未验证 提交 8d5ab1f9 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #11595 from luotao1/refine_code

add url of cuda9.0_cudnn7_avx_mkl library
...@@ -13,6 +13,7 @@ cpu_noavx_openblas `fluid.tgz <https://guest:@paddleci.ngrok.io/repository ...@@ -13,6 +13,7 @@ cpu_noavx_openblas `fluid.tgz <https://guest:@paddleci.ngrok.io/repository
cuda7.5_cudnn5_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_ cuda7.5_cudnn5_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda8.0_cudnn5_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_ cuda8.0_cudnn5_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda8.0_cudnn7_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/fluid.tgz>`_ cuda8.0_cudnn7_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda9.0_cudnn7_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/fluid.tgz>`_
====================== ======================================== ====================== ========================================
从源码编译 从源码编译
......
...@@ -21,8 +21,6 @@ namespace operators { ...@@ -21,8 +21,6 @@ namespace operators {
using batch_norm_bwd = mkldnn::batch_normalization_backward; using batch_norm_bwd = mkldnn::batch_normalization_backward;
using batch_norm_fwd = mkldnn::batch_normalization_forward; using batch_norm_fwd = mkldnn::batch_normalization_forward;
using framework::DataLayout;
using framework::Tensor;
using mkldnn::memory; using mkldnn::memory;
using mkldnn::primitive; using mkldnn::primitive;
using mkldnn::reorder; using mkldnn::reorder;
...@@ -31,18 +29,6 @@ using paddle::platform::MKLDNNDeviceContext; ...@@ -31,18 +29,6 @@ using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::MKLDNNMemDesc; using paddle::platform::MKLDNNMemDesc;
using platform::to_void_cast; using platform::to_void_cast;
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
namespace { namespace {
template <typename T> template <typename T>
struct bn_type_traits { struct bn_type_traits {
......
...@@ -22,22 +22,6 @@ limitations under the License. */ ...@@ -22,22 +22,6 @@ limitations under the License. */
namespace paddle { namespace paddle {
namespace operators { namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
class BatchNormOp : public framework::OperatorWithKernel { class BatchNormOp : public framework::OperatorWithKernel {
public: public:
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
......
...@@ -19,6 +19,22 @@ limitations under the License. */ ...@@ -19,6 +19,22 @@ limitations under the License. */
namespace paddle { namespace paddle {
namespace operators { namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename DeviceContext, typename T> template <typename DeviceContext, typename T>
class BatchNormKernel : public framework::OpKernel<T> { class BatchNormKernel : public framework::OpKernel<T> {
public: public:
......
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