Merge branch 'incubate/lite' into 'opencl_ci'

# Conflicts:
#   paddle/fluid/lite/core/CMakeLists.txt
#   paddle/fluid/lite/kernels/x86/CMakeLists.txt
#   paddle/fluid/lite/operators/CMakeLists.txt

cmake/version.cmake

@@ -3,7 +3,8 @@ set(PADDLE_VERSION $ENV{PADDLE_VERSION})
 set(tmp_version "HEAD")
 set(TAG_VERSION_REGEX "[0-9]+\\.[0-9]+\\.[0-9]+(\\.(a|b|rc)\\.[0-9]+)?")
 set(COMMIT_VERSION_REGEX "[0-9a-f]+[0-9a-f]+[0-9a-f]+[0-9a-f]+[0-9a-f]+")
-set(LATEST_PADDLE_VERSION "latest")
+# set(LATEST_PADDLE_VERSION "latest")
+set(LATEST_PADDLE_VERSION "0.0.0")
 
 while ("${PADDLE_VERSION}" STREQUAL "")
   # Check current branch name

paddle/fluid/lite/api/cxx_api.cc

@@ -79,5 +79,16 @@ const lite::Tensor *Predictor::GetTensor(const std::string &name) const {
   return &var->Get<lite::Tensor>();
 }
 
+#ifdef LITE_WITH_X86
+void Predictor::FeedVars(const std::vector<framework::Tensor> &tensors) {
+  auto var = scope_->FindVar("feed");
+  auto &feed_list = *(var->GetMutable<std::vector<lite::Tensor>>());
+  feed_list.resize(tensors.size());
+
+  for (size_t i = 0; i < tensors.size(); ++i)
+    feed_list[i].ShareDataWith(tensors[i]);
+}
+#endif
+
 }  // namespace lite
 }  // namespace paddle

paddle/fluid/lite/api/cxx_api.h

@@ -24,6 +24,10 @@
 #include "paddle/fluid/lite/core/types.h"
 #include "paddle/fluid/lite/model_parser/model_parser.h"
 
+#ifdef LITE_WITH_X86
+#include "paddle/fluid/framework/program_desc.h"
+#endif
+
 namespace paddle {
 namespace lite {
 
@@ -63,6 +67,15 @@ class Predictor {
   // This method is disabled in mobile, for unnecessary dependencies required.
   void SaveModel(const std::string& dir);
 
+#ifdef LITE_WITH_X86
+  void Run(const std::vector<framework::Tensor>& tensors) {
+    FeedVars(tensors);
+    program_->Run();
+  }
+
+  void FeedVars(const std::vector<framework::Tensor>& tensors);
+#endif
+
  private:
   Optimizer optimizer_;
   framework::proto::ProgramDesc program_desc_;
@@ -105,6 +118,16 @@ class CXXTrainer {
     return main_program_executor_;
   }
 
+#ifdef LITE_WITH_X86
+  Predictor& BuildMainProgramExecutor(framework::ProgramDesc& desc) {  // NOLINT
+    return BuildMainProgramExecutor(*desc.Proto());
+  }
+
+  void RunStartupProgram(framework::ProgramDesc& desc) {  // NOLINT
+    RunStartupProgram(*desc.Proto());
+  }
+#endif
+
   // Run the startup program. It just executes once, no cache needed.
   void RunStartupProgram(const framework::proto::ProgramDesc& desc,
                          int block_id = 0) {

paddle/fluid/lite/api/cxx_api_bin_int8.cc

@@ -34,6 +34,7 @@ double time_diff(Time t1, Time t2) {
 void Run(const char* model_dir, int repeat) {
 #ifdef LITE_WITH_ARM
   DeviceInfo::Init();
+  DeviceInfo::Global().SetRunMode(LITE_POWER_HIGH, 1);
 #endif
   lite::Predictor predictor;
   std::vector<Place> valid_places({
@@ -52,6 +53,7 @@ void Run(const char* model_dir, int repeat) {
     data[i] = 1;
   }
 
+  for (int i = 0; i < 10; i++) predictor.Run();
   auto time1 = time();
   for (int i = 0; i < repeat; i++) predictor.Run();
   auto time2 = time();
@@ -60,10 +62,16 @@ void Run(const char* model_dir, int repeat) {
 
   auto* out = predictor.GetOutput(0);
   LOG(INFO) << out << " memory size " << out->data_size();
-  LOG(INFO) << "out " << out->data<float>()[0];
-  LOG(INFO) << "out " << out->data<float>()[1];
   LOG(INFO) << "dims " << out->dims();
   LOG(INFO) << "out data size: " << out->data_size();
+  /*
+  float sum = 0.;
+  for (int i = 0; i < out->data_size(); i++) {
+     LOG(INFO) << "out " << out->data<float>()[i];
+     sum += out->data<float>()[i];
+  }
+  LOG(INFO) << sum;
+  */
 }
 
 }  // namespace lite

paddle/fluid/lite/core/CMakeLists.txt

@@ -19,15 +19,20 @@ endif()
 
 proto_library(framework_proto_lite SRCS framework.proto)
 
+if (LITE_WITH_X86)
+lite_cc_library(variable_lite SRCS variable.cc DEPS framework_proto)
+lite_cc_library(types_lite SRCS types.cc DEPS framework_proto)
+else()
 lite_cc_library(variable_lite SRCS variable.cc)
+lite_cc_library(types_lite SRCS types.cc)
+endif()
 lite_cc_library(op_registry_lite SRCS op_registry.cc DEPS framework_proto_lite)
 lite_cc_library(scope_lite SRCS scope.cc DEPS ${tensor_lite})
 lite_cc_library(cpu_info_lite SRCS cpu_info.cc)
-lite_cc_library(context_lite SRCS context.cc DEPS ${tensor_lite} any_lite cpu_info_lite X86_DEPS eigen3 CL_DEPS cl_helper)
+lite_cc_library(context_lite SRCS context.cc DEPS ${tensor_lite} any_lite cpu_info_lite eigen3 CL_DEPS cl_helper)
 lite_cc_library(kernel_lite SRCS kernel.cc DEPS context_lite type_system target_wrapper_lite any_lite op_params_lite framework_proto_lite ${tensor_lite})
 lite_cc_library(op_lite SRCS op_lite.cc DEPS scope_lite op_registry_lite target_wrapper_lite kernel_lite
   cpp_op_desc_lite ${tensor_lite})
-lite_cc_library(types_lite SRCS types.cc)
 lite_cc_library(type_system SRCS type_system.cc DEPS ${tensor_lite} target_wrapper_lite)
 
 lite_cc_library(program_lite SRCS program.cc

paddle/fluid/lite/core/mir/ssa_graph_test.cc

@@ -52,4 +52,6 @@ TEST(SSAGraph, test) {
 }  // namespace paddle
 
 USE_LITE_OP(fc);
-USE_LITE_KERNEL(fc, kHost, kFloat, kNCHW, def);
+#ifdef LITE_WITH_X86
+// USE_LITE_KERNEL(fc, kX86, kFloat, kNCHW, def);
+#endif

paddle/fluid/lite/core/program.cc

@@ -64,6 +64,7 @@ void RuntimeProgram::SaveParams(const std::string &dir,
 
 void Program::Build(const framework::proto::ProgramDesc &program) {
   CHECK(ops_.empty()) << "Executor duplicate Build found";
+
   // Create operators.
   for (const auto &proto_op_desc : program.blocks(0).ops()) {
     lite::OpDesc op_desc_dummy(proto_op_desc);
@@ -98,6 +99,7 @@ void Program::PrepareWorkspace(const framework::proto::ProgramDesc &program) {
     } else {
       if (var_desc.Name() == "feed" || var_desc.Name() == "fetch") continue;
       weights_.push_back(var_desc.Name());
+      if (var_desc.Persistable()) scope_->Var(var_desc.Name());
     }
   }
 }

paddle/fluid/lite/core/scope.h

@@ -27,6 +27,12 @@ namespace lite {
 class Scope final {
  public:
   Scope() {}
+  // delete below two functions to allow pybind to recognise it cannot make a
+  // copy
+  // link:
+  // https://stackoverflow.com/questions/53807248/pybind11-returning-a-pointer-to-a-container-of-unique-ptr
+  Scope(const Scope&) = delete;
+  Scope& operator=(const Scope&) = delete;
   ~Scope();
 
   Scope& NewScope() const;

paddle/fluid/lite/core/variable.h

@@ -15,12 +15,15 @@
 #pragma once
 #include <set>
 #include <string>
+#include <vector>
 #include "paddle/fluid/lite/core/compatible_tensor.h"
 #include "paddle/fluid/lite/utils/all.h"
 
 namespace paddle {
 namespace lite {
 
+using FeedFetchList = std::vector<lite::Tensor>;
+
 class Variable {
  public:
   template <typename T>
@@ -40,7 +43,9 @@ class Variable {
   }
 
  private:
-  variant<int, float, std::string, lite::Tensor> blob_;
+  // variant<int, float, std::string, lite::Tensor> blob_;
+  variant<int, float, std::string, lite::Tensor, std::vector<lite::Tensor>>
+      blob_;
 };
 
 }  // namespace lite

paddle/fluid/lite/kernels/arm/conv_compute.cc

@@ -74,7 +74,7 @@ void ConvCompute::PrepareForRun() {
   } else if (param.groups == 1 && kw == 3 && stride == 2 && kps_equal &&
              no_dilation) {
     // direct conv impl
-    impl_ = new lite::arm::math::DirectConv<PRECISION(kFloat)>;
+    impl_ = new lite::arm::math::GemmLikeConv<PRECISION(kFloat)>;
     VLOG(3) << "invoking direct conv";
   } else {
     impl_ = new lite::arm::math::GemmLikeConv<PRECISION(kFloat)>;
@@ -123,8 +123,7 @@ void ConvComputeInt8<Ptype_out>::PrepareForRun() {
 
   // weigth is int8 and bias is int32 so do not need trans
   if (param.groups == ic && ic == oc && kps_equal && no_dilation && flag_dw) {
-    // impl_ = new lite::arm::math::DepthwiseConvInt8<Ptype_out>;
-    impl_ = new lite::arm::math::GemmLikeConvInt8<Ptype_out>;
+    impl_ = new lite::arm::math::DepthwiseConvInt8<Ptype_out>;
     VLOG(3) << "Run DepthwiseConv Int8";
   } else if (param.groups == 1 && kw == 3 && (sw == 1 || sw == 2) &&
              kps_equal && no_dilation) {

paddle/fluid/lite/kernels/host/feed_compute.cc

@@ -29,7 +29,7 @@ class FeedCompute
     auto &param = Param<operators::FeedParam>();
     VLOG(4) << "feed_list.size: " << param.feed_list->size();
     VLOG(4) << "col " << param.col;
-    const lite::Tensor &feed_item = (*param.feed_list)[0];
+    const lite::Tensor &feed_item = (*param.feed_list)[param.col];
     param.out->ShareDataWith(feed_item);
   }
 };

paddle/fluid/lite/kernels/x86/CMakeLists.txt

@@ -18,6 +18,7 @@ lite_cc_library(concat_compute_x86 SRCS concat_compute.cc DEPS ${lite_kernel_dep
 lite_cc_library(conv_compute_x86 SRCS conv_compute.cc DEPS ${lite_kernel_deps} blas im2col vol2col)
 lite_cc_library(pool_compute_x86 SRCS pool_compute.cc DEPS ${lite_kernel_deps} pooling)
 lite_cc_library(batch_norm_compute_x86 SRCS batch_norm_compute.cc DEPS ${lite_kernel_deps})
+lite_cc_library(uniform_random_compute_x86 SRCS uniform_random_compute.cc DEPS ${lite_kernel_deps} )
 
 lite_cc_test(test_fc_compute_x86 SRCS fc_compute_test.cc DEPS fc_compute_x86)
 lite_cc_test(test_conv2d_compute_x86 SRCS conv_compute_test.cc DEPS conv_compute_x86)
@@ -47,6 +48,7 @@ set(x86_kernels
     conv_compute_x86
     pool_compute_x86
     batch_norm_compute_x86
-    )
-
-set(x86_kernels "${x86_kernels}" CACHE INTERNAL "x86 kernels")
+    uniform_random_compute_x86
+    sgd_compute_x86
+    CACHE INTERNAL "x86 kernels")
+    

paddle/fluid/lite/kernels/x86/elementwise_compute.cc

@@ -22,9 +22,19 @@ REGISTER_LITE_KERNEL(elementwise_sub, kX86, kFloat, kNCHW,
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
     .Finalize();
 
-REGISTER_LITE_KERNEL(elementwise_sub_grad, kX86, kFloat, kNCHW,
-                     paddle::lite::kernels::x86::ElementwiseSubCompute<float>,
+REGISTER_LITE_KERNEL(elementwise_add, kX86, kFloat, kNCHW,
+                     paddle::lite::kernels::x86::ElementwiseAddCompute<float>,
                      def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindInput("Y", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();
+
+#ifdef LITE_WITH_X86
+REGISTER_LITE_KERNEL(
+    elementwise_sub_grad, kX86, kFloat, kNCHW,
+    paddle::lite::kernels::x86::ElementwiseSubGradCompute<float>, def)
+    .BindInput("Y", {LiteType::GetTensorTy(TARGET(kX86))})
     .BindInput(paddle::framework::GradVarName("Out"),
                {LiteType::GetTensorTy(TARGET(kX86))})
     .BindOutput(paddle::framework::GradVarName("X"),
@@ -32,11 +42,4 @@ REGISTER_LITE_KERNEL(elementwise_sub_grad, kX86, kFloat, kNCHW,
     .BindOutput(paddle::framework::GradVarName("Y"),
                 {LiteType::GetTensorTy(TARGET(kX86))})
     .Finalize();
-
-REGISTER_LITE_KERNEL(elementwise_add, kX86, kFloat, kNCHW,
-                     paddle::lite::kernels::x86::ElementwiseAddCompute<float>,
-                     def)
-    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
-    .BindInput("Y", {LiteType::GetTensorTy(TARGET(kX86))})
-    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
-    .Finalize();
+#endif

paddle/fluid/lite/kernels/x86/elementwise_compute.h

@@ -68,6 +68,7 @@ struct SubGradDY {
   T operator()(T x, T y, T out, T dout) const { return -dout; }
 };
 
+#ifdef LITE_WITH_X86
 template <typename T>
 class ElementwiseSubGradCompute
     : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
@@ -79,20 +80,25 @@ class ElementwiseSubGradCompute
     CHECK(context.x86_device_context());
 
     param.X_grad->template mutable_data<T>();
-    param.Y_grad->template mutable_data<T>();
     // skip out, x, y
     auto dout = param.Out_grad->raw_tensor();
     auto dx = param.X_grad->raw_tensor();
-    auto dy = param.Y_grad->raw_tensor();
+
+    framework::Tensor* dy = nullptr;
+    if (param.Y_grad) {
+      param.Y_grad->template mutable_data<T>();
+      dy = &param.Y_grad->raw_tensor();
+    }
     auto& skip = dout;
     paddle::operators::ElemwiseExplicitGradCompute<
         platform::CPUDeviceContext, T, SubGradDX<T>, SubGradDY<T>>(
         *context.x86_execution_context(), skip, skip, skip, dout, param.axis,
-        &dx, &dy, SubGradDX<T>(), SubGradDY<T>());
+        &dx, dy, SubGradDX<T>(), SubGradDY<T>());
   }
 
   virtual ~ElementwiseSubGradCompute() = default;
 };
+#endif
 
 template <typename T>
 class ElementwiseAddCompute

paddle/fluid/lite/kernels/x86/sgd_compute.cc

@@ -49,6 +49,7 @@ class SGDCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
     const T *param_data = param->template data<T>();
     const T *grad_data = grad->template data<T>();
     int64_t rows_idx = 0;
+
     T *out_data = param_out->template mutable_data<T>(
         context.x86_device_context()->GetPlace());
 

paddle/fluid/lite/kernels/x86/uniform_random_compute.cc

+// Copyright (c) 2019 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/framework/eigen.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/operators/jit/kernels.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+class UniformRandomCompute
+    : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  void Run() override {
+    auto &context = ctx_->As<X86Context>();
+    auto &param = *param_.get_mutable<operators::UniformRandomParam>();
+    CHECK(context.x86_device_context());
+
+    auto *param_out = &param.Out->raw_tensor();
+
+    T *data =
+        param_out->mutable_data<T>(context.x86_device_context()->GetPlace());
+
+    unsigned int seed = static_cast<unsigned int>(param.seed);
+    std::minstd_rand engine;
+    if (seed == 0) {
+      seed = std::random_device()();
+    }
+    engine.seed(seed);
+    std::uniform_real_distribution<T> dist(static_cast<T>(param.min),
+                                           static_cast<T>(param.max));
+    int64_t size = param_out->numel();
+    for (int64_t i = 0; i < size; ++i) {
+      data[i] = dist(engine);
+    }
+  }
+
+  virtual ~UniformRandomCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+// float
+REGISTER_LITE_KERNEL(uniform_random, kX86, kFloat, kNCHW,
+                     paddle::lite::kernels::x86::UniformRandomCompute<float>,
+                     def)
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();

paddle/fluid/lite/model_parser/compatible_pb.cc

@@ -72,6 +72,10 @@ void AttrsPbToCpp(const pb::OpDesc &pb_desc, cpp::OpDesc *cpp_desc) {
         cpp_desc->SetAttr<std::vector<std::string>>(
             name, pb_desc.GetAttr<std::vector<std::string>>(name));
         break;
+      case AttrType::LONGS:
+        cpp_desc->SetAttr<std::vector<int64_t>>(
+            name, pb_desc.GetAttr<std::vector<int64_t>>(name));
+        break;
       default:
         LOG(FATAL) << "Unsupported attr type found " << static_cast<int>(type);
     }

paddle/fluid/lite/model_parser/cpp/op_desc.cc

@@ -34,6 +34,7 @@ SET_ATTR_IMPL(bool, BOOLEAN);
 SET_ATTR_IMPL(std::vector<int>, INTS);
 SET_ATTR_IMPL(std::vector<float>, FLOATS);
 SET_ATTR_IMPL(std::vector<std::string>, STRINGS);
+SET_ATTR_IMPL(std::vector<int64_t>, LONGS);
 
 std::pair<OpDesc::attrs_t::const_iterator, OpDesc::attr_types_t::const_iterator>
 FindAttr(const cpp::OpDesc& desc, const std::string& name) {

paddle/fluid/lite/model_parser/cpp/op_desc.h

@@ -58,6 +58,12 @@ class OpDesc : public OpDescAPI {
   std::map<std::string, std::vector<std::string>>* mutable_outputs() {
     return &outputs_;
   }
+
+  bool HasInput(const std::string& param) const {
+    auto it = inputs_.find(param);
+    return it != inputs_.end();
+  }
+
   std::vector<std::string> Input(const std::string& param) const override {
     auto it = inputs_.find(param);
     CHECK(it != inputs_.end());
@@ -75,6 +81,11 @@ class OpDesc : public OpDescAPI {
     return res;
   }
 
+  bool HasOutput(const std::string& param) const {
+    auto it = outputs_.find(param);
+    return it != outputs_.end();
+  }
+
   std::vector<std::string> Output(const std::string& param) const override {
     auto it = outputs_.find(param);
     CHECK(it != outputs_.end());

paddle/fluid/lite/model_parser/pb/op_desc.cc

@@ -121,6 +121,7 @@ GET_ATTRS_IMPL(std::vector<int>, ints);
 GET_ATTRS_IMPL(std::vector<float>, floats);
 GET_ATTRS_IMPL(std::vector<std::string>, strings);
 GET_ATTR_IMPL(std::string, s);
+GET_ATTRS_IMPL(std::vector<int64_t>, longs);
 
 }  // namespace pb
 }  // namespace lite

paddle/fluid/lite/operators/CMakeLists.txt

@@ -17,7 +17,9 @@ lite_cc_library(elementwise_ops_lite SRCS elementwise_ops.cc DEPS ${op_DEPS})
 lite_cc_library(fusion_elementwise_activation_ops_lite SRCS fusion_elementwise_activation_ops.cc DEPS elementwise_ops_lite ${op_DEPS})
 lite_cc_library(mean_op_lite SRCS mean_op.cc DEPS ${op_DEPS})
 lite_cc_library(fill_constant_op_lite SRCS fill_constant_op.cc DEPS ${op_DEPS})
-#lite_cc_library(sgd_op_lite SRCS sgd_op.cc DEPS ${op_DEPS})
+lite_cc_library(sgd_op_lite SRCS sgd_op.cc DEPS ${op_DEPS})
+lite_cc_library(uniform_random_op_lite SRCS uniform_random_op.cc DEPS ${op_DEPS})
+
 lite_cc_library(op_params_lite SRCS op_params.cc DEPS ${tensor_lite} any_lite framework_proto_lite)
 lite_cc_library(dropout_op_lite SRCS dropout_op.cc DEPS ${op_DEPS})
 lite_cc_library(concat_op_lite SRCS concat_op.cc DEPS ${op_DEPS})
@@ -52,7 +54,9 @@ set(ops_lite
         transpose_op_lite
         fake_quant
         fake_dequant
-        PARENT_SCOPE)
+        sgd_op_lite
+        uniform_random_op_lite
+        CACHE INTERNAL "ops lite")
 
 lite_cc_test(test_fc_op_lite SRCS fc_op_test.cc 
              DEPS fc_op_lite memory_lite 

paddle/fluid/lite/operators/activation_ops.cc

@@ -72,6 +72,21 @@ class ActivationGradOp : public OpLite {
 
     param_.Out_grad = GetVar<lite::Tensor>(scope, Out_grad_name);
     param_.X_grad = GetMutableVar<Tensor>(scope, X_grad_name);
+
+    if (opdesc.HasInput("X")) {
+      auto X_name = opdesc.Input("X").front();
+      param_.X = GetVar<lite::Tensor>(scope, X_name);
+    } else {
+      param_.X = param_.X_grad;
+    }
+
+    if (opdesc.HasInput("Out")) {
+      auto Out_name = opdesc.Input("Out").front();
+      param_.Out = GetVar<lite::Tensor>(scope, Out_name);
+    } else {
+      param_.Out = param_.Out_grad;
+    }
+
     return true;
   }
 

paddle/fluid/lite/operators/elementwise_ops.cc

@@ -48,31 +48,35 @@ bool ElementwiseOp::AttachImpl(const cpp::OpDesc& opdesc, lite::Scope* scope) {
 bool ElementwiseGradExplicitOp::CheckShape() const {
   CHECK_OR_FALSE(param_.Y);
   CHECK_OR_FALSE(param_.X_grad);
-  CHECK_OR_FALSE(param_.Y_grad);
   CHECK_OR_FALSE(param_.Out_grad);
   return true;
 }
 
 bool ElementwiseGradExplicitOp::InferShape() const {
   param_.X_grad->Resize(param_.Out_grad->dims());
-  param_.Y_grad->Resize(param_.Y->dims());
+  if (param_.Y_grad) param_.Y_grad->Resize(param_.Y->dims());
   return true;
 }
 
 bool ElementwiseGradExplicitOp::AttachImpl(const cpp::OpDesc& opdesc,
                                            lite::Scope* scope) {
-  CHECK_EQ(opdesc.InputArgumentNames().size(), 1UL);
+  CHECK_EQ(opdesc.InputArgumentNames().size(), 2UL);
+  auto Y_name = opdesc.Input("Y").front();
   auto Out_name = opdesc.Input(framework::GradVarName("Out")).front();
-  auto X_name = opdesc.Output(framework::GradVarName("X")).front();
-  auto Y_name = opdesc.Output(framework::GradVarName("Y")).front();
+  auto X_grad = opdesc.Output(framework::GradVarName("X")).front();
 
+  if (opdesc.Output(framework::GradVarName("Y")).size() > 0) {
+    auto Y_grad = opdesc.Output(framework::GradVarName("Y")).front();
+    param_.Y_grad = GetMutableVar<Tensor>(scope, Y_grad);
+  }
+  param_.Y = GetVar<lite::Tensor>(scope, Y_name);
   param_.Out_grad = GetVar<lite::Tensor>(scope, Out_name);
-  param_.X_grad = GetMutableVar<lite::Tensor>(scope, X_name);
-  param_.Y_grad = GetMutableVar<Tensor>(scope, Y_name);
+  param_.X_grad = GetMutableVar<lite::Tensor>(scope, X_grad);
   param_.axis = opdesc.GetAttr<int>("axis");
 
   return true;
 }
+
 #endif
 
 }  // namespace operators

paddle/fluid/lite/operators/fill_constant_op.cc

@@ -36,7 +36,7 @@ class FillConstantOp : public OpLite {
   bool AttachImpl(const cpp::OpDesc& opdesc, lite::Scope* scope) override {
     auto Out_name = opdesc.Output("Out").front();
 
-    param_.Out = GetMutableVar<Tensor>(scope, Out_name);
+    param_.Out = GetMutableVar<lite::Tensor>(scope, Out_name);
     param_.dtype = opdesc.GetAttr<int>("dtype");
     param_.shape = opdesc.GetAttr<std::vector<int64_t>>("shape");
     param_.value = opdesc.GetAttr<float>("value");

paddle/fluid/lite/operators/mean_op.cc

@@ -51,7 +51,7 @@ class MeanOp : public OpLite {
   std::string DebugString() const override { return "mean"; }
 
  private:
-  mutable operators::ElementwiseParam param_;
+  mutable operators::MeanParam param_;
 };
 
 #ifdef LITE_WITH_X86
@@ -73,7 +73,7 @@ class MeanGradOp : public OpLite {
   }
 
   bool AttachImpl(const cpp::OpDesc& opdesc, lite::Scope* scope) override {
-    CHECK_EQ(opdesc.InputArgumentNames().size(), 3UL);
+    CHECK_EQ(opdesc.InputArgumentNames().size(), 2UL);
     auto X_name = opdesc.Input("X").front();
     auto Out_grad_name = opdesc.Input(framework::GradVarName("Out")).front();
     auto X_grad_name = opdesc.Output(framework::GradVarName("X")).front();

paddle/fluid/lite/operators/mul_op.cc

@@ -31,16 +31,18 @@ bool MulOpLite::CheckShape() const {
   CHECK_GT_OR_FALSE(x_dims.size(), static_cast<size_t>(param_.x_num_col_dims));
   CHECK_GT_OR_FALSE(y_dims.size(), static_cast<size_t>(param_.y_num_col_dims));
 
-  // auto x_mat_dims =
-  //     framework::flatten_to_2d(x_dims.data(), param_.x_num_col_dims);
-  // auto y_mat_dims =
-  //     framework::flatten_to_2d(y_dims.data(), param_.y_num_col_dims);
-
-  // PADDLE_ENFORCE_EQ(x_mat_dims[1], y_mat_dims[0],
-  //                   "First matrix's width must be equal with second matrix's
-  //                   "
-  //                   "height. %s, %s",
-  //                   x_mat_dims[1], y_mat_dims[0]);
+#ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
+  auto x_mat_dims =
+      framework::flatten_to_2d(x_dims.data(), param_.x_num_col_dims);
+  auto y_mat_dims =
+      framework::flatten_to_2d(y_dims.data(), param_.y_num_col_dims);
+
+  PADDLE_ENFORCE_EQ(x_mat_dims[1], y_mat_dims[0],
+                    "First matrix's width must be equal with second matrix's"
+                    "height. %s, %s",
+                    x_mat_dims[1], y_mat_dims[0]);
+#endif
+
   return true;
 }
 
@@ -73,30 +75,34 @@ bool MulGradOpLite::CheckShape() const {
   CHECK_OR_FALSE(param_.x);
   CHECK_OR_FALSE(param_.y);
   CHECK_OR_FALSE(param_.output_grad);
-  CHECK_OR_FALSE(param_.x_grad);
-  CHECK_OR_FALSE(param_.y_grad);
 
   return true;
 }
 
 bool MulGradOpLite::InferShape() const {
-  param_.x_grad->Resize(param_.x->dims());
-  param_.y_grad->Resize(param_.y->dims());
+  if (param_.x_grad) param_.x_grad->Resize(param_.x->dims());
+  if (param_.y_grad) param_.y_grad->Resize(param_.y->dims());
   return true;
 }
 
 bool MulGradOpLite::AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) {
   auto X_name = op_desc.Input("X").front();
   auto Y_name = op_desc.Input("Y").front();
-  auto Out_grad_name = op_desc.Output(framework::GradVarName("Out")).front();
-  auto X_grad_name = op_desc.Output(framework::GradVarName("X")).front();
-  auto Y_grad_name = op_desc.Output(framework::GradVarName("Y")).front();
+  auto Out_grad_name = op_desc.Input(framework::GradVarName("Out")).front();
+
+  if (op_desc.Output(framework::GradVarName("X")).size()) {
+    auto X_grad_name = op_desc.Output(framework::GradVarName("X")).front();
+    param_.x_grad = GetMutableVar<lite::Tensor>(scope, X_grad_name);
+  }
+
+  if (op_desc.Output(framework::GradVarName("Y")).size()) {
+    auto Y_grad_name = op_desc.Output(framework::GradVarName("Y")).front();
+    param_.y_grad = GetMutableVar<lite::Tensor>(scope, Y_grad_name);
+  }
 
   param_.x = GetVar<lite::Tensor>(scope, X_name);
   param_.y = GetVar<lite::Tensor>(scope, Y_name);
   param_.output_grad = GetVar<lite::Tensor>(scope, Out_grad_name);
-  param_.x_grad = GetMutableVar<lite::Tensor>(scope, X_grad_name);
-  param_.y_grad = GetMutableVar<lite::Tensor>(scope, Y_grad_name);
 
   return true;
 }
@@ -107,3 +113,6 @@ bool MulGradOpLite::AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) {
 }  // namespace paddle
 
 REGISTER_LITE_OP(mul, paddle::lite::operators::MulOpLite);
+#ifdef LITE_WITH_X86
+REGISTER_LITE_OP(mul_grad, paddle::lite::operators::MulGradOpLite);
+#endif

paddle/fluid/lite/operators/mul_op.h

@@ -66,6 +66,7 @@ class MulOpLite : public OpLite {
   mutable MulParam param_;
 };
 
+#ifdef LITE_WITH_X86
 class MulGradOpLite : public OpLite {
  public:
   MulGradOpLite() {}
@@ -85,6 +86,7 @@ class MulGradOpLite : public OpLite {
  private:
   mutable MulGradParam param_;
 };
+#endif
 
 }  // namespace operators
 }  // namespace lite

paddle/fluid/lite/operators/op_params.h

@@ -36,7 +36,7 @@ using param_t = Any;
 
 /// ----------------------- Functional operators ------------------------------
 struct FeedParam {
-  const std::vector<lite::Tensor>* feed_list{};
+  std::vector<lite::Tensor>* feed_list{};
   lite::Tensor* out{};
   int col;
 };
@@ -317,6 +317,16 @@ struct SGDParam {
   lite::Tensor* ParamOut{};
 };
 
+/// ----------------------- uniform_random operators ----------------------
+struct UniformRandomParam {
+  std::vector<int64_t> shape{};
+  float min{-1.0f};
+  float max{1.0f};
+  int seed{0};
+  int dtype{framework::proto::VarType::FP32};
+  lite::Tensor* Out{};
+};
+
 }  // namespace operators
 }  // namespace lite
 }  // namespace paddle

paddle/fluid/lite/operators/sgd_op.cc

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "/paddle/paddle/fluid/lite/operators/sgd_op.h"
+#include "paddle/fluid/lite/operators/sgd_op.h"
 #include "paddle/fluid/lite/core/op_lite.h"
 #include "paddle/fluid/lite/core/op_registry.h"
 
@@ -30,13 +30,14 @@ bool SGDOpLite::CheckShape() const {
 
 bool SGDOpLite::InferShape() const {
   auto lr_dims = param_.LearningRate->dims().data();
+#ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
   CHECK_EQ_OR_FALSE(framework::product(lr_dims), 1);
+#endif
   param_.ParamOut->Resize(param_.Param->dims());
   return true;
 }
 
-bool SGDOpLite::AttachImpl(const OpDesc& opdesc, lite::Scope* scope) {
-  CHECK_EQ(opdesc.Inputs().size(), 3UL);
+bool SGDOpLite::AttachImpl(const cpp::OpDesc& opdesc, lite::Scope* scope) {
   auto Param_name = opdesc.Input("Param").front();
   auto LearningRate_name = opdesc.Input("LearningRate").front();
   auto Grad_name = opdesc.Input("Grad").front();

paddle/fluid/lite/operators/sgd_op.h

@@ -37,7 +37,7 @@ class SGDOpLite : public OpLite {
 
   void AttachKernel(KernelBase *kernel) override { kernel->SetParam(param_); }
 
-  bool AttachImpl(const OpDesc &op_desc, lite::Scope *scope) override;
+  bool AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) override;
 
   std::string DebugString() const override { return "sgd"; }
 

paddle/fluid/lite/operators/uniform_random_op.cc

+// Copyright (c) 2019 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/lite/operators/uniform_random_op.h"
+#include "paddle/fluid/lite/core/op_lite.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace operators {
+
+bool UniformRandomOpLite::CheckShape() const { return true; }
+
+bool UniformRandomOpLite::InferShape() const {
+  param_.Out->Resize(param_.shape);
+  return true;
+}
+
+bool UniformRandomOpLite::AttachImpl(const cpp::OpDesc& opdesc,
+                                     lite::Scope* scope) {
+  param_.shape = opdesc.GetAttr<std::vector<int64_t>>("shape");
+  param_.min = opdesc.GetAttr<float>("min");
+  param_.max = opdesc.GetAttr<float>("max");
+  param_.seed = opdesc.GetAttr<int>("seed");
+  param_.dtype = opdesc.GetAttr<int>("dtype");
+  param_.Out = GetMutableVar<Tensor>(scope, opdesc.Output("Out").front());
+  return true;
+}
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_LITE_OP(uniform_random, paddle::lite::operators::UniformRandomOpLite);

paddle/fluid/lite/operators/uniform_random_op.h

+// Copyright (c) 2019 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 <string>
+#include <vector>
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_lite.h"
+#include "paddle/fluid/lite/core/scope.h"
+#include "paddle/fluid/lite/operators/op_params.h"
+#include "paddle/fluid/lite/utils/all.h"
+
+namespace paddle {
+namespace lite {
+namespace operators {
+
+class UniformRandomOpLite : public OpLite {
+ public:
+  UniformRandomOpLite() {}
+
+  explicit UniformRandomOpLite(const std::string &type) : OpLite(type) {}
+
+  bool CheckShape() const override;
+
+  bool InferShape() const override;
+
+  void AttachKernel(KernelBase *kernel) override { kernel->SetParam(param_); }
+
+  bool AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) override;
+
+  std::string DebugString() const override { return "uniform_random"; }
+
+ private:
+  mutable UniformRandomParam param_;
+};
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/python/lite_test.py

+# Copyright (c) 2019 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 paddle
+import paddle.fluid as fluid
+import paddle.fluid.compiler as compiler
+import paddle.fluid.core as core
+import paddle.fluid.core.lite as lite
+import paddle.fluid.layers as layers
+import numpy as np
+import unittest
+
+from paddle.fluid.cxx_trainer import add_feed_fetch_op
+
+
+def _as_lodtensor(data, place):
+    # single tensor case
+    tensor = core.LoDTensor()
+    tensor.set(data, place)
+    return tensor
+
+
+data_label = [[
+    0.753544, 0.772977, 0.646915, 0.747543, 0.528923, 0.0517749, 0.248678,
+    0.75932, 0.960376, 0.606618
+]]
+data_a = [[
+    0.874445, 0.21623, 0.713262, 0.702672, 0.396977, 0.828285, 0.932995,
+    0.442674, 0.0321735, 0.484833, 0.045935, 0.21276, 0.556421, 0.131825,
+    0.285626, 0.741409, 0.257467, 0.975958, 0.444006, 0.114553
+]]
+
+data_loss = [0.9876687]
+
+
+class NaiveModelTest(unittest.TestCase):
+    def test_model(self):
+
+        start_prog = fluid.Program()
+        main_prog = fluid.Program()
+
+        start_prog.random_seed = 100
+        main_prog.random_seed = 100
+
+        with fluid.program_guard(main_prog, start_prog):
+            a = fluid.layers.data(name="a", shape=[1, 20], dtype='float32')
+            label = fluid.layers.data(name="label", shape=[10], dtype='float32')
+            a1 = fluid.layers.fc(input=a, size=10, act=None, bias_attr=False)
+            cost = fluid.layers.square_error_cost(a1, label)
+            avg_cost = fluid.layers.mean(cost)
+
+            optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+            optimizer.minimize(avg_cost)
+
+            x86_place = lite.Place(lite.TargetType.kX86,
+                                   lite.PrecisionType.kFloat,
+                                   lite.DataLayoutType.kNCHW, 0)
+            host_place = lite.Place(lite.TargetType.kHost,
+                                    lite.PrecisionType.kFloat,
+                                    lite.DataLayoutType.kNCHW, 0)
+            scope = lite.Scope()
+
+        trainer = lite.CXXTrainer(scope, x86_place, [x86_place, host_place])
+        trainer.run_startup_program(start_prog.desc)
+
+        cpu = fluid.core.CPUPlace()
+        main_prog = add_feed_fetch_op(
+            main_prog,
+            feed=['a', 'label'],
+            fetch_list={avg_cost},
+            scope=scope,
+            place=cpu)
+        # print(main_prog)
+        exe = trainer.build_main_program_executor(main_prog.desc)
+
+        feed_data = [
+            _as_lodtensor(np.array(data_a, object), cpu),
+            _as_lodtensor(np.array(data_label, object), cpu)
+        ]
+
+        exe.run(feed_data)
+        # print(np.array(exe.get_output(0).raw_tensor()))
+        self.assertTrue(
+            np.allclose(
+                np.array(data_loss),
+                np.array(exe.get_output(0).raw_tensor()),
+                atol=1e-8),
+            "lite result not equel to offline result")
+
+
+if __name__ == '__main__':
+    unittest.main()

paddle/fluid/lite/tools/build.sh

@@ -154,6 +154,26 @@ function build_test_server {
     test_lite $TESTS_FILE
 }
 
+function build_test_train {
+    mkdir -p ./build
+    cd ./build
+    export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/paddle/build/third_party/install/mklml/lib"
+    prepare_workspace # fake an empty __generated_code__.cc to pass cmake.
+    cmake .. -DWITH_LITE=ON -DWITH_GPU=OFF -DWITH_PYTHON=ON -DLITE_WITH_X86=ON -DLITE_WITH_LIGHT_WEIGHT_FRAMEWORK=OFF -DWITH_TESTING=ON -DWITH_MKL=OFF 
+    
+    make test_gen_code_lite -j$NUM_CORES_FOR_COMPILE
+    make test_cxx_api_lite -j$NUM_CORES_FOR_COMPILE
+    ctest -R test_cxx_api_lite
+    ctest -R test_gen_code_lite
+    make test_generated_code -j$NUM_CORES_FOR_COMPILE
+
+    make -j$NUM_CORES_FOR_COMPILE
+
+    find -name "*.whl" | xargs pip2 install 
+    python ../paddle/fluid/lite/python/lite_test.py
+
+}
+
 # test_arm_android <some_test_name> <adb_port_number>
 function test_arm_android {
     local test_name=$1
@@ -603,6 +623,10 @@ function main {
                 build_test_server
                 shift
                 ;;
+            build_test_train)
+                build_test_train
+                shift
+                ;;
             build_test_arm)
                 build_test_arm
                 shift

paddle/fluid/pybind/CMakeLists.txt

-set(PYBIND_DEPS pybind python proto_desc memory executor async_executor fleet_wrapper nccl_wrapper prune
-  feed_fetch_method pass_builder parallel_executor profiler layer scope_pool
-  tracer analysis_predictor imperative_profiler nccl_context)
+message(STATUS "use ${x86_kernels}")
+message(STATUS "use ${ops_lite}")
+
+if(WITH_PYTHON)
+  cc_library(bind_executor_lite SRCS executor_lite.cc DEPS pybind framework_proto)
+  set(PYBIND_DEPS pybind python proto_desc memory executor async_executor fleet_wrapper nccl_wrapper prune
+    feed_fetch_method pass_builder parallel_executor profiler layer scope_pool bind_executor_lite cxx_api_lite scope_lite ${ops_lite} ${host_kernels} ${x86_kernels} mir_passes kernel_lite op_lite optimizer_lite 
+    tracer analysis_predictor imperative_profiler nccl_context)
+endif(WITH_PYTHON)
+
 
 if(WITH_PYTHON)
   list(APPEND PYBIND_DEPS py_func_op)

paddle/fluid/pybind/executor_lite.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.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/pybind/executor_lite.h"
+#include <pybind11/stl.h>
+#include <memory>
+#include <vector>
+#include "paddle/fluid/lite/api/cxx_api.h"
+#include "paddle/fluid/lite/api/paddle_use_passes.h"
+#include "paddle/fluid/lite/core/hvy_tensor.h"
+#include "paddle/fluid/lite/core/scope.h"
+#include "pybind11/pybind11.h"
+
+namespace lt = paddle::lite;
+namespace py = pybind11;
+
+namespace paddle {
+namespace pybind {
+
+void BindTensor(pybind11::module* m) {
+  pybind11::class_<lt::TensorHvy>(*m, "Tensor")
+      .def(pybind11::init<>())
+      .def("raw_tensor", [](lt::TensorHvy& self) { return self.raw_tensor(); })
+      .def("share_data_with",
+           [](lt::TensorHvy& self, const framework::Tensor& other) {
+             self.ShareDataWith(other);
+           });
+}
+
+void BindVariable(pybind11::module* m) {
+  pybind11::class_<lt::Variable>(*m, "Variable")
+      .def("get_mutable_tensor",
+           [](lt::Variable& self) { return self.GetMutable<lt::Tensor>(); })
+      .def("get_mutable_fetch_list",
+           [](lt::Variable& self) -> paddle::lite::FeedFetchList* {
+             return self.GetMutable<paddle::lite::FeedFetchList>();
+           },
+           py::return_value_policy::reference);
+}
+
+void BindScope(pybind11::module* m) {
+  py::class_<lt::Scope, std::shared_ptr<lt::Scope>>(*m, "Scope")
+      .def(pybind11::init<>())
+      .def("new_scope",
+           [](lt::Scope& self) -> lt::Scope* { return &self.NewScope(); },
+           py::return_value_policy::reference)
+      .def("var", &lt::Scope::Var, pybind11::return_value_policy::reference)
+      .def("find_var", &lt::Scope::FindVar,
+           pybind11::return_value_policy::reference)
+      .def("find_local_var", &lt::Scope::FindLocalVar,
+           pybind11::return_value_policy::reference)
+      .def("parent", &lt::Scope::parent,
+           pybind11::return_value_policy::reference)
+      .def("local_var_names", &lt::Scope::LocalVarNames,
+           pybind11::return_value_policy::reference);
+}
+
+void BindExecutorLite(pybind11::module* m) {
+  py::class_<lt::Predictor>(*m, "Predictor")
+      .def(pybind11::init<>())
+      .def("__init__",
+           [](lt::Predictor& self,
+              const std::shared_ptr<lt::Scope>& root_scope) {
+             new (&self) lt::Predictor(root_scope);
+           })
+      .def("get_input", &lt::Predictor::GetInput,
+           pybind11::return_value_policy::reference)
+      .def("get_output", &lt::Predictor::GetOutput,
+           pybind11::return_value_policy::reference)
+      .def("run", [](lt::Predictor& self) { self.Run(); })
+      .def("run", [](lt::Predictor& self,
+                     const std::vector<framework::Tensor>& tensors) {
+        self.Run(tensors);
+      });
+}
+
+void BindEnums(pybind11::module* m) {
+  py::enum_<lt::TargetType>(*m, "TargetType", py::arithmetic(),
+                            "TargetType enum")
+      .value("kUnk", lt::TargetType::kUnk)
+      .value("kHost", lt::TargetType::kHost)
+      .value("kX86", lt::TargetType::kX86)
+      .value("kCUDA", lt::TargetType::kCUDA)
+      .value("kARM", lt::TargetType::kARM)
+      .value("kAny", lt::TargetType::kAny)
+      .value("NUM", lt::TargetType::NUM);
+
+  py::enum_<lt::PrecisionType>(*m, "PrecisionType", py::arithmetic(),
+                               "PrecisionType enum")
+      .value("kUnk", lt::PrecisionType::kUnk)
+      .value("kFloat", lt::PrecisionType::kFloat)
+      .value("kInt8", lt::PrecisionType::kInt8)
+      .value("kAny", lt::PrecisionType::kAny)
+      .value("NUM", lt::PrecisionType::NUM);
+
+  py::enum_<lt::DataLayoutType>(*m, "DataLayoutType", py::arithmetic(),
+                                "DataLayoutType enum")
+      .value("kUnk", lt::DataLayoutType::kUnk)
+      .value("kNCHW", lt::DataLayoutType::kNCHW)
+      .value("kAny", lt::DataLayoutType::kAny)
+      .value("NUM", lt::DataLayoutType::NUM);
+}
+
+void BindPlace(pybind11::module* m) {
+  pybind11::class_<lt::Place, std::shared_ptr<lt::Place>>(*m, "Place")
+      .def(pybind11::init<>())
+      .def("__init__",
+           [](lt::Place& self, lt::TargetType target,
+              lt::PrecisionType precision, lt::DataLayoutType layout,
+              int16_t device) {
+             new (&self) lt::Place(target, precision, layout, device);
+           })
+      .def("is_valid", &lt::Place::is_valid,
+           pybind11::return_value_policy::reference);
+}
+
+void BindCXXTrainer(pybind11::module* m) {
+  pybind11::class_<lt::CXXTrainer, std::shared_ptr<lt::CXXTrainer>>(
+      *m, "CXXTrainer")
+      .def(
+          "__init__",
+          [](lt::CXXTrainer& self, const std::shared_ptr<lt::Scope>& root_scope,
+             const lt::Place& preferred_place,
+             const std::vector<lt::Place>& valid_places) {
+            new (&self)
+                lt::CXXTrainer(root_scope, preferred_place, valid_places);
+          })
+      .def("build_main_program_executor",
+           [](lt::CXXTrainer& self,
+              framework::ProgramDesc& desc) -> lt::Predictor& {
+             return self.BuildMainProgramExecutor(desc);
+           },
+           pybind11::return_value_policy::reference)
+      .def("run_startup_program",
+           [](lt::CXXTrainer& self, framework::ProgramDesc& desc) {
+             return self.RunStartupProgram(desc);
+           });
+}
+
+void BindLite(pybind11::module* m) {
+  BindTensor(m);
+  BindVariable(m);
+  BindScope(m);
+  BindExecutorLite(m);
+  BindEnums(m);
+  BindPlace(m);
+  BindCXXTrainer(m);
+}
+
+}  // namespace pybind
+}  // namespace paddle
+
+// USE_LITE_OP(mul);
+USE_LITE_OP(elementwise_sub);
+USE_LITE_OP(uniform_random);
+USE_LITE_OP(feed);
+USE_LITE_OP(fetch);
+USE_LITE_OP(fill_constant);
+USE_LITE_OP(mul);
+USE_LITE_OP(mul_grad);
+USE_LITE_OP(mean);
+USE_LITE_OP(square);
+USE_LITE_OP(sgd);
+
+USE_LITE_KERNEL(feed, kHost, kAny, kAny, def);
+USE_LITE_KERNEL(fetch, kHost, kAny, kAny, def);
+
+#ifdef LITE_WITH_X86
+USE_LITE_KERNEL(uniform_random, kX86, kFloat, kNCHW, def);
+USE_LITE_KERNEL(fill_constant, kX86, kFloat, kNCHW, def);
+USE_LITE_KERNEL(mul, kX86, kFloat, kNCHW, def);
+USE_LITE_KERNEL(mul_grad, kX86, kFloat, kNCHW, def);
+USE_LITE_KERNEL(square, kX86, kFloat, kNCHW, def);
+USE_LITE_KERNEL(mean, kX86, kFloat, kNCHW, def);
+USE_LITE_KERNEL(sgd, kX86, kFloat, kNCHW, def);
+USE_LITE_KERNEL(elementwise_sub, kX86, kFloat, kNCHW, def);
+USE_LITE_KERNEL(elementwise_sub_grad, kX86, kFloat, kNCHW, def);
+#endif

paddle/fluid/pybind/executor_lite.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.
+See the License for the specific language governing permissions and
+limitations under the License. */
+#pragma once
+
+#include <Python.h>
+
+#include "pybind11/pybind11.h"
+
+namespace paddle {
+namespace pybind {
+
+void BindLite(pybind11::module* m);
+
+}  // namespace pybind
+}  // namespace paddle

paddle/fluid/pybind/pybind.cc

@@ -54,6 +54,7 @@ limitations under the License. */
 #include "paddle/fluid/pybind/const_value.h"
 #include "paddle/fluid/pybind/data_set_py.h"
 #include "paddle/fluid/pybind/exception.h"
+#include "paddle/fluid/pybind/executor_lite.h"
 #include "paddle/fluid/pybind/fleet_wrapper_py.h"
 #include "paddle/fluid/pybind/imperative.h"
 #include "paddle/fluid/pybind/inference_api.h"
@@ -366,6 +367,7 @@ PYBIND11_MODULE(core, m) {
       .def("set", PyCUDAPinnedTensorSetFromArray<int8_t>)
 #endif
       .def("shape", [](Tensor &self) { return vectorize(self.dims()); })
+      .def("memory_size", [](Tensor &self) { return self.memory_size(); })
       .def("_set_float_element", TensorSetElement<float>)
       .def("_get_float_element", TensorGetElement<float>)
       .def("_set_double_element", TensorSetElement<double>)
@@ -1528,6 +1530,9 @@ All parameter, weight, gradient are variables in Paddle.
   BindNode(&m);
   BindInferenceApi(&m);
   BindDataset(&m);
+
+  py::module lite = m.def_submodule("lite", "submodule lite");
+  BindLite(&lite);
 }
 }  // namespace pybind
 }  // namespace paddle

python/paddle/fluid/__init__.py

@@ -65,6 +65,7 @@ from paddle.fluid.layers.math_op_patch import monkey_patch_variable
 from . import install_check
 from .dygraph.nn import *
 from .dygraph.layers import *
+from .cxx_trainer import *
 
 Tensor = LoDTensor
 

python/paddle/fluid/backward.py

@@ -71,6 +71,7 @@ def _create_op_desc_(op_type, inputs, outputs, attrs):
             op_desc.set_block_attr(name, val.desc)
         else:
             op_desc._set_attr(name, val)
+    op_desc.check_attrs()
     return op_desc
 
 

python/paddle/fluid/cxx_trainer.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.
+
+from __future__ import print_function
+from . import core
+from . import framework
+from . import executor
+from . import compiler
+import sys
+
+from .framework import default_main_program, Variable
+
+__all__ = ['add_feed_fetch_op']
+
+
+def _has_feed_operators(block, feed_targets, feed_holder_name):
+    """ Check whether the block already has feed operators.
+
+    Return false if the block does not have any feed operators.
+    If some feed operators have been prepended to the block, check that
+    the info contained in these feed operators matches the feed_targets
+    and feed_holder_name. Raise exception when any mismatch is found.
+    Return true when the block has feed operators with matching info.
+
+    Args:
+        block: a block instance (typically global block of a program)
+        feed_targets: a dictionary of {feed_target_name: feed_target_data}
+        feed_holder_name: the name of the variable that holds the data of
+            all feed targets. The type of this feed_holder variable is
+            FEED_MINIBATCH, which is essentially vector<LoDTensor>.
+
+    Returns:
+        A boolean value that indicates whether a block has feed operators
+        that match the info contained in feed_targets and feed_holder_name.
+    """
+
+    feed_count = 0
+    for op in block.ops:
+        if op.desc.type() == 'feed':
+            feed_count += 1
+            assert op.desc.input('X')[0] == feed_holder_name
+            feed_target_name = op.desc.output('Out')[0]
+            if feed_target_name not in feed_targets:
+                raise Exception("'feed_targets' does not have {} variable".
+                                format(feed_target_name))
+        else:
+            break
+    if feed_count > 0 and feed_count != len(feed_targets):
+        raise Exception(
+            "Feed operators in program desc do not match 'feed_targets'")
+    return feed_count > 0
+
+
+def _has_fetch_operators(block, fetch_targets, fetch_holder_name):
+    """ Check whether the block already has fetch operators.
+
+    Return false if the block does not have any fetch operators.
+    If some fetch operators have been appended to the block, check that
+    the info contained in these fetch operators matches the fetch_targets
+    and fetch_holder_name. Raise exception when any mismatch is found.
+    Return true when the block has fetch operators with matching info.
+
+    Args:
+        block: a block instance (typically global block of a program)
+        fetch_targets: a dictionary of {fetch_target_name: fetch_target_data}
+        fetch_holder_name: the name of the variable that holds the data of
+            all fetch targets. The type of this fetch_holder variable is
+            FETCH_LIST, which is essentially vector<LoDTensor>.
+
+    Return:
+        A boolean value that indicates whether a block has fetch operators
+        that match the info contained in fetch_targets and fetch_holder_name.
+    """
+
+    fetch_count = 0
+    for op in block.ops:
+        if op.desc.type() == 'fetch':
+            fetch_count += 1
+            assert op.desc.output('Out')[0] == fetch_holder_name
+            fetch_target_name = op.desc.input('X')[0]
+            if fetch_target_name not in [
+                    var.desc.name() for var in fetch_targets
+            ]:
+                raise Exception("'fetch_targets' does not have {} variable".
+                                format(fetch_target_name))
+            idx = op.desc.attr('col')
+            assert fetch_target_name == fetch_targets[idx].desc.name()
+    if fetch_count > 0 and fetch_count != len(fetch_targets):
+        raise Exception(
+            "Fetch operators in program desc do not match 'fetch_targets'")
+    return fetch_count > 0
+
+
+def _add_feed_fetch_ops(program,
+                        feed,
+                        fetch_list,
+                        feed_var_name='feed',
+                        fetch_var_name='fetch'):
+    tmp_program = program.clone()
+
+    global_block = tmp_program.global_block()
+
+    if feed_var_name in global_block.vars:
+        feed_var = global_block.var(feed_var_name)
+    else:
+        feed_var = global_block.create_var(
+            name=feed_var_name,
+            type=core.VarDesc.VarType.FEED_MINIBATCH,
+            persistable=True)
+
+    if fetch_var_name in global_block.vars:
+        fetch_var = global_block.var(fetch_var_name)
+    else:
+        fetch_var = global_block.create_var(
+            name=fetch_var_name,
+            type=core.VarDesc.VarType.FETCH_LIST,
+            persistable=True)
+
+    # prepend feed operators
+    if not _has_feed_operators(global_block, feed, feed_var_name):
+        for i, name in enumerate(feed):
+            out = global_block.var(name)
+            global_block._prepend_op(
+                type='feed',
+                inputs={'X': [feed_var]},
+                outputs={'Out': [out]},
+                attrs={'col': i})
+
+    # append fetch_operators
+    if not _has_fetch_operators(global_block, fetch_list, fetch_var_name):
+        for i, var in enumerate(fetch_list):
+            assert isinstance(var, Variable) or isinstance(
+                var, six.string_types), ("Wrong type for fetch_list[%s]: %s" %
+                                         (i, type(var)))
+            global_block.append_op(
+                type='fetch',
+                inputs={'X': [var]},
+                outputs={'Out': [fetch_var]},
+                attrs={'col': i})
+
+    return tmp_program
+
+
+def add_feed_fetch_op(program, feed, fetch_list, scope, place):
+
+    if program is None:
+        program = default_main_program()
+
+    program = _add_feed_fetch_ops(
+        program=program, feed=feed, fetch_list=fetch_list)
+
+    return program