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[GPU] psi varDTC ready

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This commit is contained in:
Zhenwen Dai 2014-04-07 10:18:03 +01:00
parent 5e01b94d37
commit f776db45df
3 changed files with 8 additions and 6 deletions
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9
GPy/inference/latent_function_inference/var_dtc_parallel.py
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@ -302,18 +302,19 @@ def update_gradients(model):
while not isEnd: while not isEnd:
isEnd, n_range, grad_dict = model.inference_method.inference_minibatch(model.kern, model.X, model.Z, model.likelihood, model.Y) isEnd, n_range, grad_dict = model.inference_method.inference_minibatch(model.kern, model.X, model.Z, model.likelihood, model.Y)
if isinstance(model.X, VariationalPosterior): if isinstance(model.X, VariationalPosterior):
X_slice = model.X[n_range[0]:n_range[1]]
#gradients w.r.t. kernel #gradients w.r.t. kernel
model.kern.update_gradients_expectations(variational_posterior=model.X[n_range[0]:n_range[1]], Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2']) model.kern.update_gradients_expectations(variational_posterior=X_slice, Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'])
kern_grad += model.kern.gradient kern_grad += model.kern.gradient
#gradients w.r.t. Z #gradients w.r.t. Z
model.Z.gradient[:,model.kern.active_dims] += model.kern.gradients_Z_expectations( model.Z.gradient[:,model.kern.active_dims] += model.kern.gradients_Z_expectations(
grad_dict['dL_dpsi1'], grad_dict['dL_dpsi2'], Z=model.Z, variational_posterior=model.X[n_range[0]:n_range[1]]) grad_dict['dL_dpsi1'], grad_dict['dL_dpsi2'], Z=model.Z, variational_posterior=X_slice)
#gradients w.r.t. posterior parameters of X #gradients w.r.t. posterior parameters of X
X_grad = model.kern.gradients_qX_expectations(variational_posterior=model.X[n_range[0]:n_range[1]], Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2']) X_grad = model.kern.gradients_qX_expectations(variational_posterior=X_slice, Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'])
model.set_X_gradients(model.X[n_range[0]:n_range[1]], X_grad) model.set_X_gradients(X_slice, X_grad)
if het_noise: if het_noise:
dL_dthetaL[n_range[0]:n_range[1]] = grad_dict['dL_dthetaL'] dL_dthetaL[n_range[0]:n_range[1]] = grad_dict['dL_dthetaL']

4
GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py
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@ -328,7 +328,7 @@ class PSICOMP_SSRBF(object):
self.gpuCacheAll = None self.gpuCacheAll = None
self.gpuCache = None self.gpuCache = None
@Cache_this(limit=1) @Cache_this(limit=1,ignore_args=(0,))
def psicomputations(self, variance, lengthscale, Z, mu, S, gamma): def psicomputations(self, variance, lengthscale, Z, mu, S, gamma):
"""Compute Psi statitsitcs""" """Compute Psi statitsitcs"""
if isinstance(lengthscale, np.ndarray) and len(lengthscale)>1: if isinstance(lengthscale, np.ndarray) and len(lengthscale)>1:
@ -373,7 +373,7 @@ class PSICOMP_SSRBF(object):
return psi0_gpu, psi1_gpu, psi2_gpu return psi0_gpu, psi1_gpu, psi2_gpu
@Cache_this(limit=1) @Cache_this(limit=1,ignore_args=(0,))
def _psiDercomputations(self, variance, lengthscale, Z, mu, S, gamma): def _psiDercomputations(self, variance, lengthscale, Z, mu, S, gamma):
"""Compute the derivatives w.r.t. Psi statistics""" """Compute the derivatives w.r.t. Psi statistics"""
N, M, Q = mu.shape[0],Z.shape[0], mu.shape[1] N, M, Q = mu.shape[0],Z.shape[0], mu.shape[1]

1
GPy/util/caching.py
View file

@ -66,6 +66,7 @@ class Cacher(object):
#first make sure the depth limit isn't exceeded #first make sure the depth limit isn't exceeded
if len(self.cached_inputs) == self.limit: if len(self.cached_inputs) == self.limit:
args_ = self.cached_inputs.pop(0) args_ = self.cached_inputs.pop(0)
args_ = [a for i,a in enumerate(args_) if i not in self.ignore_args and i not in self.force_kwargs]
[a.remove_observer(self, self.on_cache_changed) for a in args_ if a is not None] [a.remove_observer(self, self.on_cache_changed) for a in args_ if a is not None]
self.inputs_changed.pop(0) self.inputs_changed.pop(0)
self.cached_outputs.pop(0) self.cached_outputs.pop(0)