updating some work on improving active subspace memory footprint

hippyflow/modeling/activeSubspaceProjector.py

@@ -40,6 +40,12 @@ def ActiveSubspaceParameterList():
  parameters['double_loop_samples'] = [20, 'Number of samples used in double loop MC approximation']
  parameters['verbose'] = [True, 'Boolean for printing']
 
+
+ parameters['initialize_samples'] = [False,'Boolean for the initialization of samples when\
+ many samples are allocated on one process ']
+ parameters['serialized_sampling'] = [False, 'Boolean for the serialization of sampling on a process\
+ to reduce memory for large problems']
+
  parameters['observable_constructor'] = [None,'observable constructor function, assumed to take a mesh, and kwargs']
  parameters['observable_kwargs'] = [{},'kwargs used when instantiating multiple local instances of observables']
 
@@ -76,6 +82,26 @@ def mult(self,x,y):
  else:
  y.axpy(1.,temp)
 
+class SeriallySampledJTJOperator:
+ '''
+ Alterantive to SummedListOperator when memory is an issue for active subspace
+ '''
+ def __init__(self,observable,nsamples,communicator=None,average=True):
+ self.observable = observable
+ self.average = average
+ if communicator is None:
+ self.temp = None
+ else:
+ self.temp = dl.Vector(communicator)
+
+ self.u = self.observable.generate_vector(STATE)
+ self.m = self.observable.generate_vector(PARAMETER)
+
+ def MatMVMult(self,x,y):
+ for i in range(nsamples):
+ pass
+ pass
+
 
 
 class ActiveSubspaceProjector:
@@ -88,7 +114,7 @@ class ActiveSubspaceProjector:
  Input active subspace: :math:`JJ' = VS^2V^*`
  """
  def __init__(self,observable, prior, mesh_constructor_comm = None ,collective = None,\
- initialize_samples = False, parameters = ActiveSubspaceParameterList()):
+ parameters = ActiveSubspaceParameterList()):
  """
  Constructor
  - :code:`observable` - object that implements the observable mapping :math:`m -> q(m)`
@@ -131,16 +157,9 @@ def __init__(self,observable, prior, mesh_constructor_comm = None ,collective =
  # and avoid the time consuming sample initialization.
 
 
-
-
-
-
-
-
-
-
-
- if self.parameters['samples_per_process'] > 1:
+ # Here we allocate many copies of the observable if serialized_sampling is not True in the
+ # active subspace parameters
+ if self.parameters['samples_per_process'] > 1 and not self.parameters['serialized_sampling']:
  assert self.parameters['observable_constructor'] is not None
  for i in range(self.parameters['samples_per_process']-1):
  new_observable = self.parameters['observable_constructor'](self.observable.problem.Vh[0].mesh(),**self.parameters['observable_kwargs'])
@@ -149,17 +168,21 @@ def __init__(self,observable, prior, mesh_constructor_comm = None ,collective =
  self.noise = dl.Vector(self.mesh_constructor_comm)
  self.prior.init_vector(self.noise,"noise")
 
-
- self.us = None
- self.ms = None
- self.Js = None
+ if self.parameters['serialized_sampling']:
+ self.u = None
+ self.m = None
+ self.J = None
+ else: 
+ self.us = None
+ self.ms = None
+ self.Js = None
 
  # Draw a new sample and set linearization point.
- if initialize_samples:
- self.initialize_samples()
+ if self.parameters['initialize_samples']:
+ if not self.parameters['serialized_sampling']:
+ self._initialize_batched_samples()
 
 
-
  self.d_GN = None
  self.V_GN = None
  self.d_GN_noprior = None
@@ -170,7 +193,7 @@ def __init__(self,observable, prior, mesh_constructor_comm = None ,collective =
  self.U_NG = None
 
 
- def initialize_samples(self):
+ def _initialize_batched_samples(self):
  """
  This method initializes the samples from the prior used in sampling
  """
@@ -204,13 +227,29 @@ def initialize_samples(self):
 
 
  def construct_input_subspace(self,prior_preconditioned = True):
+ if self.parameters['serialized_sampling']:
+ self._construct_input_subspace_serialized(prior_preconditioned = prior_preconditioned)
+ else:
+ self._construct_input_subspace_batched(prior_preconditioned = prior_preconditioned)
+
+ def _construct_input_subspace_serialized(self,prior_preconditioned = True):
+ """
+ This method implements the input subspace constructor 
+ -:code:`prior_preconditioned` - a Boolean to decide whether to include the prior covariance in the decomposition
+ The default parameter is True which is customary in active subspace construction
+ """
+ pass
+
+
+
+ def _construct_input_subspace_batched(self,prior_preconditioned = True):
  """
  This method implements the input subspace constructor 
  -:code:`prior_preconditioned` - a Boolean to decide whether to include the prior covariance in the decomposition
  The default parameter is True which is customary in active subspace construction
  """
  if self.Js is None:
- self.initialize_samples()
+ self._initialize_batched_samples()
 
  if self.parameters['verbose']:
  print(80*'#')
@@ -258,13 +297,18 @@ def construct_input_subspace(self,prior_preconditioned = True):
  axis_label = ['i',r'$\lambda_i$',\
  r'Eigenvalues of $\mathbb{E}_{\nu}[C{\nabla} q^T {\nabla} q]$'+self.parameters['plot_label_suffix']], out_name = out_name)
 
+ def construct_output_subspace(self,prior_preconditioned = True):
+ if self.parameters['serialized_sampling']:
+ pass
+ else:
+ self._construct_output_subspace_batched(prior_preconditioned = prior_preconditioned)
 
- def construct_output_subspace(self):
+ def _construct_output_subspace_batched(self):
  """
  This method implements the output subspace constructor 
  """
  if self.Js is None:
- self.initialize_samples()
+ self._initialize_batched_samples()
 
  if self.parameters['verbose']:
  print(80*'#')