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[GPU] bug fix

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This commit is contained in:
Zhenwen Dai 2014-04-03 12:27:56 +01:00
parent f07f66f1f7
commit bb5c41f64c
3 changed files with 22 additions and 23 deletions
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31
GPy/inference/latent_function_inference/var_dtc_gpu.py
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@ -61,15 +61,15 @@ class VarDTC_GPU(object):
'psi1Y_gpu' :gpuarray.empty((num_inducing,output_dim),np.float64,order='F'),
'psi2_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64,order='F'),
'beta_gpu' :gpuarray.empty((output_dim,),np.float64,order='F'),
'Y_gpu' :gpuarray.to_gpu(np.asfortranarray(Y)),
'betaY_gpu' :gpuarray.empty(Y.shape,np.float64,order='F'),
'YT_gpu' :gpuarray.to_gpu(np.asfortranarray(Y).T), # DxN
'betaYT_gpu' :gpuarray.empty(Y.T.shape,np.float64,order='F'), # DxN
'psi2_t_gpu' :gpuarray.empty((self.batchsize,num_inducing,num_inducing),np.float64,order='F'),
# inference_minibatch
}
self.gpuCache['ones_gpu'].fill(1.0)
Y_gpu = self.gpuCache['Y_gpu']
self._trYYT = cublas.cublasDdot(self.cublas_handle, Y_gpu.size, Y_gpu.gpudata, 1, Y_gpu.gpudata, 1)
YT_gpu = self.gpuCache['YT_gpu']
self._trYYT = cublas.cublasDdot(self.cublas_handle, YT_gpu.size, YT_gpu.gpudata, 1, YT_gpu.gpudata, 1)
def _get_YYTfactor(self, Y):
"""
@ -109,32 +109,32 @@ class VarDTC_GPU(object):
psi1Y_gpu = self.gpuCache['psi1Y_gpu']
psi2_gpu = self.gpuCache['psi2_gpu']
beta_gpu = self.gpuCache['beta_gpu']
Y_gpu = self.gpuCache['Y_gpu']
betaY_gpu = self.gpuCache['betaY_gpu']
YT_gpu = self.gpuCache['YT_gpu']
betaYT_gpu = self.gpuCache['betaYT_gpu']
psi2_t_gpu = self.gpuCache['psi2_t_gpu']
if het_noise:
beta_gpu.set(np.asfortranarray(beta))
mul_bcast(betaY_gpu,beta_gpu,Y_gpu,beta_gpu.size)
YRY_full = cublas.cublasDdot(self.cublas_handle, Y_gpu.size, betaY_gpu.gpudata, 1, Y_gpu.gpudata, 1)
mul_bcast(betaYT_gpu,beta_gpu,YT_gpu,beta_gpu.size)
YRY_full = cublas.cublasDdot(self.cublas_handle, YT_gpu.size, betaYT_gpu.gpudata, 1, YT_gpu.gpudata, 1)
else:
beta_gpu.fill(beta)
betaY_gpu.fill(0.)
cublas.cublasDaxpy(self.cublas_handle, betaY_gpu.size, beta, Y_gpu.gpudata, 1, betaY_gpu.gpudata, 1)
betaYT_gpu.fill(0.)
cublas.cublasDaxpy(self.cublas_handle, betaYT_gpu.size, beta, YT_gpu.gpudata, 1, betaYT_gpu.gpudata, 1)
YRY_full = trYYT*beta
if kern.useGPU:
psi1Y_gpu.fill(0.)
psi2_gpu.fill(0.)
psi0_full = 0
psi1Y_full = np.zeros((num_inducing,output_dim),order='F') # MxD
for n_start in xrange(0,num_data,self.batchsize):
n_end = min(self.batchsize+n_start, num_data)
ndata = n_end - n_start
Y_slice = Y[n_start:n_end]
X_slice = X[n_start:n_end]
beta_gpu_slice = beta_gpu[n_start:n_end]
betaY_gpu_slice = betaY_gpu[n_start:n_end]
betaYT_gpu_slice = betaYT_gpu[:,n_start:n_end]
if ndata==self.batchsize:
psi2_t_gpu_slice = psi2_t_gpu
else:
@ -147,7 +147,12 @@ class VarDTC_GPU(object):
psi0p_gpu = kern.Kdiag(X_slice)
psi1p_gpu = kern.K(X_slice, Z)
cublas.cublasDgemm(self.cublas_handle, 'T', 'N', num_inducing, output_dim, ndata, 1.0, psi1p_gpu.gpudata, ndata, betaY_gpu_slice.gpudata, ndata, 1.0, psi1Y_gpu.gpudata, num_inducing)
cublas.cublasDgemm(self.cublas_handle, 'T', 'T', num_inducing, output_dim, ndata, 1.0, psi1p_gpu.gpudata, ndata, betaYT_gpu_slice.gpudata, output_dim, 1.0, psi1Y_gpu.gpudata, num_inducing)
psi1Y_full += np.dot(psi1p_gpu.get().T,Y_slice)*beta # MxD
# print psi1Y_gpu.get()
# print psi1Y_full
print np.abs(psi1Y_gpu.get()-psi1Y_full).max()
if het_noise:
psi0_full += cublas.cublasDdot(self.cublas_handle, psi0p_gpu.size, beta_gpu_slice.gpudata, 1, psi0p_gpu.gpudata, 1)
else: