Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into port_pybind11

.gitignore

@@ -5,6 +5,7 @@ python/paddle/v2/fluid/tests/book/image_classification_resnet.inference.model/
 python/paddle/v2/fluid/tests/book/image_classification_vgg.inference.model/
 python/paddle/v2/fluid/tests/book/label_semantic_roles.inference.model/
 *.DS_Store
+*.vs
 build/
 build_doc/
 *.user
@@ -15,6 +16,7 @@ build_doc/
 .cproject
 .pydevproject
 .settings/
+CMakeSettings.json
 Makefile
 .test_env/
 third_party/

CMakeLists.txt

@@ -204,11 +204,12 @@ include(external/snappy)    # download snappy
 include(external/snappystream)
 include(external/threadpool)
 
-set(WITH_ANAKIN OFF CACHE STRING "Disable Anakin first, will add it later." FORCE)
 if(WITH_GPU)
     include(cuda)
     include(tensorrt)
     include(external/anakin)
+elseif()
+    set(WITH_ANAKIN OFF CACHE STRING "Anakin is used in GPU only now." FORCE)
 endif()
 
 include(cudnn)              # set cudnn libraries, must before configure

cmake/configure.cmake

@@ -56,6 +56,10 @@ if(NOT CMAKE_CROSSCOMPILING)
         set(SIMD_FLAG ${SSE3_FLAG})
     endif()
 endif()
+if(UNIX AND NOT APPLE)
+  # except apple from nix*Os family
+  set(LINUX TRUE)
+endif(UNIX AND NOT APPLE)
 
 if(NOT WITH_GOLANG)
     add_definitions(-DPADDLE_WITHOUT_GOLANG)
@@ -104,6 +108,10 @@ if(WITH_GPU)
         if(${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
             message(FATAL_ERROR "Anakin needs CUDNN >= 7.0 to compile")
         endif()
+        set(ENV{CUDNN_INCLUDE_DIR} ${CUDNN_INCLUDE_DIR})
+        set(ENV{CUDNN_LIBRARY} ${CUDNN_LIBRARY})
+        message(STATUS "cudnn include header is ${CUDNN_INCLUDE_DIR}/cudnn.h")
+        message(STATUS "cudnn library is ${CUDNN_LIBRARY}")
     endif()
 elseif(WITH_AMD_GPU)
     add_definitions(-DPADDLE_WITH_HIP)

cmake/external/anakin.cmake

@@ -35,9 +35,8 @@ set(ANAKIN_COMPILE_EXTRA_FLAGS
 ExternalProject_Add(
     extern_anakin
     ${EXTERNAL_PROJECT_LOG_ARGS}
-    # TODO(luotao): use PaddlePaddle/Anakin later
-    GIT_REPOSITORY      "https://github.com/luotao1/Anakin"
-    GIT_TAG             "3957ae9263eaa0b1986758dac60a88852afb09be"
+    GIT_REPOSITORY      "https://github.com/PaddlePaddle/Anakin"
+    GIT_TAG             "04256ba78fa3da0beb74e8036c8efd68c12824d6"
     PREFIX              ${ANAKIN_SOURCE_DIR}
     UPDATE_COMMAND      ""
     CMAKE_ARGS          -DUSE_GPU_PLACE=YES

paddle/fluid/API.spec

@@ -155,10 +155,11 @@ paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale',
 paddle.fluid.layers.gather ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.random_crop ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.mean_iou ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.relu ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.log ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.relu ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.log ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))

paddle/fluid/framework/attribute.h

@@ -128,7 +128,8 @@ struct ExtractAttribute {
       attr_value = &boost::get<T>(attr);
     } catch (boost::bad_get& bad_get) {
       PADDLE_THROW("Cannot get attribute %s by type %s, its type is %s",
-                   attr_name_, typeid(T).name(), attr.type().name());
+                   attr_name_, paddle::platform::demangle(typeid(T).name()),
+                   paddle::platform::demangle(attr.type().name()));
     }
     return attr_value;
   }
@@ -160,7 +161,7 @@ struct ExtractAttribute<bool> {
       attr_value = &boost::get<bool>(attr);
     } catch (boost::bad_get& bad_get) {
       PADDLE_THROW("Cannot get attribute %s by type bool, its type is %s",
-                   attr_name_, attr.type().name());
+                   attr_name_, paddle::platform::demangle(attr.type().name()));
     }
     return attr_value;
   }
@@ -186,7 +187,7 @@ struct ExtractAttribute<int64_t> {
       attr_value = &boost::get<int64_t>(attr);
     } catch (boost::bad_get& bad_get) {
       PADDLE_THROW("Cannot get attribute %s by type int64_t, its type is %s",
-                   attr_name_, attr.type().name());
+                   attr_name_, paddle::platform::demangle(attr.type().name()));
     }
     return attr_value;
   }

paddle/fluid/framework/threadpool.cc

@@ -20,6 +20,9 @@
 DEFINE_int32(io_threadpool_size, 100,
              "number of threads used for doing IO, default 100");
 
+DEFINE_int32(dist_threadpool_size, 0,
+             "number of threads used for distributed executed.");
+
 namespace paddle {
 namespace framework {
 
@@ -35,6 +38,10 @@ void ThreadPool::Init() {
   if (threadpool_.get() == nullptr) {
     // TODO(Yancey1989): specify the max threads number
     int num_threads = std::thread::hardware_concurrency();
+    if (FLAGS_dist_threadpool_size > 0) {
+      num_threads = FLAGS_dist_threadpool_size;
+      VLOG(1) << "set dist_threadpool_size to " << num_threads;
+    }
     PADDLE_ENFORCE_GT(num_threads, 0);
     threadpool_.reset(new ThreadPool(num_threads));
   }

paddle/fluid/inference/api/CMakeLists.txt

@@ -60,7 +60,7 @@ cc_library(paddle_inference_tensorrt_subgraph_engine
 inference_api_test(test_api_tensorrt_subgraph_engine SRC api_tensorrt_subgraph_engine_tester.cc ARGS test_word2vec)
 endif()
 
-if (WITH_ANAKIN) # only needed in CI
+if (WITH_ANAKIN AND WITH_GPU) # only needed in CI
     # compile the libinference_anakin_api.a and anakin.so.
     nv_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
     #nv_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin)

paddle/fluid/operators/CMakeLists.txt

@@ -170,6 +170,9 @@ function(op_library TARGET)
         file(APPEND ${pybind_file} "USE_OP(fake_dequantize_max_abs);\n")
       elseif(${TARGET} STREQUAL "tensorrt_engine_op")
           message(STATUS "Pybind skips [tensorrt_engine_op], for this OP is only used in inference")
+      elseif(${TARGET} STREQUAL "fc")
+        # HACK: fc only have mkldnn and cpu, which would mismatch the cpu only condition
+        file(APPEND ${pybind_file} "USE_CPU_ONLY_OP(${TARGET});\n")
       else()
         file(APPEND ${pybind_file} "USE_OP(${TARGET});\n")
       endif()
@@ -300,12 +303,6 @@ op_library(channel_recv_op DEPS concurrency)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 
-# The fully connected layer is deleted when the WITH_MKLDNN flag is OFF
-# Because the fully connected layer has only one MKLDNN's operator
-if(NOT WITH_MKLDNN)
-    list(REMOVE_ITEM GENERAL_OPS fc_op)
-endif(NOT WITH_MKLDNN)
-
 foreach(src ${GENERAL_OPS})
     op_library(${src})
 endforeach()

paddle/fluid/operators/activation_op.cu

@@ -26,6 +26,8 @@ namespace plat = paddle::platform;
       act_type##_grad, ops::ActivationGradKernel<plat::CUDADeviceContext,   \
                                                  ops::grad_functor<float>>, \
       ops::ActivationGradKernel<plat::CUDADeviceContext,                    \
-                                ops::grad_functor<double>>);
+                                ops::grad_functor<double>>,                 \
+      ops::ActivationGradKernel<plat::CUDADeviceContext,                    \
+                                ops::grad_functor<plat::float16>>);
 
 FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CUDA_KERNEL);

paddle/fluid/operators/activation_op.h

@@ -333,8 +333,7 @@ struct SqrtGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
-    const Out out_conj = Eigen::numext::conj(out);
-    dx.device(d) = static_cast<T>(0.5) * dout / out_conj;
+    dx.device(d) = static_cast<T>(0.5) * dout / out;
   }
 };
 
@@ -740,7 +739,7 @@ struct PowGradFunctor : public BaseActivationFunctor<T> {
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * static_cast<T>(factor) *
-                   x.pow(static_cast<T>(factor - static_cast<T>(1)));
+                   x.pow(static_cast<T>(factor) - static_cast<T>(1));
   }
 };
 
@@ -863,10 +862,11 @@ struct SwishGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
+    T b = static_cast<T>(beta);
     auto temp1 = static_cast<T>(1) /
-                 (static_cast<T>(1) + (static_cast<T>(-beta) * x).exp());
-    auto temp2 = temp1 * (static_cast<T>(1) - (beta * out));
-    dx.device(d) = dout * ((beta * out) + temp2);
+                 (static_cast<T>(1) + (static_cast<T>(-b) * x).exp());
+    auto temp2 = temp1 * (static_cast<T>(1) - (b * out));
+    dx.device(d) = dout * ((b * out) + temp2);
   }
 };
 

paddle/fluid/operators/assign_value_op.cu.cc

@@ -13,7 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/assign_value_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(assign_value, ops::AssignValueKernel<int>,
-                        ops::AssignValueKernel<float>);
+                        ops::AssignValueKernel<float>,
+                        ops::AssignValueKernel<plat::float16>);

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -39,6 +39,27 @@ using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
 static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES =
     static_cast<size_t>(1024) * 1024 * 1024;
 
+template <typename T, typename DeviceContext>
+// bool EnableFp16(const T& dummy, const DeviceContext& dev_ctx,
+bool EnableFp16(const DeviceContext& dev_ctx,
+                cudnnConvolutionDescriptor_t cudnn_conv_desc) {
+#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
+  // Tensor core is supported since the volta GPU and
+  // is only enabled when input and filter data are float16
+  if (dev_ctx.GetComputeCapability() >= 70 &&
+      std::type_index(typeid(T)) ==
+          std::type_index(typeid(platform::float16))) {
+    PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
+        cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
+    return true;
+  } else {
+    PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
+        cudnn_conv_desc, CUDNN_DEFAULT_MATH));
+  }
+#endif
+  return false;
+}
+
 template <typename T>
 class CUDNNConvOpKernel : public framework::OpKernel<T> {
  public:
@@ -128,27 +149,14 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
     cudnnConvolutionFwdAlgo_t algo;
     auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
     auto handle = dev_ctx.cudnn_handle();
-
-    CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
-        handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
-        cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-        workspace_size_limit, &algo));
-
-#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
-    // Tensor core is supported since the volta GPU and
-    // is only enabled when input and filter data are float16
-    if (dev_ctx.GetComputeCapability() >= 70 &&
-        std::type_index(typeid(T)) ==
-            std::type_index(typeid(platform::float16))) {
-      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
-          cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
-      // Currently tensor core is only enabled using this algo
+    if (EnableFp16<T>(dev_ctx, cudnn_conv_desc)) {
       algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
     } else {
-      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
-          cudnn_conv_desc, CUDNN_DEFAULT_MATH));
+      PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
+          handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
+          cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+          workspace_size_limit, &algo));
     }
-#endif
 
     // get workspace size able to allocate
     CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
@@ -288,6 +296,9 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
       } else {
         data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
       }
+      if (EnableFp16<T>(dev_ctx, cudnn_conv_desc)) {
+        data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
+      }
 
       CUDNN_ENFORCE(
           platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
@@ -307,6 +318,9 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
       } else {
         filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
       }
+      if (EnableFp16<T>(dev_ctx, cudnn_conv_desc)) {
+        filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
+      }
 
       CUDNN_ENFORCE(
           platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
@@ -362,7 +376,8 @@ REGISTER_OP_KERNEL(conv2d, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvOpKernel<plat::float16>);
 REGISTER_OP_KERNEL(conv2d_grad, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvGradOpKernel<float>,
-                   paddle::operators::CUDNNConvGradOpKernel<double>);
+                   paddle::operators::CUDNNConvGradOpKernel<double>,
+                   paddle::operators::CUDNNConvGradOpKernel<plat::float16>);
 
 REGISTER_OP_KERNEL(conv3d, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvOpKernel<float>,
@@ -370,4 +385,5 @@ REGISTER_OP_KERNEL(conv3d, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvOpKernel<plat::float16>);
 REGISTER_OP_KERNEL(conv3d_grad, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvGradOpKernel<float>,
-                   paddle::operators::CUDNNConvGradOpKernel<double>);
+                   paddle::operators::CUDNNConvGradOpKernel<double>,
+                   paddle::operators::CUDNNConvGradOpKernel<plat::float16>)

paddle/fluid/operators/cross_entropy_op.cu

@@ -13,12 +13,16 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/cross_entropy_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 using CUDACtx = paddle::platform::CUDADeviceContext;
 REGISTER_OP_CUDA_KERNEL(cross_entropy,
                         ops::CrossEntropyOpKernel<CUDACtx, float>,
-                        ops::CrossEntropyOpKernel<CUDACtx, double>);
-REGISTER_OP_CUDA_KERNEL(cross_entropy_grad,
-                        ops::CrossEntropyGradientOpKernel<CUDACtx, float>,
-                        ops::CrossEntropyGradientOpKernel<CUDACtx, double>);
+                        ops::CrossEntropyOpKernel<CUDACtx, double>,
+                        ops::CrossEntropyOpKernel<CUDACtx, plat::float16>);
+REGISTER_OP_CUDA_KERNEL(
+    cross_entropy_grad, ops::CrossEntropyGradientOpKernel<CUDACtx, float>,
+    ops::CrossEntropyGradientOpKernel<CUDACtx, double>,
+    ops::CrossEntropyGradientOpKernel<CUDACtx, plat::float16>);

paddle/fluid/operators/distributed/variable_response.cc

@@ -190,12 +190,15 @@ bool VariableResponse::ProcSerializedField(
 #endif
   }
 
+  VLOG(7) << "ProcSerializedField:" << meta_.varname()
+          << ", type:" << meta_.type() << std::endl;
   framework::DDim dims = GetDims(meta_.dims());
   if (meta_.type() == sendrecv::LOD_TENSOR) {
     PADDLE_ENFORCE(meta_.lod_size() >= 0, "lod info should be got first!");
     if (!CopyLodTensorData(input, *dev_ctx_, dims, num_bytes)) {
       return false;
     }
+
     return true;
   }
 
@@ -206,7 +209,9 @@ bool VariableResponse::ProcSerializedField(
     return true;
   }
 
-  return true;
+  PADDLE_ENFORCE("not supported var types:", meta_.varname(), meta_.type());
+
+  return false;
 }
 
 };  // namespace distributed

paddle/fluid/operators/elementwise_add_op.cu

@@ -30,4 +30,5 @@ REGISTER_OP_CUDA_KERNEL(
     ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, float>,
     ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, double>,
     ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int>,
-    ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int64_t>);
+    ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int64_t>,
+    ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, plat::float16>);

paddle/fluid/operators/elementwise_div_op.cu

@@ -14,19 +14,24 @@ limitations under the License. */
 
 #define EIGEN_USE_GPU
 #include "paddle/fluid/operators/elementwise_div_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 
 REGISTER_OP_CUDA_KERNEL(
     elementwise_div,
     ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, float>,
     ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int64_t>);
+    ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int64_t>,
+    ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext,
+                              plat::float16>);
 REGISTER_OP_CUDA_KERNEL(
     elementwise_div_grad,
     ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, float>,
     ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, int64_t>,
     ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext,
-                                  int64_t>);
+                                  plat::float16>);

paddle/fluid/operators/elementwise_mul_op.cu

@@ -14,19 +14,25 @@ limitations under the License. */
 
 #define EIGEN_USE_GPU
 #include "paddle/fluid/operators/elementwise_mul_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 
 REGISTER_OP_CUDA_KERNEL(
     elementwise_mul,
     ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, float>,
     ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int64_t>);
+    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int64_t>,
+    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext,
+                              plat::float16>);
 REGISTER_OP_CUDA_KERNEL(
     elementwise_mul_grad,
     ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, float>,
     ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext,
+                                  plat::float16>,
     ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext,
                                   int64_t>);

paddle/fluid/operators/elementwise_op_function.h

@@ -350,7 +350,7 @@ static __global__ void ElemwiseGradBroadcast1CUDAKernel(
   int j = blockIdx.x;
   int i = threadIdx.x;
   int tid = threadIdx.x;
-  T val = 0;
+  T val(0);
 
   do {
     int x_offset = i * w + j;
@@ -418,7 +418,7 @@ static __global__ void ElemwiseGradBroadcast2CUDAKernel(
   int tid = threadIdx.x;
   int j = blockIdx.x;
 
-  T val = 0;
+  T val(0);
   int ttid = tid;
 
   while (true) {

paddle/fluid/operators/elementwise_sub_op.cu

@@ -14,19 +14,25 @@ limitations under the License. */
 
 #define EIGEN_USE_GPU
 #include "paddle/fluid/operators/elementwise_sub_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 
 REGISTER_OP_CUDA_KERNEL(
     elementwise_sub,
     ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, float>,
     ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, int64_t>);
+    ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, int64_t>,
+    ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext,
+                              plat::float16>);
 REGISTER_OP_CUDA_KERNEL(
     elementwise_sub_grad,
     ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext, float>,
     ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext,
+                                  plat::float16>,
     ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext,
                                   int64_t>);

paddle/fluid/operators/fc_mkldnn_op.cc

@@ -125,13 +125,16 @@ class FCMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
 
     auto input = ctx.Input<Tensor>("Input");
     auto w = ctx.Input<Tensor>("W");
+    auto bias = ctx.Input<Tensor>("Bias");
 
     PADDLE_ENFORCE(input->dims().size() == 2 || input->dims().size() == 4,
                    "Input must be with 2 or 4 dimensions, i.e. NCHW");
+    // TODO(intel friends): the native weight format is io,
+    // but the mkldnn weight format is oihw, which may need be transposed.
     PADDLE_ENFORCE(w->dims().size() == 2 || w->dims().size() == 4,
                    "Weights must be with 2 or 4 dimensions, i.e. OI or OIHW");
 
-    bool with_bias = ctx.Attr<bool>("bias_attr");
+    bool with_bias = bias != nullptr;
     MKLDNNMD<Tensor> md(input, w, with_bias);
 
     std::shared_ptr<mkldnn::inner_product_forward::primitive_desc> pd =
@@ -154,6 +157,7 @@ class FCMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     auto dst_memory = mem.dst(output_data);
     auto src_memory = mem.src(input_data);
     auto weights_memory = mem.weights(w_data);
+    // TODO(intel friends): bias memory should also be obtain from bias->data()
     auto bias_memory = mem.bias();
 
     auto forward = with_bias ? mkldnn::inner_product_forward(
@@ -216,7 +220,8 @@ class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
     const T* out_grad_data = out_grad->data<T>();
 
-    bool with_bias = ctx.Attr<bool>("bias_attr");
+    auto bias = ctx.Input<Tensor>("Bias");
+    bool with_bias = bias != nullptr;
 
     MKLDNNMD<Tensor> md(input, w, with_bias);
     MKLDNNMemory mem(&md, mkldnn_engine);

paddle/fluid/operators/fc_op.cc

@@ -14,6 +14,9 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/fc_op.h"
 #include <vector>
+#include "paddle/fluid/operators/math/blas.h"
+
+DECLARE_int32(paddle_num_threads);
 
 namespace paddle {
 namespace operators {
@@ -25,16 +28,24 @@ void FCOp::InferShape(framework::InferShapeContext* ctx) const {
                  "Out(Output) of Fully Connected should not be null.");
   PADDLE_ENFORCE(ctx->HasInput("W"),
                  "W(Input) of Fully Connected should not be null.");
-
+  // NCHW
   auto in_dims = ctx->GetInputDim("Input");
+  // IO, I=C*H*W
   auto w_dims = ctx->GetInputDim("W");
   std::vector<int64_t> output_shape({in_dims[0], w_dims[1]});
 
+  if (ctx->HasInput("Bias")) {
+    auto bias_dims = ctx->GetInputDim("Bias");
+    PADDLE_ENFORCE_EQ(bias_dims[0], 1, "The shape of Bias must be [1, dim].");
+    PADDLE_ENFORCE_EQ(bias_dims[1], w_dims[1],
+                      "The shape of Bias must be [1, dim].");
+  }
   PADDLE_ENFORCE(in_dims.size() == 2 || in_dims.size() == 4,
                  "Fully Connected input should be 2-D or 4-D tensor.");
-
-  PADDLE_ENFORCE(w_dims.size() == 2 || w_dims.size() == 4,
-                 "Fully Connected input should be 2-D or 4-D tensor.");
+  PADDLE_ENFORCE_EQ(w_dims.size(), 2UL,
+                    "Fully Connected input should be 2-D tensor.");
+  PADDLE_ENFORCE_EQ(framework::product(in_dims) / in_dims[0], w_dims[0],
+                    "Fully Connected input and weigth size do not match.");
 
   ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
   ctx->ShareLoD("Input", "Out");
@@ -42,9 +53,12 @@ void FCOp::InferShape(framework::InferShapeContext* ctx) const {
 
 framework::OpKernelType FCOp::GetExpectedKernelType(
     const framework::ExecutionContext& ctx) const {
-  framework::LibraryType library{framework::LibraryType::kMKLDNN};
-  framework::DataLayout layout{framework::DataLayout::kMKLDNN};
-
+  framework::LibraryType library = framework::LibraryType::kPlain;
+  framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+  if (ctx.Attr<bool>("use_mkldnn")) {
+    library = framework::LibraryType::kMKLDNN;
+    layout = framework::DataLayout::kMKLDNN;
+  }
   return framework::OpKernelType(
       framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
       layout, library);
@@ -60,27 +74,39 @@ void FCOpGrad::InferShape(framework::InferShapeContext* ctx) const {
   if (ctx->HasOutput(framework::GradVarName("W"))) {
     ctx->SetOutputDim(framework::GradVarName("W"), w_dims);
   }
+
+  if (ctx->HasInput("Bias")) {
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Bias")),
+                   "Should have bias grad");
+    auto bias_dims = ctx->GetInputDim("Bias");
+    ctx->SetOutputDim(framework::GradVarName("Bias"), bias_dims);
+  }
 }
 
 framework::OpKernelType FCOpGrad::GetExpectedKernelType(
     const framework::ExecutionContext& ctx) const {
-  framework::LibraryType library{framework::LibraryType::kMKLDNN};
-  framework::DataLayout layout{framework::DataLayout::kMKLDNN};
-
+  framework::LibraryType library = framework::LibraryType::kPlain;
+  framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+  if (ctx.Attr<bool>("use_mkldnn")) {
+    library = framework::LibraryType::kMKLDNN;
+    layout = framework::DataLayout::kMKLDNN;
+  }
   return framework::OpKernelType(
       framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
       layout, library);
 }
 
 void FCOpMaker::Make() {
-  AddInput("Input", "(Tensor) The input tensor of fully connected operator. ");
-  AddInput("W", "(Tensor), The second input tensor of fc op.");
+  AddInput("Input",
+           "(Tensor), The input tensor of fully connected operator with format "
+           "(NCHW). ");
+  AddInput("W", "(Tensor), The weight fc op with shape (I, O).");
+  AddInput("Bias", "(Tensor, optional) Bias vector with shape (1 x O")
+      .AsDispensable();
   AddOutput("Out", "(Tensor) The output tensor of fully connected operator. ");
   AddAttr<bool>("use_mkldnn",
                 "(bool, default false) Only used in mkldnn kernel")
       .SetDefault(false);
-  AddAttr<bool>("bias_attr", "(bool, default false) Only used in mkldnn kernel")
-      .SetDefault(false);
   AddComment(R"DOC(
   Fully Connected Operator.
 
@@ -94,9 +120,47 @@ void FCOpMaker::Make() {
 )DOC");
 }
 
+template <typename T>
+class FCOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+    auto input = ctx.Input<Tensor>("Input");
+    auto w = ctx.Input<Tensor>("W");
+    auto bias = ctx.Input<Tensor>("Bias");
+    auto output = ctx.Output<Tensor>("Out");
+    auto in_dims = input->dims();
+    auto w_dims = w->dims();
+
+    auto& dev_ctx = ctx.template device_context<platform::CPUDeviceContext>();
+    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(dev_ctx);
+    const T* input_data = input->data<T>();
+    const T* w_data = w->data<T>();
+    T* output_data = output->mutable_data<T>(ctx.GetPlace());
+
+    blas.GEMM(CblasNoTrans, CblasNoTrans, in_dims[0], w_dims[1], w_dims[0],
+              static_cast<T>(1), input_data, w_data, static_cast<T>(0),
+              output_data);
+
+    if (bias) {
+      const T* bias_data = bias->data<T>();
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
+#endif
+      for (int bs = 0; bs < in_dims[0]; bs++) {
+        blas.AXPY(w_dims[1], static_cast<T>(1), bias_data,
+                  output_data + bs * w_dims[1]);
+      }
+    }
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OPERATOR(fc, paddle::operators::FCOp, paddle::operators::FCOpMaker,
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(fc, ops::FCOp, ops::FCOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(fc_grad, paddle::operators::FCOpGrad);
+REGISTER_OPERATOR(fc_grad, ops::FCOpGrad);
+REGISTER_OP_CPU_KERNEL(fc, ops::FCOpKernel<float>, ops::FCOpKernel<double>);

paddle/fluid/operators/fill_constant_op.cc

@@ -12,48 +12,28 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/framework/data_type.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/math/math_function.h"
-#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/operators/fill_constant_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
 
-class FillConstantInferShape : public framework::InferShapeBase {
+class FillConstantOp : public framework::OperatorWithKernel {
  public:
-  void operator()(framework::InferShapeContext *ctx) const override {
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of FillConstantOp should not be null.");
-    auto &shape = ctx->Attrs().Get<std::vector<int>>("shape");
+    auto& shape = ctx->Attrs().Get<std::vector<int>>("shape");
     ctx->SetOutputDim("Out", framework::make_ddim(shape));
   }
-};
-
-class FillConstantOp : public framework::OperatorBase {
- public:
-  using framework::OperatorBase::OperatorBase;
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &dev_place) const override {
-    auto data_type =
-        static_cast<framework::proto::VarType::Type>(Attr<int>("dtype"));
-    auto value = Attr<float>("value");
-    auto force_cpu = Attr<bool>("force_cpu");
-    auto &out =
-        *scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
-    out.Resize(framework::make_ddim(Attr<std::vector<int>>("shape")));
-    if (force_cpu) {
-      auto cpu = platform::CPUPlace();
-      out.mutable_data(cpu, framework::ToTypeIndex(data_type));
-    } else {
-      out.mutable_data(dev_place, framework::ToTypeIndex(data_type));
-    }
 
-    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-    auto &dev_ctx = *pool.Get(dev_place);
-    math::set_constant(dev_ctx, &out, value);
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype")),
+        ctx.device_context());
   }
 };
 
@@ -87,6 +67,11 @@ Fill up a variable with specified constant value.
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(fill_constant, ops::FillConstantOp,
-                  ops::FillConstantInferShape, ops::FillConstantOpMaker,
+REGISTER_OPERATOR(fill_constant, ops::FillConstantOp, ops::FillConstantOpMaker,
                   paddle::framework::EmptyGradOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    fill_constant,
+    ops::FillConstantOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::FillConstantOpKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::FillConstantOpKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::FillConstantOpKernel<paddle::platform::CPUDeviceContext, int64_t>)

paddle/fluid/operators/fill_constant_op.cu.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/operators/fill_constant_op.h"
+#include "paddle/fluid/platform/float16.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    fill_constant,
+    ops::FillConstantOpKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::FillConstantOpKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::FillConstantOpKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::FillConstantOpKernel<paddle::platform::CUDADeviceContext, int64_t>,
+    ops::FillConstantOpKernel<paddle::platform::CUDADeviceContext,
+                              paddle::platform::float16>)

paddle/fluid/operators/fill_constant_op.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <vector>
+
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class FillConstantOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto data_type =
+        static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype"));
+    auto value = ctx.Attr<float>("value");
+    auto force_cpu = ctx.Attr<bool>("force_cpu");
+    auto* out = ctx.Output<framework::Tensor>("Out");
+    out->Resize(framework::make_ddim(ctx.Attr<std::vector<int>>("shape")));
+    if (force_cpu) {
+      auto cpu = platform::CPUPlace();
+      out->mutable_data(cpu, framework::ToTypeIndex(data_type));
+    } else {
+      out->mutable_data(ctx.GetPlace(), framework::ToTypeIndex(data_type));
+    }
+
+    math::set_constant(ctx.template device_context<DeviceContext>(), out,
+                       value);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/fill_op.cc

@@ -16,6 +16,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
 #include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
@@ -69,7 +70,6 @@ class FillOp : public framework::OperatorBase {
 
     framework::VisitDataType(
         dtype, FillOpVisitor(&tensor, Attr<std::vector<float>>("value")));
-
     if (!force_cpu && platform::is_gpu_place(place)) {
       // Copy tensor to out
       platform::DeviceContextPool &pool =

paddle/fluid/operators/gaussian_random_op.cu

@@ -15,6 +15,7 @@ limitations under the License. */
 #include <thrust/transform.h>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
@@ -60,6 +61,7 @@ class GPUGaussianRandomKernel : public framework::OpKernel<T> {
 }  // namespace operators
 }  // namespace paddle
 
+namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(gaussian_random,
                         paddle::operators::GPUGaussianRandomKernel<float>,
                         paddle::operators::GPUGaussianRandomKernel<double>);

paddle/fluid/operators/gru_op.cc

@@ -14,6 +14,11 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/gru_op.h"
 #include <string>
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/operators/math/detail/gru_cpu_kernel.h"
+#include "paddle/fluid/operators/math/detail/gru_kernel.h"
+
+DECLARE_int32(paddle_num_threads);
 
 namespace paddle {
 namespace operators {
@@ -211,6 +216,158 @@ class GRUGradOp : public framework::OperatorWithKernel {
   }
 };
 
+template <typename T>
+class GRUCPUKernel : public framework::OpKernel<T> {
+ public:
+  void BatchCompute(const framework::ExecutionContext& context) const {
+    using DeviceContext = paddle::platform::CPUDeviceContext;
+    auto* input = context.Input<LoDTensor>("Input");
+    auto* h0 = context.Input<Tensor>("H0");
+    auto* weight = context.Input<Tensor>("Weight");
+    const T* weight_data = weight->data<T>();
+    auto* bias = context.Input<Tensor>("Bias");
+    auto* batch_gate = context.Output<LoDTensor>("BatchGate");
+    batch_gate->mutable_data<T>(context.GetPlace());
+    auto* batch_reset_hidden_prev =
+        context.Output<LoDTensor>("BatchResetHiddenPrev");
+    batch_reset_hidden_prev->mutable_data<T>(context.GetPlace());
+    auto* batch_hidden = context.Output<LoDTensor>("BatchHidden");
+    batch_hidden->mutable_data<T>(context.GetPlace());
+    auto* hidden = context.Output<LoDTensor>("Hidden");
+    hidden->mutable_data<T>(context.GetPlace());
+
+    auto hidden_dims = hidden->dims();
+
+    bool is_reverse = context.Attr<bool>("is_reverse");
+    math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+    to_batch(dev_ctx, *input, batch_gate, true, is_reverse);
+
+    if (bias) {
+      math::RowwiseAdd<DeviceContext, T> add_bias;
+      add_bias(dev_ctx, *batch_gate, *bias, batch_gate);
+    }
+
+    int frame_size = hidden_dims[1];
+    math::GRUMetaValue<T> gru_value;
+    gru_value.gate_weight = const_cast<T*>(weight_data);
+    gru_value.state_weight =
+        const_cast<T*>(weight_data + 2 * frame_size * frame_size);
+    Tensor ordered_h0;
+
+    framework::Vector<size_t> order(batch_gate->lod()[2]);
+
+    if (h0) {
+      // Since the batch computing for GRU reorders the input sequences
+      // according to their length. The initialized cell state also needs
+      // to reorder.
+      ReorderInitState<DeviceContext, T>(
+          context.template device_context<DeviceContext>(), *h0, order,
+          &ordered_h0, true);
+      gru_value.prev_out_value = ordered_h0.data<T>();
+    } else {
+      gru_value.prev_out_value = nullptr;
+    }
+    auto batch_starts = batch_gate->lod()[0];
+    size_t seq_len = batch_starts.size() - 1;
+    auto active_node = math::detail::GetActivationType(
+        context.Attr<std::string>("activation"));
+    auto active_gate = math::detail::GetActivationType(
+        context.Attr<std::string>("gate_activation"));
+
+#ifdef PADDLE_WITH_MKLML
+    // use MKL packed to speedup GEMM
+    if (FLAGS_paddle_num_threads >= 4) {
+      auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
+      T* packed_gate = blas.GEMM_ALLOC(CblasBMatrix, 1 /*height of C*/,
+                                       frame_size * 2 /*width of weight*/,
+                                       frame_size /*height of height*/);
+      PADDLE_ENFORCE(packed_gate);
+      blas.GEMM_PACK(CblasBMatrix, CblasNoTrans, 1 /*cur bs?*/, frame_size * 2,
+                     frame_size, T(1.0), gru_value.gate_weight, frame_size * 2,
+                     packed_gate);
+      T* packed_state = blas.GEMM_ALLOC(CblasBMatrix, 1 /*height of C*/,
+                                        frame_size /*width of weight*/,
+                                        frame_size /*height of height*/);
+      PADDLE_ENFORCE(packed_state);
+      blas.GEMM_PACK(CblasBMatrix, CblasNoTrans, 1 /*cur bs?*/, frame_size,
+                     frame_size, T(1.0), gru_value.state_weight, frame_size,
+                     packed_state);
+      for (size_t n = 0; n < seq_len; n++) {
+        int bstart = static_cast<int>(batch_starts[n]);
+        int bend = static_cast<int>(batch_starts[n + 1]);
+        int cur_batch_size = bend - bstart;
+
+        Tensor gate_t = batch_gate->Slice(bstart, bend);
+        Tensor reset_hidden_prev_t =
+            batch_reset_hidden_prev->Slice(bstart, bend);
+        Tensor hidden_t = batch_hidden->Slice(bstart, bend);
+        gru_value.output_value = hidden_t.data<T>();
+        gru_value.gate_value = gate_t.data<T>();
+        gru_value.reset_output_value = reset_hidden_prev_t.data<T>();
+
+        if (gru_value.prev_out_value) {
+          blas.GEMM_COMPUTE(
+              CblasNoTrans, CblasPacked, cur_batch_size, frame_size * 2,
+              frame_size, gru_value.prev_out_value, frame_size, packed_gate,
+              frame_size * 2, T(1), gru_value.gate_value, frame_size * 3);
+        }
+
+        math::detail::forward_reset_output(
+            math::detail::forward::gru_resetOutput<T>(), gru_value, frame_size,
+            cur_batch_size, active_gate);
+
+        if (gru_value.prev_out_value) {
+          blas.GEMM_COMPUTE(
+              CblasNoTrans, CblasPacked, cur_batch_size, frame_size, frame_size,
+              gru_value.reset_output_value, frame_size, packed_state,
+              frame_size, T(1), gru_value.gate_value + frame_size * 2,
+              frame_size * 3);
+        }
+
+        math::detail::forward_final_output(
+            math::detail::forward::gru_finalOutput<T>(), gru_value, frame_size,
+            cur_batch_size, active_node);
+
+        gru_value.prev_out_value = gru_value.output_value;
+      }
+
+      blas.GEMM_FREE(packed_gate);
+      blas.GEMM_FREE(packed_state);
+    } else {
+#endif
+      for (size_t n = 0; n < seq_len; n++) {
+        int bstart = static_cast<int>(batch_starts[n]);
+        int bend = static_cast<int>(batch_starts[n + 1]);
+        int cur_batch_size = bend - bstart;
+
+        Tensor gate_t = batch_gate->Slice(bstart, bend);
+        Tensor reset_hidden_prev_t =
+            batch_reset_hidden_prev->Slice(bstart, bend);
+        Tensor hidden_t = batch_hidden->Slice(bstart, bend);
+        gru_value.output_value = hidden_t.data<T>();
+        gru_value.gate_value = gate_t.data<T>();
+        gru_value.reset_output_value = reset_hidden_prev_t.data<T>();
+
+        math::GRUUnitFunctor<DeviceContext, T>::compute(
+            dev_ctx, gru_value, frame_size, cur_batch_size, active_node,
+            active_gate);
+
+        gru_value.prev_out_value = gru_value.output_value;
+      }
+#ifdef PADDLE_WITH_MKLML
+    }
+#endif
+    math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
+    batch_hidden->set_lod(batch_gate->lod());
+    to_seq(dev_ctx, *batch_hidden, hidden);
+  }
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    BatchCompute(context);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -218,9 +375,8 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(gru, ops::GRUOp, ops::GRUOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(gru_grad, ops::GRUGradOp);
-REGISTER_OP_CPU_KERNEL(
-    gru, ops::GRUKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::GRUKernel<paddle::platform::CPUDeviceContext, double>);
+REGISTER_OP_CPU_KERNEL(gru, ops::GRUCPUKernel<float>,
+                       ops::GRUCPUKernel<double>);
 REGISTER_OP_CPU_KERNEL(
     gru_grad, ops::GRUGradKernel<paddle::platform::CPUDeviceContext, float>,
     ops::GRUGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/gru_op.cu.cc

@@ -14,6 +14,96 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/gru_op.h"
 
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class GRUKernel : public framework::OpKernel<T> {
+ public:
+  void BatchCompute(const framework::ExecutionContext& context) const {
+    auto* input = context.Input<LoDTensor>("Input");
+    auto* h0 = context.Input<Tensor>("H0");
+    auto* weight = context.Input<Tensor>("Weight");
+    const T* weight_data = weight->data<T>();
+    auto* bias = context.Input<Tensor>("Bias");
+    auto* batch_gate = context.Output<LoDTensor>("BatchGate");
+    batch_gate->mutable_data<T>(context.GetPlace());
+    auto* batch_reset_hidden_prev =
+        context.Output<LoDTensor>("BatchResetHiddenPrev");
+    batch_reset_hidden_prev->mutable_data<T>(context.GetPlace());
+    auto* batch_hidden = context.Output<LoDTensor>("BatchHidden");
+    batch_hidden->mutable_data<T>(context.GetPlace());
+    auto* hidden = context.Output<LoDTensor>("Hidden");
+    hidden->mutable_data<T>(context.GetPlace());
+
+    auto hidden_dims = hidden->dims();
+
+    bool is_reverse = context.Attr<bool>("is_reverse");
+    math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+    to_batch(dev_ctx, *input, batch_gate, true, is_reverse);
+
+    if (bias) {
+      math::RowwiseAdd<DeviceContext, T> add_bias;
+      add_bias(dev_ctx, *batch_gate, *bias, batch_gate);
+    }
+
+    int frame_size = hidden_dims[1];
+    math::GRUMetaValue<T> gru_value;
+    gru_value.gate_weight = const_cast<T*>(weight_data);
+    gru_value.state_weight =
+        const_cast<T*>(weight_data + 2 * frame_size * frame_size);
+    Tensor ordered_h0;
+
+    framework::Vector<size_t> order(batch_gate->lod()[2]);
+
+    if (h0) {
+      // Since the batch computing for GRU reorders the input sequences
+      // according to their length. The initialized cell state also needs
+      // to reorder.
+      ReorderInitState<DeviceContext, T>(
+          context.template device_context<DeviceContext>(), *h0, order,
+          &ordered_h0, true);
+      gru_value.prev_out_value = ordered_h0.data<T>();
+    } else {
+      gru_value.prev_out_value = nullptr;
+    }
+    auto batch_starts = batch_gate->lod()[0];
+    size_t num_batch = batch_starts.size() - 1;
+    auto active_node = math::detail::GetActivationType(
+        context.Attr<std::string>("activation"));
+    auto active_gate = math::detail::GetActivationType(
+        context.Attr<std::string>("gate_activation"));
+    for (size_t n = 0; n < num_batch; n++) {
+      int bstart = static_cast<int>(batch_starts[n]);
+      int bend = static_cast<int>(batch_starts[n + 1]);
+      int cur_batch_size = bend - bstart;
+
+      Tensor gate_t = batch_gate->Slice(bstart, bend);
+      Tensor reset_hidden_prev_t = batch_reset_hidden_prev->Slice(bstart, bend);
+      Tensor hidden_t = batch_hidden->Slice(bstart, bend);
+      gru_value.output_value = hidden_t.data<T>();
+      gru_value.gate_value = gate_t.data<T>();
+      gru_value.reset_output_value = reset_hidden_prev_t.data<T>();
+      math::GRUUnitFunctor<DeviceContext, T>::compute(
+          dev_ctx, gru_value, frame_size, cur_batch_size, active_node,
+          active_gate);
+      gru_value.prev_out_value = gru_value.output_value;
+    }
+
+    math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
+    batch_hidden->set_lod(batch_gate->lod());
+    to_seq(dev_ctx, *batch_hidden, hidden);
+  }
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    BatchCompute(context);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     gru, ops::GRUKernel<paddle::platform::CUDADeviceContext, float>,

paddle/fluid/operators/gru_op.h

@@ -37,90 +37,6 @@ inline void ReorderInitState(const DeviceContext& ctx,
   row_shuffle(ctx, src, index_lod, dst, indexed_src);
 }
 
-template <typename DeviceContext, typename T>
-class GRUKernel : public framework::OpKernel<T> {
- public:
-  void BatchCompute(const framework::ExecutionContext& context) const {
-    auto* input = context.Input<LoDTensor>("Input");
-    auto* h0 = context.Input<Tensor>("H0");
-    auto* weight = context.Input<Tensor>("Weight");
-    const T* weight_data = weight->data<T>();
-    auto* bias = context.Input<Tensor>("Bias");
-    auto* batch_gate = context.Output<LoDTensor>("BatchGate");
-    batch_gate->mutable_data<T>(context.GetPlace());
-    auto* batch_reset_hidden_prev =
-        context.Output<LoDTensor>("BatchResetHiddenPrev");
-    batch_reset_hidden_prev->mutable_data<T>(context.GetPlace());
-    auto* batch_hidden = context.Output<LoDTensor>("BatchHidden");
-    batch_hidden->mutable_data<T>(context.GetPlace());
-    auto* hidden = context.Output<LoDTensor>("Hidden");
-    hidden->mutable_data<T>(context.GetPlace());
-
-    auto hidden_dims = hidden->dims();
-
-    bool is_reverse = context.Attr<bool>("is_reverse");
-    math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
-    auto& dev_ctx = context.template device_context<DeviceContext>();
-    to_batch(dev_ctx, *input, batch_gate, true, is_reverse);
-
-    if (bias) {
-      math::RowwiseAdd<DeviceContext, T> add_bias;
-      add_bias(dev_ctx, *batch_gate, *bias, batch_gate);
-    }
-
-    int frame_size = hidden_dims[1];
-    math::GRUMetaValue<T> gru_value;
-    gru_value.gate_weight = const_cast<T*>(weight_data);
-    gru_value.state_weight =
-        const_cast<T*>(weight_data + 2 * frame_size * frame_size);
-    Tensor ordered_h0;
-
-    framework::Vector<size_t> order(batch_gate->lod()[2]);
-
-    if (h0) {
-      // Since the batch computing for GRU reorders the input sequences
-      // according to their length. The initialized cell state also needs
-      // to reorder.
-      ReorderInitState<DeviceContext, T>(
-          context.template device_context<DeviceContext>(), *h0, order,
-          &ordered_h0, true);
-      gru_value.prev_out_value = ordered_h0.data<T>();
-    } else {
-      gru_value.prev_out_value = nullptr;
-    }
-    auto batch_starts = batch_gate->lod()[0];
-    size_t num_batch = batch_starts.size() - 1;
-    auto active_node = math::detail::GetActivationType(
-        context.Attr<std::string>("activation"));
-    auto active_gate = math::detail::GetActivationType(
-        context.Attr<std::string>("gate_activation"));
-    for (size_t n = 0; n < num_batch; n++) {
-      int bstart = static_cast<int>(batch_starts[n]);
-      int bend = static_cast<int>(batch_starts[n + 1]);
-      int cur_batch_size = bend - bstart;
-
-      Tensor gate_t = batch_gate->Slice(bstart, bend);
-      Tensor reset_hidden_prev_t = batch_reset_hidden_prev->Slice(bstart, bend);
-      Tensor hidden_t = batch_hidden->Slice(bstart, bend);
-      gru_value.output_value = hidden_t.data<T>();
-      gru_value.gate_value = gate_t.data<T>();
-      gru_value.reset_output_value = reset_hidden_prev_t.data<T>();
-      math::GRUUnitFunctor<DeviceContext, T>::compute(
-          dev_ctx, gru_value, frame_size, cur_batch_size, active_node,
-          active_gate);
-      gru_value.prev_out_value = gru_value.output_value;
-    }
-
-    math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
-    batch_hidden->set_lod(batch_gate->lod());
-    to_seq(dev_ctx, *batch_hidden, hidden);
-  }
-
-  void Compute(const framework::ExecutionContext& context) const override {
-    BatchCompute(context);
-  }
-};
-
 template <typename DeviceContext, typename T>
 class GRUGradKernel : public framework::OpKernel<T> {
  public:

paddle/fluid/operators/listen_and_serv_op.cc

@@ -123,8 +123,11 @@ void ListenAndServOp::RunSyncLoop(
       optimize_prepared.begin(),
       std::shared_ptr<framework::ExecutorPrepareContext>(nullptr));
 
+  // Trainers will get all parameters from pserver in the
+  // startup program, so we will wait RequestGet first
+  rpc_service_->SetCond(distributed::kRequestGet);
+  rpc_service_->WaitBarrier(distributed::kRequestGet);
   rpc_service_->ResetBarrierCounter();
-
   while (true) {
     rpc_service_->Profiler().OneStep();
     // Get from multiple trainers, we don't care about the order in which

paddle/fluid/operators/math/blas.h

@@ -90,6 +90,25 @@ class Blas {
   void GEMM(bool transA, bool transB, int M, int N, int K, T alpha, const T* A,
             int lda, const T* B, int ldb, T beta, T* C, int ldc) const;
 
+#ifdef PADDLE_WITH_MKLML
+  template <typename T>
+  T* GEMM_ALLOC(const CBLAS_IDENTIFIER id, const int M, const int N,
+                const int K) const;
+
+  template <typename T>
+  void GEMM_PACK(const CBLAS_IDENTIFIER id, const CBLAS_TRANSPOSE trans, int M,
+                 int N, int K, const T alpha, const T* src, const int ld,
+                 T* dst) const;
+
+  template <typename T>
+  void GEMM_COMPUTE(int transA, int transB, int M, int N, int K, const T* A,
+                    const int lda, const T* B, const int ldb, T beta, T* C,
+                    const int ldc) const;
+
+  template <typename T>
+  void GEMM_FREE(T* data) const;
+#endif
+
   template <typename T>
   void MatMul(const framework::Tensor& mat_a, bool trans_a,
               const framework::Tensor& mat_b, bool trans_b, T alpha,
@@ -146,6 +165,28 @@ class BlasT : private Blas<DeviceContext> {
     Base()->template GEMM<T>(args...);
   }
 
+#ifdef PADDLE_WITH_MKLML
+  template <typename... ARGS>
+  T* GEMM_ALLOC(ARGS... args) const {
+    return Base()->template GEMM_ALLOC<T>(args...);
+  }
+
+  template <typename... ARGS>
+  void GEMM_PACK(ARGS... args) const {
+    Base()->template GEMM_PACK<T>(args...);
+  }
+
+  template <typename... ARGS>
+  void GEMM_COMPUTE(ARGS... args) const {
+    Base()->template GEMM_COMPUTE<T>(args...);
+  }
+
+  template <typename... ARGS>
+  void GEMM_FREE(ARGS... args) const {
+    Base()->template GEMM_FREE<T>(args...);
+  }
+#endif
+
   template <typename... ARGS>
   void MatMul(ARGS... args) const {
     Base()->template MatMul<T>(args...);

paddle/fluid/operators/math/blas_impl.h

@@ -31,6 +31,26 @@ struct CBlas<float> {
     platform::dynload::cblas_sgemm(args...);
   }
 
+  template <typename... ARGS>
+  static float *GEMM_ALLOC(ARGS... args) {
+    return platform::dynload::cblas_sgemm_alloc(args...);
+  }
+
+  template <typename... ARGS>
+  static void GEMM_PACK(ARGS... args) {
+    platform::dynload::cblas_sgemm_pack(args...);
+  }
+
+  template <typename... ARGS>
+  static void GEMM_COMPUTE(ARGS... args) {
+    platform::dynload::cblas_sgemm_compute(args...);
+  }
+
+  template <typename... ARGS>
+  static void GEMM_FREE(ARGS... args) {
+    platform::dynload::cblas_sgemm_free(args...);
+  }
+
 #ifdef PADDLE_WITH_LIBXSMM
   template <typename... ARGS>
   static void SMM_GEMM(ARGS... args) {
@@ -71,6 +91,26 @@ struct CBlas<double> {
     platform::dynload::cblas_dgemm(args...);
   }
 
+  template <typename... ARGS>
+  static double *GEMM_ALLOC(ARGS... args) {
+    return platform::dynload::cblas_dgemm_alloc(args...);
+  }
+
+  template <typename... ARGS>
+  static void GEMM_PACK(ARGS... args) {
+    platform::dynload::cblas_dgemm_pack(args...);
+  }
+
+  template <typename... ARGS>
+  static void GEMM_COMPUTE(ARGS... args) {
+    platform::dynload::cblas_dgemm_compute(args...);
+  }
+
+  template <typename... ARGS>
+  static void GEMM_FREE(ARGS... args) {
+    platform::dynload::cblas_dgemm_free(args...);
+  }
+
 #ifdef PADDLE_WITH_LIBXSMM
   template <typename... ARGS>
   static void SMM_GEMM(ARGS... args) {
@@ -224,6 +264,41 @@ inline void GEMM_WARP(CBLAS_ORDER order, CBLAS_TRANSPOSE transA,
                  beta, C, ldc);
 }
 
+#ifdef PADDLE_WITH_MKLML
+template <>
+template <typename T>
+T *Blas<platform::CPUDeviceContext>::GEMM_ALLOC(const CBLAS_IDENTIFIER id,
+                                                const int M, const int N,
+                                                const int K) const {
+  return CBlas<T>::GEMM_ALLOC(id, M, N, K);
+}
+
+template <>
+template <typename T>
+void Blas<platform::CPUDeviceContext>::GEMM_PACK(const CBLAS_IDENTIFIER id,
+                                                 const CBLAS_TRANSPOSE trans,
+                                                 int M, int N, int K,
+                                                 const T alpha, const T *src,
+                                                 const int ld, T *dst) const {
+  CBlas<T>::GEMM_PACK(CblasRowMajor, id, trans, M, N, K, alpha, src, ld, dst);
+}
+
+template <>
+template <typename T>
+void Blas<platform::CPUDeviceContext>::GEMM_COMPUTE(
+    int transA, int transB, int M, int N, int K, const T *A, const int lda,
+    const T *B, const int ldb, T beta, T *C, const int ldc) const {
+  CBlas<T>::GEMM_COMPUTE(CblasRowMajor, transA, transB, M, N, K, A, lda, B, ldb,
+                         beta, C, ldc);
+}
+
+template <>
+template <typename T>
+void Blas<platform::CPUDeviceContext>::GEMM_FREE(T *data) const {
+  CBlas<T>::GEMM_FREE(data);
+}
+#endif
+
 template <>
 template <typename T>
 void Blas<platform::CPUDeviceContext>::GEMM(CBLAS_TRANSPOSE transA,

paddle/fluid/operators/math/cross_entropy.cu

@@ -15,11 +15,25 @@ limitations under the License. */
 #include "paddle/fluid/operators/math/cross_entropy.h"
 #include "paddle/fluid/platform/cuda_device_function.h"
 #include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
 namespace math {
 
+template <typename T>
+HOSTDEVICE T log(const T& val) {
+  return std::log(val);
+}
+
+template <>
+HOSTDEVICE platform::float16 log(const platform::float16& val) {
+  // strage bug, hlog is not exists.
+  return static_cast<float16>(0);
+  // half tmp = static_cast<half>(val);
+  // return static_cast<platform::float16>(hlog(tmp));
+}
+
 namespace {
 template <typename T>
 __global__ void CrossEntropyKernel(T* Y, const T* X, const int64_t* label,
@@ -35,12 +49,12 @@ template <typename T>
 __global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
                                        const int class_num) {
   int tid = threadIdx.x;
-  T val = 0;
+  T val(0);
 
   int idx = blockIdx.x * class_num + tid;
   int end = blockIdx.x * class_num + class_num;
   for (; idx < end; idx += blockDim.x) {
-    val += math::TolerableValue<T>()(std::log(X[idx])) * label[idx];
+    val += math::TolerableValue<T>()(log(X[idx])) * label[idx];
   }
 
   val = paddle::platform::reduceSum(val, tid, blockDim.x);
@@ -84,6 +98,8 @@ class CrossEntropyFunctor<platform::CUDADeviceContext, T> {
 
 template class CrossEntropyFunctor<platform::CUDADeviceContext, float>;
 template class CrossEntropyFunctor<platform::CUDADeviceContext, double>;
+template class CrossEntropyFunctor<platform::CUDADeviceContext,
+                                   platform::float16>;
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/math/cross_entropy.h

@@ -13,8 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <limits>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/platform/float16.h"
 #include "paddle/fluid/platform/hostdevice.h"
 
 namespace paddle {
@@ -33,6 +35,21 @@ struct TolerableValue {
   }
 };
 
+// float16 value clip behave different.
+using paddle::platform::float16;
+using paddle::platform::isfinite;
+template <>
+struct TolerableValue<float16> {
+  HOSTDEVICE float16 operator()(const float16& x) const {
+    if (isfinite(x))
+      return x;
+    else if (x > static_cast<float16>(0))
+      return std::numeric_limits<float16>::max();
+    else
+      return std::numeric_limits<float16>::min();
+  }
+};
+
 template <typename DeviceContext, typename T>
 class CrossEntropyFunctor {
  public:

paddle/fluid/operators/math/selected_rows_functor.cu

@@ -18,6 +18,7 @@ limitations under the License. */
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/operators/math/selected_rows_functor.h"
 #include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
@@ -76,6 +77,7 @@ struct SelectedRowsAdd<platform::CUDADeviceContext, T> {
 
 template struct SelectedRowsAdd<platform::CUDADeviceContext, float>;
 template struct SelectedRowsAdd<platform::CUDADeviceContext, double>;
+template struct SelectedRowsAdd<platform::CUDADeviceContext, platform::float16>;
 
 namespace {
 template <typename T, int block_size>
@@ -120,7 +122,7 @@ struct SelectedRowsAddTensor<platform::CUDADeviceContext, T> {
     auto* out_data = output->data<T>();
 
     SetConstant<platform::CUDADeviceContext, T> functor;
-    functor(context, output, 0.0);
+    functor(context, output, static_cast<T>(0));
 
     const int block_size = 256;
     dim3 threads(block_size, 1);
@@ -138,6 +140,8 @@ struct SelectedRowsAddTensor<platform::CUDADeviceContext, T> {
 
 template struct SelectedRowsAddTensor<platform::CUDADeviceContext, float>;
 template struct SelectedRowsAddTensor<platform::CUDADeviceContext, double>;
+template struct SelectedRowsAddTensor<platform::CUDADeviceContext,
+                                      platform::float16>;
 
 template <typename T>
 struct SelectedRowsAddTo<platform::CUDADeviceContext, T> {
@@ -177,6 +181,8 @@ template struct SelectedRowsAddTo<platform::CUDADeviceContext, float>;
 template struct SelectedRowsAddTo<platform::CUDADeviceContext, double>;
 template struct SelectedRowsAddTo<platform::CUDADeviceContext, int>;
 template struct SelectedRowsAddTo<platform::CUDADeviceContext, int64_t>;
+template struct SelectedRowsAddTo<platform::CUDADeviceContext,
+                                  platform::float16>;
 
 namespace {
 template <typename T, int block_size>
@@ -229,6 +235,8 @@ template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, float>;
 template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, double>;
 template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, int>;
 template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, int64_t>;
+template struct SelectedRowsAddToTensor<platform::CUDADeviceContext,
+                                        platform::float16>;
 
 namespace scatter {
 
@@ -276,7 +284,7 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
         context.GetPlace());
 
     math::SetConstant<platform::CUDADeviceContext, T> constant_functor;
-    constant_functor(context, out.mutable_value(), 0.0);
+    constant_functor(context, out.mutable_value(), static_cast<T>(0));
 
     auto* out_data = out.mutable_value()->data<T>();
     auto* input_data = input.value().data<T>();
@@ -300,6 +308,7 @@ template struct MergeAdd<platform::CUDADeviceContext, float>;
 template struct MergeAdd<platform::CUDADeviceContext, double>;
 template struct MergeAdd<platform::CUDADeviceContext, int>;
 template struct MergeAdd<platform::CUDADeviceContext, int64_t>;
+template struct MergeAdd<platform::CUDADeviceContext, platform::float16>;
 
 template <typename T, int block_size>
 __global__ void UpdateToTensorKernel(const T* selected_rows,

paddle/fluid/operators/math/softmax.cu

@@ -94,12 +94,15 @@ void SoftmaxGradCUDNNFunctor<T>::operator()(
 template class SoftmaxCUDNNFunctor<platform::float16>;
 template class SoftmaxCUDNNFunctor<float>;
 template class SoftmaxCUDNNFunctor<double>;
+template class SoftmaxGradCUDNNFunctor<platform::float16>;
 template class SoftmaxGradCUDNNFunctor<float>;
 template class SoftmaxGradCUDNNFunctor<double>;
 
 template class SoftmaxFunctor<platform::CUDADeviceContext, platform::float16>;
 template class SoftmaxFunctor<platform::CUDADeviceContext, float>;
 template class SoftmaxFunctor<platform::CUDADeviceContext, double>;
+template class SoftmaxGradFunctor<platform::CUDADeviceContext,
+                                  platform::float16>;
 template class SoftmaxGradFunctor<platform::CUDADeviceContext, float>;
 template class SoftmaxGradFunctor<platform::CUDADeviceContext, double>;
 

paddle/fluid/operators/mean_op.cu

@@ -12,14 +12,16 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#define EIGEN_USE_GPU
-
 #include "paddle/fluid/operators/mean_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(
     mean, ops::MeanKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::MeanKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::MeanKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::MeanKernel<paddle::platform::CUDADeviceContext, plat::float16>);
 REGISTER_OP_CUDA_KERNEL(
     mean_grad, ops::MeanGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::MeanGradKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::MeanGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::MeanGradKernel<paddle::platform::CUDADeviceContext, plat::float16>);

paddle/fluid/operators/mean_op.h

@@ -55,7 +55,7 @@ class MeanGradKernel : public framework::OpKernel<T> {
     IG->mutable_data<T>(context.GetPlace());
 
     T ig_size = static_cast<T>(IG->numel());
-    Eigen::DSizes<int, 1> bcast(ig_size);
+    Eigen::DSizes<int, 1> bcast(static_cast<int>(ig_size));
 
     EigenVector<T>::Flatten(*IG).device(
         *context.template device_context<DeviceContext>().eigen_device()) =

paddle/fluid/operators/mul_op.cu.cc

@@ -20,6 +20,7 @@ namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(mul, ops::MulKernel<plat::CUDADeviceContext, float>,
                         ops::MulKernel<plat::CUDADeviceContext, double>,
                         ops::MulKernel<plat::CUDADeviceContext, plat::float16>);
-REGISTER_OP_CUDA_KERNEL(mul_grad,
-                        ops::MulGradKernel<plat::CUDADeviceContext, float>,
-                        ops::MulGradKernel<plat::CUDADeviceContext, double>);
+REGISTER_OP_CUDA_KERNEL(
+    mul_grad, ops::MulGradKernel<plat::CUDADeviceContext, float>,
+    ops::MulGradKernel<plat::CUDADeviceContext, double>,
+    ops::MulGradKernel<plat::CUDADeviceContext, plat::float16>);

paddle/fluid/operators/pool_cudnn_op.cu.cc

@@ -174,7 +174,8 @@ REGISTER_OP_KERNEL(pool2d, CUDNN, plat::CUDAPlace,
                    ops::PoolCUDNNOpKernel<plat::float16>);
 REGISTER_OP_KERNEL(pool2d_grad, CUDNN, plat::CUDAPlace,
                    ops::PoolCUDNNGradOpKernel<float>,
-                   ops::PoolCUDNNGradOpKernel<double>);
+                   ops::PoolCUDNNGradOpKernel<double>,
+                   ops::PoolCUDNNGradOpKernel<plat::float16>);
 
 REGISTER_OP_KERNEL(pool3d, CUDNN, plat::CUDAPlace,
                    ops::PoolCUDNNOpKernel<float>,
@@ -182,4 +183,5 @@ REGISTER_OP_KERNEL(pool3d, CUDNN, plat::CUDAPlace,
                    ops::PoolCUDNNOpKernel<plat::float16>);
 REGISTER_OP_KERNEL(pool3d_grad, CUDNN, plat::CUDAPlace,
                    ops::PoolCUDNNGradOpKernel<float>,
-                   ops::PoolCUDNNGradOpKernel<double>);
+                   ops::PoolCUDNNGradOpKernel<double>,
+                   ops::PoolCUDNNGradOpKernel<plat::float16>);

paddle/fluid/operators/prelu_op.cc

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
-
     http://www.apache.org/licenses/LICENSE-2.0
-
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -26,14 +23,40 @@ class PReluOp : public framework::OperatorWithKernel {
       : OperatorWithKernel(type, inputs, outputs, attrs) {}
 
   void InferShape(framework::InferShapeContext *ctx) const override {
+    std::string mode = ctx->Attrs().Get<std::string>("mode");
+
+    auto x_dim = ctx->GetInputDim("X");
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
     PADDLE_ENFORCE(ctx->HasInput("Alpha"), "Input(Alpha) should not be null");
-    PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == 1,
-                   "Size of weight Alpha must be one.");
+
     PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
-    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+    if (mode == "all") {
+      PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == 1,
+                     "For mode 'all', size of weight Alpha must be one.");
+    } else if (mode == "channel") {
+      PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == x_dim[1],
+                     "For channel-wise mode, size of weight Alpha must be "
+                     "equal to the number of channels, should be %d",
+                     x_dim[1]);
+    } else if (mode == "element") {
+      PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == product(x_dim),
+                     "For element-wise mode, size of weight Alpha must be "
+                     "equal to the number of input, should be %d",
+                     product(x_dim));
+    } else {
+      PADDLE_THROW("Unkown mode %s", mode);
+    }
+    ctx->SetOutputDim("Out", x_dim);
     ctx->ShareLoD("X", /*->*/ "Out");
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        platform::CPUPlace());
+  }
 };
 
 class PReluOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -44,9 +67,7 @@ class PReluOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("Out", "The output tensor of prelu operator.");
     AddComment(R"DOC(
 PRelu Operator.
-
 The equation is:
-
 $$
 f(x) =
 \begin{cases}
@@ -54,11 +75,15 @@ f(x) =
 x,         \qquad  \text{if} \ x >= 0
 \end{cases}
 $$
-
 The input `X` can carry the LoD (Level of Details) information,
 or not. And the output shares the LoD information with input `X`.
-
+There are modes: 
+  all: all elements share same weight
+  channel: elements in a channel share same weight
+  element: each element has a weight 
 )DOC");
+    AddAttr<std::string>("mode", "The mode for inputs to share weights.")
+        .SetDefault("all");
   }
 };
 
@@ -71,9 +96,23 @@ class PReluGradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                    "Input(Out@GRAD) should not be null");
-    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
-    ctx->SetOutputDim(framework::GradVarName("Alpha"),
-                      ctx->GetInputDim("Alpha"));
+    auto x_grad_name = framework::GradVarName("X");
+    auto alpha_grad_name = framework::GradVarName("Alpha");
+
+    if (ctx->HasOutput(x_grad_name)) {
+      ctx->SetOutputDim(x_grad_name, ctx->GetInputDim("X"));
+    }
+    if (ctx->HasOutput(alpha_grad_name)) {
+      ctx->SetOutputDim(alpha_grad_name, ctx->GetInputDim("Alpha"));
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        platform::CPUPlace());
   }
 };
 

paddle/fluid/operators/prelu_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include "paddle/fluid/operators/prelu_op.h"
-
-REGISTER_OP_CUDA_KERNEL(
-    prelu,
-    paddle::operators::PReluKernel<paddle::platform::CUDADeviceContext, float>);
-REGISTER_OP_CUDA_KERNEL(prelu_grad,
-                        paddle::operators::PReluGradKernel<
-                            paddle::platform::CUDADeviceContext, float>);

paddle/fluid/operators/prelu_op.h

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
-
     http://www.apache.org/licenses/LICENSE-2.0
-
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -13,32 +10,16 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <string>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/platform/transform.h"
-
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
 using platform::Transform;
 
-template <typename T>
-class PReluFunctor {
- public:
-  explicit PReluFunctor(const T* alpha) : alpha_(alpha) {}
-
-  HOSTDEVICE T operator()(const T& x) const {
-    if (x > 0)
-      return x;
-    else
-      return x * (*alpha_);
-  }
-
- private:
-  const T* alpha_;
-};
-
 template <typename DeviceContext, typename T>
 class PReluKernel : public framework::OpKernel<T> {
  public:
@@ -50,53 +31,93 @@ class PReluKernel : public framework::OpKernel<T> {
     const T* x_ptr = x->data<T>();
     T* o_ptr = out->mutable_data<T>(context.GetPlace());
 
-    auto* alpha_ptr = alpha->data<T>();
+    const T* alpha_ptr = alpha->data<T>();
+    std::string mode = context.Attr<std::string>("mode");
 
     int numel = x->numel();
-
-    Transform<DeviceContext> trans;
-    trans(context.template device_context<DeviceContext>(), x_ptr,
-          x_ptr + numel, o_ptr, PReluFunctor<T>(alpha_ptr));
-  }
-};
-
-template <typename T>
-class PReluGradFunctor {
- public:
-  explicit PReluGradFunctor(const T* alpha) : alpha_(alpha) {}
-
-  HOSTDEVICE T operator()(const T& out, const T& dout) const {
-    if (out > 0)
-      return dout;
-    else
-      return dout * (*alpha_);
+    auto dim = x->dims();
+    int index = 0;
+    int i = 0;
+    int temp = 0;
+    if (mode == "channel") {
+      for (i = 0; i < numel; i++) {
+        temp = numel / (dim[0] * dim[1]);
+        index = (i / temp) % dim[1];
+        o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[index] * x_ptr[i];
+      }
+    } else if (mode == "element") {
+      for (i = 0; i < numel; i++) {
+        o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[i] * x_ptr[i];
+      }
+    } else {
+      for (i = 0; i < numel; i++) {
+        o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[0] * x_ptr[i];
+      }
+    }
   }
-
- private:
-  const T* alpha_;
 };
 
 template <typename DeviceContext, typename T>
 class PReluGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<Tensor>("X");
     auto* dx = context.Output<Tensor>(framework::GradVarName("X"));
     auto* dout = context.Input<Tensor>(framework::GradVarName("Out"));
-
+    auto* dalpha = context.Output<Tensor>(framework::GradVarName("Alpha"));
     auto* out = context.Input<Tensor>("Out");
     auto* alpha = context.Input<Tensor>("Alpha");
-    auto* alpha_ptr = alpha->data<T>();
-
-    T* dx_ptr = dx->mutable_data<T>(context.GetPlace());
+    const T* alpha_ptr = alpha->data<T>();
+    const T* x_ptr = x->data<T>();
     const T* dout_ptr = dout->data<T>();
     const T* out_ptr = out->data<T>();
-    int numel = dx->numel();
-
-    Transform<DeviceContext> trans;
-    trans(context.template device_context<DeviceContext>(), out_ptr,
-          out_ptr + numel, dout_ptr, dx_ptr, PReluGradFunctor<T>(alpha_ptr));
-
-    // TODO(Zhuoyuan): add dalpha upgrade when GPU kernels ready
+    std::string mode = context.Attr<std::string>("mode");
+    int numel = x->numel();
+    auto dim = x->dims();
+    int index = 0;
+    int i = 0;
+    int temp = 0;
+    if (dx) {
+      T* dx_ptr = dx->mutable_data<T>(context.GetPlace());
+      if (mode == "channel") {
+        for (i = 0; i < numel; i++) {
+          temp = numel / (dim[0] * dim[1]);
+          index = (i / temp) % dim[1];
+          dx_ptr[i] =
+              out_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[index] * dout_ptr[i];
+        }
+      } else if (mode == "element") {
+        for (i = 0; i < numel; i++) {
+          dx_ptr[i] = out_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[i] * dout_ptr[i];
+        }
+      } else {
+        for (i = 0; i < numel; i++) {
+          dx_ptr[i] = out_ptr[i] > 0 ? dout_ptr[i] : alpha_ptr[0] * dout_ptr[i];
+        }
+      }
+    }
+
+    index = 0;
+    if (dalpha) {
+      T* dalpha_ptr = dalpha->mutable_data<T>(context.GetPlace());
+      if (mode == "channel") {
+        for (i = 0; i < numel; i++) {
+          temp = numel / (dim[0] * dim[1]);
+          index = (i / temp) % dim[1];
+          dalpha_ptr[index] += out_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
+        }
+      } else if (mode == "element") {
+        for (i = 0; i < numel; i++) {
+          dalpha_ptr[i] += out_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
+        }
+      } else {
+        for (i = 0; i < numel; i++) {
+          dalpha_ptr[0] += out_ptr[i] > 0 ? 0 : x_ptr[i] * dout_ptr[i];
+        }
+      }
+    }
+
+    // TODO(Guanzhong): add GPU kernels
   }
 };
 

paddle/fluid/operators/scale_op.cu

@@ -13,11 +13,15 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/scale_op.h"
+#include "paddle/fluid/platform/float16.h"
 
+namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(
     scale,
     paddle::operators::ScaleKernel<paddle::platform::CUDADeviceContext, float>,
     paddle::operators::ScaleKernel<paddle::platform::CUDADeviceContext, double>,
     paddle::operators::ScaleKernel<paddle::platform::CUDADeviceContext, int>,
     paddle::operators::ScaleKernel<paddle::platform::CUDADeviceContext,
-                                   int64_t>);
+                                   int64_t>,
+    paddle::operators::ScaleKernel<paddle::platform::CUDADeviceContext,
+                                   plat::float16>);

paddle/fluid/operators/scatter_op.h

@@ -35,7 +35,7 @@ class ScatterOpKernel : public framework::OpKernel<T> {
     auto *Out = ctx.Output<Tensor>("Out");
 
     // In place output: Out = X, Out[Ids] += Updates
-    Out->ShareDataWith(*X);
+    framework::TensorCopySync(*X, ctx.GetPlace(), Out);
     // Apply ScatterUpdate: Out[index] += Updates[:]
     ScatterAssign<T>(ctx.device_context(), *Updates, *Ids, Out);
   }
@@ -53,7 +53,7 @@ class ScatterGradientOpKernel : public framework::OpKernel<T> {
     auto *dOut = ctx.Input<Tensor>(framework::GradVarName("Out"));
 
     // In place gradient: dX = dO
-    dX->ShareDataWith(*dOut);
+    framework::TensorCopySync(*dOut, ctx.GetPlace(), dX);
     dUpdates->mutable_data<T>(ctx.GetPlace());
     // Gradient by Gather: dUpdates += dO[Ids]
     CPUGather<T>(ctx.device_context(), *dOut, *Ids, dUpdates);

paddle/fluid/operators/softmax_cudnn_op.cu.cc

@@ -78,4 +78,5 @@ REGISTER_OP_KERNEL(softmax, CUDNN, plat::CUDAPlace,
                    ops::SoftmaxCUDNNKernel<float>,
                    ops::SoftmaxCUDNNKernel<plat::float16>);
 REGISTER_OP_KERNEL(softmax_grad, CUDNN, plat::CUDAPlace,
-                   ops::SoftmaxGradCUDNNKernel<float>);
+                   ops::SoftmaxGradCUDNNKernel<float>,
+                   ops::SoftmaxGradCUDNNKernel<plat::float16>);

paddle/fluid/operators/softmax_op.cu.cc

@@ -23,4 +23,5 @@ REGISTER_OP_CUDA_KERNEL(
     ops::SoftmaxKernel<plat::CUDADeviceContext, plat::float16>);
 REGISTER_OP_CUDA_KERNEL(
     softmax_grad, ops::SoftmaxGradKernel<plat::CUDADeviceContext, float>,
-    ops::SoftmaxGradKernel<plat::CUDADeviceContext, double>);
+    ops::SoftmaxGradKernel<plat::CUDADeviceContext, double>,
+    ops::SoftmaxGradKernel<plat::CUDADeviceContext, plat::float16>);

paddle/fluid/operators/sum_op.cu

@@ -11,10 +11,13 @@ limitations under the License. */
 
 #define EIGEN_USE_GPU
 #include "paddle/fluid/operators/sum_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(
     sum, ops::SumKernel<paddle::platform::CUDADeviceContext, float>,
     ops::SumKernel<paddle::platform::CUDADeviceContext, double>,
     ops::SumKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::SumKernel<paddle::platform::CUDADeviceContext, int64_t>);
+    ops::SumKernel<paddle::platform::CUDADeviceContext, int64_t>,
+    ops::SumKernel<paddle::platform::CUDADeviceContext, plat::float16>);

paddle/fluid/operators/sum_op.h

@@ -46,7 +46,7 @@ class SumKernel : public framework::OpKernel<T> {
       if (!in_place) {
         math::SetConstant<DeviceContext, T> constant_functor;
         constant_functor(context.template device_context<DeviceContext>(), out,
-                         0.0);
+                         static_cast<T>(0));
       }
 
       math::SelectedRowsAddToTensor<DeviceContext, T> functor;

paddle/fluid/operators/top_k_op.cu

@@ -11,16 +11,19 @@ distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
+#include <limits>
 
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/top_k_op.h"
 #include "paddle/fluid/platform/assert.h"
 #include "paddle/fluid/platform/cuda_device_function.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+using paddle::platform::float16;
 
 template <typename T>
 struct Pair {
@@ -32,6 +35,11 @@ struct Pair {
     id = id;
   }
 
+  __device__ __forceinline__ void clear() {
+    v = -INFINITY;
+    id = -1;
+  }
+
   __device__ __forceinline__ void operator=(const Pair<T>& in) {
     v = in.v;
     id = in.id;
@@ -53,6 +61,12 @@ struct Pair {
   int64_t id;
 };
 
+template <>
+__device__ __forceinline__ void Pair<float16>::clear() {
+  v = platform::raw_uint16_to_float16(0x400);
+  id = -1;
+}
+
 template <typename T>
 __device__ __forceinline__ void AddTo(Pair<T> topk[], const Pair<T>& p,
                                       int beam_size) {
@@ -150,7 +164,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
         if (k < MaxLength - (*beam)) {
           topk[k] = topk[k + *beam];
         } else {
-          topk[k].set(-INFINITY, -1);
+          topk[k].clear();
         }
       }
       if (!(*is_empty)) {
@@ -160,7 +174,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
     }
 
     *max = topk[MaxLength - 1];
-    if ((*max).v == -1) *is_empty = true;
+    if ((*max).v == static_cast<T>(-1)) *is_empty = true;
     *beam = 0;
   }
 }
@@ -181,7 +195,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
         if (k < MaxLength - *beam) {
           topk[k] = topk[k + *beam];
         } else {
-          topk[k].set(-INFINITY, -1);
+          topk[k].set(std::numeric_limits<T>::min(), -1);
         }
       }
       if (!(*is_empty)) {
@@ -273,7 +287,7 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
   bool firststep = true;
 
   for (int k = 0; k < MaxLength; k++) {
-    topk[k].set(-INFINITY, -1);
+    topk[k].clear();
   }
   while (k) {
     ThreadGetTopK<T, MaxLength, BlockSize>(topk, &beam, k,
@@ -325,5 +339,7 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP_CUDA_KERNEL(top_k, paddle::operators::TopkOpCUDAKernel<float>,
-                        paddle::operators::TopkOpCUDAKernel<double>);
+REGISTER_OP_CUDA_KERNEL(
+    top_k, paddle::operators::TopkOpCUDAKernel<float>,
+    paddle::operators::TopkOpCUDAKernel<double>,
+    paddle::operators::TopkOpCUDAKernel<paddle::platform::float16>);

paddle/fluid/operators/uniform_random_op.cu

@@ -11,10 +11,14 @@ distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
+#include <glog/logging.h>
 #include <thrust/random.h>
 #include <thrust/transform.h>
+#include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/platform/float16.h"
+#include "paddle/fluid/platform/transform.h"
 
 namespace paddle {
 namespace operators {
@@ -36,6 +40,11 @@ struct UniformGenerator {
   }
 };
 
+template <typename T, typename V>
+struct CastFunctor {
+  HOSTDEVICE V operator()(const T& a) { return static_cast<V>(a); }
+};
+
 // It seems that Eigen::Tensor::random in GPU will SEGFAULT.
 // Use std::random and thrust::random(thrust is a std library in CUDA) to
 // implement uniform random.
@@ -66,18 +75,50 @@ class GPUUniformRandomKernel : public framework::OpKernel<T> {
     T max = static_cast<T>(context.Attr<float>("max"));
     thrust::counting_iterator<unsigned int> index_sequence_begin(0);
     int64_t size = tensor->numel();
-    thrust::transform(index_sequence_begin, index_sequence_begin + size,
-                      thrust::device_ptr<T>(data),
-                      UniformGenerator<T>(min, max, seed));
+    if (out_var->IsType<framework::LoDTensor>() &&
+        std::type_index(typeid(T)) ==
+            std::type_index(typeid(platform::float16))) {
+      framework::Tensor master_copy_tensor;
+      master_copy_tensor.Resize(tensor->dims());
+      float* master_copy_tensor_data =
+          master_copy_tensor.mutable_data<float>(context.GetPlace());
+      thrust::transform(index_sequence_begin, index_sequence_begin + size,
+                        thrust::device_ptr<float>(master_copy_tensor_data),
+                        UniformGenerator<float>(static_cast<float>(min),
+                                                static_cast<float>(max), seed));
+      platform::Transform<platform::CUDADeviceContext> trans;
+      auto* in_begin = master_copy_tensor.data<float>();
+      auto* in_end = in_begin + master_copy_tensor.numel();
+      auto* out_begin = tensor->mutable_data<T>(context.GetPlace());
+      trans(context.template device_context<platform::CUDADeviceContext>(),
+            in_begin, in_end, out_begin, CastFunctor<float, T>());
+    } else {
+      thrust::transform(index_sequence_begin, index_sequence_begin + size,
+                        thrust::device_ptr<T>(data),
+                        UniformGenerator<T>(min, max, seed));
+    }
+    if (VLOG_IS_ON(5)) {
+      framework::Tensor cpu_tensor;
+      framework::TensorCopySync(*tensor, platform::CPUPlace(), &cpu_tensor);
+      auto& dev_ctx =
+          *platform::DeviceContextPool::Instance().Get(context.GetPlace());
+      dev_ctx.Wait();
+      auto x = framework::EigenVector<T>::Flatten(cpu_tensor);
+      VLOG(5) << "The Uniform output " << x;
+    }
   }
 };
 
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP_CUDA_KERNEL(uniform_random,
-                        paddle::operators::GPUUniformRandomKernel<float>,
-                        paddle::operators::GPUUniformRandomKernel<double>);
-REGISTER_OP_CUDA_KERNEL(uniform_random_batch_size_like,
-                        paddle::operators::GPUUniformRandomKernel<float>,
-                        paddle::operators::GPUUniformRandomKernel<double>);
+namespace plat = paddle::platform;
+REGISTER_OP_CUDA_KERNEL(
+    uniform_random, paddle::operators::GPUUniformRandomKernel<float>,
+    paddle::operators::GPUUniformRandomKernel<double>,
+    paddle::operators::GPUUniformRandomKernel<plat::float16>);
+REGISTER_OP_CUDA_KERNEL(
+    uniform_random_batch_size_like,
+    paddle::operators::GPUUniformRandomKernel<float>,
+    paddle::operators::GPUUniformRandomKernel<double>,
+    paddle::operators::GPUUniformRandomKernel<plat::float16>);

paddle/fluid/platform/dynload/mklml.h

@@ -60,6 +60,14 @@ extern void* mklml_dso_handle;
   __macro(cblas_dgemm_batch);       \
   __macro(vsAdd);                   \
   __macro(vdAdd);                   \
+  __macro(cblas_sgemm_alloc);       \
+  __macro(cblas_sgemm_pack);        \
+  __macro(cblas_sgemm_compute);     \
+  __macro(cblas_sgemm_free);        \
+  __macro(cblas_dgemm_alloc);       \
+  __macro(cblas_dgemm_pack);        \
+  __macro(cblas_dgemm_compute);     \
+  __macro(cblas_dgemm_free);        \
   __macro(MKL_Set_Num_Threads)
 
 MKLML_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_MKLML_WRAP);

paddle/fluid/platform/enforce.h

@@ -263,7 +263,8 @@ inline void throw_on_error(T e) {
  *    PADDLE_ENFORCE_EQ(a, b);
  *
  *    will raise an expression described as follows:
- *    "enforce a == b failed, 1 != 2" with detailed stack information.
+ *    "Enforce failed. Expected input a == b, but received a(1) != b(2)."
+ *      with detailed stack information.
  *
  *    extra messages is also supported, for example:
  *    PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)
@@ -292,9 +293,10 @@ inline void throw_on_error(T e) {
 #define __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, __CMP, __INV_CMP, ...)  \
   do {                                                                  \
     if (UNLIKELY(!((__VAL0)__CMP(__VAL1)))) {                           \
-      PADDLE_THROW("enforce %s " #__CMP " %s failed, %s " #__INV_CMP    \
-                   " %s\n%s",                                           \
-                   #__VAL0, #__VAL1, paddle::string::to_string(__VAL0), \
+      PADDLE_THROW("Enforce failed. Expected %s " #__CMP                \
+                   " %s, but received %s:%s " #__INV_CMP " %s:%s.\n%s", \
+                   #__VAL0, #__VAL1, #__VAL0,                           \
+                   paddle::string::to_string(__VAL0), #__VAL1,          \
                    paddle::string::to_string(__VAL1),                   \
                    paddle::string::Sprintf("" __VA_ARGS__));            \
     }                                                                   \

paddle/fluid/platform/enforce_test.cc

@@ -54,7 +54,9 @@ TEST(ENFORCE_EQ, NO_EXTRA_MSG_FAIL) {
     PADDLE_ENFORCE_EQ(a, 1 + 3);
   } catch (paddle::platform::EnforceNotMet error) {
     caught_exception = true;
-    HasPrefix(StringPiece(error.what()), "enforce a == 1 + 3 failed, 2 != 4");
+    HasPrefix(
+        StringPiece(error.what()),
+        "Enforce failed. Expected a == 1 + 3, but received a:2 != 1 + 3:4.");
   }
   EXPECT_TRUE(caught_exception);
 }
@@ -67,7 +69,8 @@ TEST(ENFORCE_EQ, EXTRA_MSG_FAIL) {
   } catch (paddle::platform::EnforceNotMet error) {
     caught_exception = true;
     HasPrefix(StringPiece(error.what()),
-              "enforce a == 1 + 3 failed, 2 != 4\ntheir size not match");
+              "Enforce failed. Expected a == 1 + 3, but received a:2 != 1 + "
+              "3:4.\ntheir size not match");
   }
   EXPECT_TRUE(caught_exception);
 }
@@ -84,8 +87,9 @@ TEST(ENFORCE_NE, FAIL) {
     PADDLE_ENFORCE_NE(1.0, 1UL);
   } catch (paddle::platform::EnforceNotMet error) {
     caught_exception = true;
-    EXPECT_TRUE(HasPrefix(StringPiece(error.what()),
-                          "enforce 1.0 != 1UL failed, 1 == 1"))
+    EXPECT_TRUE(HasPrefix(
+        StringPiece(error.what()),
+        "Enforce failed. Expected 1.0 != 1UL, but received 1.0:1 == 1UL:1."))
         << error.what() << " does not have expected prefix";
   }
   EXPECT_TRUE(caught_exception);
@@ -98,8 +102,9 @@ TEST(ENFORCE_GT, FAIL) {
     PADDLE_ENFORCE_GT(1, 2UL);
   } catch (paddle::platform::EnforceNotMet error) {
     caught_exception = true;
-    EXPECT_TRUE(
-        HasPrefix(StringPiece(error.what()), "enforce 1 > 2UL failed, 1 <= 2"));
+    EXPECT_TRUE(HasPrefix(
+        StringPiece(error.what()),
+        "Enforce failed. Expected 1 > 2UL, but received 1:1 <= 2UL:2."));
   }
   EXPECT_TRUE(caught_exception);
 }
@@ -116,8 +121,9 @@ TEST(ENFORCE_GE, FAIL) {
     PADDLE_ENFORCE_GE(1, 2UL);
   } catch (paddle::platform::EnforceNotMet error) {
     caught_exception = true;
-    EXPECT_TRUE(
-        HasPrefix(StringPiece(error.what()), "enforce 1 >= 2UL failed, 1 < 2"));
+    EXPECT_TRUE(HasPrefix(
+        StringPiece(error.what()),
+        "Enforce failed. Expected 1 >= 2UL, but received 1:1 < 2UL:2."));
   }
   EXPECT_TRUE(caught_exception);
 }
@@ -135,8 +141,9 @@ TEST(ENFORCE_LE, FAIL) {
     PADDLE_ENFORCE_GT(1, 2UL);
   } catch (paddle::platform::EnforceNotMet error) {
     caught_exception = true;
-    EXPECT_TRUE(
-        HasPrefix(StringPiece(error.what()), "enforce 1 > 2UL failed, 1 <= 2"));
+    EXPECT_TRUE(HasPrefix(
+        StringPiece(error.what()),
+        "Enforce failed. Expected 1 > 2UL, but received 1:1 <= 2UL:2."));
   }
   EXPECT_TRUE(caught_exception);
 }
@@ -153,7 +160,8 @@ TEST(ENFORCE_LT, FAIL) {
   } catch (paddle::platform::EnforceNotMet error) {
     caught_exception = true;
     EXPECT_TRUE(HasPrefix(StringPiece(error.what()),
-                          "enforce 1UL < 0.12 failed, 1 >= 0.12"));
+                          "Enforce failed. Expected 1UL < 0.12, but "
+                          "received 1UL:1 >= 0.12:0.12."));
   }
   EXPECT_TRUE(caught_exception);
 }

paddle/fluid/platform/gpu_info.cc

@@ -116,7 +116,8 @@ size_t GpuMaxChunkSize() {
   size_t allocating = static_cast<size_t>(FLAGS_fraction_of_gpu_memory_to_use *
                                           (total - reserving));
 
-  PADDLE_ENFORCE_LE(allocating, available);
+  PADDLE_ENFORCE_LE(allocating, available,
+                    "Insufficient GPU memory to allocation.");
 
   return allocating;
 }

python/CMakeLists.txt

@@ -97,10 +97,11 @@ if(APPLE)
   if(NOT INSTALL_NAME_TOOL_EXECUTABLE)
     message(FATAL_ERROR "install_name_tool not found, please check.\n")
   endif()
-else(APPLE)
+endif()
+if(LINUX)
   find_program(PATCHELF_EXECUTABLE patchelf)
   if(NOT PATCHELF_EXECUTABLE)
     message(FATAL_ERROR "patchelf not found, please install it.\n"
             "For Ubuntu, the command is: apt-get install -y patchelf.")
   endif()
-endif(APPLE)
+endif(LINUX)

python/paddle/fluid/__init__.py

@@ -122,7 +122,7 @@ def __bootstrap__():
         'use_pinned_memory', 'check_nan_inf', 'benchmark', 'warpctc_dir',
         'eager_delete_scope', 'use_mkldnn', 'initial_cpu_memory_in_mb',
         'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads',
-        'cpu_deterministic'
+        "dist_threadpool_size", 'cpu_deterministic'
     ]
     if core.is_compiled_with_dist():
         read_env_flags.append('rpc_deadline')

python/paddle/fluid/initializer.py

@@ -17,7 +17,6 @@ from __future__ import print_function
 from . import framework
 import numpy as np
 import contextlib
-from .framework import convert_np_dtype_to_dtype_
 from .core import VarDesc
 
 __all__ = [

python/paddle/fluid/layers/nn.py

@@ -100,6 +100,7 @@ __all__ = [
     'log',
     'crop',
     'rank_loss',
+    'prelu',
     'flatten',
 ]
 
@@ -5077,7 +5078,7 @@ def random_crop(x, shape, seed=None):
     return out
 
 
-def log(x):
+def log(x, name=None):
     """
     Calculates the natural log of the given input tensor, element-wise.
 
@@ -5087,6 +5088,8 @@ def log(x):
 
     Args:
         x (Variable): Input tensor.
+        name (str|None, default None): A name for this layer If set None,
+            the layer will be named automatically.
 
     Returns:
         Variable: The natural log of the input tensor computed element-wise.
@@ -5104,7 +5107,7 @@ def log(x):
     return out
 
 
-def relu(x):
+def relu(x, name=None):
     """
     Relu takes one input data (Tensor) and produces one output data (Tensor)
     where the rectified linear function, y = max(0, x), is applied to
@@ -5116,6 +5119,8 @@ def relu(x):
 
     Args:
         x (Variable): The input tensor.
+        name (str|None, default None): A name for this layer If set None,
+            the layer will be named automatically.
 
     Returns:
         Variable: The output tensor with the same shape as input.
@@ -5352,6 +5357,59 @@ def rank_loss(label, left, right, name=None):
     return out
 
 
+def prelu(x, mode, param_attr=None, name=None):
+    """
+    Equation:
+
+        y = \max(0, x) + alpha \min(0, x)
+
+    Args:
+        x (Variable): The input tensor.
+	  param_attr(ParamAttr|None): The parameter attribute for the learnable
+                                    weight (alpha).
+        mode (string): The mode for weight sharing
+		       all: all elements share same weight
+ 		       channel:elements in a channel share same weight
+ 		       element:each element has a weight
+	  name(str|None): A name for this layer(optional). If set None, the layer
+                        will be named automatically. 
+
+    Returns:
+        Variable: The output tensor with the same shape as input.
+
+    Examples:
+
+        .. code-block:: python
+
+         x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
+            mode = 'channel'
+            output = fluid.layers.prelu(x,mode)
+    """
+    helper = LayerHelper('prelu', **locals())
+    if mode not in ['all', 'channel', 'element']:
+        raise ValueError('mode should be one of all, channel, element.')
+    alpha_shape = [1]
+    if mode == 'channel':
+        alpha_shape = [1, x.shape[1], 1, 1]
+    elif mode == 'element':
+        alpha_shape = x.shape
+    dtype = helper.input_dtype(input_param_name='x')
+    alpha = helper.create_parameter(
+        attr=param_attr,
+        shape=alpha_shape,
+        dtype='float32',
+        is_bias=False,
+        default_initializer=Constant(1.0))
+    out = helper.create_tmp_variable(dtype)
+    helper.append_op(
+        type="prelu",
+        inputs={"X": x,
+                'Alpha': alpha},
+        attrs={"mode": mode},
+        outputs={"Out": out})
+    return out
+
+
 def flatten(x, axis=1, name=None):
     """
     **Flatten layer**

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -59,8 +59,8 @@ py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=$
 if(WITH_DISTRIBUTE)
     py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
     set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)
-    set_tests_properties(test_dist_mnist PROPERTIES TIMEOUT 180)
-    set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 180)
+    set_tests_properties(test_dist_mnist PROPERTIES TIMEOUT 200)
+    set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 200)
 endif()
 py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
 py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)

python/paddle/fluid/tests/unittests/test_dist_train.py

@@ -28,6 +28,12 @@ from paddle.fluid.layers.io import ListenAndServ
 from paddle.fluid.layers.io import Recv
 from paddle.fluid.layers.io import Send
 
+from paddle.fluid import core
+
+RPC_OP_ROLE_ATTR_NAME = op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName(
+)
+RPC_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.RPC
+
 
 class TestSendOp(unittest.TestCase):
     def test_send(self):
@@ -91,18 +97,29 @@ class TestSendOp(unittest.TestCase):
     def init_client(self, place, port):
         main = fluid.Program()
         with fluid.program_guard(main):
+            main.global_block().append_op(
+                type="fetch_barrier",
+                inputs={},
+                outputs={},
+                attrs={
+                    "endpoints": ["127.0.0.1:{0}".format(port)],
+                    RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
+                })
+
             x = layers.data(
                 shape=[32, 32],
                 dtype='float32',
                 name='X',
                 append_batch_size=False)
             fluid.initializer.Constant(value=2.3)(x, main.global_block())
+
             get_var = main.global_block().create_var(
                 name="scale_0.tmp_0",  # server side var
                 dtype="float32",
                 persistable=False,
                 shape=[32, 32])
             fluid.initializer.Constant(value=2.3)(get_var, main.global_block())
+
             Send("127.0.0.1:%d" % port, [x])
             o = Recv("127.0.0.1:%d" % port, [get_var])
 

python/paddle/fluid/tests/unittests/test_dist_transpiler.py

@@ -20,6 +20,7 @@ import unittest
 import paddle.fluid as fluid
 from paddle.fluid.transpiler.distribute_transpiler import delete_ops
 import traceback
+import collections
 
 
 class TranspilerTest(unittest.TestCase):
@@ -56,9 +57,18 @@ class TranspilerTest(unittest.TestCase):
         self.origin_prog = main.clone()
         return main
 
-    def get_trainer(self, config=None, sync_mode=True):
-        t = self._transpiler_instance(config, sync_mode)
-        return t.get_trainer_program()
+    def get_trainer(self, config=None):
+        src = fluid.default_startup_program().clone()
+
+        t = self._transpiler_instance(config)
+
+        trainer_main = t.get_trainer_program()
+        trainer_startup = fluid.default_startup_program()
+
+        assert (src.num_blocks == 1)
+        assert (trainer_startup.num_blocks == src.num_blocks)
+
+        return trainer_main, trainer_startup
 
     def get_pserver(self, ep, config=None, sync_mode=True):
         t = self._transpiler_instance(config, sync_mode)
@@ -94,7 +104,21 @@ class TestBasicModel(TranspilerTest):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         pserver2, startup2 = self.get_pserver(self.pserver2_ep)
 
-        trainer = self.get_trainer()
+        trainer, trainer_startup = self.get_trainer()
+
+        # splited var blocks should be in startup program
+        self.assertTrue("fc_w.block0" in trainer_startup.global_block().vars)
+        self.assertTrue("fc_w.block1" in trainer_startup.global_block().vars)
+        self.assertTrue("fc_w" in trainer_startup.global_block().vars)
+        self.assertTrue("fc_b" in trainer_startup.global_block().vars)
+        self.assertTrue("fc_w@GRAD" not in trainer_startup.global_block().vars)
+        self.assertTrue("fc_b@GRAD" not in trainer_startup.global_block().vars)
+
+        src = [op.type for op in trainer_startup.global_block().ops]
+        dst = ['fill_constant', 'fill_constant', 'uniform_random', 'recv', 'recv', \
+               'fetch_barrier', 'concat']
+
+        self.assertEqual(src, dst)
 
         self.assertEqual([op.type for op in trainer.global_block().ops], [
             'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
@@ -145,7 +169,7 @@ class TestBasicModelWithLargeBlockSize(TranspilerTest):
         pserver, startup = self.get_pserver(self.pserver1_ep, config)
         pserver2, startup2 = self.get_pserver(self.pserver2_ep, config)
 
-        trainer = self.get_trainer(config)
+        trainer, _ = self.get_trainer(config)
 
         self.assertEqual([op.type for op in trainer.global_block().ops], [
             'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
@@ -229,7 +253,7 @@ class TestLRDecay(TranspilerTest):
 
     def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
 
         self.assertEqual(len(pserver.blocks), 4)
         lr_decay_ops = [op.type for op in pserver.blocks[1].ops]
@@ -259,7 +283,7 @@ class TestLRDecayConditional(TranspilerTest):
 
     def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
 
         serv_op = pserver.blocks[0].ops[0]
         sub_blocks = []
@@ -308,7 +332,7 @@ class TestL2Decay(TranspilerTest):
 
     def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
 
         self.assertEqual(len(pserver.blocks), 3)
         self.assertEqual([op.type for op in pserver.blocks[1].ops],
@@ -343,7 +367,7 @@ class TestL2DecayWithPiecewise(TranspilerTest):
 
     def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
 
         self.assertEqual(len(pserver.blocks), 9)
         self.assertEqual([op.type for op in pserver.blocks[1].ops], [
@@ -418,7 +442,7 @@ class TestLocalLookupTable(TestDistLookupTableBase):
         self.assertEqual([op.type for op in pserver1.blocks[2].ops],
                          ["sum", "adam", "scale", "scale"])
 
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
@@ -456,7 +480,7 @@ class TestDistLookupTable(TestDistLookupTableBase):
         # 5 save table
         self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
 
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'split_ids',
@@ -489,7 +513,7 @@ class TestAsyncLocalLookupTable(TestDistLookupTableBase):
         self.assertEqual([op.type for op in pserver1.blocks[2].ops],
                          ["adam", "scale", "scale"])
 
-        trainer = self.get_trainer(config)
+        trainer, _ = self.get_trainer(config)
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
@@ -528,7 +552,7 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
         # 5 save table
         self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
 
-        trainer = self.get_trainer(config)
+        trainer, _ = self.get_trainer(config)
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'split_ids',

python/paddle/fluid/tests/unittests/test_fc_mkldnn_op.py

@@ -24,6 +24,7 @@ def fully_connected_naive(input, weights, bias_data=None):
     w_h, w_c = weights.shape
 
     x_data = np.reshape(input, [in_n, in_c * in_h * in_w])
+    # this transpose should be implemented at C code
     w_data = np.transpose(np.reshape(weights, (w_c, in_c * in_h * in_w)))
     result = None
 
@@ -45,15 +46,11 @@ class TestFCMKLDNNOp(OpTest):
     def setUp(self):
         self.op_type = "fc"
         self.use_mkldnn = True
-        self.with_bias = True
         self.matrix = MatrixGenerate(1, 10, 15, 3, 3)
 
         self.inputs = {'Input': self.matrix.input, 'W': self.matrix.weights}
 
-        self.attrs = {
-            'use_mkldnn': self.use_mkldnn,
-            'with_bias': self.with_bias
-        }
+        self.attrs = {'use_mkldnn': self.use_mkldnn, }
 
         self.outputs = {
             'Out': fully_connected_naive(self.matrix.input, self.matrix.weights)
@@ -87,13 +84,11 @@ class TestFCMKLDNNOp3(TestFCMKLDNNOp):
 
 class TestFCMKLDNNOp4(TestFCMKLDNNOp):
     def init_op_type(self):
-        self.with_bias = False
         self.matrix = MatrixGenerate(2, 32, 48, 2, 2)
 
 
 class TestFCMKLDNNOp4(TestFCMKLDNNOp):
     def init_op_type(self):
-        self.with_bias = False
         self.matrix = MatrixGenerate(2, 32, 1000, 6, 6)
 
 

python/paddle/fluid/tests/unittests/test_fc_op.py

+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def fc_refer(matrix, with_bias):
+    in_n, in_c, in_h, in_w = matrix.input.shape
+    w_i, w_o = matrix.weights.shape
+
+    x_data = np.reshape(matrix.input, [in_n, in_c * in_h * in_w])
+    w_data = np.reshape(matrix.weights, [w_i, w_o])
+    b_data = np.reshape(matrix.bias, [1, w_o])
+    result = None
+
+    if with_bias:
+        result = np.dot(x_data, w_data) + b_data
+    else:
+        result = np.dot(x_data, w_data)
+
+    return result
+
+
+class MatrixGenerate:
+    def __init__(self, mb, ic, oc, h, w):
+        self.input = np.random.random((mb, ic, h, w)).astype("float32")
+        self.weights = np.random.random((ic * h * w, oc)).astype("float32")
+        self.bias = np.random.random((1, oc)).astype("float32")
+
+
+class TestFCOp(OpTest):
+    def setUp(self):
+        self.op_type = "fc"
+        self.matrix = MatrixGenerate(1, 10, 15, 3, 3)
+
+        self.with_bias = True
+        if self.with_bias:
+            self.inputs = {
+                'Input': self.matrix.input,
+                'W': self.matrix.weights,
+                'Bias': self.matrix.bias
+            }
+        else:
+            self.inputs = {'Input': self.matrix.input, 'W': self.matrix.weights}
+
+        self.attrs = {'use_mkldnn': False}
+
+        self.outputs = {'Out': fc_refer(self.matrix, self.with_bias)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestFCOpBiasBoth(TestFCOp):
+    def init_shapes(self, mb, ic, oc, h, w):
+        for with_bias in {True, False}:
+            self.with_bias = with_bias
+            self.matrix = MatrixGenerate(mb, ic, oc, h, w)
+
+
+class TestFCOp1(TestFCOpBiasBoth):
+    def init_op_type(self):
+        self.init_shapes(2, 8, 10, 1, 1)
+
+
+class TestFCOp2(TestFCOpBiasBoth):
+    def init_op_type(self):
+        self.init_shapes(4, 5, 6, 2, 2)
+
+
+class TestFCOp4(TestFCOpBiasBoth):
+    def init_op_type(self):
+        self.init_shapes(1, 32, 64, 3, 3)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_layers.py

@@ -21,6 +21,7 @@ import paddle.fluid.nets as nets
 from paddle.fluid.framework import Program, program_guard, default_main_program
 from paddle.fluid.param_attr import ParamAttr
 import decorators
+from paddle.fluid.initializer import Constant
 
 
 class TestBook(unittest.TestCase):
@@ -485,6 +486,20 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(out)
         print(str(program))
 
+    def test_prelu(self):
+        program = Program()
+        with program_guard(program):
+            input = layers.data(
+                name="input", shape=[5, 200, 100, 100], dtype="float32")
+            mode = 'channel'
+            out = layers.prelu(
+                input,
+                mode,
+                param_attr=ParamAttr(initializer=Constant(1.0)),
+                name='prelu')
+            self.assertIsNotNone(out)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_prelu_op.py

@@ -22,30 +22,58 @@ from op_test import OpTest
 class PReluTest(OpTest):
     def setUp(self):
         self.op_type = "prelu"
-        x_np = np.random.normal(size=(10, 10)).astype("float32")
-
-        for pos, val in np.ndenumerate(x_np):
-            # Since zero point in prelu is not differentiable, avoid randomize
-            # zero.
-            while abs(val) < 1e-3:
-                x_np[pos] = np.random.normal()
-                val = x_np[pos]
-
-        x_np_sign = np.sign(x_np)
-        x_np = x_np_sign * np.maximum(x_np, .005)
-        alpha_np = np.array([.1], dtype="float32")
-        self.inputs = {'X': x_np, 'Alpha': alpha_np}
+        self.initTestCase()
+        x_np = np.random.normal(size=(3, 5, 5, 10)).astype("float32")
+
+        # Since zero point in prelu is not differentiable, avoid randomize
+        # zero.
+        x_np[np.abs(x_np) < 0.005] = 0.02
+
+        if self.attrs == {'mode': "all"}:
+            alpha_np = np.random.rand(1).astype("float32")
+            self.inputs = {'X': x_np, 'Alpha': alpha_np}
+        elif self.attrs == {'mode': "channel"}:
+            alpha_np = np.random.rand(1, x_np.shape[1], 1, 1).astype("float32")
+            self.inputs = {'X': x_np, 'Alpha': alpha_np}
+        else:
+            alpha_np = np.random.rand(*x_np.shape).astype("float32")
+            self.inputs = {'X': x_np, 'Alpha': alpha_np}
+
         out_np = np.maximum(self.inputs['X'], 0.)
         out_np = out_np + np.minimum(self.inputs['X'],
                                      0.) * self.inputs['Alpha']
         assert out_np is not self.inputs['X']
         self.outputs = {'Out': out_np}
 
+    def initTestCase(self):
+        self.attrs = {'mode': "channel"}
+
     def test_check_output(self):
         self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
+        self.check_grad(['X', 'Alpha'], 'Out')
+
+    def test_check_grad_ignore_x(self):
+        self.check_grad(['Alpha'], 'Out', no_grad_set=set('X'))
+
+    def test_check_grad_ignore_alpha(self):
+        self.check_grad(['X'], 'Out', no_grad_set=set('Alpha'))
+
+
+class TestCase1(PReluTest):
+    def initTestCase(self):
+        self.attrs = {'mode': "all"}
+
+
+class TestCase2(PReluTest):
+    def initTestCase(self):
+        self.attrs = {'mode': "channel"}
+
+
+class TestCase3(PReluTest):
+    def initTestCase(self):
+        self.attrs = {'mode': "element"}
 
 
 if __name__ == "__main__":

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -198,6 +198,9 @@ class DistributeTranspiler(object):
         if program is None:
             program = default_main_program()
         self.origin_program = program
+        self.origin_startup_program = default_startup_program().clone()
+
+        self.startup_program = default_startup_program()
         self.trainer_num = trainers
         self.sync_mode = sync_mode
         self.trainer_id = trainer_id
@@ -208,10 +211,10 @@ class DistributeTranspiler(object):
         ps_dispatcher = self.config.split_method(self.pserver_endpoints)
         self.has_distributed_lookup_table = self._has_distributed_lookup_table()
 
-        # split and create vars, then put splited vars in dicts for later use.
+        # step 1: split and create vars, then put splited vars in dicts for later use.
         self._init_splited_vars()
 
-        # step 3.1: insert send op to send gradient vars to parameter servers
+        # step 2: insert send op to send gradient vars to parameter servers
         ps_dispatcher.reset()
         send_vars = []
 
@@ -269,7 +272,7 @@ class DistributeTranspiler(object):
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
 
-        # step 3.2: insert recv op to receive parameters from parameter server
+        # step 3: insert recv op to receive parameters from parameter server
         recv_vars = []
         for _, var in enumerate(send_vars):
             recv_vars.append(self.grad_param_mapping[var])
@@ -316,6 +319,8 @@ class DistributeTranspiler(object):
                 outputs={"Out": [orig_param]},
                 attrs={"axis": 0})
 
+        self._get_trainer_startup_program(recv_vars=recv_vars, eplist=eplist)
+
         if self.has_distributed_lookup_table:
             self._replace_lookup_table_op_with_prefetch(program,
                                                         pserver_endpoints)
@@ -332,8 +337,78 @@ class DistributeTranspiler(object):
         # FIXME(typhoonzero): Also ops like clip_gradient, lrn_decay?
         delete_ops(self.origin_program.global_block(), self.optimize_ops)
         self.origin_program.__str__()
+
         return self.origin_program
 
+    def _get_trainer_startup_program(self,
+                                     recv_vars,
+                                     eplist,
+                                     startup_program=None):
+        """
+        Get transpiled trainer side startup program.
+
+        Args:
+            startup_program(Program): Startup program.
+
+        Returns:
+            Program: trainer side startup program.
+        """
+        if startup_program is None:
+            startup_program = self.startup_program
+
+        # FIXME(gongwb): delete not need ops.
+        # note that: some parameter is not trainable and those ops can't be deleted.
+
+        for varname, splited_var in self.param_var_mapping.iteritems():
+            # Get the eplist of recv vars
+            eps = []
+            for var in splited_var:
+                index = [v.name for v in recv_vars].index(var.name)
+                eps.append(eplist[index])
+
+            for var in splited_var:
+                if startup_program.global_block().has_var(var.name):
+                    continue
+
+                startup_program.global_block().create_var(
+                    name=var.name,
+                    persistable=False,
+                    type=var.type,
+                    dtype=var.dtype,
+                    shape=var.shape,
+                    lod_level=var.lod_level)
+
+            op = startup_program.global_block().append_op(
+                type="recv",
+                inputs={},
+                outputs={"Out": splited_var},
+                attrs={
+                    "epmap": eps,
+                    RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
+                })
+
+        startup_program.global_block().append_op(
+            type="fetch_barrier",
+            inputs={},
+            outputs={},
+            attrs={
+                "endpoints": self.pserver_endpoints,
+                RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
+            })
+
+        for varname, splited_var in self.param_var_mapping.iteritems():
+            #add concat ops to merge splited parameters received from parameter servers.
+            if len(splited_var) <= 1:
+                continue
+            orig_param = startup_program.global_block().vars[varname]
+            startup_program.global_block().append_op(
+                type="concat",
+                inputs={"X": splited_var},
+                outputs={"Out": [orig_param]},
+                attrs={"axis": 0})
+
+        return startup_program
+
     def get_pserver_program(self, endpoint):
         """
         Get parameter server side program.
@@ -580,14 +655,16 @@ class DistributeTranspiler(object):
             new_outputs = collections.OrderedDict()
             # do not append startup op if var is not on this pserver
             op_on_pserver = False
-            for key in op.output_names:
-                newname, _ = _get_splited_name_and_shape(op.output(key)[0])
-                if newname:
-                    op_on_pserver = True
-                    new_outputs[key] = created_var_map[newname]
-                elif op.output(key)[0] in pserver_vars:
-                    op_on_pserver = True
-                    new_outputs[key] = pserver_vars[op.output(key)[0]]
+            # TODO(gongwb): remove this line.
+            if op.type not in ["recv", "fetch_barrier", "concat"]:
+                for key in op.output_names:
+                    newname, _ = _get_splited_name_and_shape(op.output(key)[0])
+                    if newname:
+                        op_on_pserver = True
+                        new_outputs[key] = created_var_map[newname]
+                    elif op.output(key)[0] in pserver_vars:
+                        op_on_pserver = True
+                        new_outputs[key] = pserver_vars[op.output(key)[0]]
 
             if op_on_pserver:
                 # most startup program ops have no inputs
@@ -1026,7 +1103,6 @@ class DistributeTranspiler(object):
                     var_mapping[varname] = \
                         [program.global_block().var(orig_var.name)]
                 continue
-
             var_mapping[varname] = []
             orig_shape = orig_var.shape
             orig_dim1_flatten = 1

tools/manylinux1/Dockerfile.x64

@@ -13,7 +13,7 @@ ENV PATH /opt/rh/devtoolset-2/root/usr/bin:$PATH
 ENV LD_LIBRARY_PATH /opt/rh/devtoolset-2/root/usr/lib64:/opt/rh/devtoolset-2/root/usr/lib:/usr/local/lib64:/usr/local/lib:${LD_LIBRARY_PATH}
 ENV PKG_CONFIG_PATH=/usr/local/lib/pkgconfig
 
-RUN yum install -y sqlite-devel zlib-devel openssl-devel pcre-devel vim tk-devel tkinter libtool xz graphviz
+RUN yum install -y sqlite-devel zlib-devel openssl-devel pcre-devel vim tk-devel tkinter libtool xz freetype-devel libpng-devel graphviz
 COPY build_scripts /build_scripts
 RUN bash build_scripts/build.sh && \
   bash build_scripts/install_nccl2.sh && rm -r build_scripts

tools/manylinux1/build_scripts/build.sh

@@ -105,7 +105,7 @@ curl-config --features
 rm -rf /usr/local/ssl
 
 # Install patchelf (latest with unreleased bug fixes)
-curl -sLO https://nipy.bic.berkeley.edu/manylinux/patchelf-0.9njs2.tar.gz
+curl -sLO http://nipy.bic.berkeley.edu/manylinux/patchelf-0.9njs2.tar.gz
 check_sha256sum patchelf-0.9njs2.tar.gz $PATCHELF_HASH
 tar -xzf patchelf-0.9njs2.tar.gz
 (cd patchelf-0.9njs2 && ./configure && make && make install)

tools/manylinux1/build_scripts/install_nccl2.sh

@@ -21,5 +21,5 @@ for sub_deb in $DEBS; do
   ar x $sub_deb && tar xf data.tar.xz
 done
 mv -f usr/include/nccl.h /usr/local/include/
-mv -f usr/lib/libnccl* /usr/local/lib/
+mv -f usr/lib/x86_64-linux-gnu/libnccl* /usr/local/lib/
 rm -rf $DIR