def setup_vars(self, ofs, wrts):
matrix = self.sparsity
isplit = self.isplit
osplit = self.osplit
isizes, _ = evenly_distrib_idxs(isplit, matrix.shape[1])
for i, sz in enumerate(isizes):
self.add_input('x%d' % i, np.zeros(sz))
osizes, _ = evenly_distrib_idxs(osplit, matrix.shape[0])
for i, sz in enumerate(osizes):
self.add_output('y%d' % i, np.zeros(sz))
self.declare_partials(of=ofs, wrt=wrts, method=self.method)
def setup(self):
arr_size = self.options['arr_size']
comm = self.comm
rank = comm.rank
sizes, offsets = evenly_distrib_idxs(comm.size, arr_size)
start = offsets[rank]
io_size = sizes[rank]
self.offset = offsets[rank]
end = start + io_size
self.add_input('x',
val=np.ones(io_size),
src_indices=np.arange(start, end, dtype=int))
self.add_input('y',
val=np.ones(io_size),
src_indices=np.arange(start, end, dtype=int))
self.add_input('offset',
val=-3.0 * np.ones(io_size),
src_indices=np.arange(start, end, dtype=int))
self.add_output('f_xy', val=np.ones(io_size))
row_col = np.arange(io_size)
self.declare_partials('f_xy', ['x', 'y', 'offset'],
rows=row_col,
cols=row_col)
def verify(inputs, outputs, in_vals=1., out_vals=1., pathnames=False, comm=None, final=True, rank=None):
global_shape = (size, ) if final else 'Unavailable'
inputs = sorted(inputs)
outputs = sorted(outputs)
with multi_proc_exception_check(comm):
if comm is not None:
sizes, offsets = evenly_distrib_idxs(comm.size, size)
local_size = sizes[comm.rank]
else:
local_size = size
if rank is None or comm is None or rank == comm.rank:
test.assertEqual(len(inputs), 1)
name, meta = inputs[0]
test.assertEqual(name, 'C2.invec' if pathnames else 'invec')
test.assertEqual(meta['shape'], (local_size,))
test.assertEqual(meta['global_shape'], global_shape)
test.assertTrue(all(meta['val'] == in_vals*np.ones(size)))
test.assertEqual(len(outputs), 1)
name, meta = outputs[0]
test.assertEqual(name, 'C2.outvec' if pathnames else 'outvec')
test.assertEqual(meta['shape'], (local_size,))
test.assertEqual(meta['global_shape'], global_shape)
test.assertTrue(all(meta['val'] == out_vals*np.ones(size)))
def verify(inputs,
outputs,
in_vals=1.,
out_vals=1.,
pathnames=False,
comm=None,
final=True):
global_shape = (size, ) if final else 'Unavailable'
if comm is not None:
sizes, offsets = evenly_distrib_idxs(comm.size, size)
local_size = sizes[comm.rank]
else:
local_size = size
test.assertEqual(len(inputs), 1)
name, meta = inputs[0]
test.assertEqual(name, 'C2.invec' if pathnames else 'invec')
test.assertEqual(meta['shape'], (local_size, ))
test.assertEqual(meta['global_shape'], global_shape)
test.assertTrue(all(meta['value'] == in_vals *
np.ones(local_size)))
test.assertEqual(len(outputs), 1)
name, meta = outputs[0]
test.assertEqual(name, 'C2.outvec' if pathnames else 'outvec')
test.assertEqual(meta['shape'], (local_size, ))
test.assertEqual(meta['global_shape'], global_shape)
test.assertTrue(
all(meta['value'] == out_vals * np.ones(local_size)))
def setup(self):
comm = self.comm
rank = comm.rank
# this results in 8 entries for proc 0 and 7 entries for proc 1 when using 2 processes.
sizes, offsets = evenly_distrib_idxs(comm.size, self.size)
start = offsets[rank]
end = start + sizes[rank]
self.add_input('invec', np.ones(sizes[rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('outvec', np.ones(sizes[rank], float))
self.input_file = 'distrib_comp_input.dat'
self.output_file = 'distrib_comp_output.dat'
self.options['external_input_files'] = [self.input_file,]
self.options['external_output_files'] = [self.output_file,]
self.options['command'] = [
'python',
os.path.join(DIRECTORY, 'extcode_distrib_comp.py'),
self.input_file, self.output_file
]
# at setup time set unique folder to run in
subdir_name = 'distrib_{}'.format(rank)
self.run_directory = os.path.join(self.options['toplevel_run_directory'], subdir_name)
try:
os.mkdir(self.run_directory)
except:
pass
def compute(self, inputs, outputs):
rank = self.comm.rank
sizes, offsets = evenly_distrib_idxs(self.comm.size, N)
outputs['y'] = inputs['x'] * np.ones((sizes[rank], ))
if rank == 0:
outputs['y'][0] = 2.
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)
start = offsets[rank]
io_size = sizes[rank]
self.offset = offsets[rank]
end = start + io_size
self.add_input('x',
val=np.ones(io_size),
src_indices=np.arange(start, end, dtype=int))
self.add_input('y',
val=np.ones(io_size),
src_indices=np.arange(start, end, dtype=int))
self.add_input('a',
val=-3.0 * np.ones(io_size),
src_indices=np.arange(start, end, dtype=int))
self.add_output('f_xy', val=np.ones(io_size))
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)
def setup(self):
N = self.options['size']
rank = self.comm.rank
self.add_input('x', shape=1, src_indices=rank)
sizes, offsets = evenly_distrib_idxs(self.comm.size, N)
self.add_output('y', shape=sizes[rank])
# automatically infer dimensions without specifying rows, cols
self.declare_partials('y', 'x')
def setup(self):
comm = self.comm
rank = comm.rank
self.sizes, self.offsets = evenly_distrib_idxs(comm.size, self.arr_size)
start = self.offsets[rank]
end = start + self.sizes[rank]
self.add_input('invec', np.ones(self.sizes[rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('outvec', np.ones(self.arr_size, float), shape=np.int32(self.arr_size))
def setup_indeps(isplit, ninputs, indeps_name, comp_name):
isizes, _ = evenly_distrib_idxs(isplit, ninputs)
indeps = IndepVarComp()
conns = []
for i, sz in enumerate(isizes):
indep_var = 'x%d' % i
indeps.add_output(indep_var, np.random.random(sz))
conns.append(
(indeps_name + '.' + indep_var, comp_name + '.' + indep_var))
return indeps, conns
def setup(self):
comm = self.comm
rank = comm.rank
self.sizes, self.offsets = evenly_distrib_idxs(comm.size, self.arr_size)
start = self.offsets[rank]
end = start + self.sizes[rank]
self.add_input('invec', np.ones(self.sizes[rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('outvec', np.ones(self.arr_size, float), shape=np.int32(self.arr_size))
def setup(self):
comm = self.comm
rank = comm.rank
# results in 8 entries for proc 0 and 7 entries for proc 1 when using 2 processes.
sizes, offsets = evenly_distrib_idxs(comm.size, self.size)
start = offsets[rank]
end = start + sizes[rank]
self.add_input('invec', np.ones(sizes[rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('outvec', np.ones(sizes[rank], float))
def setup(self):
comm = self.comm
rank = comm.rank
# results in 8 entries for proc 0 and 7 entries for proc 1 when using 2 processes.
sizes, offsets = evenly_distrib_idxs(comm.size, self.size)
start = offsets[rank]
end = start + sizes[rank]
self.add_input('invec', np.ones(sizes[rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('outvec', np.ones(sizes[rank], float))
def setup(self):
# this results in 8 entries for proc 0 and 7 entries for proc 1
# when using 2 processes.
sizes, offsets = evenly_distrib_idxs(self.comm.size, self.size)
start = offsets[rank]
end = start + sizes[rank]
# NOTE: you must specify src_indices here for the input. Otherwise,
# you'll connect the input to [0:local_input_size] of the
# full distributed output!
self.add_input('invec', np.ones(sizes[self.comm.rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('out', 0.0)
def setup(self):
# this results in 8 entries for proc 0 and 7 entries for proc 1
# when using 2 processes.
sizes, offsets = evenly_distrib_idxs(self.comm.size, self.size)
start = offsets[rank]
end = start + sizes[rank]
# NOTE: you must specify src_indices here for the input. Otherwise,
# you'll connect the input to [0:local_input_size] of the
# full distributed output!
self.add_input('invec', np.ones(sizes[self.comm.rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('out', 0.0)
def setup(self):
comm = self.comm
rank = comm.rank
arr_size = self.options['arr_size']
sizes, offsets = evenly_distrib_idxs(comm.size, arr_size)
start = offsets[rank]
end = start + sizes[rank]
self.add_input('invec', np.ones(sizes[rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('outvec', np.ones(sizes[rank], float))
def setup(self):
comm = self.comm
rank = comm.rank
self.sizes, self.offsets = evenly_distrib_idxs(comm.size,
self.arr_size)
start = self.offsets[rank]
end = start + self.sizes[rank]
# need to initialize the variable to have the correct local size
self.add_input('invec', np.ones(self.sizes[rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('outvec', np.ones(self.arr_size, float))
def setup(self):
comm = self.comm
rank = comm.rank
arr_size = self.options['arr_size']
sizes, offsets = evenly_distrib_idxs(comm.size, arr_size)
start = offsets[rank]
end = start + sizes[rank]
self.add_input('invec', np.ones(sizes[rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('outvec', np.ones(sizes[rank], float))
def setup(self):
comm = self.comm
rank = comm.rank
self.sizes, self.offsets = evenly_distrib_idxs(comm.size,
self.arr_size)
start = self.offsets[rank]
end = start + self.sizes[rank]
# need to initialize the variable to have the correct local size
self.add_input('invec', np.ones(self.sizes[rank], float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('outvec', np.ones(self.arr_size, float))
def setup(self):
comm = self.comm
rank = comm.rank
size = self.options['size']
# if comm.size is 2 and size is 15, this results in
# 8 entries for proc 0 and 7 entries for proc 1
sizes, _ = evenly_distrib_idxs(comm.size, size)
self.mysize = mysize = sizes[rank]
# don't set src_indices on the input, just use default behavior
self.add_input('invec', np.ones(mysize, float))
self.add_output('outvec', np.ones(mysize, float))
def setup(self):
self.add_input('a', val=10., units='m')
rank = self.comm.rank
GLOBAL_SIZE = 5
sizes, offsets = evenly_distrib_idxs(self.comm.size, GLOBAL_SIZE)
self.add_output('states', shape=int(sizes[rank]))
self.add_output('out_var', shape=1)
self.local_size = sizes[rank]
self.linear_solver = PETScKrylov()
def setup(self):
self.add_input('a', val=10., units='m')
rank = self.comm.rank
GLOBAL_SIZE = 15
sizes, offsets = evenly_distrib_idxs(self.comm.size, GLOBAL_SIZE)
self.add_output('states', shape=int(sizes[rank]))
self.add_output('out_var', shape=1)
self.local_size = sizes[rank]
self.linear_solver = om.PETScKrylov()
self.linear_solver.precon = om.LinearUserDefined(solve_function=self.mysolve)
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')
def setup(self):
self.add_input('a', val=10., units='m')
rank = self.comm.rank
GLOBAL_SIZE = 15
sizes, offsets = evenly_distrib_idxs(self.comm.size, GLOBAL_SIZE)
self.add_output('states', shape=int(sizes[rank]))
self.add_output('out_var', shape=1)
self.local_size = sizes[rank]
self.linear_solver = PETScKrylov()
self.linear_solver.precon = LinearUserDefined(self.mysolve)
def setup(self):
nn = self.options['num_nodes']
comm = self.comm
rank = comm.rank
sizes, offsets = evenly_distrib_idxs(comm.size, nn)
start = offsets[rank]
end = start + sizes[rank]
self.add_input('x1', val=np.ones(sizes[rank]),
src_indices=np.arange(start, end, dtype=int))
self.add_output('x0dot', val=np.ones(sizes[rank]))
r = c = np.arange(sizes[rank])
self.declare_partials(of='x0dot', wrt='x1', rows=r, cols=c)
def test_distrib_record_driver(self):
# create distributed variables of different sizes to catch mismatched collective calls
sizes = [7, 10, 12, 25, 33, 42]
prob = om.Problem()
ivc = prob.model.add_subsystem('ivc', om.IndepVarComp(), promotes_outputs=['*'])
for n, size in enumerate(sizes):
local_sizes, _ = evenly_distrib_idxs(prob.comm.size, size)
local_size = local_sizes[prob.comm.rank]
ivc.add_output(f'in{n}', np.ones(local_size), distributed=True)
prob.model.add_design_var(f'in{n}')
prob.model.add_subsystem('adder', DistributedAdder(sizes), promotes=['*'])
prob.model.add_subsystem('summer', Summer(sizes), promotes_outputs=['sum'])
for n, size in enumerate(sizes):
prob.model.promotes('summer', inputs=[f'summand{n}'], src_indices=om.slicer[:], src_shape=size)
prob.model.add_objective('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'] = [f'out{n}' for n in range(len(sizes))]
prob.driver.add_recorder(self.recorder)
prob.setup()
t0, t1 = run_driver(prob)
prob.cleanup()
coordinate = [0, 'Driver', (0,)]
expected_desvars = {}
for n in range(len(sizes)):
expected_desvars[f'ivc.in{n}'] = prob.get_val(f'ivc.in{n}', get_remote=True)
expected_objectives = { "summer.sum": prob['summer.sum'] }
expected_outputs = expected_desvars.copy()
for n in range(len(sizes)):
expected_outputs[f'adder.out{n}'] = prob.get_val(f'adder.out{n}', get_remote=True)
if prob.comm.rank == 0:
expected_outputs.update(expected_objectives)
expected_data = ((coordinate, (t0, t1), expected_outputs, None, None),)
assertDriverIterDataRecorded(self, expected_data, self.eps)
def setup(self):
comm = self.comm
rank = comm.rank
size = self.options['size']
# if comm.size is 2 and size is 15, this results in
# 8 entries for proc 0 and 7 entries for proc 1
sizes, _ = evenly_distrib_idxs(comm.size, size)
mysize = sizes[rank]
self.add_input('invec', np.ones(mysize, float), distributed=True)
self.add_output(
'outvec',
np.ones(mysize, float),
distributed=True,
)
def setup(self):
self.options['distributed'] = True
self.add_input('a', val=10., units='m', src_indices=[0])
rank = self.comm.rank
GLOBAL_SIZE = 5
sizes, offsets = evenly_distrib_idxs(self.comm.size, GLOBAL_SIZE)
self.add_output('states', shape=int(sizes[rank]))
self.add_output('out_var', shape=1)
self.local_size = sizes[rank]
self.linear_solver = om.PETScKrylov()
def setup(self):
self.options['distributed'] = True
self.add_input('a', val=10., units='m', src_indices=[0])
rank = self.comm.rank
GLOBAL_SIZE = 5
sizes, offsets = evenly_distrib_idxs(self.comm.size, GLOBAL_SIZE)
self.add_output('states', shape=int(sizes[rank]))
self.add_output('out_var', shape=1)
self.local_size = sizes[rank]
self.linear_solver = PETScKrylov()
def setup(self):
"""
specify the local sizes of the variables and which specific indices this specific
distributed component will handle. Indices do NOT need to be sequential or
contiguous!
"""
comm = self.comm
rank = comm.rank
for n, size in enumerate(self.sizes):
# NOTE: evenly_distrib_idxs is a helper function to split the array
# up as evenly as possible
local_sizes, _ = evenly_distrib_idxs(comm.size, size)
local_size = local_sizes[rank]
self.add_input(f'in{n}', val=np.zeros(local_size, float), distributed=True)
self.add_output(f'out{n}', val=np.zeros(local_size, float), distributed=True)
def setup(self):
comm = self.comm
rank = comm.rank
arr_size = self.options['arr_size']
sizes, offsets = evenly_distrib_idxs(comm.size, arr_size)
self.sizes = sizes
self.offsets = offsets
start = offsets[rank]
end = start + sizes[rank]
# don't set src_indices on the input and just use default behavior
self.add_input('invec', np.ones(sizes[rank], float), distributed=True)
self.add_output('outvec', np.ones(sizes[rank], float), distributed=True)
def setup(self):
outs = set()
allvars = set()
exprs = self._exprs
kwargs = self._kwargs
comm = self.comm
rank = comm.rank
if len(self._exprs) > comm.size:
raise RuntimeError(
"DistribExecComp only supports up to 1 expression per MPI process."
)
if len(self._exprs) < comm.size:
# repeat the last expression for any leftover procs
self._exprs.extend([self._exprs[-1]] *
(comm.size - len(self._exprs)))
self._exprs = [self._exprs[rank]]
# find all of the variables and which ones are outputs
for expr in exprs:
lhs, _ = expr.split('=', 1)
outs.update(self._parse_for_out_vars(lhs))
allvars.update(self._parse_for_vars(expr))
sizes, offsets = evenly_distrib_idxs(comm.size, self.arr_size)
start = offsets[rank]
end = start + sizes[rank]
for name in outs:
if name not in kwargs or not isinstance(kwargs[name], dict):
kwargs[name] = {}
kwargs[name]['value'] = numpy.ones(sizes[rank], float)
for name in allvars:
if name not in outs:
if name not in kwargs or not isinstance(kwargs[name], dict):
kwargs[name] = {}
meta = kwargs[name]
meta['value'] = numpy.ones(sizes[rank], float)
meta['src_indices'] = numpy.arange(start, end, dtype=int)
super(DistribExecComp, self).setup()
def setup(self):
comm = self.comm
rank = comm.rank
size = self.options['size']
# if comm.size is 2 and size is 15, this results in
# 8 entries for proc 0 and 7 entries for proc 1
sizes, offsets = evenly_distrib_idxs(comm.size, size)
mysize = sizes[rank]
start = offsets[rank]
end = start + mysize
self.add_input('invec',
np.ones(mysize, float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('outvec', np.ones(mysize, float))
def setup(self):
comm = self.comm
rank = comm.rank
size_total = self.options['size']
# Distribute x and y vectors across each processor as evenly as possible
sizes, offsets = evenly_distrib_idxs(comm.size, size_total)
start = offsets[rank]
end = start + sizes[rank]
self.size_local = size_local = sizes[rank]
# Get the local slice of A that this processor will be working with
self.A_local = A[start:end,:]
self.add_input('x', np.ones(size_local, float), distributed=True,
src_indices=np.arange(start, end, dtype=int))
self.add_output('y', np.ones(size_local, float), distributed=True)
def setup(self):
comm = self.comm
rank = comm.rank
arr_size = self.options['arr_size']
sizes, offsets = evenly_distrib_idxs(comm.size, arr_size)
self.sizes = sizes
self.offsets = offsets
start = offsets[rank]
end = start + sizes[rank]
# don't set src_indices on the input and just use default behavior
self.add_input('invec', np.ones(sizes[rank], float))
self.add_output('outvec', np.ones(sizes[rank], float))
self.declare_partials('outvec', 'invec', rows=np.arange(0, sizes[rank]),
cols=np.arange(0, sizes[rank]))
def setup(self):
outs = set()
allvars = set()
exprs = self._exprs
kwargs = self._kwargs
comm = self.comm
rank = comm.rank
if len(self._exprs) > comm.size:
raise RuntimeError("DistribExecComp only supports up to 1 expression per MPI process.")
if len(self._exprs) < comm.size:
# repeat the last expression for any leftover procs
self._exprs.extend([self._exprs[-1]] * (comm.size - len(self._exprs)))
self._exprs = [self._exprs[rank]]
# find all of the variables and which ones are outputs
for expr in exprs:
lhs, _ = expr.split('=', 1)
outs.update(self._parse_for_out_vars(lhs))
allvars.update(self._parse_for_vars(expr))
sizes, offsets = evenly_distrib_idxs(comm.size, self.arr_size)
start = offsets[rank]
end = start + sizes[rank]
for name in outs:
if name not in kwargs or not isinstance(kwargs[name], dict):
kwargs[name] = {}
kwargs[name]['value'] = numpy.ones(sizes[rank], float)
for name in allvars:
if name not in outs:
if name not in kwargs or not isinstance(kwargs[name], dict):
kwargs[name] = {}
meta = kwargs[name]
meta['value'] = numpy.ones(sizes[rank], float)
meta['src_indices'] = numpy.arange(start, end, dtype=int)
super(DistribExecComp, self).setup()
def setup(self):
"""
specify the local sizes of the variables and which specific indices this specific
distributed component will handle. Indices do NOT need to be sequential or
contiguous!
"""
comm = self.comm
rank = comm.rank
# NOTE: evenly_distrib_idxs is a helper function to split the array
# up as evenly as possible
sizes, offsets = evenly_distrib_idxs(comm.size,self.options['size'])
local_size, local_offset = sizes[rank], offsets[rank]
start = local_offset
end = local_offset + local_size
self.add_input('x', val=np.zeros(local_size, float), distributed=True,
src_indices=np.arange(start, end, dtype=int))
self.add_output('y', val=np.zeros(local_size, float), distributed=True)
def setup(self):
comm = self.comm
rank = comm.rank
size = self.options['size']
# if comm.size is 2 and size is 15, this results in
# 8 entries for proc 0 and 7 entries for proc 1
sizes, _ = evenly_distrib_idxs(comm.size, size)
self.mysize = mysize = sizes[rank]
# don't set src_indices on the input, just use default behavior
self.add_input('invec', np.ones(mysize, float))
self.add_output('outvec', np.ones(mysize, float))
# declare partial derivatives (diagonal of mysize)
self.declare_partials('outvec',
'invec',
rows=np.arange(0, mysize),
cols=np.arange(0, mysize))
def setup(self):
"""
specify the local sizes of the variables and which specific indices this specific
distributed component will handle. Indices do NOT need to be sequential or
contiguous!
"""
comm = self.comm
rank = comm.rank
# NOTE: evenly_distrib_idxs is a helper function to split the array
# up as evenly as possible
sizes, offsets = evenly_distrib_idxs(comm.size,self.options['size'])
local_size, local_offset = sizes[rank], offsets[rank]
start = local_offset
end = local_offset + local_size
self.add_input('x', val=np.zeros(local_size, float),
src_indices=np.arange(start, end, dtype=int))
self.add_output('y', val=np.zeros(local_size, float))
