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https://github.com/SheffieldML/GPy.git
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[GPU] finish infere_likelihood
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Zhenwen Dai
parent
88277f6b67
commit
b5b17b9715
1 changed files with 48 additions and 20 deletions
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@ -15,6 +15,7 @@ try:
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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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from ...util.linalg_gpu import logDiagSum
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except:
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print 'Error in importing GPU modules!'
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@ -44,6 +45,27 @@ class VarDTC_GPU(object):
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# Initialize GPU environment
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culinalg.init()
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self.cublas_handle = cublas.cublasCreate()
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# Initialize GPU caches
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self.gpuCache = None
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def _initGPUCache(self, num_inducing, output_dim):
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if self.gpuCache == None:
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self.gpuCache = {# inference_likelihood
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'Kmm_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
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'Lm_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
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'ones_gpu' :gpuarray.empty(num_inducing, np.float64),
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'LL_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
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'b_gpu' :gpuarray.empty((num_inducing,output_dim),np.float64),
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'v_gpu' :gpuarray.empty((num_inducing,output_dim),np.float64),
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'vvt_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
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'KmmInvPsi2LLInvT_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
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'KmmInvPsi2P_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
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'dL_dpsi2R_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
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'dL_dKmm_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
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# inference_minibatch
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}
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self.gpuCache['ones_gpu'].fill(1.0)
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def set_limit(self, limit):
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self.get_trYYT.limit = limit
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@ -74,6 +96,8 @@ class VarDTC_GPU(object):
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num_inducing = Z.shape[0]
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num_data, output_dim = Y.shape
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self._initGPUCache(num_inducing, output_dim)
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if isinstance(X, VariationalPosterior):
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uncertain_inputs = True
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@ -142,33 +166,34 @@ class VarDTC_GPU(object):
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#======================================================================
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Kmm = kern.K(Z).copy()
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Kmm_gpu = gpuarray.to_gpu(np.asfortranarray(Kmm))
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Kmm_gpu = self.gpuCache['Kmm_gpu']
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Kmm_gpu.set(Kmm)
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diag.add(Kmm, self.const_jitter)
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ones_gpu = gpuarray.empty(num_inducing, np.float64)
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ones_gpu.fill(1.0)
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ones_gpu = self.gpuCache['ones_gpu']
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cublas.cublasDaxpy(self.cublas_handle, num_inducing, self.const_jitter, ones_gpu.gpudata, 1, Kmm_gpu.gpudata, num_inducing+1)
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assert np.allclose(Kmm, Kmm_gpu.get())
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Lm = jitchol(Kmm)
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#
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Lm_gpu = Kmm_gpu.copy()
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Lm_gpu = self.gpuCache['Lm_gpu']
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cublas.cublasDcopy(self.cublas_handle, Kmm_gpu.size, Kmm_gpu.gpudata, 1, Lm_gpu.gpudata, 1)
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culinalg.cho_factor(Lm_gpu,'L')
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print np.abs(np.tril(Lm)-np.tril(Lm_gpu.get())).max()
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Lambda = Kmm+psi2_full
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LL = jitchol(Lambda)
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#
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Lambda_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64)
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Lambda_gpu = self.gpuCache['LL_gpu']
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cublas.cublasDcopy(self.cublas_handle, Kmm_gpu.size, Kmm_gpu.gpudata, 1, Lambda_gpu.gpudata, 1)
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cublas.cublasDaxpy(self.cublas_handle, psi2_gpu.size, np.float64(1.0), psi2_gpu.gpudata, 1, Lambda_gpu.gpudata, 1)
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LL_gpu = Lambda_gpu.copy()
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LL_gpu = Lambda_gpu
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culinalg.cho_factor(LL_gpu,'L')
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print np.abs(np.tril(LL)-np.tril(LL_gpu.get())).max()
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b,_ = dtrtrs(LL, psi1Y_full)
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bbt_cpu = np.square(b).sum()
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#
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b_gpu = gpuarray.empty((num_inducing,output_dim),np.float64)
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b_gpu = self.gpuCache['b_gpu']
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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.gpudata, 1, b_gpu.gpudata, 1)
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@ -178,12 +203,12 @@ class VarDTC_GPU(object):
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vvt = np.einsum('md,od->mo',v,v)
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LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right')
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#
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v_gpu = gpuarray.empty((num_inducing,output_dim),np.float64)
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v_gpu = self.gpuCache['v_gpu']
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cublas.cublasDcopy(self.cublas_handle, v_gpu.size, b_gpu.gpudata, 1, v_gpu.gpudata, 1)
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cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'T', 'N', num_inducing, output_dim, np.float64(1.0), LL_gpu.gpudata, num_inducing, v_gpu.gpudata, num_inducing)
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vvt_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64)
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vvt_gpu = self.gpuCache['vvt_gpu']
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cublas.cublasDgemm(self.cublas_handle, 'N', 'T', num_inducing, num_inducing, output_dim, np.float64(1.0), v_gpu.gpudata, num_inducing, v_gpu.gpudata, num_inducing, np.float64(0.), vvt_gpu.gpudata, num_inducing)
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LmInvPsi2LmInvT_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64)
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LmInvPsi2LmInvT_gpu = self.gpuCache['KmmInvPsi2LLInvT_gpu']
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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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@ -201,24 +226,24 @@ class VarDTC_GPU(object):
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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 , '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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KmmInvPsi2P_gpu = self.gpuCache['KmmInvPsi2P_gpu']
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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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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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#
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dL_dpsi2R_gpu = self.gpuCache['dL_dpsi2R_gpu']
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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_gpu
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self.midRes['v'] = v_gpu
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self.midRes['dL_dpsi2R'] = dL_dpsi2R
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self.midRes['v'] = v
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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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#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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@ -240,10 +265,10 @@ class VarDTC_GPU(object):
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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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dL_dKmm_gpu = self.gpuCache['dL_dKmm_gpu']
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cublas.cublasDgemm(self.cublas_handle, 'N', 'T', num_inducing, num_inducing, num_inducing, 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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cublas.cublasDscal(self.cublas_handle, dL_dKmm_gpu.size, np.float64(-output_dim/2.), dL_dKmm_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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@ -303,6 +328,9 @@ class VarDTC_GPU(object):
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betaY = beta*Y_slice
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betapsi1 = np.einsum('n,nm->nm',beta,psi1)
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betaY_gpu = gpuarray.to_gpu(betaY)
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betapsi1_gpu = gpuarray.to_gpu(betapsi1)
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#======================================================================
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# Load Intermediate Results
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#======================================================================
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