apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-11 21:12:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[GPU] in progress

Browse source
This commit is contained in:
Zhenwen Dai 2014-03-24 16:19:30 +00:00
parent 2b1c1614d9
commit 029abe8536
1 changed files with 43 additions and 12 deletions
Download patch file Download diff file Expand all files Collapse all files

55
GPy/inference/latent_function_inference/var_dtc_gpu.py
View file

@ -86,7 +86,7 @@ class VarDTC_GPU(object):
psi2_full = np.zeros((num_inducing,num_inducing))
psi1Y_full = np.zeros((output_dim,num_inducing)) # DxM
psi1Y_full = np.zeros((num_inducing,output_dim)) # DxM
psi0_full = 0
YRY_full = 0
@ -109,11 +109,11 @@ class VarDTC_GPU(object):
if het_noise:
beta_slice = beta[n_start:n_end]
psi0_full += (beta_slice*psi0).sum()
psi1Y_full += np.dot(beta_slice*Y_slice.T,psi1) # DxM
psi1Y_full += np.dot(psi1,beta_slice[:,None]*Y_slice) # DxM
YRY_full += (beta_slice*np.square(Y_slice).sum(axis=-1)).sum()
else:
psi0_full += psi0.sum()
psi1Y_full += np.dot(Y_slice.T,psi1) # DxM
psi1Y_full += np.dot(psi1,Y_slice) # DxM
if uncertain_inputs:
@ -144,37 +144,68 @@ class VarDTC_GPU(object):
Kmm = kern.K(Z).copy()
Kmm_gpu = gpuarray.to_gpu(np.asfortranarray(Kmm))
diag.add(Kmm, self.const_jitter)
ones_gpu = gpuarray.empty(num_inducing, np.float64)
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)
assert np.allclose(Kmm, Kmm_gpu.get())
Lm = jitchol(Kmm)
#
Lm_gpu = Kmm_gpu.copy()
Lm_gpu = culinalg.cho_factor(Lm_gpu,'L')
assert np.allclose(Lm,Lm_gpu.get())
culinalg.cho_factor(Lm_gpu,'L')
print np.abs(np.tril(Lm)-np.tril(Lm_gpu.get())).max()
Lambda = Kmm+psi2_full
LL = jitchol(Lambda)
#
Lambda_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64)
cublas.cublasDaxpy(self.cublas_handle, Kmm_gpu.size, np.float64(1.0), Kmm_gpu.gpudata, 1, psi2_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)
LL_gpu = Lambda_gpu.copy()
LL_gpu = culinalg.cho_factor(LL_gpu,'L')
assert np.allclose(LL,LL_gpu.get())
b,_ = dtrtrs(LL, psi1Y_full.T)
bbt = np.square(b).sum()
culinalg.cho_factor(LL_gpu,'L')
print np.abs(np.tril(LL)-np.tril(LL_gpu.get())).max()
b,_ = dtrtrs(LL, psi1Y_full)
bbt_cpu = np.square(b).sum()
#
b_gpu = gpuarray.empty((num_inducing,output_dim),np.float64)
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)
bbt = cublas.cublasDdot(self.cublas_handle, b_gpu.size, b_gpu, 1, b_gpu, 1)
print np.abs(bbt-bbt_cpu)
v,_ = dtrtrs(LL.T,b,lower=False)
vvt = np.einsum('md,od->mo',v,v)
LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right')
#
v_gpu = gpuarray.empty((num_inducing,output_dim),np.float64)
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)
vvt_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64)
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)
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 , 'R', 'L', 'T', 'N', num_inducing, num_inducing, np.float64(1.0), Lm_gpu.gpudata, num_inducing, LmInvPsi2LmInvT_gpu.gpudata, num_inducing)
tr_LmInvPsi2LmInvT = cublas.cublasDasum(self.cublas_handle, num_inducing, LmInvPsi2LmInvT_gpu.gpudata, num_inducing+1)
print np.abs(vvt-vvt_gpu.get()).max()
print np.abs(np.trace(LmInvPsi2LmInvT)-tr_LmInvPsi2LmInvT)
Psi2LLInvT = dtrtrs(LL,psi2_full)[0].T
LmInvPsi2LLInvT= dtrtrs(Lm,Psi2LLInvT)[0]
KmmInvPsi2LLInvT = dtrtrs(Lm,LmInvPsi2LLInvT,trans=True)[0]
KmmInvPsi2P = dtrtrs(LL,KmmInvPsi2LLInvT.T, trans=True)[0].T
#
KmmInvPsi2LLInvT_gpu = LmInvPsi2LmInvT_gpu # Reuse GPU memory (size:MxM)
cublas.cublasDcopy(self.cublas_handle, psi2_gpu.size, psi2_gpu.gpudata, 1, KmmInvPsi2LLInvT_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, 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)
KmmInvPsi2P_gpu = gpuarray.empty((num_inducing,num_inducing),np.float64)
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)
print np.abs(KmmInvPsi2P-KmmInvPsi2P_gpu.get()).max()
dL_dpsi2R = (output_dim*KmmInvPsi2P - vvt)/2. # dL_dpsi2 with R inside psi2