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change BGPLVM to use sparsegp_mpi

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This commit is contained in:
Zhenwen Dai 2014-09-22 18:12:19 +01:00
parent ac4dbb851d
commit dd987d9428
1 changed files with 5 additions and 60 deletions
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65
GPy/models/bayesian_gplvm.py
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@ -3,7 +3,7 @@
import numpy as np
from .. import kern
from ..core import SparseGP
from ..core.sparse_gp_mpi import SparseGP_MPI
from ..likelihoods import Gaussian
from ..inference.optimization import SCG
from ..util import linalg
@ -12,7 +12,7 @@ from ..inference.latent_function_inference.var_dtc_parallel import update_gradie
from ..inference.latent_function_inference.var_dtc_gpu import VarDTC_GPU
import logging
class BayesianGPLVM(SparseGP):
class BayesianGPLVM(SparseGP_MPI):
"""
Bayesian Gaussian Process Latent Variable Model
@ -76,18 +76,7 @@ class BayesianGPLVM(SparseGP):
if kernel.useGPU and isinstance(inference_method, VarDTC_GPU):
kernel.psicomp.GPU_direct = True
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, name, normalizer=normalizer)
self.logger.info("Adding X as parameter")
self.link_parameter(self.X, index=0)
if mpi_comm != None:
from ..util.mpi import divide_data
N_start, N_end, N_list = divide_data(Y.shape[0], mpi_comm)
self.N_range = (N_start, N_end)
self.N_list = np.array(N_list)
self.Y_local = self.Y[N_start:N_end]
print 'MPI RANK: '+str(self.mpi_comm.rank)+' with datasize: '+str(self.N_range)
mpi_comm.Bcast(self.param_array, root=0)
super(BayesianGPLVM,self).__init__(X, Y, Z, kernel, likelihood=likelihood, name=name, inference_method=inference_method, normalizer=normalizer, mpi_comm=mpi_comm, variational_prior=self.variational_prior)
def set_X_gradients(self, X, X_grad):
"""Set the gradients of the posterior distribution of X in its specific form."""
@ -98,8 +87,8 @@ class BayesianGPLVM(SparseGP):
return X.mean.gradient, X.variance.gradient
def parameters_changed(self):
if isinstance(self.inference_method, VarDTC_GPU) or isinstance(self.inference_method, VarDTC_minibatch):
update_gradients(self, mpi_comm=self.mpi_comm)
super(BayesianGPLVM,self).parameters_changed()
if isinstance(self.inference_method, VarDTC_minibatch):
return
super(BayesianGPLVM, self).parameters_changed()
@ -211,50 +200,6 @@ class BayesianGPLVM(SparseGP):
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_steepest_gradient_map(self,*args,**kwargs)
def __getstate__(self):
dc = super(BayesianGPLVM, self).__getstate__()
dc['mpi_comm'] = None
if self.mpi_comm != None:
del dc['N_range']
del dc['N_list']
del dc['Y_local']
return dc
def __setstate__(self, state):
return super(BayesianGPLVM, self).__setstate__(state)
#=====================================================
# The MPI parallelization
# - can move to model at some point
#=====================================================
def _set_params_transformed(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)
super(BayesianGPLVM, self)._set_params_transformed(p)
def optimize(self, optimizer=None, start=None, **kwargs):
self.__IN_OPTIMIZATION__ = True
if self.mpi_comm==None:
super(BayesianGPLVM, self).optimize(optimizer,start,**kwargs)
elif self.mpi_comm.rank==0:
super(BayesianGPLVM, self).optimize(optimizer,start,**kwargs)
self.mpi_comm.Bcast(np.int32(-1),root=0)
elif self.mpi_comm.rank>0:
x = self._get_params_transformed().copy()
flag = np.empty(1,dtype=np.int32)
while True:
self.mpi_comm.Bcast(flag,root=0)
if flag==1:
self._set_params_transformed(x)
elif flag==-1:
break
else:
self.__IN_OPTIMIZATION__ = False
raise Exception("Unrecognizable flag for synchronization!")
self.__IN_OPTIMIZATION__ = False
def latent_cost_and_grad(mu_S, input_dim, kern, Z, dL_dpsi0, dL_dpsi1, dL_dpsi2):