL/R7VK9/src/model.jl:404 [inlined]
  [7] evaluate!!(model::DynamicPPL.Model{TuringGLM.var"#normal_model_ranef#16"{Int64, Int64, CustomPrior, Vector{String}, Int64, Vector{Int64}}, (:y, :X, :predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (:predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (), Tuple{Vector{Float64}, Matrix{Float64}, Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, Tuple{Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, DynamicPPL.DefaultContext}, rng::Random._GLOBAL_RNG, varinfo::DynamicPPL.UntypedVarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}, Vector{Set{DynamicPPL.Selector}}}, Float64}, sampler::DynamicPPL.SampleFromUniform, context::DynamicPPL.DefaultContext)
    @ DynamicPPL ~/.julia/packages/DynamicPPL/R7VK9/src/model.jl:415
  [8] (::DynamicPPL.Model{TuringGLM.var"#normal_model_ranef#16"{Int64, Int64, CustomPrior, Vector{String}, Int64, Vector{Int64}}, (:y, :X, :predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (:predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (), Tuple{Vector{Float64}, Matrix{Float64}, Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, Tuple{Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, DynamicPPL.DefaultContext})(::Random._GLOBAL_RNG, ::Vararg{Any})
    @ DynamicPPL ~/.julia/packages/DynamicPPL/R7VK9/src/model.jl:375
  [9] VarInfo
    @ ~/.julia/packages/DynamicPPL/R7VK9/src/varinfo.jl:127 [inlined]
 [10] VarInfo
    @ ~/.julia/packages/DynamicPPL/R7VK9/src/varinfo.jl:126 [inlined]
 [11] step(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{TuringGLM.var"#normal_model_ranef#16"{Int64, Int64, CustomPrior, Vector{String}, Int64, Vector{Int64}}, (:y, :X, :predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (:predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (), Tuple{Vector{Float64}, Matrix{Float64}, Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, Tuple{Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ReverseDiffAD{true}, (), AdvancedHMC.DiagEuclideanMetric}}; resume_from::Nothing, init_params::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}})
    @ DynamicPPL ~/.julia/packages/DynamicPPL/R7VK9/src/sampler.jl:86
 [12] macro expansion
    @ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:120 [inlined]
 [13] macro expansion
    @ ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:328 [inlined]
 [14] (::AbstractMCMC.var"#21#22"{Bool, String, Nothing, Int64, Int64, Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}}, Random._GLOBAL_RNG, DynamicPPL.Model{TuringGLM.var"#normal_model_ranef#16"{Int64, Int64, CustomPrior, Vector{String}, Int64, Vector{Int64}}, (:y, :X, :predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (:predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (), Tuple{Vector{Float64}, Matrix{Float64}, Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, Tuple{Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ReverseDiffAD{true}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64})()
    @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:12
 [15] with_logstate(f::Function, logstate::Any)
    @ Base.CoreLogging ./logging.jl:511
 [16] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{ConsoleProgressMonitor.ProgressLogger, AbstractMCMC.var"#1#3"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.SimpleLogger, AbstractMCMC.var"#2#4"{Module}}}})
    @ Base.CoreLogging ./logging.jl:623
 [17] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.SimpleLogger)
    @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:36
 [18] macro expansion
    @ ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:11 [inlined]
 [19] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{TuringGLM.var"#normal_model_ranef#16"{Int64, Int64, CustomPrior, Vector{String}, Int64, Vector{Int64}}, (:y, :X, :predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (:predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (), Tuple{Vector{Float64}, Matrix{Float64}, Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, Tuple{Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ReverseDiffAD{true}, (), 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/fnRmh/src/sample.jl:111
 [20] sample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{TuringGLM.var"#normal_model_ranef#16"{Int64, Int64, CustomPrior, Vector{String}, Int64, Vector{Int64}}, (:y, :X, :predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (:predictors, :idxs, :n_gr, :intercept_ranef, :μ_X, :σ_X, :prior, :residual), (), Tuple{Vector{Float64}, Matrix{Float64}, Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, Tuple{Int64, Vector{Int64}, Int64, Vector{String}, Int64, Int64, CustomPrior, Float64}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ReverseDiffAD{true}, (), 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/GxgQ1/src/inference/hmc.jl:133
 [21] #sample#2
    @ ~/.julia/packages/Turing/GxgQ1/src/inference/Inference.jl:145 [inlined]
 [22] #sample#1
    @ ~/.julia/packages/Turing/GxgQ1/src/inference/Inference.jl:135 [inlined]
 [23] top-level scope
    @ In[98]:1
 [24] eval
    @ ./boot.jl:373 [inlined]
 [25] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
    @ Base ./loading.jl:1196

```