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rbf gpu usable
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Zhenwen Dai
parent
e486f3fd99
commit
ca1edecce4
5 changed files with 140 additions and 146 deletions
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@ -66,7 +66,6 @@ class VarDTC_GPU(LatentFunctionInference):
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'beta_gpu' :gpuarray.empty((ndata,),np.float64,order='F'),
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'YT_gpu' :gpuarray.to_gpu(np.asfortranarray(Y.T)), # DxN
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'betaYT_gpu' :gpuarray.empty(Y.T.shape,np.float64,order='F'), # DxN
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'psi2_t_gpu' :gpuarray.empty((num_inducing*num_inducing*self.batchsize),np.float64,order='F'),
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# inference_minibatch
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'dL_dpsi0_gpu' :gpuarray.empty((self.batchsize,),np.float64,order='F'),
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'dL_dpsi1_gpu' :gpuarray.empty((self.batchsize,num_inducing),np.float64,order='F'),
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@ -122,6 +121,89 @@ class VarDTC_GPU(LatentFunctionInference):
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else:
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return jitchol(tdot(Y))
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def gatherPsiStat(self, kern, X, Z, Y, beta, uncertain_inputs, het_noise):
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num_inducing, input_dim = Z.shape[0], Z.shape[1]
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num_data, output_dim = Y.shape
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trYYT = self._trYYT
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psi1Y_gpu = self.gpuCache['psi1Y_gpu']
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psi2_gpu = self.gpuCache['psi2_gpu']
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beta_gpu = self.gpuCache['beta_gpu']
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YT_gpu = self.gpuCache['YT_gpu']
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betaYT_gpu = self.gpuCache['betaYT_gpu']
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beta_gpu.fill(beta)
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betaYT_gpu.fill(0.)
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cublas.cublasDaxpy(self.cublas_handle, betaYT_gpu.size, beta, YT_gpu.gpudata, 1, betaYT_gpu.gpudata, 1)
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YRY_full = trYYT*beta
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if kern.useGPU:
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psi1Y_gpu.fill(0.)
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psi2_gpu.fill(0.)
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psi0_full = 0
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for n_start in xrange(0,num_data,self.batchsize):
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n_end = min(self.batchsize+n_start, num_data)
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ndata = n_end - n_start
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X_slice = X[n_start:n_end]
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betaYT_gpu_slice = betaYT_gpu[:,n_start:n_end]
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if uncertain_inputs:
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psi0 = kern.psi0(Z, X_slice)
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psi1p_gpu = kern.psi1(Z, X_slice)
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psi2p_gpu = kern.psi2(Z, X_slice)
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else:
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psi0 = kern.Kdiag(X_slice)
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psi1p_gpu = kern.K(X_slice, Z)
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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)
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psi0_full += psi0.sum()
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if uncertain_inputs:
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sum_axis(psi2_gpu,psi2p_gpu,1,1)
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else:
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cublas.cublasDgemm(self.cublas_handle, 'T', 'N', num_inducing, num_inducing, ndata, beta, psi1p_gpu.gpudata, ndata, psi1p_gpu.gpudata, ndata, 1.0, psi2_gpu.gpudata, num_inducing)
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psi0_full *= beta
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if uncertain_inputs:
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cublas.cublasDscal(self.cublas_handle, psi2_gpu.size, beta, psi2_gpu.gpudata, 1)
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else:
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psi2_full = np.zeros((num_inducing,num_inducing))
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psi1Y_full = np.zeros((output_dim,num_inducing)) # DxM
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psi0_full = 0.
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YRY_full = 0.
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for n_start in xrange(0,num_data,self.batchsize):
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n_end = min(self.batchsize+n_start, num_data)
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Y_slice = Y[n_start:n_end]
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X_slice = X[n_start:n_end]
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if het_noise:
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b = beta[n_start]
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YRY_full += np.inner(Y_slice, Y_slice)*b
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else:
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b = beta
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if uncertain_inputs:
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psi0 = kern.psi0(Z, X_slice)
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psi1 = kern.psi1(Z, X_slice)
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psi2_full += kern.psi2(Z, X_slice)*b
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else:
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psi0 = kern.Kdiag(X_slice)
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psi1 = kern.K(X_slice, Z)
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psi2_full += np.dot(psi1.T,psi1)*b
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psi0_full += psi0.sum()*b
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psi1Y_full += np.dot(Y_slice.T,psi1)*b # DxM
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if not het_noise:
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YRY_full = trYYT*beta
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psi1Y_gpu.set(psi1Y_full)
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psi2_gpu.set(psi2_full)
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return psi0_full, YRY_full
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def inference_likelihood(self, kern, X, Z, likelihood, Y):
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"""
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The first phase of inference:
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@ -146,118 +228,10 @@ class VarDTC_GPU(LatentFunctionInference):
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else:
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uncertain_inputs = False
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trYYT = self._trYYT
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psi1Y_gpu = self.gpuCache['psi1Y_gpu']
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psi2_gpu = self.gpuCache['psi2_gpu']
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beta_gpu = self.gpuCache['beta_gpu']
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YT_gpu = self.gpuCache['YT_gpu']
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betaYT_gpu = self.gpuCache['betaYT_gpu']
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psi2_t_gpu = self.gpuCache['psi2_t_gpu']
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if het_noise:
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beta_gpu.set(np.asfortranarray(beta))
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mul_bcast(betaYT_gpu,beta_gpu,YT_gpu,beta_gpu.size)
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YRY_full = cublas.cublasDdot(self.cublas_handle, YT_gpu.size, betaYT_gpu.gpudata, 1, YT_gpu.gpudata, 1)
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else:
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beta_gpu.fill(beta)
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betaYT_gpu.fill(0.)
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cublas.cublasDaxpy(self.cublas_handle, betaYT_gpu.size, beta, YT_gpu.gpudata, 1, betaYT_gpu.gpudata, 1)
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YRY_full = trYYT*beta
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if kern.useGPU:
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psi1Y_gpu.fill(0.)
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psi2_gpu.fill(0.)
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psi0_full = 0
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for n_start in xrange(0,num_data,self.batchsize):
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n_end = min(self.batchsize+n_start, num_data)
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ndata = n_end - n_start
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X_slice = X[n_start:n_end]
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beta_gpu_slice = beta_gpu[n_start:n_end]
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betaYT_gpu_slice = betaYT_gpu[:,n_start:n_end]
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if ndata==self.batchsize:
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psi2_t_gpu_slice = psi2_t_gpu
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else:
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psi2_t_gpu_slice = psi2_t_gpu[:num_inducing*num_inducing*ndata]
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if uncertain_inputs:
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psi0p_gpu = kern.psi0(Z, X_slice)
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psi1p_gpu = kern.psi1(Z, X_slice)
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psi2p_gpu = kern.psi2(Z, X_slice)
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else:
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psi0p_gpu = kern.Kdiag(X_slice)
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psi1p_gpu = kern.K(X_slice, Z)
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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)
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if het_noise:
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psi0_full += cublas.cublasDdot(self.cublas_handle, psi0p_gpu.size, beta_gpu_slice.gpudata, 1, psi0p_gpu.gpudata, 1)
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else:
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psi0_full += gpuarray.sum(psi0p_gpu).get()
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if uncertain_inputs:
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if het_noise:
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mul_bcast(psi2_t_gpu_slice,beta_gpu_slice,psi2p_gpu,beta_gpu_slice.size)
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sum_axis(psi2_gpu,psi2_t_gpu_slice,1,ndata)
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else:
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sum_axis(psi2_gpu,psi2p_gpu,1,ndata)
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else:
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if het_noise:
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psi1_t_gpu = psi2_t_gpu_slice[:,num_inducing*ndata]
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mul_bcast(psi1_t_gpu,beta_gpu_slice,psi1p_gpu,beta_gpu_slice.size)
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cublas.cublasDgemm(self.cublas_handle, 'T', 'N', num_inducing, num_inducing, ndata, 1.0, psi1p_gpu.gpudata, ndata, psi1_t_gpu.gpudata, ndata, 1.0, psi2_gpu.gpudata, num_inducing)
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else:
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cublas.cublasDgemm(self.cublas_handle, 'T', 'N', num_inducing, num_inducing, ndata, beta, psi1p_gpu.gpudata, ndata, psi1p_gpu.gpudata, ndata, 1.0, psi2_gpu.gpudata, num_inducing)
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if not het_noise:
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psi0_full *= beta
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if uncertain_inputs:
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cublas.cublasDscal(self.cublas_handle, psi2_gpu.size, beta, psi2_gpu.gpudata, 1)
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else:
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psi2_full = np.zeros((num_inducing,num_inducing),order='F')
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psi1Y_full = np.zeros((num_inducing,output_dim),order='F') # MxD
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psi0_full = 0
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for n_start in xrange(0,num_data,self.batchsize):
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n_end = min(self.batchsize+n_start, num_data)
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Y_slice = Y[n_start:n_end]
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X_slice = X[n_start:n_end]
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if uncertain_inputs:
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psi0 = kern.psi0(Z, X_slice)
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psi1 = kern.psi1(Z, X_slice)
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psi2 = kern.psi2(Z, X_slice)
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else:
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psi0 = kern.Kdiag(X_slice)
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psi1 = kern.K(X_slice, Z)
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if het_noise:
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beta_slice = beta[n_start:n_end]
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psi0_full += (beta_slice*psi0).sum()
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psi1Y_full += np.dot(psi1.T,beta_slice[:,None]*Y_slice) # MxD
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else:
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psi0_full += psi0.sum()
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psi1Y_full += np.dot(psi1.T,Y_slice) # MxD
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if uncertain_inputs:
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if het_noise:
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psi2_full += np.einsum('n,nmo->mo',beta_slice,psi2)
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else:
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psi2_full += psi2
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else:
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if het_noise:
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psi2_full += np.einsum('n,nm,no->mo',beta_slice,psi1,psi1)
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else:
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psi2_full += np.outer(psi1.T, psi1.T)
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if not het_noise:
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psi0_full *= beta
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psi1Y_full *= beta
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psi2_full *= beta
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psi1Y_gpu.set(psi1Y_full)
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psi2_gpu.set(psi2_full)
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psi0_full, YRY_full = self.gatherPsiStat(kern, X, Z, Y, beta, uncertain_inputs, het_noise)
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#======================================================================
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# Compute Common Components
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