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[GPU] inference function part1
This commit is contained in:
Zhenwen Dai
parent
029abe8536
commit
88277f6b67
3 changed files with 32 additions and 10 deletions
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@ -32,6 +32,7 @@ from expectation_propagation import EP
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from dtc import DTC
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from fitc import FITC
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from var_dtc_parallel import VarDTC_minibatch
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from var_dtc_gpu import VarDTC_GPU
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# class FullLatentFunctionData(object):
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#
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@ -14,6 +14,7 @@ try:
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import pycuda.gpuarray as gpuarray
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from scikits.cuda import cublas
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import pycuda.autoinit
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from pycuda.reduction import ReductionKernel
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except:
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print 'Error in importing GPU modules!'
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@ -133,10 +134,8 @@ class VarDTC_GPU(object):
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psi2_full *= beta
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YRY_full = trYYT*beta
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psi0_gpu = gpuarray.to_gpu(np.asfortranarray(psi0_full))
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psi1Y_gpu = gpuarray.to_gpu(np.asfortranarray(psi1Y_full))
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psi2_gpu = gpuarray.to_gpu(np.asfortranarray(psi2_full))
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YRY_gpu = gpuarray.to_gpu(np.asfortranarray(YRY_full))
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#======================================================================
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# Compute Common Components
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@ -172,7 +171,7 @@ class VarDTC_GPU(object):
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b_gpu = gpuarray.empty((num_inducing,output_dim),np.float64)
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cublas.cublasDcopy(self.cublas_handle, b_gpu.size, psi1Y_gpu.gpudata, 1, b_gpu.gpudata, 1)
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cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'N', 'N', num_inducing, output_dim, np.float64(1.0), LL_gpu.gpudata, num_inducing, b_gpu.gpudata, num_inducing)
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bbt = cublas.cublasDdot(self.cublas_handle, b_gpu.size, b_gpu, 1, b_gpu, 1)
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bbt = cublas.cublasDdot(self.cublas_handle, b_gpu.size, b_gpu.gpudata, 1, b_gpu.gpudata, 1)
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print np.abs(bbt-bbt_cpu)
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v,_ = dtrtrs(LL.T,b,lower=False)
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@ -187,7 +186,7 @@ class VarDTC_GPU(object):
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LmInvPsi2LmInvT_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64)
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cublas.cublasDcopy(self.cublas_handle, psi2_gpu.size, psi2_gpu.gpudata, 1, LmInvPsi2LmInvT_gpu.gpudata, 1)
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cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'N', 'N', num_inducing, num_inducing, np.float64(1.0), Lm_gpu.gpudata, num_inducing, LmInvPsi2LmInvT_gpu.gpudata, num_inducing)
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cublas.cublasDtrsm(self.cublas_handle , 'R', 'L', 'T', 'N', num_inducing, num_inducing, np.float64(1.0), Lm_gpu.gpudata, num_inducing, LmInvPsi2LmInvT_gpu.gpudata, num_inducing)
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cublas.cublasDtrsm(self.cublas_handle , 'r', 'L', 'T', 'N', num_inducing, num_inducing, np.float64(1.0), Lm_gpu.gpudata, num_inducing, LmInvPsi2LmInvT_gpu.gpudata, num_inducing)
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tr_LmInvPsi2LmInvT = cublas.cublasDasum(self.cublas_handle, num_inducing, LmInvPsi2LmInvT_gpu.gpudata, num_inducing+1)
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print np.abs(vvt-vvt_gpu.get()).max()
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print np.abs(np.trace(LmInvPsi2LmInvT)-tr_LmInvPsi2LmInvT)
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@ -200,18 +199,26 @@ class VarDTC_GPU(object):
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KmmInvPsi2LLInvT_gpu = LmInvPsi2LmInvT_gpu # Reuse GPU memory (size:MxM)
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cublas.cublasDcopy(self.cublas_handle, psi2_gpu.size, psi2_gpu.gpudata, 1, KmmInvPsi2LLInvT_gpu.gpudata, 1)
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cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'N', 'N', num_inducing, num_inducing, np.float64(1.0), Lm_gpu.gpudata, num_inducing, KmmInvPsi2LLInvT_gpu.gpudata, num_inducing)
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cublas.cublasDtrsm(self.cublas_handle , 'R', 'L', 'T', 'N', num_inducing, num_inducing, np.float64(1.0), LL_gpu.gpudata, num_inducing, KmmInvPsi2LLInvT_gpu.gpudata, num_inducing)
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cublas.cublasDtrsm(self.cublas_handle , 'r', 'L', 'T', 'N', num_inducing, num_inducing, np.float64(1.0), LL_gpu.gpudata, num_inducing, KmmInvPsi2LLInvT_gpu.gpudata, num_inducing)
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cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'T', 'N', num_inducing, num_inducing, np.float64(1.0), Lm_gpu.gpudata, num_inducing, KmmInvPsi2LLInvT_gpu.gpudata, num_inducing)
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KmmInvPsi2P_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64)
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cublas.cublasDcopy(self.cublas_handle, KmmInvPsi2LLInvT_gpu.size, KmmInvPsi2LLInvT_gpu.gpudata, 1, KmmInvPsi2P_gpu.gpudata, 1)
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cublas.cublasDtrsm(self.cublas_handle , 'R', 'L', 'N', 'N', num_inducing, num_inducing, np.float64(1.0), LL_gpu.gpudata, num_inducing, KmmInvPsi2P_gpu.gpudata, num_inducing)
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cublas.cublasDtrsm(self.cublas_handle , 'r', 'L', 'N', 'N', num_inducing, num_inducing, np.float64(1.0), LL_gpu.gpudata, num_inducing, KmmInvPsi2P_gpu.gpudata, num_inducing)
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print np.abs(KmmInvPsi2P-KmmInvPsi2P_gpu.get()).max()
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dL_dpsi2R = (output_dim*KmmInvPsi2P - vvt)/2. # dL_dpsi2 with R inside psi2
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dL_dpsi2R_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64)
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cublas.cublasDcopy(self.cublas_handle, vvt_gpu.size, vvt_gpu.gpudata, 1, dL_dpsi2R_gpu.gpudata, 1)
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cublas.cublasDaxpy(self.cublas_handle, KmmInvPsi2P_gpu.size, np.float64(-output_dim), KmmInvPsi2P_gpu.gpudata, 1, dL_dpsi2R_gpu.gpudata, 1)
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cublas.cublasDscal(self.cublas_handle, dL_dpsi2R_gpu.size, np.float64(-0.5), dL_dpsi2R_gpu.gpudata, 1)
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print np.abs(dL_dpsi2R_gpu.get()-dL_dpsi2R).max()
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# Cache intermediate results
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self.midRes['dL_dpsi2R'] = dL_dpsi2R
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self.midRes['v'] = v
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self.midRes['dL_dpsi2R'] = dL_dpsi2R_gpu
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self.midRes['v'] = v_gpu
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logDiagSum = ReductionKernel(np.float64, neutral="0", reduce_expr="a+b", map_expr="i%step==0?log(x[i]):0", arguments="double *x, int step")
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#======================================================================
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# Compute log-likelihood
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@ -220,19 +227,30 @@ class VarDTC_GPU(object):
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logL_R = -np.log(beta).sum()
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else:
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logL_R = -num_data*np.log(beta)
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logL = -(output_dim*(num_data*log_2_pi+logL_R+psi0_full-np.trace(LmInvPsi2LmInvT))+YRY_full-bbt)/2.-output_dim*(-np.log(np.diag(Lm)).sum()+np.log(np.diag(LL)).sum())
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logL_old = -(output_dim*(num_data*log_2_pi+logL_R+psi0_full-np.trace(LmInvPsi2LmInvT))+YRY_full-bbt)/2.-output_dim*(-np.log(np.diag(Lm)).sum()+np.log(np.diag(LL)).sum())
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logdetKmm = logDiagSum(Lm_gpu,num_inducing+1)
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logdetLambda = logDiagSum(LL_gpu,num_inducing+1)
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logL = -(output_dim*(num_data*log_2_pi+logL_R+psi0_full-tr_LmInvPsi2LmInvT)+YRY_full-bbt)/2.+output_dim*(logdetKmm-logdetLambda)
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print np.abs(logL_old - logL)
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#======================================================================
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# Compute dL_dKmm
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#======================================================================
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dL_dKmm = -(output_dim*np.einsum('md,od->mo',KmmInvPsi2LLInvT,KmmInvPsi2LLInvT) + vvt)/2.
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#
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dL_dKmm_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64)
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cublas.cublasDgemm(self.cublas_handle, 'N', 'T', num_inducing, num_inducing, output_dim, np.float64(1.0), KmmInvPsi2LLInvT_gpu.gpudata, num_inducing, KmmInvPsi2LLInvT_gpu.gpudata, num_inducing, np.float64(0.), dL_dKmm_gpu.gpudata, num_inducing)
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cublas.cublasDaxpy(self.cublas_handle, dL_dKmm_gpu.size, np.float64(1./output_dim), vvt_gpu.gpudata, 1, dL_dKmm_gpu.gpudata, 1)
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cublas.cublasDscal(self.cublas_handle, dL_dKmm_gpu.size, np.float64(-output_dim/2.), dL_dpsi2R_gpu.gpudata, 1)
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print np.abs(dL_dKmm - dL_dKmm_gpu.get()).max()
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#======================================================================
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# Compute the Posterior distribution of inducing points p(u|Y)
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#======================================================================
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post = Posterior(woodbury_inv=KmmInvPsi2P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=Lm)
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post = Posterior(woodbury_inv=KmmInvPsi2P_gpu.get(), woodbury_vector=v_gpu.get(), K=Kmm_gpu.get(), mean=None, cov=None, K_chol=Lm.get())
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return logL, dL_dKmm, post
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@ -67,6 +67,9 @@ class BayesianGPLVM(SparseGP):
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X.mean.gradient, X.variance.gradient = X_grad
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def parameters_changed(self):
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update_gradients(self)
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return
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super(BayesianGPLVM, self).parameters_changed()
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self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
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