mirror of
https://github.com/SheffieldML/GPy.git
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[GPU]
This commit is contained in:
Zhenwen Dai
parent
06336bf0d4
commit
ddbf15d763
6 changed files with 122 additions and 86 deletions
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@ -15,7 +15,7 @@ try:
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import scikits.cuda.linalg as culinalg
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import pycuda.gpuarray as gpuarray
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from scikits.cuda import cublas
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from ...util.linalg_gpu import logDiagSum, strideSum, mul_bcast, sum_axis, outer_prod, mul_bcast_first, join_prod
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from ...util.linalg_gpu import logDiagSum, strideSum, mul_bcast, sum_axis, outer_prod, mul_bcast_first, join_prod, traceDot
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except:
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pass
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@ -69,14 +69,14 @@ class VarDTC_GPU(object):
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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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'dL_dpsi2_gpu' :gpuarray.empty((self.batchsize,num_inducing,num_inducing),np.float64,order='F'),
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'dL_dpsi2_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64,order='F'),
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'dL_dthetaL_gpu' :gpuarray.empty((self.batchsize,),np.float64,order='F'),
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'betapsi1_gpu' :gpuarray.empty((self.batchsize,num_inducing),np.float64,order='F'),
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'thetaL_t_gpu' :gpuarray.empty((self.batchsize,),np.float64,order='F'),
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'betaYT2_gpu' :gpuarray.empty((output_dim,self.batchsize),np.float64,order='F'),
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'psi0p_gpu' :gpuarray.empty((self.batchsize,),np.float64,order='F'),
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'psi1p_gpu' :gpuarray.empty((self.batchsize,num_inducing),np.float64,order='F'),
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'psi2p_gpu' :gpuarray.empty((self.batchsize,num_inducing,num_inducing),np.float64,order='F'),
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'psi2p_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64,order='F'),
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}
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self.gpuCache['ones_gpu'].fill(1.0)
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@ -136,6 +136,12 @@ class VarDTC_GPU(object):
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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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#see whether we've got a different noise variance for each datum
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beta = 1./np.fmax(likelihood.variance, 1e-6)
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het_noise = beta.size > 1
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if het_noise:
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self.batchsize=0
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self._initGPUCache(kern, num_inducing, input_dim, output_dim, Y)
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if isinstance(X, VariationalPosterior):
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@ -143,9 +149,7 @@ class VarDTC_GPU(object):
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else:
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uncertain_inputs = False
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#see whether we've got a different noise variance for each datum
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beta = 1./np.fmax(likelihood.variance, 1e-6)
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het_noise = beta.size > 1
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trYYT = self._trYYT
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psi1Y_gpu = self.gpuCache['psi1Y_gpu']
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@ -218,7 +222,6 @@ class VarDTC_GPU(object):
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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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# 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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@ -236,7 +239,6 @@ class VarDTC_GPU(object):
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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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# YRY_full += (beta_slice*np.square(Y_slice).sum(axis=-1)).sum()
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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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@ -245,18 +247,17 @@ class VarDTC_GPU(object):
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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.sum(axis=0)
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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 += tdot(psi1.T)
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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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# 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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@ -372,6 +373,17 @@ class VarDTC_GPU(object):
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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_gpu.get())
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#======================================================================
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# Compute dL_dthetaL for uncertian input and non-heter noise
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#======================================================================
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if uncertain_inputs and not het_noise:
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dL_dthetaL = (YRY_full*beta + beta*output_dim*psi0_full - num_data*output_dim*beta)/2.
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dL_dthetaL += -beta*cublas.cublasDdot(self.cublas_handle,dL_dpsi2R_gpu.size, dL_dpsi2R_gpu.gpudata,1,psi2_gpu.gpudata,1)
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dL_dthetaL += -beta*cublas.cublasDdot(self.cublas_handle,dL_dpsi2R_gpu.size, dL_dpsi2R_gpu.gpudata,1,psi2_gpu.gpudata,1)
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dL_dthetaL += traceDot(v_gpu, psi1Y_gpu, num_inducing, output_dim)
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self.midRes['dL_dthetaL'] = dL_dthetaL
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return logL, dL_dKmm_gpu.get(), post
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def inference_minibatch(self, kern, X, Z, likelihood, Y):
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@ -403,6 +415,8 @@ class VarDTC_GPU(object):
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nSlice = n_end-n_start
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X_slice = X[n_start:n_end]
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if het_noise:
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beta = beta[n_start] # nSlice==1
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if kern.useGPU:
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if uncertain_inputs:
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@ -413,8 +427,6 @@ class VarDTC_GPU(object):
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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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psi2p_gpu = self.gpuCache['psi2p_gpu']
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if psi2p_gpu.shape[0] > nSlice:
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psi2p_gpu = psi2p_gpu.ravel()[:nSlice*num_inducing*num_inducing].reshape(nSlice,num_inducing,num_inducing)
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else:
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if uncertain_inputs:
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psi0 = kern.psi0(Z, X_slice)
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@ -430,7 +442,6 @@ class VarDTC_GPU(object):
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if psi0p_gpu.shape[0] > nSlice:
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psi0p_gpu = psi0p_gpu[:nSlice]
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psi1p_gpu = psi1p_gpu.ravel()[:nSlice*num_inducing].reshape(nSlice,num_inducing)
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psi2p_gpu = psi2p_gpu.ravel()[:nSlice*num_inducing*num_inducing].reshape(nSlice,num_inducing,num_inducing)
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psi0p_gpu.set(np.asfortranarray(psi0))
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psi1p_gpu.set(np.asfortranarray(psi1))
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if uncertain_inputs:
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@ -473,13 +484,13 @@ class VarDTC_GPU(object):
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# Compute dL_dpsi
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#======================================================================
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dL_dpsi0_gpu.fill(0.)
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cublas.cublasDaxpy(self.cublas_handle, dL_dpsi0_gpu.size, output_dim/(-2.), beta_gpu_slice.gpudata, 1, dL_dpsi0_gpu.gpudata, 1)
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dL_dpsi0_gpu.fill(-output_dim *beta/2.)
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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)
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if uncertain_inputs:
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outer_prod(dL_dpsi2_gpu,beta_gpu_slice,dL_dpsi2R_gpu,beta_gpu_slice.size)
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cublas.cublasDcopy(self.cublas_handle, dL_dpsi2R_gpu.size, dL_dpsi2R_gpu.gpudata, 1, dL_dpsi2_gpu.gpudata, 1)
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cublas.cublasDscal(self.cublas_handle, dL_dpsi2_gpu.szie, beta, dL_dpsi2_gpu.gpudata, 1)
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else:
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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)
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@ -487,34 +498,44 @@ class VarDTC_GPU(object):
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# Compute dL_dthetaL
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#======================================================================
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if not uncertain_inputs:
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join_prod(psi2p_gpu,psi1p_gpu,psi1p_gpu,nSlice,num_inducing)
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if uncertain_inputs and not het_noise:
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if isEnd:
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dL_dthetaL = self.midRes['dL_dthetaL']
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else:
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dL_dthetaL = 0
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mul_bcast_first(psi2R_gpu,dL_dpsi2R_gpu,psi2p_gpu,nSlice)
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# if not uncertain_inputs:
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# cublas.cublasDgemm(self.cublas_handle, 'T', 'N', num_inducing, num_inducing, nSlice, beta, psi1p_gpu.gpudata, nSlice, psi1p_gpu.gpudata, nSlice, 1.0, psi2p_gpu.gpudata, num_inducing)
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# join_prod(psi2p_gpu,psi1p_gpu,psi1p_gpu,nSlice,num_inducing)
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dL_dthetaL_gpu.fill(0.)
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cublas.cublasDcopy(self.cublas_handle, betaYT_gpu_slice.size, betaYT_gpu_slice.gpudata, 1, betaYT2_gpu.gpudata, 1)
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mul_bcast(betaYT2_gpu,betaYT2_gpu,betaYT2_gpu,betaYT2_gpu.size)
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cublas.cublasDscal(self.cublas_handle, betaYT2_gpu.size, 0.5, betaYT2_gpu.gpudata, 1)
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sum_axis(dL_dthetaL_gpu, betaYT2_gpu, 1, output_dim)
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cublas.cublasDaxpy(self.cublas_handle, dL_dthetaL_gpu.size, output_dim/(-2.0), beta_gpu_slice.gpudata, 1, dL_dthetaL_gpu.gpudata, 1)
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cublas.cublasDcopy(self.cublas_handle, beta_gpu_slice.size, beta_gpu_slice.gpudata, 1, thetaL_t_gpu.gpudata, 1)
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mul_bcast(thetaL_t_gpu,thetaL_t_gpu,thetaL_t_gpu,thetaL_t_gpu.size)
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mul_bcast(thetaL_t_gpu,thetaL_t_gpu,psi0p_gpu,thetaL_t_gpu.size)
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cublas.cublasDaxpy(self.cublas_handle, dL_dthetaL_gpu.size, output_dim/2.0, thetaL_t_gpu.gpudata, 1, dL_dthetaL_gpu.gpudata, 1)
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thetaL_t_gpu.fill(0.)
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sum_axis(thetaL_t_gpu, psi2R_gpu, nSlice, num_inducing*num_inducing)
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mul_bcast(thetaL_t_gpu,thetaL_t_gpu,beta_gpu_slice,thetaL_t_gpu.size)
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mul_bcast(thetaL_t_gpu,thetaL_t_gpu,beta_gpu_slice,thetaL_t_gpu.size)
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cublas.cublasDaxpy(self.cublas_handle, dL_dthetaL_gpu.size, -1.0, thetaL_t_gpu.gpudata, 1, dL_dthetaL_gpu.gpudata, 1)
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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)
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mul_bcast(betaYT2_gpu,betaYT2_gpu,betaYT_gpu_slice,betaYT2_gpu.size)
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sum_axis(dL_dthetaL_gpu, betaYT2_gpu, 1, output_dim)
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# psiR = cublas.cublasDdot(self.cublas_handle, dL_dpsi2R_gpu.size, dL_dpsi2R_gpu.gpudata, 1, psi2p_gpu.gpudata, 1)
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# # mul_bcast_first(psi2R_gpu,dL_dpsi2R_gpu,psi2p_gpu,nSlice)
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#
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# dL_dthetaL_gpu.fill(0.)
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# dL_dthetaL = 0.
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#
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# dL_dthetaL += cublas.cublasDdot(self.cublas_handle, betaYT_gpu_slice.size, betaYT_gpu_slice.gpudata,1,betaYT_gpu_slice.gpudata,1)
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#
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# cublas.cublasDcopy(self.cublas_handle, betaYT_gpu_slice.size, betaYT_gpu_slice.gpudata, 1, betaYT2_gpu.gpudata, 1)
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# mul_bcast(betaYT2_gpu,betaYT2_gpu,betaYT2_gpu,betaYT2_gpu.size)
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# cublas.cublasDscal(self.cublas_handle, betaYT2_gpu.size, 0.5, betaYT2_gpu.gpudata, 1)
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# sum_axis(dL_dthetaL_gpu, betaYT2_gpu, 1, output_dim)
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#
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# cublas.cublasDaxpy(self.cublas_handle, dL_dthetaL_gpu.size, output_dim/(-2.0), beta_gpu_slice.gpudata, 1, dL_dthetaL_gpu.gpudata, 1)
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# cublas.cublasDcopy(self.cublas_handle, beta_gpu_slice.size, beta_gpu_slice.gpudata, 1, thetaL_t_gpu.gpudata, 1)
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# mul_bcast(thetaL_t_gpu,thetaL_t_gpu,thetaL_t_gpu,thetaL_t_gpu.size)
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# mul_bcast(thetaL_t_gpu,thetaL_t_gpu,psi0p_gpu,thetaL_t_gpu.size)
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# cublas.cublasDaxpy(self.cublas_handle, dL_dthetaL_gpu.size, output_dim/2.0, thetaL_t_gpu.gpudata, 1, dL_dthetaL_gpu.gpudata, 1)
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#
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# thetaL_t_gpu.fill(0.)
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# sum_axis(thetaL_t_gpu, psi2R_gpu, nSlice, num_inducing*num_inducing)
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# mul_bcast(thetaL_t_gpu,thetaL_t_gpu,beta_gpu_slice,thetaL_t_gpu.size)
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# mul_bcast(thetaL_t_gpu,thetaL_t_gpu,beta_gpu_slice,thetaL_t_gpu.size)
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# cublas.cublasDaxpy(self.cublas_handle, dL_dthetaL_gpu.size, -1.0, thetaL_t_gpu.gpudata, 1, dL_dthetaL_gpu.gpudata, 1)
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#
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# 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)
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# mul_bcast(betaYT2_gpu,betaYT2_gpu,betaYT_gpu_slice,betaYT2_gpu.size)
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# sum_axis(dL_dthetaL_gpu, betaYT2_gpu, 1, output_dim)
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if kern.useGPU:
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dL_dpsi0 = dL_dpsi0_gpu
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@ -116,7 +116,7 @@ class VarDTC_minibatch(object):
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if het_noise:
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psi2_full += beta_slice*np.outer(psi1,psi1)
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else:
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psi2_full += np.outer(psi1,psi1)
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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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@ -170,13 +170,16 @@ class VarDTC_minibatch(object):
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# Compute the Posterior distribution of inducing points p(u|Y)
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#======================================================================
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# phi_u_mean = np.dot(Kmm,v)
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# LLInvKmm,_ = dtrtrs(LL,Kmm)
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# # phi_u_var = np.einsum('ma,mb->ab',LLInvKmm,LLInvKmm)
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# phi_u_var = Kmm - np.dot(LLInvKmm.T,LLInvKmm)
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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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#======================================================================
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# Compute dL_dthetaL for uncertian input and non-heter noise
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#======================================================================
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if uncertain_inputs and not het_noise:
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dL_dthetaL = (YRY_full*beta + beta*output_dim*psi0_full - num_data*output_dim*beta)/2. - beta*(dL_dpsi2R*psi2_full).sum() - beta*(v.T*psi1Y_full).sum()
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self.midRes['dL_dthetaL'] = dL_dthetaL
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return logL, dL_dKmm, post
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def inference_minibatch(self, kern, X, Z, likelihood, Y):
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@ -213,20 +216,21 @@ class VarDTC_minibatch(object):
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Y_slice = YYT_factor[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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if not uncertain_inputs:
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psi0 = kern.Kdiag(X_slice)
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psi1 = kern.K(X_slice, Z)
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psi2 = None
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betapsi1 = np.einsum('n,nm->nm',beta,psi1)
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elif het_noise:
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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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betapsi1 = np.einsum('n,nm->nm',beta,psi1)
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if het_noise:
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beta = beta[n_start] # assuming batchsize==1
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betaY = beta*Y_slice
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betapsi1 = np.einsum('n,nm->nm',beta,psi1)
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#======================================================================
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# Load Intermediate Results
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@ -239,7 +243,7 @@ class VarDTC_minibatch(object):
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# Compute dL_dpsi
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#======================================================================
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dL_dpsi0 = -0.5 * output_dim * (beta * np.ones((n_end-n_start,)))
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dL_dpsi0 = -output_dim * (beta * np.ones((n_end-n_start,)))/2.
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dL_dpsi1 = np.dot(betaY,v.T)
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@ -254,19 +258,21 @@ class VarDTC_minibatch(object):
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#======================================================================
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if het_noise:
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if uncertain_inputs:
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psiR = np.einsum('mo,nmo->n',dL_dpsi2R,psi2)
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else:
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psiR = np.einsum('nm,no,mo->n',psi1,psi1,dL_dpsi2R)
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dL_dthetaL = ((np.square(betaY)).sum(axis=-1) + np.square(beta)*(output_dim*psi0)-output_dim*beta)/2. - np.square(beta)*psiR- (betaY*np.dot(betapsi1,v)).sum(axis=-1)
|
||||
else:
|
||||
if uncertain_inputs:
|
||||
psiR = np.einsum('mo,mo->',dL_dpsi2R,psi2)
|
||||
else:
|
||||
psiR = np.einsum('nm,no,mo->',psi1,psi1,dL_dpsi2R)
|
||||
|
||||
dL_dthetaL = ((np.square(betaY)).sum() + beta*beta*output_dim*(psi0.sum())-num_slice*output_dim*beta)/2. - beta*beta*psiR- (betaY*np.dot(betapsi1,v)).sum()
|
||||
dL_dthetaL = ((np.square(betaY)).sum(axis=-1) + np.square(beta)*(output_dim*psi0)-output_dim*beta)/2. - np.square(beta)*psiR- (betaY*np.dot(betapsi1,v)).sum(axis=-1)
|
||||
else:
|
||||
if uncertain_inputs:
|
||||
if isEnd:
|
||||
dL_dthetaL = self.midRes['dL_dthetaL']
|
||||
else:
|
||||
dL_dthetaL = 0.
|
||||
else:
|
||||
psiR = np.einsum('nm,no,mo->',psi1,psi1,dL_dpsi2R)
|
||||
dL_dthetaL = ((np.square(betaY)).sum() + beta*beta*output_dim*(psi0.sum())-num_slice*output_dim*beta)/2. - beta*beta*psiR- (betaY*np.dot(betapsi1,v)).sum()
|
||||
|
||||
if uncertain_inputs:
|
||||
grad_dict = {'dL_dpsi0':dL_dpsi0,
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ The package for the psi statistics computation
|
|||
"""
|
||||
|
||||
import numpy as np
|
||||
from GPy.util.caching import Cache_this
|
||||
from GPy.util.caching import Cache_this,Cacher
|
||||
|
||||
@Cache_this(limit=1)
|
||||
def psicomputations(variance, lengthscale, Z, mu, S, gamma):
|
||||
|
|
@ -88,7 +88,7 @@ def _psi2computations(variance, lengthscale, Z, mu, S, gamma):
|
|||
|
||||
return _psi2
|
||||
|
||||
def psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
|
||||
def _psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
|
||||
ARD = (len(lengthscale)!=1)
|
||||
|
||||
dvar_psi1, dl_psi1, dZ_psi1, dmu_psi1, dS_psi1, dgamma_psi1 = _psi1compDer(dL_dpsi1, variance, lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
|
|
@ -211,3 +211,5 @@ def _psi2compDer(dL_dpsi2, variance, lengthscale, Z, mu, S, gamma):
|
|||
# _dpsi2_dlengthscale = 2.*lengthscale* _psi2_q * (_psi2_common*(S[:,None,None,:]/lengthscale2+_psi2_Zdist_sq*_psi2_denom+_psi2_mudist_sq)*_psi2_exp_dist_sq+(1-gamma[:,None,None,:])*_psi2_Z_sq_sum*0.5/lengthscale2*_psi2_exp_Z) # NxMxMxQ
|
||||
|
||||
return _dL_dvariance, _dL_dlengthscale, _dL_dZ, _dL_dmu, _dL_dS, _dL_dgamma
|
||||
|
||||
psiDerivativecomputations = Cacher(_psiDerivativecomputations, limit=1, ignore_args=(0,1,2,))
|
||||
|
|
|
|||
|
|
@ -58,19 +58,22 @@ try:
|
|||
__device__ double comp_psi2_element(double var, double *l, double *Z, double *mu, double *S, double *logGamma, double *log1Gamma, double *logpsi2denom, int N, int M, int Q, int idx)
|
||||
{
|
||||
// psi2 (n,m1,m2)
|
||||
int m2 = idx/(M*N);
|
||||
int m1 = (idx%(M*N))/N;
|
||||
int n = idx%N;
|
||||
int m2 = idx/M;
|
||||
int m1 = idx%M;
|
||||
|
||||
double psi2_exp=0;
|
||||
for(int q=0;q<Q;q++){
|
||||
double dZ = Z[IDX_MQ(m1,q)]-Z[IDX_MQ(m2,q)];
|
||||
double muZ = mu[IDX_NQ(n,q)] - (Z[IDX_MQ(m1,q)]+Z[IDX_MQ(m2,q)])/2.0;
|
||||
double exp1 = logGamma[IDX_NQ(n,q)] - (logpsi2denom[IDX_NQ(n,q)])/2.0 - dZ*dZ/(l[q]*4.0) - muZ*muZ/(2*S[IDX_NQ(n,q)]+l[q]);
|
||||
double exp2 = log1Gamma[IDX_NQ(n,q)] - (Z[IDX_MQ(m1,q)]*Z[IDX_MQ(m1,q)]+Z[IDX_MQ(m2,q)]*Z[IDX_MQ(m2,q)])/(l[q]*2.0);
|
||||
psi2_exp += LOGEXPSUM(exp1,exp2);
|
||||
double psi2=0;
|
||||
for(int n=0;n<N;n++){
|
||||
double psi2_exp=0;
|
||||
for(int q=0;q<Q;q++){
|
||||
double dZ = Z[IDX_MQ(m1,q)]-Z[IDX_MQ(m2,q)];
|
||||
double muZ = mu[IDX_NQ(n,q)] - (Z[IDX_MQ(m1,q)]+Z[IDX_MQ(m2,q)])/2.0;
|
||||
double exp1 = logGamma[IDX_NQ(n,q)] - (logpsi2denom[IDX_NQ(n,q)])/2.0 - dZ*dZ/(l[q]*4.0) - muZ*muZ/(2*S[IDX_NQ(n,q)]+l[q]);
|
||||
double exp2 = log1Gamma[IDX_NQ(n,q)] - (Z[IDX_MQ(m1,q)]*Z[IDX_MQ(m1,q)]+Z[IDX_MQ(m2,q)]*Z[IDX_MQ(m2,q)])/(l[q]*2.0);
|
||||
psi2_exp += LOGEXPSUM(exp1,exp2);
|
||||
}
|
||||
psi2 += exp(psi2_exp);
|
||||
}
|
||||
return var*var*exp(psi2_exp);
|
||||
return var*var*psi2;
|
||||
}
|
||||
""")
|
||||
|
||||
|
|
@ -281,7 +284,7 @@ class PSICOMP_SSRBF(object):
|
|||
'logpsi2denom_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
'psi0_gpu' :gpuarray.empty((N,),np.float64,order='F'),
|
||||
'psi1_gpu' :gpuarray.empty((N,M),np.float64,order='F'),
|
||||
'psi2_gpu' :gpuarray.empty((N,M,M),np.float64,order='F'),
|
||||
'psi2_gpu' :gpuarray.empty((M,M),np.float64,order='F'),
|
||||
# derivatives psi1
|
||||
'psi1_neq_gpu' :gpuarray.empty((N,M,Q),np.float64, order='F'),
|
||||
'psi1exp1_gpu' :gpuarray.empty((N,M,Q),np.float64, order='F'),
|
||||
|
|
|
|||
|
|
@ -123,7 +123,8 @@ class Cacher_wrap(object):
|
|||
def __get__(self, obj, objtype=None):
|
||||
return partial(self, obj)
|
||||
def __call__(self, *args, **kwargs):
|
||||
obj = args[0]
|
||||
obj = args[0] # <------------------- WHAT IF IT IS ONLY A FUNCTION!
|
||||
|
||||
#import ipdb;ipdb.set_trace()
|
||||
try:
|
||||
caches = obj.__cachers
|
||||
|
|
|
|||
|
|
@ -19,6 +19,9 @@ try:
|
|||
|
||||
strideSum = ReductionKernel(np.float64, neutral="0", reduce_expr="a+b", map_expr="i%step==0?x[i]:0", arguments="double *x, int step")
|
||||
|
||||
# np.trace(np.dot(A,B)) (also equivalent to (A*B.T).sum() ) A - n x m, B - m x n
|
||||
traceDot = ReductionKernel(np.float64, neutral="0", reduce_expr="a+b", map_expr="A[i]*B[(i%n)*m+i/n]", arguments="double *A, double *B, int n, int m")
|
||||
|
||||
#=======================================================================================
|
||||
# Element-wise functions
|
||||
#=======================================================================================
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue