Nested task error: AssertionError

marouane1994 · May 13, 2022, 9:39pm

recently i updated Julia to 1.7.2 which means that i had to update all the used packages, i am getting an assertion error, that is not clear to me knowing that the same exact code was running fine in Julia 1.5

using Plots
using Random
using SymPy
using CSV
using DataFrames
using Main.QuantumRelay
using Distributions

#calling the github code 
function quantumrelay(alpha,delta,name::String,n)   #n is the number of sources for 1 relay we have 2 sources
   
    chi = fill(sqrt(0.06), n)                  # the parameter chi 
    phi = im*tanh.(chi)
    omega = -log(prod(cosh.(chi)))
    syms, op = qrelay_op(n, phi, alpha, delta)  #operators.jl
    op_a, op_ab, mat, coef = op_mat(op)         #operators.jl

    op_q2 = [syms.apH[1], syms.apV[1], syms.bpH[end], syms.bpV[end]]    # array in operators.jl
    op_q1 = [syms.apH[2:end]..., syms.apV[2:end]..., syms.bpH[1:end-1]..., syms.bpV[1:end-1]...]      #array in operators.jl
    mask_q1 = [op in op_q1 for op in op_a];      #array in operators.jl
    
    mask_q2 = [op in op_q2 for op in op_a];       #array in operators.jl
    qq = [x in syms.apH || x in syms.bpV ? 1 : 0 for x in op_a];      #array in operators.jl
           
    pdet0 = pdet_maker(0.04, 3e-5)      #function utility.jl
    qrs = QRelaySampler(mat, coef, omega, pdet0).         #QuantumRelay.jl  
    targetcache=Dict{Vector{Int}, Float64}()
    target(x::Vector)= log(qrs.prob(qq, x, mask_q1))+ log(qrs.prob(x))      #the log target function of MCMC the function prob is in QuantumRelay.jl 
    Iteration=2^17
    burnin=2^10
    samples=Iteration+burnin
    step=5
    save_iter=burnin:samples
    dist= qrs.psetproposal           #the proposal distribution from the distribution.jl     
    selected=Array[]
    Q = Float64[]
    
    current_x = zeros(8)
    @time for i in 2:samples            #from this line the MCMC algorithm starts
        
        proposed_x= rand(dist(current_x))
        
        prop_proposed= logpdf(dist(current_x), proposed_x)
        
        prop_current= logpdf(dist(proposed_x), current_x)
        
        
        A= min(1,target(proposed_x)+prop_current-target(current_x)-prop_proposed)
        
        
        if log(rand()) < A
            #if i in save_iter
                
                
            push!(selected, proposed_x)
            push!(Q, qrs.prob(qq, proposed_x, mask_q2))
            
            #end
    
            current_x = proposed_x
        else
            current_x= current_x
        end        
    
        
    end
    
    return selected, Q
end

prob= Float64[]
accepted= Array[]
for i = 0:14
    beta = i*pi/14
    name = string(i)
    selected, Q = quantumrelay(pi/4, beta, name,2)
    push!(accepted,length(selected))
    println("beta:", beta)
    push!(prob,Q)
    df=DataFrame(selected=selected)

end

i get this error:

2425.699067 seconds (737.51 M allocations: 57.773 GiB, 0.52% gc time, 0.38% compilation time)
beta:0.0
TaskFailedException

    nested task error: AssertionError: termination_status(m) == MOI.OPTIMAL || termination_status(m) == MOI.DUAL_INFEASIBLE
    Stacktrace:
     [1] (::Main.QuantumRelay.var"#scan#6"{Vector{JuMP.VariableRef}, Vector{Int64}, Vector{Int64}, Vector{Int64}, Int64, JuMP.Model})(c::Channel{Any})
       @ Main.QuantumRelay ~/Downloads/Clp.jl:34
     [2] (::Base.var"#560#561"{Main.QuantumRelay.var"#scan#6"{Vector{JuMP.VariableRef}, Vector{Int64}, Vector{Int64}, Vector{Int64}, Int64, JuMP.Model}, Channel{Any}})()
       @ Base ./channels.jl:132

Stacktrace:
  [1] try_yieldto(undo::typeof(Base.ensure_rescheduled))
    @ Base ./task.jl:777
  [2] wait()
    @ Base ./task.jl:837
  [3] wait(c::Base.GenericCondition{ReentrantLock})
    @ Base ./condition.jl:123
  [4] take_unbuffered(c::Channel{Any})
    @ Base ./channels.jl:405
  [5] take!
    @ ./channels.jl:383 [inlined]
  [6] iterate(c::Channel{Any}, state::Nothing)
    @ Base ./channels.jl:466
  [7] iterate
    @ ./channels.jl:465 [inlined]
  [8] (::Main.QuantumRelay.var"#prob#8"{Vector{Complex}, Float64, Main.QuantumRelay.var"#scan#6"{Vector{JuMP.VariableRef}, Vector{Int64}, Vector{Int64}, Vector{Int64}, Int64, JuMP.Model}, Main.QuantumRelay.var"#setc#5"{Vector{JuMP.ConstraintRef{JuMP.Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.GreaterThan{Float64}}, JuMP.ScalarShape}}}, Matrix{Float64}, Matrix{Float64}, Main.QuantumRelay.var"#pdet0#2"{Float64, Float64}})(na::Vector{Float64})
    @ Main.QuantumRelay ./In[2]:63
  [9] (::var"#target#7"{QRelaySampler, Vector{Int64}, Vector{Bool}})(x::Vector{Float64})
    @ Main ./In[3]:28
 [10] macro expansion
    @ ./In[3]:48 [inlined]
 [11] macro expansion
    @ ./timing.jl:220 [inlined]
 [12] quantumrelay(alpha::Float64, delta::Float64, name::String, n::Int64)
    @ Main ./In[3]:39
 [13] top-level scope
    @ ./In[4]:6
 [14] eval
    @ ./boot.jl:373 [inlined]
 [15] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
    @ Base ./loading.jl:1196

all the included files are at the begining of this code can be found here https://github.com/marouanehanhasse/QComm
from the error message it is indicated that there is a problem in the Clp.jl file exactly in line 34 @ Main.QuantumRelay ~/Downloads/Clp.jl:34 termination_status(m) == MOI.OPTIMAL || termination_status(m) == MOI.DUAL_INFEASIBLE and i don’t know what i am doing wrong there, it was totally working in the previous version of julia. The code has comment about the variables and where can they be found, i’ve been trying with it these past two days, i changed the syntax but i don’t know what to do in this kind of assertion errors.

odow · May 14, 2022, 10:11pm

Hi @marouane1994, I moved this to the “Optimization (mathematical)” section.

Your problem is that the solver found a solution that wasn’t optimal or dual infeasible.

Try changing

github.com

marouanehanhasse/QComm/blob/b0e4ecc7a1c9d85e32e089f0b8b44d6b0b681f83/Clp.jl#L34


      
          i = 1
          init = 1
          while i > 0
              if i >= init
                  @objective(m, Max, x[i])
                  res = JuMP.optimize!(m)
                  if MOI.get(m, MOI.TerminationStatus()) == MOI.OPTIMAL || MOI.get(m, MOI.TerminationStatus()) == MOI.DUAL_INFEASIBLE
                      ub[i] = round(Int, value(x[i]))
                      set_objective_sense(m, MOI.MIN_SENSE)
                      res = JuMP.optimize!(m)
                      @assert termination_status(m) == MOI.OPTIMAL || termination_status(m) == MOI.DUAL_INFEASIBLE
                      lb[i] = round(Int, value(x[i]))
          
          
            v[i] = lb[i]
                      init += 1
                  else
                      @assert termination_status(m) == MOI.INFEASIBLE
                      i -= 1
                      continue
                  end
              elseif v[i] < ub[i]

to something like

status = termination_status(m)
if status != MOI.OPTIMAL && status != MOI.DUAL_INFEASIBLE
    print(solution_summary(m))
    error("Unexpected status $status")
end

marouane1994 · May 15, 2022, 2:37pm

But why would it work for earlier versions in your opinion with the same input, but it doesn’t work now and is there a better solver than Clp for LP?

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