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[GPU] finish infere_likelihood

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Zhenwen Dai 2014-03-24 18:23:11 +00:00
parent 88277f6b67
commit b5b17b9715
1 changed files with 48 additions and 20 deletions
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66
GPy/inference/latent_function_inference/var_dtc_gpu.py
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@ -15,6 +15,7 @@ try:
from scikits.cuda import cublas from scikits.cuda import cublas
import pycuda.autoinit import pycuda.autoinit
from pycuda.reduction import ReductionKernel from pycuda.reduction import ReductionKernel
from ...util.linalg_gpu import logDiagSum
except: except:
print 'Error in importing GPU modules!' print 'Error in importing GPU modules!'
@ -45,6 +46,27 @@ class VarDTC_GPU(object):
culinalg.init() culinalg.init()
self.cublas_handle = cublas.cublasCreate() self.cublas_handle = cublas.cublasCreate()
# Initialize GPU caches
self.gpuCache = None
def _initGPUCache(self, num_inducing, output_dim):
if self.gpuCache == None:
self.gpuCache = {# inference_likelihood
'Kmm_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
'Lm_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
'ones_gpu' :gpuarray.empty(num_inducing, np.float64),
'LL_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
'b_gpu' :gpuarray.empty((num_inducing,output_dim),np.float64),
'v_gpu' :gpuarray.empty((num_inducing,output_dim),np.float64),
'vvt_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
'KmmInvPsi2LLInvT_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
'KmmInvPsi2P_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
'dL_dpsi2R_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
'dL_dKmm_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64),
# inference_minibatch
}
self.gpuCache['ones_gpu'].fill(1.0)
def set_limit(self, limit): def set_limit(self, limit):
self.get_trYYT.limit = limit self.get_trYYT.limit = limit
self.get_YYTfactor.limit = limit self.get_YYTfactor.limit = limit
@ -75,6 +97,8 @@ class VarDTC_GPU(object):
num_inducing = Z.shape[0] num_inducing = Z.shape[0]
num_data, output_dim = Y.shape num_data, output_dim = Y.shape
self._initGPUCache(num_inducing, output_dim)
if isinstance(X, VariationalPosterior): if isinstance(X, VariationalPosterior):
uncertain_inputs = True uncertain_inputs = True
else: else:
@ -142,33 +166,34 @@ class VarDTC_GPU(object):
#====================================================================== #======================================================================
Kmm = kern.K(Z).copy() Kmm = kern.K(Z).copy()
Kmm_gpu = gpuarray.to_gpu(np.asfortranarray(Kmm)) Kmm_gpu = self.gpuCache['Kmm_gpu']
Kmm_gpu.set(Kmm)
diag.add(Kmm, self.const_jitter) diag.add(Kmm, self.const_jitter)
ones_gpu = gpuarray.empty(num_inducing, np.float64) ones_gpu = self.gpuCache['ones_gpu']
ones_gpu.fill(1.0)
cublas.cublasDaxpy(self.cublas_handle, num_inducing, self.const_jitter, ones_gpu.gpudata, 1, Kmm_gpu.gpudata, num_inducing+1) cublas.cublasDaxpy(self.cublas_handle, num_inducing, self.const_jitter, ones_gpu.gpudata, 1, Kmm_gpu.gpudata, num_inducing+1)
assert np.allclose(Kmm, Kmm_gpu.get()) assert np.allclose(Kmm, Kmm_gpu.get())
Lm = jitchol(Kmm) Lm = jitchol(Kmm)
# #
Lm_gpu = Kmm_gpu.copy() Lm_gpu = self.gpuCache['Lm_gpu']
cublas.cublasDcopy(self.cublas_handle, Kmm_gpu.size, Kmm_gpu.gpudata, 1, Lm_gpu.gpudata, 1)
culinalg.cho_factor(Lm_gpu,'L') culinalg.cho_factor(Lm_gpu,'L')
print np.abs(np.tril(Lm)-np.tril(Lm_gpu.get())).max() print np.abs(np.tril(Lm)-np.tril(Lm_gpu.get())).max()
Lambda = Kmm+psi2_full Lambda = Kmm+psi2_full
LL = jitchol(Lambda) LL = jitchol(Lambda)
# #
Lambda_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64) Lambda_gpu = self.gpuCache['LL_gpu']
cublas.cublasDcopy(self.cublas_handle, Kmm_gpu.size, Kmm_gpu.gpudata, 1, Lambda_gpu.gpudata, 1) cublas.cublasDcopy(self.cublas_handle, Kmm_gpu.size, Kmm_gpu.gpudata, 1, Lambda_gpu.gpudata, 1)
cublas.cublasDaxpy(self.cublas_handle, psi2_gpu.size, np.float64(1.0), psi2_gpu.gpudata, 1, Lambda_gpu.gpudata, 1) cublas.cublasDaxpy(self.cublas_handle, psi2_gpu.size, np.float64(1.0), psi2_gpu.gpudata, 1, Lambda_gpu.gpudata, 1)
LL_gpu = Lambda_gpu.copy() LL_gpu = Lambda_gpu
culinalg.cho_factor(LL_gpu,'L') culinalg.cho_factor(LL_gpu,'L')
print np.abs(np.tril(LL)-np.tril(LL_gpu.get())).max() print np.abs(np.tril(LL)-np.tril(LL_gpu.get())).max()
b,_ = dtrtrs(LL, psi1Y_full) b,_ = dtrtrs(LL, psi1Y_full)
bbt_cpu = np.square(b).sum() bbt_cpu = np.square(b).sum()
# #
b_gpu = gpuarray.empty((num_inducing,output_dim),np.float64) b_gpu = self.gpuCache['b_gpu']
cublas.cublasDcopy(self.cublas_handle, b_gpu.size, psi1Y_gpu.gpudata, 1, b_gpu.gpudata, 1) cublas.cublasDcopy(self.cublas_handle, b_gpu.size, psi1Y_gpu.gpudata, 1, b_gpu.gpudata, 1)
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) 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)
bbt = cublas.cublasDdot(self.cublas_handle, b_gpu.size, b_gpu.gpudata, 1, b_gpu.gpudata, 1) bbt = cublas.cublasDdot(self.cublas_handle, b_gpu.size, b_gpu.gpudata, 1, b_gpu.gpudata, 1)
@ -178,12 +203,12 @@ class VarDTC_GPU(object):
vvt = np.einsum('md,od->mo',v,v) vvt = np.einsum('md,od->mo',v,v)
LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right') LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right')
# #
v_gpu = gpuarray.empty((num_inducing,output_dim),np.float64) v_gpu = self.gpuCache['v_gpu']
cublas.cublasDcopy(self.cublas_handle, v_gpu.size, b_gpu.gpudata, 1, v_gpu.gpudata, 1) cublas.cublasDcopy(self.cublas_handle, v_gpu.size, b_gpu.gpudata, 1, v_gpu.gpudata, 1)
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) 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)
vvt_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64) vvt_gpu = self.gpuCache['vvt_gpu']
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) 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)
LmInvPsi2LmInvT_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64) LmInvPsi2LmInvT_gpu = self.gpuCache['KmmInvPsi2LLInvT_gpu']
cublas.cublasDcopy(self.cublas_handle, psi2_gpu.size, psi2_gpu.gpudata, 1, LmInvPsi2LmInvT_gpu.gpudata, 1) cublas.cublasDcopy(self.cublas_handle, psi2_gpu.size, psi2_gpu.gpudata, 1, LmInvPsi2LmInvT_gpu.gpudata, 1)
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) 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)
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) 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)
@ -201,24 +226,24 @@ class VarDTC_GPU(object):
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) 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)
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) 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)
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) 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)
KmmInvPsi2P_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64) KmmInvPsi2P_gpu = self.gpuCache['KmmInvPsi2P_gpu']
cublas.cublasDcopy(self.cublas_handle, KmmInvPsi2LLInvT_gpu.size, KmmInvPsi2LLInvT_gpu.gpudata, 1, KmmInvPsi2P_gpu.gpudata, 1) cublas.cublasDcopy(self.cublas_handle, KmmInvPsi2LLInvT_gpu.size, KmmInvPsi2LLInvT_gpu.gpudata, 1, KmmInvPsi2P_gpu.gpudata, 1)
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) 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)
print np.abs(KmmInvPsi2P-KmmInvPsi2P_gpu.get()).max() print np.abs(KmmInvPsi2P-KmmInvPsi2P_gpu.get()).max()
dL_dpsi2R = (output_dim*KmmInvPsi2P - vvt)/2. # dL_dpsi2 with R inside psi2 dL_dpsi2R = (output_dim*KmmInvPsi2P - vvt)/2. # dL_dpsi2 with R inside psi2
dL_dpsi2R_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64) #
dL_dpsi2R_gpu = self.gpuCache['dL_dpsi2R_gpu']
cublas.cublasDcopy(self.cublas_handle, vvt_gpu.size, vvt_gpu.gpudata, 1, dL_dpsi2R_gpu.gpudata, 1) cublas.cublasDcopy(self.cublas_handle, vvt_gpu.size, vvt_gpu.gpudata, 1, dL_dpsi2R_gpu.gpudata, 1)
cublas.cublasDaxpy(self.cublas_handle, KmmInvPsi2P_gpu.size, np.float64(-output_dim), KmmInvPsi2P_gpu.gpudata, 1, dL_dpsi2R_gpu.gpudata, 1) cublas.cublasDaxpy(self.cublas_handle, KmmInvPsi2P_gpu.size, np.float64(-output_dim), KmmInvPsi2P_gpu.gpudata, 1, dL_dpsi2R_gpu.gpudata, 1)
cublas.cublasDscal(self.cublas_handle, dL_dpsi2R_gpu.size, np.float64(-0.5), dL_dpsi2R_gpu.gpudata, 1) cublas.cublasDscal(self.cublas_handle, dL_dpsi2R_gpu.size, np.float64(-0.5), dL_dpsi2R_gpu.gpudata, 1)
print np.abs(dL_dpsi2R_gpu.get()-dL_dpsi2R).max() print np.abs(dL_dpsi2R_gpu.get()-dL_dpsi2R).max()
# Cache intermediate results # Cache intermediate results
self.midRes['dL_dpsi2R'] = dL_dpsi2R_gpu self.midRes['dL_dpsi2R'] = dL_dpsi2R
self.midRes['v'] = v_gpu self.midRes['v'] = v
logDiagSum = ReductionKernel(np.float64, neutral="0", reduce_expr="a+b", map_expr="i%step==0?log(x[i]):0", arguments="double *x, int step") #logDiagSum = ReductionKernel(np.float64, neutral="0", reduce_expr="a+b", map_expr="i%step==0?log(x[i]):0", arguments="double *x, int step")
#====================================================================== #======================================================================
# Compute log-likelihood # Compute log-likelihood
@ -240,10 +265,10 @@ class VarDTC_GPU(object):
dL_dKmm = -(output_dim*np.einsum('md,od->mo',KmmInvPsi2LLInvT,KmmInvPsi2LLInvT) + vvt)/2. dL_dKmm = -(output_dim*np.einsum('md,od->mo',KmmInvPsi2LLInvT,KmmInvPsi2LLInvT) + vvt)/2.
# #
dL_dKmm_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64) dL_dKmm_gpu = self.gpuCache['dL_dKmm_gpu']
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) 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)
cublas.cublasDaxpy(self.cublas_handle, dL_dKmm_gpu.size, np.float64(1./output_dim), vvt_gpu.gpudata, 1, dL_dKmm_gpu.gpudata, 1) cublas.cublasDaxpy(self.cublas_handle, dL_dKmm_gpu.size, np.float64(1./output_dim), vvt_gpu.gpudata, 1, dL_dKmm_gpu.gpudata, 1)
cublas.cublasDscal(self.cublas_handle, dL_dKmm_gpu.size, np.float64(-output_dim/2.), dL_dpsi2R_gpu.gpudata, 1) cublas.cublasDscal(self.cublas_handle, dL_dKmm_gpu.size, np.float64(-output_dim/2.), dL_dKmm_gpu.gpudata, 1)
print np.abs(dL_dKmm - dL_dKmm_gpu.get()).max() print np.abs(dL_dKmm - dL_dKmm_gpu.get()).max()
#====================================================================== #======================================================================
@ -303,6 +328,9 @@ class VarDTC_GPU(object):
betaY = beta*Y_slice betaY = beta*Y_slice
betapsi1 = np.einsum('n,nm->nm',beta,psi1) betapsi1 = np.einsum('n,nm->nm',beta,psi1)
betaY_gpu = gpuarray.to_gpu(betaY)
betapsi1_gpu = gpuarray.to_gpu(betapsi1)
#====================================================================== #======================================================================
# Load Intermediate Results # Load Intermediate Results
#====================================================================== #======================================================================