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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
View file

@ -61,15 +61,15 @@ 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((output_dim,),np.float64,order='F'), 'beta_gpu' :gpuarray.empty((output_dim,),np.float64,order='F'),
'Y_gpu' :gpuarray.to_gpu(np.asfortranarray(Y)), 'YT_gpu' :gpuarray.to_gpu(np.asfortranarray(Y).T), # DxN
'betaY_gpu' :gpuarray.empty(Y.shape,np.float64,order='F'), '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'), 'psi2_t_gpu' :gpuarray.empty((self.batchsize,num_inducing,num_inducing),np.float64,order='F'),
# inference_minibatch # inference_minibatch
} }
self.gpuCache['ones_gpu'].fill(1.0) self.gpuCache['ones_gpu'].fill(1.0)
Y_gpu = self.gpuCache['Y_gpu'] YT_gpu = self.gpuCache['YT_gpu']
self._trYYT = cublas.cublasDdot(self.cublas_handle, Y_gpu.size, Y_gpu.gpudata, 1, Y_gpu.gpudata, 1) self._trYYT = cublas.cublasDdot(self.cublas_handle, YT_gpu.size, YT_gpu.gpudata, 1, YT_gpu.gpudata, 1)
def _get_YYTfactor(self, Y): def _get_YYTfactor(self, Y):
""" """
@ -109,32 +109,32 @@ class VarDTC_GPU(object):
psi1Y_gpu = self.gpuCache['psi1Y_gpu'] psi1Y_gpu = self.gpuCache['psi1Y_gpu']
psi2_gpu = self.gpuCache['psi2_gpu'] psi2_gpu = self.gpuCache['psi2_gpu']
beta_gpu = self.gpuCache['beta_gpu'] beta_gpu = self.gpuCache['beta_gpu']
Y_gpu = self.gpuCache['Y_gpu'] YT_gpu = self.gpuCache['YT_gpu']
betaY_gpu = self.gpuCache['betaY_gpu'] betaYT_gpu = self.gpuCache['betaYT_gpu']
psi2_t_gpu = self.gpuCache['psi2_t_gpu'] psi2_t_gpu = self.gpuCache['psi2_t_gpu']
if het_noise: if het_noise:
beta_gpu.set(np.asfortranarray(beta)) beta_gpu.set(np.asfortranarray(beta))
mul_bcast(betaY_gpu,beta_gpu,Y_gpu,beta_gpu.size) mul_bcast(betaYT_gpu,beta_gpu,YT_gpu,beta_gpu.size)
YRY_full = cublas.cublasDdot(self.cublas_handle, Y_gpu.size, betaY_gpu.gpudata, 1, Y_gpu.gpudata, 1) YRY_full = cublas.cublasDdot(self.cublas_handle, YT_gpu.size, betaYT_gpu.gpudata, 1, YT_gpu.gpudata, 1)
else: else:
beta_gpu.fill(beta) beta_gpu.fill(beta)
betaY_gpu.fill(0.) betaYT_gpu.fill(0.)
cublas.cublasDaxpy(self.cublas_handle, betaY_gpu.size, beta, Y_gpu.gpudata, 1, betaY_gpu.gpudata, 1) cublas.cublasDaxpy(self.cublas_handle, betaYT_gpu.size, beta, YT_gpu.gpudata, 1, betaYT_gpu.gpudata, 1)
YRY_full = trYYT*beta YRY_full = trYYT*beta
if kern.useGPU: if kern.useGPU:
psi1Y_gpu.fill(0.) psi1Y_gpu.fill(0.)
psi2_gpu.fill(0.) psi2_gpu.fill(0.)
psi0_full = 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): for n_start in xrange(0,num_data,self.batchsize):
n_end = min(self.batchsize+n_start, num_data) n_end = min(self.batchsize+n_start, num_data)
ndata = n_end - n_start ndata = 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]
beta_gpu_slice = beta_gpu[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: if ndata==self.batchsize:
psi2_t_gpu_slice = psi2_t_gpu psi2_t_gpu_slice = psi2_t_gpu
else: else:
@ -147,7 +147,12 @@ class VarDTC_GPU(object):
psi0p_gpu = kern.Kdiag(X_slice) psi0p_gpu = kern.Kdiag(X_slice)
psi1p_gpu = kern.K(X_slice, Z) 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: if het_noise:
psi0_full += cublas.cublasDdot(self.cublas_handle, psi0p_gpu.size, beta_gpu_slice.gpudata, 1, psi0p_gpu.gpudata, 1) psi0_full += cublas.cublasDdot(self.cublas_handle, psi0p_gpu.size, beta_gpu_slice.gpudata, 1, psi0p_gpu.gpudata, 1)
else: else:

12
GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py
View file

@ -260,7 +260,7 @@ class PSICOMP_SSRBF(object):
self.gpuCache = None self.gpuCache = None
def _initGPUCache(self, N, M, Q): def _initGPUCache(self, N, M, Q):
if self.gpuCache and self.gpuCacheAll['mu_gpu'].shape[0]<N: if self.gpuCache and self.gpuCacheAll['mu_gpu'].shape[0]!=N:
self._releaseMemory() self._releaseMemory()
if self.gpuCache == None: if self.gpuCache == None:
@ -304,13 +304,6 @@ class PSICOMP_SSRBF(object):
'grad_S_gpu' :gpuarray.empty((N,Q),np.float64,order='F'), 'grad_S_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
'grad_gamma_gpu' :gpuarray.empty((N,Q),np.float64,order='F'), 'grad_gamma_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
} }
nonN_list = ['l_gpu','Z_gpu','psi2exp2_gpu','grad_l_gpu','grad_Z_gpu']
self._gpuCache_Nlist = [k for k in self.gpuCacheAll.keys() if k not in nonN_list]
self.gpuCache = self.gpuCacheAll
elif self.gpuCacheAll['mu_gpu'].shape[0]>N:
self.gpuCache = self.gpuCacheAll.copy()
for k in self._gpuCache_Nlist:
self.gpuCache[k] = self.gpuCacheAll[k][0:N]
def _releaseMemory(self): def _releaseMemory(self):
if not self.gpuCacheAll: if not self.gpuCacheAll:
@ -361,7 +354,8 @@ class PSICOMP_SSRBF(object):
comp_psi1(psi1_gpu, variance, l_gpu, Z_gpu, mu_gpu, S_gpu, logGamma_gpu, log1Gamma_gpu, logpsi1denom_gpu, N, M, Q) comp_psi1(psi1_gpu, variance, l_gpu, Z_gpu, mu_gpu, S_gpu, logGamma_gpu, log1Gamma_gpu, logpsi1denom_gpu, N, M, Q)
comp_psi2(psi2_gpu, variance, l_gpu, Z_gpu, mu_gpu, S_gpu, logGamma_gpu, log1Gamma_gpu, logpsi2denom_gpu, N, M, Q) comp_psi2(psi2_gpu, variance, l_gpu, Z_gpu, mu_gpu, S_gpu, logGamma_gpu, log1Gamma_gpu, logpsi2denom_gpu, N, M, Q)
return psi0_gpu.get(), psi1_gpu.get(), psi2_gpu.get() # return psi0_gpu.get(), psi1_gpu.get(), psi2_gpu.get()
return psi0_gpu, psi1_gpu, psi2_gpu
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"""

2
GPy/util/linalg_gpu.py
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@ -28,7 +28,7 @@ try:
# log(1.0-X) # log(1.0-X)
logOne = ElementwiseKernel("double *in, double *out", "out[i] = log(1.-in[i])", "logOne_element") logOne = ElementwiseKernel("double *in, double *out", "out[i] = log(1.-in[i])", "logOne_element")
# multiplication with broadcast on the last dimension # multiplication with broadcast on the last dimension (out = shorter[:,None]*longer)
mul_bcast = ElementwiseKernel("double *out, double *shorter, double *longer, int shorter_size", "out[i] = longer[i]*shorter[i%shorter_size]", "mul_bcast") mul_bcast = ElementwiseKernel("double *out, double *shorter, double *longer, int shorter_size", "out[i] = longer[i]*shorter[i%shorter_size]", "mul_bcast")
# sum through the middle dimension (size_2) of a 3D matrix (size_1, size_2, size_3) # sum through the middle dimension (size_2) of a 3D matrix (size_1, size_2, size_3)