""" A distributed version of the paraboloid model with an extra input that can be used to shift
each index.
This version is used for testing, so it will have different options.
"""
import numpy as np
import openmdao.api as om
from openmdao.utils.mpi import MPI
from openmdao.utils.array_utils import evenly_distrib_idxs
[docs]class DistParab(om.ExplicitComponent):
[docs] def initialize(self):
self.options.declare('arr_size', types=int, default=10,
desc="Size of input and output vectors.")
self.options.declare('deriv_type', default='dense',
values=['dense', 'fd', 'cs', 'sparse'],
desc="Method for computing derivatives.")
[docs] def setup(self):
arr_size = self.options['arr_size']
deriv_type = self.options['deriv_type']
comm = self.comm
rank = comm.rank
sizes, offsets = evenly_distrib_idxs(comm.size, arr_size)
io_size = sizes[rank]
self.offset = offsets[rank]
# src_indices will be computed automatically
self.add_input('x', val=np.ones(io_size), distributed=True)
self.add_input('y', val=np.ones(io_size), distributed=True)
self.add_input('a', val=-3.0 * np.ones(io_size), distributed=True)
self.add_output('f_xy', val=np.ones(io_size), distributed=True)
if deriv_type == 'dense':
self.declare_partials('f_xy', ['x', 'y', 'a'])
elif deriv_type == 'sparse':
row_col = np.arange(io_size)
self.declare_partials('f_xy', ['x', 'y', 'a'], rows=row_col, cols=row_col)
else:
self.declare_partials('f_xy', ['x', 'y', 'a'], method=deriv_type)
[docs] def compute(self, inputs, outputs):
x = inputs['x']
y = inputs['y']
a = inputs['a']
outputs['f_xy'] = (x + a)**2 + x * y + (y + 4.0)**2 - 3.0
[docs] def compute_partials(self, inputs, partials):
deriv_type = self.options['deriv_type']
x = inputs['x']
y = inputs['y']
a = inputs['a']
if deriv_type == 'dense':
partials['f_xy', 'x'] = np.diag(2.0 * x + 2.0 * a + y)
partials['f_xy', 'y'] = np.diag(2.0 * y + 8.0 + x)
partials['f_xy', 'a'] = np.diag(2.0 * a + 2.0 * x)
elif deriv_type == 'sparse':
partials['f_xy', 'x'] = 2.0 * x + 2.0 * a + y
partials['f_xy', 'y'] = 2.0 * y + 8.0 + x
partials['f_xy', 'a'] = 2.0 * a + 2.0 * x
# Simplified version for feature docs without the extra testing args.
[docs]class DistParabFeature(om.ExplicitComponent):
[docs] def initialize(self):
self.options.declare('arr_size', types=int, default=10,
desc="Size of input and output vectors.")
[docs] def setup(self):
arr_size = self.options['arr_size']
self.add_input('x', val=1., distributed=False,
shape=arr_size)
self.add_input('y', val=1., distributed=False,
shape=arr_size)
sizes, offsets = evenly_distrib_idxs(self.comm.size, arr_size)
self.start = offsets[self.comm.rank]
self.end = self.start + sizes[self.comm.rank]
self.a = -3.0 + 0.6 * np.arange(self.start,self.end)
self.add_output('f_xy', shape=len(self.a), distributed=True)
self.add_output('f_sum', shape=1, distributed=False)
self.declare_coloring(wrt='*', method='fd')
[docs] def compute(self, inputs, outputs):
x = inputs['x'][self.start:self.end]
y = inputs['y'][self.start:self.end]
outputs['f_xy'] = (x + self.a)**2 + x * y + (y + 4.0)**2 - 3.0
local_sum = np.sum(outputs['f_xy'])
global_sum = np.zeros(1)
self.comm.Allreduce(local_sum, global_sum, op=MPI.SUM)
outputs['f_sum'] = global_sum
[docs]class DistParabDeprecated(om.ExplicitComponent):
[docs] def initialize(self):
self.options.declare('arr_size', types=int, default=10,
desc="Size of input and output vectors.")
[docs] def setup(self):
arr_size = self.options['arr_size']
comm = self.comm
rank = comm.rank
sizes, offsets = evenly_distrib_idxs(comm.size, arr_size)
io_size = sizes[rank]
self.offset = offsets[rank]
# src_indices will be computed automatically
self.add_input('x', val=np.ones(io_size), distributed=True)
self.add_input('y', val=np.ones(io_size), distributed=True)
self.add_input('offset', val=-3.0 * np.ones(io_size), distributed=True)
self.add_output('f_xy', val=np.ones(io_size), distributed=True)
row_col = np.arange(io_size)
self.declare_partials('f_xy', ['x', 'y', 'offset'], rows=row_col, cols=row_col)
[docs] def compute(self, inputs, outputs):
x = inputs['x']
y = inputs['y']
a = inputs['offset']
outputs['f_xy'] = (x + a)**2 + x * y + (y + 4.0)**2 - 3.0
[docs] def compute_partials(self, inputs, partials):
x = inputs['x']
y = inputs['y']
a = inputs['offset']
partials['f_xy', 'x'] = 2.0 * x + 2.0 * a + y
partials['f_xy', 'y'] = 2.0 * y + 8.0 + x
partials['f_xy', 'offset'] = 2.0 * a + 2.0 * x
