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enable the mpi capability of ssmrd

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Zhenwen Dai 2014-10-17 15:53:33 +01:00
parent 3971e68b9c
commit eed647bec3
1 changed files with 37 additions and 0 deletions
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37
GPy/models/ss_mrd.py
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@ -8,6 +8,7 @@ from .ss_gplvm import SSGPLVM
from ..core.parameterization.variational import SpikeAndSlabPrior from ..core.parameterization.variational import SpikeAndSlabPrior
from ..util.misc import param_to_array from ..util.misc import param_to_array
from ..kern import RBF from ..kern import RBF
from numpy.linalg.linalg import LinAlgError
class SSMRD(Model): class SSMRD(Model):
@ -15,6 +16,7 @@ class SSMRD(Model):
num_inducing=10, Zs=None, kernel=None, inference_methods=None, likelihoods=None, num_inducing=10, Zs=None, kernel=None, inference_methods=None, likelihoods=None,
pi=0.5, name='ss_mrd', Ynames=None, mpi_comm=None): pi=0.5, name='ss_mrd', Ynames=None, mpi_comm=None):
super(SSMRD, self).__init__(name) super(SSMRD, self).__init__(name)
self.mpi_comm = mpi_comm
# initialize X for individual models # initialize X for individual models
X, X_variance, Gammas, fracs = self._init_X(Ylist, input_dim, X, X_variance, Gammas, initx) X, X_variance, Gammas, fracs = self._init_X(Ylist, input_dim, X, X_variance, Gammas, initx)
@ -87,6 +89,41 @@ class SSMRD(Model):
Gammas.append(gamma) Gammas.append(gamma)
return X, X_variance, Gammas, fracs return X, X_variance, Gammas, fracs
@Model.optimizer_array.setter
def optimizer_array(self, p):
if self.mpi_comm != None:
if self._IN_OPTIMIZATION_ and self.mpi_comm.rank==0:
self.mpi_comm.Bcast(np.int32(1),root=0)
self.mpi_comm.Bcast(p, root=0)
Model.optimizer_array.fset(self,p)
def optimize(self, optimizer=None, start=None, **kwargs):
self._IN_OPTIMIZATION_ = True
if self.mpi_comm==None:
super(SSMRD, self).optimize(optimizer,start,**kwargs)
elif self.mpi_comm.rank==0:
super(SSMRD, self).optimize(optimizer,start,**kwargs)
self.mpi_comm.Bcast(np.int32(-1),root=0)
elif self.mpi_comm.rank>0:
x = self.optimizer_array.copy()
flag = np.empty(1,dtype=np.int32)
while True:
self.mpi_comm.Bcast(flag,root=0)
if flag==1:
try:
self.optimizer_array = x
self._fail_count = 0
except (LinAlgError, ZeroDivisionError, ValueError):
if self._fail_count >= self._allowed_failures:
raise
self._fail_count += 1
elif flag==-1:
break
else:
self._IN_OPTIMIZATION_ = False
raise Exception("Unrecognizable flag for synchronization!")
self._IN_OPTIMIZATION_ = False