def matvecREV(self, arg):
'''Callback function for performing the matrix vector product of the
workflow's full Jacobian with an incoming vector arg.'''
dgraph = self._derivative_graph
comps = self._comp_edge_list()
result = zeros(len(arg))
# We can call applyJ on each component one-at-a-time, and poke the
# results into the result vector.
for compname, data in comps.iteritems():
if compname == '@fake':
continue
comp_inputs = data['inputs']
comp_outputs = data['outputs']
comp_residuals = data['residuals']
inputs = {}
outputs = {}
out_bounds = []
for varname in comp_outputs:
node = '%s.%s' % (compname, varname)
# Ouputs define unique edges, so don't duplicate anything
if is_subvar_node(dgraph, node):
if dgraph.base_var(node).split('.', 1)[1] in comp_outputs:
continue
i1, i2 = self.get_bounds(node)
if isinstance(i1, list):
inputs[varname] = arg[i1].copy()
if varname not in comp_residuals:
outputs[varname] = zeros(len(i1))
out_bounds.append((varname, i1, i2))
else:
inputs[varname] = arg[i1:i2].copy()
if varname not in comp_residuals:
outputs[varname] = zeros(i2 - i1)
out_bounds.append((varname, i1, i2))
for varname in comp_inputs:
node = '%s.%s' % (compname, varname)
i1, i2 = self.get_bounds(node)
if isinstance(i1, list):
outputs[varname] = zeros(len(i1))
else:
outputs[varname] = zeros(i2 - i1)
out_bounds.append((varname, i1, i2))
if '~' in compname:
comp = self._derivative_graph.node[compname]['pa_object']
else:
comp = self.scope.get(compname)
# Preconditioning
if hasattr(comp, 'applyMinvT'):
inputs = applyMinvT(comp, inputs, self._shape_cache)
applyJT(comp, inputs, outputs, comp_residuals, self._shape_cache,
self._J_cache.get(compname))
#print inputs, outputs
for varname, i1, i2 in out_bounds:
if isinstance(i1, list):
result[i1] += outputs[varname]
else:
result[i1:i2] += outputs[varname]
# Each parameter adds an equation
for src, target in self._edges.iteritems():
if '@in' in src or '@fake' in src:
if isinstance(target, list):
target = target[0]
i1, i2 = self.get_bounds(target)
result[i1:i2] += arg[i1:i2]
# A fake output needs to make it into the result vector to prevent
# the solution from blowing up. Its derivative will be zero
# regardless.
if '@fake' in target:
i1, i2 = self.get_bounds(src)
result[i1:i2] += arg[i1:i2]
#print arg, result
return result
def matvecREV(self, arg):
'''Callback function for performing the matrix vector product of the
workflow's full Jacobian with an incoming vector arg.'''
dgraph = self._derivative_graph
comps = self._comp_edge_list()
result = zeros(len(arg))
# We can call applyJ on each component one-at-a-time, and poke the
# results into the result vector.
for compname, data in comps.iteritems():
if compname == '@fake':
continue
comp_inputs = data['inputs']
comp_outputs = data['outputs']
comp_residuals = data['residuals']
inputs = {}
outputs = {}
out_bounds = []
for varname in comp_outputs:
node = '%s.%s' % (compname, varname)
# Ouputs define unique edges, so don't duplicate anything
if is_subvar_node(dgraph, node):
if dgraph.base_var(node).split('.', 1)[1] in comp_outputs:
continue
i1, i2 = self.get_bounds(node)
if isinstance(i1, list):
inputs[varname] = arg[i1].copy()
if varname not in comp_residuals:
outputs[varname] = zeros(len(i1))
out_bounds.append((varname, i1, i2))
else:
inputs[varname] = arg[i1:i2].copy()
if varname not in comp_residuals:
outputs[varname] = zeros(i2-i1)
out_bounds.append((varname, i1, i2))
for varname in comp_inputs:
node = '%s.%s' % (compname, varname)
i1, i2 = self.get_bounds(node)
if isinstance(i1, list):
outputs[varname] = zeros(len(i1))
else:
outputs[varname] = zeros(i2-i1)
out_bounds.append((varname, i1, i2))
if '~' in compname:
comp = self._derivative_graph.node[compname]['pa_object']
else:
comp = self.scope.get(compname)
# Preconditioning
if hasattr(comp, 'applyMinvT'):
inputs = applyMinvT(comp, inputs, self._shape_cache)
applyJT(comp, inputs, outputs, comp_residuals,
self._shape_cache, self._J_cache.get(compname))
#print inputs, outputs
for varname, i1, i2 in out_bounds:
if isinstance(i1, list):
result[i1] += outputs[varname]
else:
result[i1:i2] += outputs[varname]
# Each parameter adds an equation
for src, target in self._edges.iteritems():
if '@in' in src or '@fake' in src:
if isinstance(target, list):
target = target[0]
i1, i2 = self.get_bounds(target)
result[i1:i2] += arg[i1:i2]
#print arg, result
return result
def applyJT(self, system, variables):
""" Wrapper for component derivative specification methods.
Adjoint Mode.
"""
applyJT(system, variables)
def applyJT(self, system, variables):
""" Wrapper for component derivative specification methods.
Adjoint Mode.
"""
applyJT(system, variables)
