def test_distrib_record_driver(self):
size = 100 # how many items in the array
prob = Problem()
prob.model.add_subsystem('des_vars',
IndepVarComp('x', np.ones(size)),
promotes=['x'])
prob.model.add_subsystem('plus',
DistributedAdder(size),
promotes=['x', 'y'])
prob.model.add_subsystem('summer', Summer(size), promotes=['y', 'sum'])
prob.driver.recording_options['record_desvars'] = True
prob.driver.recording_options['record_objectives'] = True
prob.driver.recording_options['record_constraints'] = True
prob.driver.recording_options['includes'] = ['y']
prob.driver.add_recorder(self.recorder)
prob.model.add_design_var('x')
prob.model.add_objective('sum')
prob.setup()
prob['x'] = np.ones(size)
t0, t1 = run_driver(prob)
prob.cleanup()
coordinate = [0, 'Driver', (0, )]
expected_desvars = {
"des_vars.x": prob['des_vars.x'],
}
expected_objectives = {
"summer.sum": prob['summer.sum'],
}
expected_outputs = expected_desvars.copy()
expected_outputs['plus.y'] = prob.get_val('plus.y', get_remote=True)
if prob.comm.rank == 0:
expected_outputs.update(expected_objectives)
expected_data = ((coordinate, (t0, t1), expected_outputs, None), )
assertDriverIterDataRecorded(self, expected_data, self.eps)
def test_distrib_record_driver(self):
size = 100 # how many items in the array
prob = Problem()
prob.model = Group()
prob.model.add_subsystem('des_vars', IndepVarComp('x', np.ones(size)), promotes=['x'])
prob.model.add_subsystem('plus', DistributedAdder(size), promotes=['x', 'y'])
prob.model.add_subsystem('summer', Summer(size), promotes=['y', 'sum'])
prob.driver.recording_options['record_desvars'] = True
prob.driver.recording_options['record_responses'] = True
prob.driver.recording_options['record_objectives'] = True
prob.driver.recording_options['record_constraints'] = True
prob.driver.recording_options['includes'] = []
prob.driver.add_recorder(self.recorder)
prob.model.add_design_var('x')
prob.model.add_objective('sum')
prob.setup(check=False)
prob['x'] = np.ones(size)
t0, t1 = run_driver(prob)
prob.cleanup()
if prob.comm.rank == 0:
coordinate = [0, 'Driver', (0,)]
expected_desvars = {
"des_vars.x": prob['des_vars.x'],
}
expected_objectives = {
"summer.sum": prob['summer.sum'],
}
expected_outputs = expected_desvars
expected_outputs.update(expected_objectives)
expected_data = ((coordinate, (t0, t1), expected_outputs, None),)
assertDriverIterDataRecorded(self, expected_data, self.eps)
def test_recording_remote_voi(self):
# Create a parallel model
model = Group()
model.add_subsystem('par', ParallelGroup())
model.par.add_subsystem('G1', Mygroup())
model.par.add_subsystem('G2', Mygroup())
model.connect('par.G1.y', 'Obj.y1')
model.connect('par.G2.y', 'Obj.y2')
model.add_subsystem('Obj', ExecComp('obj=y1+y2'))
model.add_objective('Obj.obj')
# Configure driver to record VOIs on both procs
driver = ScipyOptimizeDriver(disp=False)
driver.recording_options['record_desvars'] = True
driver.recording_options['record_responses'] = True
driver.recording_options['record_objectives'] = True
driver.recording_options['record_constraints'] = True
driver.recording_options['includes'] = ['par.G1.y', 'par.G2.y']
driver.add_recorder(self.recorder)
# Create problem and run driver
prob = Problem(model, driver)
prob.setup()
t0, t1 = run_driver(prob)
prob.cleanup()
# Since the test will compare the last case recorded, just check the
# current values in the problem. This next section is about getting those values
# These involve collective gathers so all ranks need to run this
expected_outputs = prob.driver.get_design_var_values()
expected_outputs.update(prob.driver.get_objective_values())
expected_outputs.update(prob.driver.get_constraint_values())
# includes for outputs are specified as promoted names but we need absolute names
prom2abs = model._var_allprocs_prom2abs_list['output']
abs_includes = [
prom2abs[n][0] for n in prob.driver.recording_options['includes']
]
# Absolute path names of includes on this rank
rrank = model.comm.rank
rowned = model._owning_rank
local_includes = [n for n in abs_includes if rrank == rowned[n]]
# Get values for all vars on this rank
inputs, outputs, residuals = model.get_nonlinear_vectors()
# Get values for includes on this rank
local_vars = {n: outputs[n] for n in local_includes}
# Gather values for includes on all ranks
all_vars = model.comm.gather(local_vars, root=0)
if prob.comm.rank == 0:
# Only on rank 0 do we have all the values. The all_vars variable is a list of
# dicts from all ranks 0,1,... In this case, just ranks 0 and 1
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
expected_includes = {
'par.G1.Cy.y': dct['par.G1.Cy.y'],
'par.G2.Cy.y': dct['par.G2.Cy.y'],
}
expected_outputs.update(expected_includes)
coordinate = [0, 'ScipyOptimize_SLSQP', (driver.iter_count - 1, )]
expected_data = ((coordinate, (t0, t1), expected_outputs, None), )
assertDriverIterDataRecorded(self, expected_data, self.eps)
def test_recording_remote_voi(self):
# Create a parallel model
model = Group()
model.add_subsystem('par', ParallelGroup())
model.par.add_subsystem('G1', Mygroup())
model.par.add_subsystem('G2', Mygroup())
model.connect('par.G1.y', 'Obj.y1')
model.connect('par.G2.y', 'Obj.y2')
model.add_subsystem('Obj', ExecComp('obj=y1+y2'))
model.add_objective('Obj.obj')
# Configure driver to record VOIs on both procs
driver = ScipyOptimizeDriver(disp=False)
driver.recording_options['record_desvars'] = True
driver.recording_options['record_responses'] = True
driver.recording_options['record_objectives'] = True
driver.recording_options['record_constraints'] = True
driver.recording_options['includes'] = ['par.G1.y', 'par.G2.y']
driver.add_recorder(self.recorder)
# Create problem and run driver
prob = Problem(model, driver)
prob.add_recorder(self.recorder)
prob.setup()
t0, t1 = run_driver(prob)
prob.record_iteration('final')
t2 = time()
prob.cleanup()
# Since the test will compare the last case recorded, just check the
# current values in the problem. This next section is about getting those values
# These involve collective gathers so all ranks need to run this
expected_outputs = driver.get_design_var_values()
expected_outputs.update(driver.get_objective_values())
expected_outputs.update(driver.get_constraint_values())
# includes for outputs are specified as promoted names but we need absolute names
prom2abs = model._var_allprocs_prom2abs_list['output']
abs_includes = [prom2abs[n][0] for n in prob.driver.recording_options['includes']]
# Absolute path names of includes on this rank
rrank = model.comm.rank
rowned = model._owning_rank
local_includes = [n for n in abs_includes if rrank == rowned[n]]
# Get values for all vars on this rank
inputs, outputs, residuals = model.get_nonlinear_vectors()
# Get values for includes on this rank
local_vars = {n: outputs[n] for n in local_includes}
# Gather values for includes on all ranks
all_vars = model.comm.gather(local_vars, root=0)
if prob.comm.rank == 0:
# Only on rank 0 do we have all the values. The all_vars variable is a list of
# dicts from all ranks 0,1,... In this case, just ranks 0 and 1
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
expected_includes = {
'par.G1.Cy.y': dct['par.G1.Cy.y'],
'par.G2.Cy.y': dct['par.G2.Cy.y'],
}
expected_outputs.update(expected_includes)
coordinate = [0, 'ScipyOptimize_SLSQP', (driver.iter_count-1,)]
expected_data = ((coordinate, (t0, t1), expected_outputs, None),)
assertDriverIterDataRecorded(self, expected_data, self.eps)
expected_data = (('final', (t1, t2), expected_outputs),)
assertProblemDataRecorded(self, expected_data, self.eps)
def test_recording_remote_voi(self):
prob = Problem()
prob.model.add_subsystem('par', ParallelGroup())
prob.model.par.add_subsystem('G1', Mygroup())
prob.model.par.add_subsystem('G2', Mygroup())
prob.model.add_subsystem('Obj', ExecComp('obj=y1+y2'))
prob.model.connect('par.G1.y', 'Obj.y1')
prob.model.connect('par.G2.y', 'Obj.y2')
prob.model.add_objective('Obj.obj')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.recording_options['record_desvars'] = True
prob.driver.recording_options['record_responses'] = True
prob.driver.recording_options['record_objectives'] = True
prob.driver.recording_options['record_constraints'] = True
prob.driver.recording_options['includes'] = [
'par.G1.Cy.y', 'par.G2.Cy.y'
]
prob.driver.add_recorder(self.recorder)
prob.setup()
t0, t1 = run_driver(prob)
prob.cleanup()
# Since the test will compare the last case recorded, just check the
# current values in the problem. This next section is about getting those values
# These involve collective gathers so all ranks need to run this
expected_outputs = prob.driver.get_design_var_values()
expected_outputs.update(prob.driver.get_objective_values())
expected_outputs.update(prob.driver.get_constraint_values())
# Determine the expected values for the sysincludes
# this gets all of the outputs but just locally
rrank = prob.comm.rank # root ( aka model ) rank.
rowned = prob.model._owning_rank
# names of sysincl vars on this rank
local_inclnames = [
n for n in prob.driver.recording_options['includes']
if rrank == rowned[n]
]
# Get values for vars on this rank
inputs, outputs, residuals = prob.model.get_nonlinear_vectors()
# Potential local sysvars are in this
sysvars = outputs._views
# Just get the values for the sysincl vars on this rank
local_vars = {c: sysvars[c] for c in local_inclnames}
# Gather up the values for all the sysincl vars on all ranks
all_vars = prob.model.comm.gather(local_vars, root=0)
if prob.comm.rank == 0:
# Only on rank 0 do we have all the values. The all_vars variable is a list of
# dicts from all ranks 0,1,... In this case, just ranks 0 and 1
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
expected_includes = {
'par.G1.Cy.y': dct['par.G1.Cy.y'],
'par.G2.Cy.y': dct['par.G2.Cy.y'],
}
expected_outputs.update(expected_includes)
coordinate = [0, 'SLSQP', (48, )]
expected_data = ((coordinate, (t0, t1), expected_outputs, None), )
assertDriverIterDataRecorded(self, expected_data, self.eps)
