OpenMDAO並列サンプリング/なぜtest_brute_force.pyが失敗しない

Question

test_brute_force.pyを変更して、パラボラモデルのサンプリングを並列化しようとしました（以下のコードを参照）。エラー'sellars': promoted name 'sellar99.p1.f_xy' matches multiple unknowns: ('sellars.sellar99.p1.f_xy', 'sellars.sellar99.p1.f_xy')が表示されます。このエラーは何を意味しますか？なぜtest_brute_force.pyにこのエラーがありますか？

from __future__ import print_function 
from florisse.floris import AEPGroup 
import unittest 

from six.moves import range 
from six import iteritems 
import numpy as np 
from openmdao.api import Problem, Group, ParallelGroup, \ 
         Component, IndepVarComp, ExecComp, \ 
         Driver, ScipyOptimizer, SqliteRecorder 
from openmdao.core.mpi_wrap import MPI 

if MPI: 
    from openmdao.core.petsc_impl import PetscImpl as impl 
else: 
    from openmdao.api import BasicImpl as impl 

class Paraboloid(Component): 
    """ Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 """ 

    def __init__(self): 
     super(Paraboloid, self).__init__() 

     self.add_param('x_p', val=6.0) 
     self.add_param('y', val=-7.0) 

     self.add_output('f_xy', val=0.0) 

    def solve_nonlinear(self, params, unknowns, resids): 
     """f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 
     """ 

     x = params['x_p'] 
     y = params['y'] 

     unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0 

    def linearize(self, params, unknowns, resids): 
     """ Jacobian for our paraboloid.""" 

     x = params['x_p'] 
     y = params['y'] 
     J = {} 

     J['f_xy', 'x_p'] = 2.0*x - 6.0 + y 
     J['f_xy', 'y'] = 2.0*y + 8.0 + x 
     return J 

pboidGroup = Group() 
pboidGroup.add('p1', Paraboloid(), promotes=['x_p', 'y']) 
pboidGroup.add('p2', Paraboloid(), promotes=['x_p', 'y']) 
pboidGroup.connect('p1.x_p', 'p2.x_p') 
pboidGroup.connect('p1.x_p', 'p2.y') 

class Randomize(Component): 
    """ add random uncertainty to params and distribute 

    Args 
    ---- 
    n : number of points to generate for each param 

    params : collection of (name, value, std_dev) specifying the params 
      that are to be randommized. 
    """ 
    def __init__(self, n=0, params=[]): 
     super(Randomize, self).__init__() 

     self.dists = {} 

     for name, value, std_dev in params: 
      # add param 
      self.add_param(name, val=value) 

      # add an output array var to distribute the modified param values 
      if isinstance(value, np.ndarray): 
       shape = (n, value.size) 
      else: 
       shape = (n, 1) 

      # generate a standard normal distribution (size n) for this param 
      self.dists[name] = np.random.normal(0.0, std_dev, n*shape[1]).reshape(shape) 
      #self.dists[name] = std_dev*np.random.normal(0.0, 1.0, n*shape[1]).reshape(shape) 

      self.add_output('dist_'+name, val=np.zeros(shape)) 

    def solve_nonlinear(self, params, unknowns, resids): 
     """ add random uncertainty to params 
     """ 
     for name, dist in iteritems(self.dists): 
      unknowns['dist_'+name] = params[name] + dist 

    def linearize(self, params, unknowns, resids): 
     """ derivatives 
     """ 
     J = {} 
     for u in unknowns: 
      name = u.split('_', 1)[1] 
      for p in params: 
       shape = (unknowns[u].size, params[p].size) 
       if p == name: 
        J[u, p] = np.eye(shape[0], shape[1]) 
       else: 
        J[u, p] = np.zeros(shape) 
     return J 


class Collector(Component): 
    """ collect the inputs and compute the mean of each 

    Args 
    ---- 
    n : number of points to collect for each input 

    names : collection of `Str` specifying the names of the inputs to 
      collect and the resulting outputs. 
    """ 
    def __init__(self, n=10, names=[]): 
     super(Collector, self).__init__() 

     self.names = names 

     # create n params for each input 
     for i in range(n): 
      for name in names: 
       self.add_param('%s_%i' % (name, i), val=0.) 

     # create an output for the mean of each input 
     for name in names: 
      self.add_output(name, val=0.) 

    def solve_nonlinear(self, params, unknowns, resids): 
     """ compute the mean of each input 
     """ 
     inputs = {} 

     for p in params: 
      name = p.split('_', 1)[0] 
      if name not in inputs: 
       inputs[name] = data = [0.0, 0.0] 
      else: 
       data = inputs[name] 
      data[0] += 1 
      data[1] += params[p] 

     for name in self.names: 
      unknowns[name] = inputs[name][1]/inputs[name][0] 

    def linearize(self, params, unknowns, resids): 
     """ derivatives 
     """ 
     J = {} 
     for p in params: 
      name, idx = p.split('_', 1) 
      for u in unknowns: 
       if u == name: 
        J[u, p] = 1 
       else: 
        J[u, p] = 0 
     return J 


class BruteForceSellarProblem(Problem): 
    """ Performs optimization on the Sellar problem. 

     Applies a normal distribution to the design vars and runs all of the 
     samples, then collects the values of all of the outputs, calculates 
     the mean of those and stuffs that back into the unknowns vector. 

     This is the brute force version that just stamps out N separate 
     sellar models in a parallel group and sets the input of each 
     one to be one of these random design vars. 

    Args 
    ---- 
    n : number of randomized points to generate for each input value 

    derivs : if True, use user-defined derivatives, else use Finite Difference 
    """ 
    def __init__(self, n=10, derivs=False): 
     super(BruteForceSellarProblem, self).__init__(impl=impl) 

     root = self.root = Group() 
     if not derivs: 
      root.deriv_options['type'] = 'fd' 

     sellars = root.add('sellars', ParallelGroup()) 
     for i in range(n): 
      name = 'sellar%i' % i 
      sellars.add(name, pboidGroup) 
      #sellars.add(name, SellarDerivatives()) 

      root.connect('x_p', 'sellars.'+name+'.x')#, src_indices=[i]) 
      #root.connect('yaw0', 'sellars.'+name+'.yaw0')#, src_indices=[i]) 
      #root.connect('dist_z', 'sellars.'+name+'.z', src_indices=[i*2, i*2+1]) 

      root.connect('sellars.'+name+'.f_xy', 'collect.obj_%i' % i) 
      #root.connect('sellars.'+name+'.con1', 'collect.con1_%i' % i) 
      #root.connect('sellars.'+name+'.con2', 'collect.con2_%i' % i) 

     root.add('indep', IndepVarComp([ 
        ('x', 1.0), 
        ('z', np.array([5.0, 2.0])) 
       ]), 
       promotes=['x', 'z']) 

     root.add('random', Randomize(n=n, params=[ 
        # name, value, std dev 
        ('x', 1.0, 1e-2), 
        ('z', np.array([5.0, 2.0]), 1e-2) 
       ]), 
       promotes=['x', 'z', 'dist_x', 'dist_z']) 

     root.add('collect', Collector(n=n, names=['obj', 'con1', 'con2']), 
       promotes=['obj', 'con1', 'con2']) 

     # top level driver setup 
     self.driver = ScipyOptimizer() 
     self.driver.options['optimizer'] = 'SLSQP' 
     self.driver.options['tol'] = 1.0e-8 
     self.driver.options['maxiter'] = 50 
     self.driver.options['disp'] = False 

     self.driver.add_desvar('z', lower=np.array([-10.0, 0.0]), 
            upper=np.array([ 10.0, 10.0])) 
     self.driver.add_desvar('x', lower=0.0, upper=10.0) 

     self.driver.add_objective('obj') 
     self.driver.add_constraint('con1', upper=0.0) 
     self.driver.add_constraint('con2', upper=0.0) 

prob = BruteForceSellarProblem(100, derivs=False) 
prob.setup(check=False) 
prob.run() 
print(prob["obj"]) 

Answer 1

あなたがモデルを作成しようとしていること、または何を何に接続するべきかは完全にはわかりませんが、そのエラーを回避できます。問題は、放物面群pboidGroupの同じインスタンスをループ内に複数回追加することであり、OpenMDAOは複数の場所で同じコンポーネントインスタンスを使用することをサポートしていないということです。毎回新しいインスタンスを作成する必要があります。今

 pboidGroup = Group() 
     pboidGroup.add('p1', Paraboloid()) 
     pboidGroup.add('p2', Paraboloid()) 
     pboidGroup.connect('p1.x_p', 'p2.x_p') 
     pboidGroup.connect('p1.x_p', 'p2.y') 

     name = 'sellar%i' % i 
     sellars.add(name, pboidGroup) 
     #sellars.add(name, SellarDerivatives()) 

私は、私はエラーを取得することをやったこと：ループ内で我々が得るよう

はそれを修正するには、私はちょうど、近くにそれが使用されている場所までループにコードを移動します接続し、私はx_pがルートにあることを意図しているのかどうか分からない（多分IndepVarCompが必要です）が、これは停止点を過ぎてしまうかもしれません。