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WIP: Parallel SAMIN #843

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60ba344
Draft for distributed SAMIN
MatFi Aug 14, 2020
9ddf656
fix deps
MatFi Aug 14, 2020
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1 change: 1 addition & 0 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@ version = "0.22.0"

[deps]
Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
LineSearches = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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2 changes: 1 addition & 1 deletion src/Optim.jl
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Expand Up @@ -37,7 +37,7 @@ using FillArrays # For handling scalar bounds in Fminbox

# for extensions of functions defined in Base.
import Base: length, push!, show, getindex, setindex!, maximum, minimum

import Distributed: RemoteChannel, remote_do, fetch, put!, take!, @async, @sync
# objective and constraints types and functions relevant to them.
import NLSolversBase: NonDifferentiable, OnceDifferentiable, TwiceDifferentiable,
nconstraints, nconstraints_x, NotInplaceObjective, InplaceObjective
Expand Down
248 changes: 152 additions & 96 deletions src/multivariate/solvers/constrained/samin.jl
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Expand Up @@ -47,6 +47,7 @@ algorithm
x_tol::T = 1e-6 # the required tolerance level for x
coverage_ok::Bool = false # if false, increase temperature until initial parameter space is covered
verbosity::Int = 1 # scalar: 0, 1, 2 or 3 (default = 1: see final results).
workers::Array{Int,1} = [] # associated worker processes for parallel evaluation
end
# * verbosity: scalar: 0, 1, 2 or 3 (default = 1).
# * 0 = no screen output
Expand All @@ -56,17 +57,42 @@ end
# covered by the trial values. 1: start decreasing temperature immediately
Base.summary(::SAMIN) = "SAMIN"



function remote_eval(d,Tx,lb_ub_bounds_chan,xp_fold_i_chan,ret_chan,stop_sig)
while !fetch(stop_sig)
lnobds = 0
if timedwait(()->isready(xp_fold_i_chan),0.1) ==:ok
xp ,f_old, i = take!(xp_fold_i_chan)
lb, ub, bounds = fetch(lb_ub_bounds_chan)
xp[i] += (Tx(2.0) * rand(Tx) - Tx(1.0)) * bounds[i]
if (xp[i] < lb[i]) || (xp[i] > ub[i])
xp[i] = lb[i] + (ub[i] - lb[i]) * rand(Tx)
lnobds =1
end
f_proposal = value(d, xp)
put!(ret_chan,(xp,f_old,f_proposal,lnobds,i))

elseif fetch(stop_sig) # exit on stop
return nothing
end
end
end



function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray{Tx}, method::SAMIN, options::Options = Options()) where Tx


t0 = time() # Initial time stamp used to control early stopping by options.time_limit

hline = "="^80
d = NonDifferentiable(obj_fn, x)

tr = OptimizationTrace{typeof(value(d)), typeof(method)}()
tracing = options.store_trace || options.show_trace || options.extended_trace || options.callback != nothing

@unpack nt, ns, rt, neps, f_tol, x_tol, coverage_ok, verbosity = method
@unpack nt, ns, rt, neps, f_tol, x_tol, coverage_ok, verbosity ,workers = method
verbose = verbosity > 0

x0 = copy(x)
Expand Down Expand Up @@ -96,7 +122,22 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
trace!(tr, d, (x=xopt, iteration=iteration), iteration, method, options, _time-t0)

stopped_by_callback = false


if !isempty(workers) #prepare parallel evaluation
xp_fold_i_chan = RemoteChannel(()->Channel{Tuple}(1024)); #put and take jobs from here
ret_chan = RemoteChannel(()->Channel{Tuple}(1024)); #put and take results from here
lb_ub_bounds_chan = RemoteChannel(()->Channel{Tuple}(1)); #put and fetch current bounds here
stop_sig = RemoteChannel(()->Channel{Bool}(1)); # signal the workers to end the eval loop
put!(stop_sig,false)
for p in workers # start tasks on the workers to process evals in parallel
remote_do(remote_eval, p, d,Tx,lb_ub_bounds_chan, xp_fold_i_chan, ret_chan,stop_sig)
end
put!(lb_ub_bounds_chan,(lb,ub,bounds)) # Fill channel with initial bounds
#start the initial evaluations at same x
for i in (length(workers) < (n*ns) ? (1:length(workers)) : (1:(n*ns)))
put!(xp_fold_i_chan,(x,f_old,(i % n) + 1))
end
end
# main loop, first increase temperature until parameter space covered, then reduce until convergence
while converge==0
# statistics to report at each temp change, set back to zero
Expand All @@ -105,109 +146,117 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
nnew = 0
ndown = 0
lnobds = 0

# repeat nt times then adjust temperature
for m = 1:nt
@sync for m = 1:nt
h=0
# repeat ns times, then adjust bounds
nacp = zeros(n)
for j = 1:ns
# generate new point by taking last and adding a random value
# to each of elements, in turn
for h = 1:n
iteration += 1
# new Sept 2011, if bounds are same, skip the search for that vbl.
# Allows restrictions without complicated programming
if (lb[h] != ub[h])
while h <n*ns
h += 1
iteration += 1
i = (h % n)+1
i_vbl = copy(i)
if (lb[i] != ub[i])

if isempty(workers)
xp = copy(x)
xp[h] += (Tx(2.0) * rand(Tx) - Tx(1.0)) * bounds[h]
if (xp[h] < lb[h]) || (xp[h] > ub[h])
xp[h] = lb[h] + (ub[h] - lb[h]) * rand(Tx)
lnobds += 1
xp[i] += (Tx(2.0) * rand(Tx) - Tx(1.0)) * bounds[i]
if (xp[i] < lb[i]) || (xp[i] > ub[i])
xp[i] = lb[i] + (ub[i] - lb[i]) * rand(Tx)
lnobds =1
end
# Evaluate function at new point
f_proposal = value(d, xp)
# Accept the new point if the function value decreases
if (f_proposal <= f_old)

else
xp, f_old_r, f_proposal, lnobds, i = take!(ret_chan)
end
if (f_proposal <= f_old)
x = copy(xp)
f_old = f_proposal
nacc += 1 # total number of acceptances
nacp[i] += 1 # acceptances for this parameter
nup += 1
# If lower than any other point, record as new optimum
if f_proposal < fopt
xopt = copy(xp)
fopt = f_proposal
d.F = f_proposal
nnew +=1
details = [details; [iteration t f_proposal xp']]
end
# If the point is higher, use the Metropolis criteria to decide on
# acceptance or rejection.
else
p = exp(-(f_proposal - f_old) / t)
if rand(Tx) < p
x = copy(xp)
f_old = f_proposal
nacc += 1 # total number of acceptances
nacp[h] += 1 # acceptances for this parameter
nup += 1
# If lower than any other point, record as new optimum
if f_proposal < fopt
xopt = copy(xp)
fopt = f_proposal
d.F = f_proposal
nnew +=1
details = [details; [f_calls(d) t f_proposal xp']]
end
# If the point is higher, use the Metropolis criteria to decide on
# acceptance or rejection.
f_old = copy(f_proposal)
d.F = f_proposal
nacc += 1
nacp[i] += 1
ndown += 1
else
p = exp(-(f_proposal - f_old) / t)
if rand(Tx) < p
x = copy(xp)
f_old = copy(f_proposal)
d.F = f_proposal
nacc += 1
nacp[h] += 1
ndown += 1
else
nrej += 1
end
nrej += 1
end
end

if tracing
# update trace; callbacks can stop routine early by returning true
stopped_by_callback = trace!(tr, d, (x=xopt,iteration=iteration), iteration, method, options, time()-t0)
end

# If options.iterations exceeded, terminate the algorithm
_time = time()
if f_calls(d) >= options.iterations || _time-t0 > options.time_limit || stopped_by_callback
!isempty(workers) && @async put!(xp_fold_i_chan,(x,f_old,i_vbl)) # next iteration, @async rusults in better scaling with workers
end
end

if verbose
println(hline)
println("SAMIN results")
println("NO CONVERGENCE: MAXEVALS exceeded")
@printf("\n Obj. value: %16.5f\n\n", fopt)
println(" parameter search width")
for i=1:n
@printf("%16.5f %16.5f \n", xopt[i], bounds[i])
end
println(hline)
end
converge = 0
if tracing
# update trace; callbacks can stop routine early by returning true
stopped_by_callback = trace!(tr, d, (x=xopt,iteration=iteration), iteration, method, options, time()-t0)
end
# If options.iterations exceeded, terminate the algorithm
_time = time()
if iteration >= options.iterations || _time-t0 > options.time_limit || stopped_by_callback

# tell the workers to stop
!isempty(workers) && take!(stop_sig)
!isempty(workers) && put!(stop_sig,true)

return MultivariateOptimizationResults(method,
x0,# initial_x,
xopt, #pick_best_x(f_incr_pick, state),
fopt, # pick_best_f(f_incr_pick, state, d),
f_calls(d), #iteration,
f_calls(d) >= options.iterations, #iteration == options.iterations,
false, # x_converged,
0.0,#T(options.x_tol),
0.0,#T(options.x_tol),
NaN,# x_abschange(state),
NaN,# x_abschange(state),
false,# f_converged,
0.0,#T(options.f_tol),
0.0,#T(options.f_tol),
NaN,#f_abschange(d, state),
NaN,#f_abschange(d, state),
false,#g_converged,
0.0,#T(options.g_tol),
NaN,#g_residual(d),
false, #f_increased,
tr,
f_calls(d),
g_calls(d),
h_calls(d),
true,
options.time_limit,
_time-t0,)
if verbose
println(hline)
println("SAMIN results")
println("NO CONVERGENCE: MAXEVALS exceeded")
@printf("\n Obj. value: %16.5f\n\n", fopt)
println(" parameter search width")
for i=1:n
@printf("%16.5f %16.5f \n", xopt[i], bounds[i])
end
println(hline)
end
converge = 0

return MultivariateOptimizationResults(method,
x0,# initial_x,
xopt, #pick_best_x(f_incr_pick, state),
fopt, # pick_best_f(f_incr_pick, state, d),
iteration, #iteration,
iteration >= options.iterations, #iteration == options.iterations,
false, # x_converged,
0.0,#T(options.x_tol),
0.0,#T(options.x_tol),
NaN,# x_abschange(state),
NaN,# x_abschange(state),
false,# f_converged,
0.0,#T(options.f_tol),
0.0,#T(options.f_tol),
NaN,#f_abschange(d, state),
NaN,#f_abschange(d, state),
false,#g_converged,
0.0,#T(options.g_tol),
NaN,#g_residual(d),
false, #f_increased,
tr,
iteration,
0,
0,
true,
options.time_limit,
_time-t0,)
end
# Adjust bounds so that approximately half of all evaluations are accepted
test = 0
Expand All @@ -225,6 +274,9 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
test += 1 # make sure coverage check passes for the fixed parameters
end
end
!isempty(workers) && take!(lb_ub_bounds_chan) # clear bounds
!isempty(workers) && put!(lb_ub_bounds_chan,(lb,ub,bounds)) # populate new bounds

nacp = 0 # set back to zero
# check if we cover parameter space, if we have yet to do so
if !coverage_ok
Expand Down Expand Up @@ -318,12 +370,16 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
x = xopt
end
end
# tell the workers to stop
!isempty(workers) && take!(stop_sig)
!isempty(workers) && put!(stop_sig,true)

return MultivariateOptimizationResults(method,
x0,# initial_x,
xopt, #pick_best_x(f_incr_pick, state),
fopt, # pick_best_f(f_incr_pick, state, d),
f_calls(d), #iteration,
f_calls(d) >= options.iterations, #iteration == options.iterations,
iteration, #iteration,
iteration >= options.iterations, #iteration == options.iterations,
false, # x_converged,
0.0,#T(options.x_tol),
0.0,#T(options.x_tol),
Expand All @@ -339,9 +395,9 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
NaN,#g_residual(d),
false, #f_increased,
tr,
f_calls(d),
g_calls(d),
h_calls(d),
iteration,
0,
0,
true,
options.time_limit,
_time-t0,)
Expand Down
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