# Problem with reversediff when using Turing

**URL:** <https://discourse.julialang.org/t/problem-with-reversediff-when-using-turing/88798>\
**Category:** Modelling & Simulations\
**Tags:** question, turing\
**Created:** [October 16, 2022, 10:49am UTC](https://discourse.julialang.org/t/problem-with-reversediff-when-using-turing/88798 "2022-10-16T10:49:42Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![neuro\_enthusiast](https://avatars.discourse-cdn.com/v4/letter/n/43a26b/32.png) [@neuro\_enthusiast](https://discourse.julialang.org/u/neuro_enthusiast)\
**Post date:** [October 16, 2022, 10:49am UTC](https://discourse.julialang.org/t/problem-with-reversediff-when-using-turing/88798/1 "2022-10-16T10:49:42Z")

</div>

I’m receiving the following error when using reversediff for fitting my model. Does anyone have any clue what may be causing this? I’m using reversediff 1.7.0. Forward diff works fine for my model. The sampler is `NUTS(200, 0.65)` and I’m using `MCMCDistributed()` for parallelism.

```julia
Sampling (3 processes) 0%| | ETA: N/A
Sampling (3 processes) 100%|████████████████████████████| Time: 0:01:24
ERROR: LoadError: TaskFailedException

    nested task error: On worker 2:
    MethodError: no method matching increment_deriv!(::Int64, ::Float64)
    Closest candidates are:
      increment_deriv!(!Matched::ReverseDiff.TrackedArray, ::Real, !Matched::Any) at ~/.julia/packages/ReverseDiff/iHmB4/src/derivatives/propagation.jl:34
      increment_deriv!(!Matched::AbstractArray, ::Real, !Matched::Any) at ~/.julia/packages/ReverseDiff/iHmB4/src/derivatives/propagation.jl:36
      increment_deriv!(!Matched::AbstractArray, ::Any) at ~/.julia/packages/ReverseDiff/iHmB4/src/derivatives/propagation.jl:38
      ...
    Stacktrace:
      [1] increment_deriv!
        @ ~/.julia/packages/ReverseDiff/iHmB4/src/derivatives/propagation.jl:36 [inlined]
      [2] broadcast_increment_deriv!
        @ ~/.julia/packages/ReverseDiff/iHmB4/src/derivatives/propagation.jl:145
      [3] special_reverse_exec!
        @ ~/.julia/packages/ReverseDiff/iHmB4/src/derivatives/elementwise.jl:442
      [4] reverse_exec!
        @ ~/.julia/packages/ReverseDiff/iHmB4/src/tape.jl:93
      [5] reverse_pass!
        @ ~/.julia/packages/ReverseDiff/iHmB4/src/tape.jl:87
      [6] reverse_pass!
        @ ~/.julia/packages/ReverseDiff/iHmB4/src/api/tape.jl:36 [inlined]
      [7] seeded_reverse_pass!
        @ ~/.julia/packages/ReverseDiff/iHmB4/src/api/utils.jl:31
      [8] seeded_reverse_pass!
        @ ~/.julia/packages/ReverseDiff/iHmB4/src/api/tape.jl:47
      [9] gradient!
        @ ~/.julia/packages/ReverseDiff/iHmB4/src/api/gradients.jl:42
     [10] gradient!
        @ ~/.julia/packages/ReverseDiff/iHmB4/src/api/gradients.jl:41 [inlined]
     [11] logdensity_and_gradient
        @ ~/.julia/packages/LogDensityProblems/oAYeE/src/AD_ReverseDiff.jl:55
     [12] ∂logπ∂θ
        @ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:166 [inlined]
     [13] ∂H∂θ
        @ ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:31 [inlined]
     [14] phasepoint
        @ ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:76 [inlined]
     [15] phasepoint
        @ ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:153
     [16] #initialstep#43
        @ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:170
     [17] #step#23
        @ ~/.julia/packages/DynamicPPL/zPOYL/src/sampler.jl:104
     [18] macro expansion
        @ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:120 [inlined]
     [19] macro expansion
        @ ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:16 [inlined]
     [20] #mcmcsample#20
        @ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:111
     [21] #sample#42
        @ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:133 [inlined]
     [22] sample_chain
        @ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:448
     [23] sample_chain
        @ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:445 [inlined]
     [24] #exec_from_cache#213
        @ /cluster/apps/nss/gcc-6.3.0/julia/1.7.3/x86_64/share/julia/stdlib/v1.7/Distributed/src/workerpool.jl:343
     [25] exec_from_cache
        @ /cluster/apps/nss/gcc-6.3.0/julia/1.7.3/x86_64/share/julia/stdlib/v1.7/Distributed/src/workerpool.jl:342
     [26] #110
        @ /cluster/apps/nss/gcc-6.3.0/julia/1.7.3/x86_64/share/julia/stdlib/v1.7/Distributed/src/process_messages.jl:278
     [27] run_work_thunk
        @ /cluster/apps/nss/gcc-6.3.0/julia/1.7.3/x86_64/share/julia/stdlib/v1.7/Distributed/src/process_messages.jl:63
     [28] macro expansion
        @ /cluster/apps/nss/gcc-6.3.0/julia/1.7.3/x86_64/share/julia/stdlib/v1.7/Distributed/src/process_messages.jl:278 [inlined]
     [29] #109
        @ ./task.jl:429
    Stacktrace:
      [1] (::Base.var"#898#900")(x::Task)
        @ Base ./asyncmap.jl:177
      [2] foreach(f::Base.var"#898#900", itr::Vector{Any})
        @ Base ./abstractarray.jl:2712
      [3] maptwice(wrapped_f::Function, chnl::Channel{Any}, worker_tasks::Vector{Any}, c::Vector{UInt64})
        @ Base ./asyncmap.jl:177
      [4] wrap_n_exec_twice
        @ ./asyncmap.jl:153 [inlined]
      [5] #async_usemap#883
        @ ./asyncmap.jl:103 [inlined]
      [6] #asyncmap#882
        @ ./asyncmap.jl:81 [inlined]
      [7] pmap(f::Function, p::CachingPool, c::Vector{UInt64}; distributed::Bool, batch_size::Int64, on_error::Nothing, retry_delays::Vector{Any}, retry_check::Nothing)
        @ Distributed /cluster/apps/nss/gcc-6.3.0/julia/1.7.3/x86_64/share/julia/stdlib/v1.7/Distributed/src/pmap.jl:126
      [8] pmap
        @ /cluster/apps/nss/gcc-6.3.0/julia/1.7.3/x86_64/share/julia/stdlib/v1.7/Distributed/src/pmap.jl:101 [inlined]
      [9] macro expansion
        @ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:465 [inlined]
     [10] (::AbstractMCMC.var"#60#71"{Bool, Nothing, Base.Pairs{Symbol, UnionAll, Tuple{Symbol}, NamedTuple{(:chain_type,), Tuple{UnionAll}}}, Random._GLOBAL_RNG, DynamicPPL.Model{typeof(optimal_observer_model), (:sa_bi, :a_response_bi, :sv_bi, :v_response_bi, :behavioral_params), (), (), Tuple{Vector{Int64}, Vector{Float64}, Vector{Int64}, Vector{Float64}, Dict{Symbol, Int64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ReverseDiffAD{false}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, CachingPool, Vector{UInt64}})()
        @ AbstractMCMC ./task.jl:429
Stacktrace:
  [1] sync_end(c::Channel{Any})
    @ Base ./task.jl:381
  [2] macro expansion
    @ ./task.jl:400 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:421 [inlined]
  [4] macro expansion
    @ ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:328 [inlined]
  [5] (::AbstractMCMC.var"#57#68"{Bool, String, Nothing, Base.Pairs{Symbol, UnionAll, Tuple{Symbol}, NamedTuple{(:chain_type,), Tuple{UnionAll}}}, Random._GLOBAL_RNG, DynamicPPL.Model{typeof(optimal_observer_model), (:sa_bi, :a_response_bi, :sv_bi, :v_response_bi, :behavioral_params), (), (), Tuple{Vector{Int64}, Vector{Float64}, Vector{Int64}, Vector{Float64}, Dict{Symbol, Int64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ReverseDiffAD{false}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64, CachingPool, Vector{UInt64}})()
    @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:12
  [6] with_logstate(f::Function, logstate::Any)
    @ Base.CoreLogging ./logging.jl:511
  [7] 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:623
  [8] with_progresslogger(f::Function, _module::Module, logger::Logging.ConsoleLogger)
    @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:36
  [9] macro expansion
    @ ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:11 [inlined]
 [10] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(optimal_observer_model), (:sa_bi, :a_response_bi, :sv_bi, :v_response_bi, :behavioral_params), (), (), Tuple{Vector{Int64}, Vector{Float64}, Vector{Int64}, Vector{Float64}, Dict{Symbol, Int64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ReverseDiffAD{false}, (), AdvancedHMC.DiagEuclideanMetric}}, ::MCMCDistributed, N::Int64, nchains::Int64; progress::Bool, progressname::String, init_params::Nothing, kwargs::Base.Pairs{Symbol, UnionAll, 
Tuple{Symbol}, NamedTuple{(:chain_type,), Tuple{UnionAll}}})
    @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:415
 [11] sample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(optimal_observer_model), (:sa_bi, :a_response_bi, :sv_bi, :v_response_bi, :behavioral_params), (), (), Tuple{Vector{Int64}, Vector{Float64}, Vector{Int64}, Vector{Float64}, Dict{Symbol, Int64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ReverseDiffAD{false}, (), AdvancedHMC.DiagEuclideanMetric}}, ensemble::MCMCDistributed, N::Int64, n_chains::Int64; chain_type::Type, progress::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:220
 [12] sample
    @ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:220 [inlined]
 [13] #sample#6
    @ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:205 [inlined]
 [14] sample
    @ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:205 [inlined]
 [15] #sample#5
    @ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:192 [inlined]
 [16] sample(model::DynamicPPL.Model{typeof(optimal_observer_model), (:sa_bi, :a_response_bi, :sv_bi, :v_response_bi, :behavioral_params), (), (), Tuple{Vector{Int64}, Vector{Float64}, Vector{Int64}, Vector{Float64}, Dict{Symbol, Int64}}, Tuple{}, DynamicPPL.DefaultContext}, alg::NUTS{Turing.Essential.ReverseDiffAD{false}, (), AdvancedHMC.DiagEuclideanMetric}, ensemble::MCMCDistributed, N::Int64, n_chains::Int64)
    @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:192
 [17] macro expansion
    @ /cluster/work/tnu/jachou/msi_project/model_inversion/cluster_uniform_bimodal_fit_all.jl:106 [inlined]
 [18] top-level scope
    @ timing.jl:220

```

My model is approximately the following form:

```julia
@everywhere @model function optimal_observer_model(x1, y1, x2, y2, fixed_params)

        # params that I set
        sigma_m2 = behavioral_params[:sigma_m2]
    
        param1 ~ Uniform(0, 1) 
        var_1~ Uniform(0.1, 200)
        var_2 ~ Uniform(0.1, 500)
    

        trial_num = length(x1) # x2 has the same length

        z1 ~ MvNormal(x1, sqrt(var_1) .* ones(trial_num ))
        z2 ~ MvNormal(x2, sqrt(var_2) .* ones(trial_num ))

        x1_est, x2_est = some_deterministic_transform(z1, z2, var_1, var_2, param1)
        y1 ~ MvNormal(x1_est, sqrt(sigma_m2) .* ones(trial_num))
        y2 ~ MvNormal(x2_est , sqrt(sigma_m2) .* ones(trial_num))
        
    end

```

both x looks like the following

```julia
x=collect(-45:15:45)

```

---

<div class="post-metadata">

**Author:** ![Christopher\_Fisher](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/christopher_fisher/32/26132_2.png) [@Christopher\_Fisher](https://discourse.julialang.org/u/Christopher_Fisher)\
**Post date:** [October 16, 2022, 5:22pm UTC](https://discourse.julialang.org/t/problem-with-reversediff-when-using-turing/88798/2 "2022-10-16T17:22:15Z")

</div>

Without a MWE, it is difficult to diagnose. My suspicion is that `ReverseDiff` is not loaded on the other workers. Typically, all of the dependencies are loaded via the `@everywhere` macro

```julia
@everywhere begin 
  using ReverseDiff
  ....
  include("my_code.jl")
end

```

Another solution is to use `MCMCthreads()` instead of `MCMCDistributed()`. In that case, you will not need to use `@everywhere`.

---

<div class="post-metadata">

**Author:** ![neuro\_enthusiast](https://avatars.discourse-cdn.com/v4/letter/n/43a26b/32.png) [@neuro\_enthusiast](https://discourse.julialang.org/u/neuro_enthusiast)\
**Post date:** [October 20, 2022, 10:44am UTC](https://discourse.julialang.org/t/problem-with-reversediff-when-using-turing/88798/3 "2022-10-20T10:44:41Z")

</div>

Thank you again for the help! I will definitely provide a MWE if some other approaches I’m trying out doesn’t work. Sorry for the vague descriptions in the question.