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[GPU] bug fix

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This commit is contained in:
Zhenwen Dai 2014-04-02 10:52:04 +01:00
parent 5875a33d21
commit c20cd69c4e
2 changed files with 4 additions and 4 deletions
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4
GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py
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@ -414,7 +414,7 @@ class PSICOMP_SSRBF(object):
grad_dl_gpu = self.gpuCache['grad_l_gpu'] grad_dl_gpu = self.gpuCache['grad_l_gpu']
# variance # variance
variance.gradient = gpuarray.sum(dL_dpsi0) \ variance.gradient = gpuarray.sum(dL_dpsi0).get() \
+ cublas.cublasDdot(self.cublas_handle, dL_dpsi1.size, dL_dpsi1.gpudata, 1, dpsi1_dvar_gpu.gpudata, 1) \ + cublas.cublasDdot(self.cublas_handle, dL_dpsi1.size, dL_dpsi1.gpudata, 1, dpsi1_dvar_gpu.gpudata, 1) \
+ cublas.cublasDdot(self.cublas_handle, dL_dpsi2.size, dL_dpsi2.gpudata, 1, dpsi2_dvar_gpu.gpudata, 1) + cublas.cublasDdot(self.cublas_handle, dL_dpsi2.size, dL_dpsi2.gpudata, 1, dpsi2_dvar_gpu.gpudata, 1)
@ -429,7 +429,7 @@ class PSICOMP_SSRBF(object):
else: else:
linalg_gpu.mul_bcast(psi1_comb_gpu, dL_dpsi1, dpsi1_dl_gpu, dL_dpsi1.size) linalg_gpu.mul_bcast(psi1_comb_gpu, dL_dpsi1, dpsi1_dl_gpu, dL_dpsi1.size)
linalg_gpu.mul_bcast(psi2_comb_gpu, dL_dpsi2, dpsi2_dl_gpu, dL_dpsi2.size) linalg_gpu.mul_bcast(psi2_comb_gpu, dL_dpsi2, dpsi2_dl_gpu, dL_dpsi2.size)
lengthscale.gradient = gpuarray.sum(psi1_comb_gpu) + gpuarray.sum(psi2_comb_gpu) lengthscale.gradient = gpuarray.sum(psi1_comb_gpu).get() + gpuarray.sum(psi2_comb_gpu).get()
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, mu, S, gamma): def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, mu, S, gamma):
pass pass

4
GPy/util/linalg_gpu.py
View file

@ -28,8 +28,8 @@ try:
# log(1.0-X) # log(1.0-X)
logOne = ElementwiseKernel("double *in, double *out", "out[i] = log(1.-in[i])", "logOne_element") logOne = ElementwiseKernel("double *in, double *out", "out[i] = log(1.-in[i])", "logOne_element")
# multiplication with broadcast on the last dimension # multiplication with broadcast on the last dimension (a has to be smaller than b)
mul_bcast = ElementwiseKernel("double *out, double *shorter, double *longer, int shorter_size", "out[i] = longer[i]*shorter[i%shorter_size]", "mul_bcast") mul_bcast = ElementwiseKernel("double *out, double *a, double *b, int a_size", "out[i] = b[i]*a[i % a_size ]", "mul_bcast")
# sum through the middle dimension (size_2) of a 3D matrix (size_1, size_2, size_3) # sum through the middle dimension (size_2) of a 3D matrix (size_1, size_2, size_3)
sum_axis = ElementwiseKernel("double *out, double *in, int size_1, int size_2", "out[i] += sum_axis_element(in, size_1, size_2, i)", "sum_axis",preamble=""" sum_axis = ElementwiseKernel("double *out, double *in, int size_1, int size_2", "out[i] += sum_axis_element(in, size_1, size_2, i)", "sum_axis",preamble="""