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[GPU] varDTC_gpu almost done

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Zhenwen Dai 2014-04-04 18:02:53 +01:00
parent 954af5a6c2
commit 7a74c0b80d
2 changed files with 46 additions and 37 deletions
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63
GPy/inference/latent_function_inference/var_dtc_gpu.py
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@ -62,7 +62,7 @@ class VarDTC_GPU(object):
'psi1Y_gpu' :gpuarray.empty((num_inducing,output_dim),np.float64,order='F'), 'psi1Y_gpu' :gpuarray.empty((num_inducing,output_dim),np.float64,order='F'),
'psi2_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64,order='F'), 'psi2_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64,order='F'),
'beta_gpu' :gpuarray.empty((ndata,),np.float64,order='F'), 'beta_gpu' :gpuarray.empty((ndata,),np.float64,order='F'),
'YT_gpu' :gpuarray.to_gpu(np.asfortranarray(Y).T), # DxN 'YT_gpu' :gpuarray.to_gpu(np.asfortranarray(Y.T)), # DxN
'betaYT_gpu' :gpuarray.empty(Y.T.shape,np.float64,order='F'), # DxN 'betaYT_gpu' :gpuarray.empty(Y.T.shape,np.float64,order='F'), # DxN
'psi2_t_gpu' :gpuarray.empty((num_inducing*num_inducing*self.batchsize),np.float64,order='F'), 'psi2_t_gpu' :gpuarray.empty((num_inducing*num_inducing*self.batchsize),np.float64,order='F'),
# inference_minibatch # inference_minibatch
@ -70,10 +70,12 @@ class VarDTC_GPU(object):
'dL_dpsi1_gpu' :gpuarray.empty((self.batchsize,num_inducing),np.float64,order='F'), 'dL_dpsi1_gpu' :gpuarray.empty((self.batchsize,num_inducing),np.float64,order='F'),
'dL_dpsi2_gpu' :gpuarray.empty((self.batchsize,num_inducing,num_inducing),np.float64,order='F'), 'dL_dpsi2_gpu' :gpuarray.empty((self.batchsize,num_inducing,num_inducing),np.float64,order='F'),
'dL_dthetaL_gpu' :gpuarray.empty((self.batchsize,),np.float64,order='F'), 'dL_dthetaL_gpu' :gpuarray.empty((self.batchsize,),np.float64,order='F'),
'psi2p_gpu' :gpuarray.empty((self.batchsize,num_inducing,num_inducing),np.float64,order='F'), 'betapsi1_gpu' :gpuarray.empty((self.batchsize,num_inducing),np.float64,order='F'),
'betapsi1_gpu' :gpuarray.empty((self.batchsize,num_inducing),order='F'),
'thetaL_t_gpu' :gpuarray.empty((self.batchsize,),np.float64,order='F'), 'thetaL_t_gpu' :gpuarray.empty((self.batchsize,),np.float64,order='F'),
'betaYT2_gpu' :gpuarray.empty((output_dim,self.batchsize),order='F'), 'betaYT2_gpu' :gpuarray.empty((output_dim,self.batchsize),np.float64,order='F'),
'psi0p_gpu' :gpuarray.empty((self.batchsize,),np.float64,order='F'),
'psi1p_gpu' :gpuarray.empty((self.batchsize,num_inducing),np.float64,order='F'),
'psi2p_gpu' :gpuarray.empty((self.batchsize,num_inducing,num_inducing),np.float64,order='F'),
} }
self.gpuCache['ones_gpu'].fill(1.0) self.gpuCache['ones_gpu'].fill(1.0)
@ -371,24 +373,38 @@ class VarDTC_GPU(object):
self.batch_pos = n_end self.batch_pos = n_end
nSlice = n_end-n_start nSlice = n_end-n_start
Y_slice = Y[n_start:n_end]
X_slice = X[n_start:n_end] X_slice = X[n_start:n_end]
if uncertain_inputs: if kern.useGPU:
psi0p_gpu = kern.psi0(Z, X_slice) if uncertain_inputs:
psi1p_gpu = kern.psi1(Z, X_slice) psi0p_gpu = kern.psi0(Z, X_slice)
psi2p_gpu = kern.psi2(Z, X_slice) psi1p_gpu = kern.psi1(Z, X_slice)
psi2p_gpu = kern.psi2(Z, X_slice)
else:
psi0p_gpu = kern.Kdiag(X_slice)
psi1p_gpu = kern.K(X_slice, Z)
psi2p_gpu = self.gpuCache['psi2p_gpu']
if psi2p_gpu.shape[0] > nSlice:
psi2p_gpu = psi2p_gpu.ravel()[:nSlice*num_inducing*num_inducing].reshape(nSlice,num_inducing,num_inducing)
else: else:
psi0p_gpu = kern.Kdiag(X_slice) if uncertain_inputs:
psi1p_gpu = kern.K(X_slice, Z) psi0 = kern.psi0(Z, X_slice)
psi1 = kern.psi1(Z, X_slice)
psi2 = kern.psi2(Z, X_slice)
else:
psi0 = kern.Kdiag(X_slice)
psi1 = kern.K(X_slice, Z)
if het_noise: psi0p_gpu = self.gpuCache['psi0p_gpu']
beta = beta[n_start:n_end] psi1p_gpu = self.gpuCache['psi1p_gpu']
psi2p_gpu = self.gpuCache['psi2p_gpu']
# betapsi1 = np.einsum('n,nm->nm',beta,psi1) if psi0p_gpu > nSlice:
# psi0p_gpu = psi0p_gpu[:nSlice]
# # betaY_gpu = gpuarray.to_gpu(betaY) psi1p_gpu = psi1p_gpu.ravel()[:nSlice*num_inducing].reshape(nSlice,num_inducing)
# betapsi1_gpu = gpuarray.to_gpu(betapsi1) psi2p_gpu = psi2p_gpu.ravel()[:nSlice*num_inducing*num_inducing].reshape(nSlice,num_inducing,num_inducing)
psi0p_gpu.get(psi0)
psi1p_gpu.get(psi1)
psi2p_gpu.get(psi2)
#====================================================================== #======================================================================
# Prepare gpu memory # Prepare gpu memory
@ -403,7 +419,6 @@ class VarDTC_GPU(object):
dL_dpsi2_gpu = self.gpuCache['dL_dpsi2_gpu'] dL_dpsi2_gpu = self.gpuCache['dL_dpsi2_gpu']
dL_dthetaL_gpu = self.gpuCache['dL_dthetaL_gpu'] dL_dthetaL_gpu = self.gpuCache['dL_dthetaL_gpu']
psi2R_gpu = self.gpuCache['psi2_t_gpu'][:nSlice*num_inducing*num_inducing].reshape(nSlice,num_inducing,num_inducing) psi2R_gpu = self.gpuCache['psi2_t_gpu'][:nSlice*num_inducing*num_inducing].reshape(nSlice,num_inducing,num_inducing)
psi2p_gpu = self.gpuCache['psi2p_gpu']
betapsi1_gpu = self.gpuCache['betapsi1_gpu'] betapsi1_gpu = self.gpuCache['betapsi1_gpu']
thetaL_t_gpu = self.gpuCache['thetaL_t_gpu'] thetaL_t_gpu = self.gpuCache['thetaL_t_gpu']
betaYT2_gpu = self.gpuCache['betaYT2_gpu'] betaYT2_gpu = self.gpuCache['betaYT2_gpu']
@ -412,7 +427,7 @@ class VarDTC_GPU(object):
beta_gpu_slice = beta_gpu[n_start:n_end] beta_gpu_slice = beta_gpu[n_start:n_end]
# Adjust to the batch size # Adjust to the batch size
if dL_dpsi0_gpu.shape[0] < nSlice: if dL_dpsi0_gpu.shape[0] > nSlice:
betaYT2_gpu = betaYT2_gpu[:,:nSlice] betaYT2_gpu = betaYT2_gpu[:,:nSlice]
dL_dpsi0_gpu = dL_dpsi0_gpu.ravel()[:nSlice] dL_dpsi0_gpu = dL_dpsi0_gpu.ravel()[:nSlice]
dL_dpsi1_gpu = dL_dpsi1_gpu.ravel()[:nSlice*num_inducing].reshape(nSlice,num_inducing) dL_dpsi1_gpu = dL_dpsi1_gpu.ravel()[:nSlice*num_inducing].reshape(nSlice,num_inducing)
@ -421,8 +436,6 @@ class VarDTC_GPU(object):
psi2R_gpu = psi2R_gpu.ravel()[:nSlice*num_inducing*num_inducing].reshape(nSlice,num_inducing,num_inducing) psi2R_gpu = psi2R_gpu.ravel()[:nSlice*num_inducing*num_inducing].reshape(nSlice,num_inducing,num_inducing)
thetaL_t_gpu = thetaL_t_gpu.ravel()[:nSlice] thetaL_t_gpu = thetaL_t_gpu.ravel()[:nSlice]
betapsi1_gpu = betapsi1_gpu.ravel()[:nSlice*num_inducing].reshape(nSlice,num_inducing) betapsi1_gpu = betapsi1_gpu.ravel()[:nSlice*num_inducing].reshape(nSlice,num_inducing)
if not uncertain_inputs:
psi2p_gpu = psi2p_gpu.ravel()[:nSlice*num_inducing*num_inducing].reshape(nSlice,num_inducing,num_inducing)
mul_bcast(betapsi1_gpu,beta_gpu_slice,psi1p_gpu,beta_gpu_slice.size) mul_bcast(betapsi1_gpu,beta_gpu_slice,psi1p_gpu,beta_gpu_slice.size)
@ -432,17 +445,13 @@ class VarDTC_GPU(object):
dL_dpsi0_gpu.fill(0.) dL_dpsi0_gpu.fill(0.)
cublas.cublasDaxpy(self.cublas_handle, dL_dpsi0_gpu.size, output_dim/(-2.), beta_gpu_slice.gpudata, 1, dL_dpsi0_gpu.gpudata, 1) cublas.cublasDaxpy(self.cublas_handle, dL_dpsi0_gpu.size, output_dim/(-2.), beta_gpu_slice.gpudata, 1, dL_dpsi0_gpu.gpudata, 1)
# dL_dpsi0_gpu = -0.5 * output_dim * (beta * np.ones((n_end-n_start,)))
cublas.cublasDgemm(self.cublas_handle, 'T', 'T', nSlice, num_inducing, output_dim, 1.0, betaYT_gpu_slice.gpudata, output_dim, v_gpu.gpudata, num_inducing, 0., dL_dpsi1_gpu.gpudata, nSlice) cublas.cublasDgemm(self.cublas_handle, 'T', 'T', nSlice, num_inducing, output_dim, 1.0, betaYT_gpu_slice.gpudata, output_dim, v_gpu.gpudata, num_inducing, 0., dL_dpsi1_gpu.gpudata, nSlice)
# dL_dpsi1 = np.dot(betaY,v.T)
if uncertain_inputs: if uncertain_inputs:
outer_prod(dL_dpsi2_gpu,beta_gpu_slice,dL_dpsi2R_gpu,beta_gpu_slice.size) outer_prod(dL_dpsi2_gpu,beta_gpu_slice,dL_dpsi2R_gpu,beta_gpu_slice.size)
# dL_dpsi2 = np.einsum('n,mo->nmo',beta * np.ones((n_end-n_start,)),dL_dpsi2R)
else: else:
cublas.cublasDgemm(self.cublas_handle, 'N', 'N', nSlice, num_inducing, output_dim, 1.0, betapsi1_gpu.gpudata, nSlice, dL_dpsi2R_gpu.gpudata, num_inducing, 1.0, dL_dpsi1_gpu.gpudata, nSlice) cublas.cublasDgemm(self.cublas_handle, 'N', 'N', nSlice, num_inducing, output_dim, 1.0, betapsi1_gpu.gpudata, nSlice, dL_dpsi2R_gpu.gpudata, num_inducing, 1.0, dL_dpsi1_gpu.gpudata, nSlice)
# dL_dpsi1 += np.dot(betapsi1,dL_dpsi2R)*2.
#====================================================================== #======================================================================
# Compute dL_dthetaL # Compute dL_dthetaL
@ -473,7 +482,7 @@ class VarDTC_GPU(object):
mul_bcast(thetaL_t_gpu,thetaL_t_gpu,beta_gpu_slice,thetaL_t_gpu.size) mul_bcast(thetaL_t_gpu,thetaL_t_gpu,beta_gpu_slice,thetaL_t_gpu.size)
cublas.cublasDaxpy(self.cublas_handle, dL_dthetaL_gpu.size, -1.0, thetaL_t_gpu.gpudata, 1, dL_dthetaL_gpu.gpudata, 1) cublas.cublasDaxpy(self.cublas_handle, dL_dthetaL_gpu.size, -1.0, thetaL_t_gpu.gpudata, 1, dL_dthetaL_gpu.gpudata, 1)
cublas.cublasDgemm(self.cublas_handle, 'T', 'T', output_dim, nSlice, num_inducing, 1.0, betapsi1_gpu.gpudata, nSlice, v_gpu.gpudata, num_inducing, 0.0, betaYT2_gpu.gpudata, output_dim) cublas.cublasDgemm(self.cublas_handle, 'T', 'T', output_dim, nSlice, num_inducing, -1.0, v_gpu.gpudata, num_inducing, betapsi1_gpu.gpudata, nSlice, 0.0, betaYT2_gpu.gpudata, output_dim)
mul_bcast(betaYT2_gpu,betaYT2_gpu,betaYT_gpu_slice,betaYT2_gpu.size) mul_bcast(betaYT2_gpu,betaYT2_gpu,betaYT_gpu_slice,betaYT2_gpu.size)
sum_axis(dL_dthetaL_gpu, betaYT2_gpu, 1, output_dim) sum_axis(dL_dthetaL_gpu, betaYT2_gpu, 1, output_dim)

20
GPy/kern/_src/rbf.py
View file

@ -74,10 +74,10 @@ class RBF(Stationary):
# Spike-and-Slab GPLVM # Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
if self.useGPU: if self.useGPU:
dL_dpsi0_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi0)) # dL_dpsi0_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi0))
dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1)) # dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2)) # dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
self.psicomp.update_gradients_expectations(dL_dpsi0_gpu, dL_dpsi1_gpu, dL_dpsi2_gpu, self.variance, self.lengthscale, Z, variational_posterior) self.psicomp.update_gradients_expectations(dL_dpsi0, dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
else: else:
_, _dpsi1_dvariance, _, _, _, _, _dpsi1_dlengthscale = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) _, _dpsi1_dvariance, _, _, _, _, _dpsi1_dlengthscale = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
@ -139,9 +139,9 @@ class RBF(Stationary):
# Spike-and-Slab GPLVM # Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
if self.useGPU: if self.useGPU:
dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1)) # dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2)) # dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
return self.psicomp.gradients_Z_expectations(dL_dpsi1_gpu, dL_dpsi2_gpu, self.variance, self.lengthscale, Z, variational_posterior) return self.psicomp.gradients_Z_expectations(dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
else: else:
_, _, _, _, _, _dpsi1_dZ, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) _, _, _, _, _, _dpsi1_dZ, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
_, _, _, _, _, _dpsi2_dZ, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) _, _, _, _, _, _dpsi2_dZ, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
@ -177,9 +177,9 @@ class RBF(Stationary):
# Spike-and-Slab GPLVM # Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
if self.useGPU: if self.useGPU:
dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1)) # dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2)) # dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
return self.psicomp.gradients_qX_expectations(dL_dpsi1_gpu, dL_dpsi2_gpu, self.variance, self.lengthscale, Z, variational_posterior) return self.psicomp.gradients_qX_expectations(dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
else: else:
ndata = variational_posterior.mean.shape[0] ndata = variational_posterior.mean.shape[0]