make an adapter for TensorLite and framework::LoDTensor and DDim

paddle/fluid/lite/api/cxx_api.h

@@ -45,17 +45,17 @@ class LightPredictor {
   void SaveModel(const std::string& dir);
 
   // Get offset-th col of feed.
-  Tensor* GetInput(size_t offset) {
+  lite::Tensor* GetInput(size_t offset) {
     auto* _feed_list = program_->exec_scope()->FindVar("feed");
     CHECK(_feed_list) << "no feed variable in exec_scope";
-    auto* feed_list = _feed_list->GetMutable<std::vector<Tensor>>();
+    auto* feed_list = _feed_list->GetMutable<std::vector<lite::Tensor>>();
     if (offset >= feed_list->size()) {
       feed_list->resize(offset + 1);
     }
     return &feed_list->at(offset);
   }
 
-  const Tensor* GetOutput(size_t offset) {
+  const lite::Tensor* GetOutput(size_t offset) {
     auto* _fetch_list = program_->exec_scope()->FindVar("fetch");
     CHECK(_fetch_list) << "no fatch variable in exec_scope";
     auto& fetch_list = *_fetch_list->GetMutable<std::vector<lite::Tensor>>();

paddle/fluid/lite/api/cxx_api_test.cc

@@ -13,11 +13,14 @@
 // limitations under the License.
 
 #include "paddle/fluid/lite/api/cxx_api.h"
+#include <gflags/gflags.h>
 #include <gtest/gtest.h>
 #include "paddle/fluid/lite/core/mir/passes.h"
 #include "paddle/fluid/lite/core/op_executor.h"
 #include "paddle/fluid/lite/core/op_registry.h"
 
+DEFINE_string(model_dir, "", "");
+
 namespace paddle {
 namespace lite {
 
@@ -36,24 +39,22 @@ TEST(CXXApi, test) {
   });
 #endif
 
-  predictor.Build("/home/chunwei/project/models/model2",
-                  Place{TARGET(kCUDA), PRECISION(kFloat)}, valid_places);
+  predictor.Build(FLAGS_model_dir, Place{TARGET(kCUDA), PRECISION(kFloat)},
+                  valid_places);
 
   auto* input_tensor = predictor.GetInput(0);
-  input_tensor->Resize({100, 100});
-  auto* data = TensorMutableData<float>(input_tensor, TARGET(kHost),
-                                        product(input_tensor->dims()));
+  input_tensor->Resize(DDim(std::vector<DDim::value_type>({100, 100})));
+  auto* data = input_tensor->mutable_data<float>();
   for (int i = 0; i < 100 * 100; i++) {
     data[i] = i;
   }
 
-  LOG(INFO) << "input " << input_tensor;
   LOG(INFO) << "input " << *input_tensor;
 
   predictor.Run();
 
   auto* out = predictor.GetOutput(0);
-  LOG(INFO) << out << " memory size " << out->memory_size();
+  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();
@@ -63,8 +64,8 @@ TEST(CXXApi, test) {
 TEST(CXXApi, save_model) {
   lite::LightPredictor predictor;
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)}});
-  predictor.Build("/home/chunwei/project/models/model2",
-                  Place{TARGET(kCUDA), PRECISION(kFloat)}, valid_places);
+  predictor.Build(FLAGS_model_dir, Place{TARGET(kCUDA), PRECISION(kFloat)},
+                  valid_places);
 
   predictor.SaveModel("./optimized_model");
 }

paddle/fluid/lite/api/light_api.h

@@ -41,20 +41,21 @@ class LightPredictor {
   void Run() { program_->Run(); }
 
   // Get offset-th col of feed.
-  Tensor* GetInput(size_t offset) {
+  TensorBase* GetInput(size_t offset) {
     auto* _feed_list = program_->exec_scope()->FindVar("feed");
     CHECK(_feed_list) << "no feed variable in exec_scope";
-    auto* feed_list = _feed_list->GetMutable<std::vector<Tensor>>();
+    auto* feed_list = _feed_list->GetMutable<std::vector<TensorBase>>();
     if (offset >= feed_list->size()) {
       feed_list->resize(offset + 1);
     }
     return &feed_list->at(offset);
   }
 
-  const Tensor* GetOutput(size_t offset) {
+  const TensorBase* GetOutput(size_t offset) {
     auto* _fetch_list = program_->exec_scope()->FindVar("fetch");
     CHECK(_fetch_list) << "no fatch variable in exec_scope";
-    auto& fetch_list = *_fetch_list->GetMutable<std::vector<lite::Tensor>>();
+    auto& fetch_list =
+        *_fetch_list->GetMutable<std::vector<lite::TensorBase>>();
     CHECK_LT(offset, fetch_list.size()) << "offset " << offset << " overflow";
     return &fetch_list.at(offset);
   }

paddle/fluid/lite/core/CMakeLists.txt

@@ -2,22 +2,26 @@ cc_library(lite_gtest_main SRCS lite_gtest_main.cc)
 cc_library(memory_lite SRCS memory.cc)
 cc_library(target_wrapper_lite SRCS target_wrapper.cc)
 
+cc_library(lite_tensor SRCS lite_tensor.cc DEPS memory_lite target_wrapper_lite)
+cc_library(hvy_tensor SRCS hvy_tensor.cc DEPS lod_tensor)
+
 if(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
-  cc_library(tensor_lite SRCS lite_tensor.cc DEPS memory_lite target_wrapper_lite)
+  set(tensor_lite lite_tensor)
 else()
-  cc_library(tensor_lite DEPS lod_tensor)
+  set(tensor_lite hvy_tensor)
 endif()
+
 cc_library(kernel_lite SRCS kernel.cc DEPS type_system target_wrapper_lite)
 cc_library(variable_lite SRCS variable.cc)
 cc_library(op_registry_lite SRCS op_registry.cc)
 cc_library(scope_lite SRCS scope.cc)
 cc_library(op_lite SRCS op_lite.cc DEPS scope_lite op_registry_lite compatible_pb_lite)
-cc_library(op_executor_lite SRCS op_executor.cc DEPS scope_lite tensor_lite op_lite op_registry_lite
+cc_library(op_executor_lite SRCS op_executor.cc DEPS scope_lite ${tensor_lite} op_lite op_registry_lite
   #TODO(Superjomn) remove these dependencies from original framework
   )
 cc_library(kernel_executor_lite SRCS kernel_executor.cc DEPS mir_ssa_graph kernel_lite)
 cc_library(types_lite SRCS types.cc)
-cc_library(type_system SRCS type_system.cc DEPS tensor_lite)
+cc_library(type_system SRCS type_system.cc DEPS ${tensor_lite})
 cc_library(program_fake_utils SRCS program_fake_utils.cc DEPS mir_ssa_graph
   scope_lite op_registry_lite proto_desc op_lite
   ops_lite

paddle/fluid/lite/core/compatible_tensor.h

@@ -14,83 +14,24 @@
 
 #pragma once
 
-#include <vector>
-#include "paddle/fluid/lite/core/target_wrapper.h"
+#include "paddle/fluid/lite/core/tensor.h"
+
 #ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
 #include "paddle/fluid/lite/core/lite_tensor.h"
 #else
-#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/lite/core/hvy_tensor.h"
 #endif
 
 namespace paddle {
 namespace lite {
 
 #ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
-using Tensor = details::Tensor;
-using DDim = details::DDim;
-#else
-using Tensor = framework::LoDTensor;
-using DDim = framework::DDim;
-
-static TargetType TensorGetTarget(const Tensor &x) {
-  if (platform::is_gpu_place(x.place())) {
-    return TARGET(kCUDA);
-  } else if (platform::is_cpu_place(x.place())) {
-    return TARGET(kX86);
-  }
-  return TARGET(kUnk);
-}
-
-template <typename T>
-T *TensorMutableData(Tensor *x, TargetType target, size_t size) {
-  if (target == TARGET(kX86) || target == TARGET(kHost)) {
-    return x->mutable_data<T>(platform::CPUPlace(), memory::Allocator::kDefault,
-                              size);
-  } else if (target == TARGET(kCUDA)) {
-    return x->mutable_data<T>(platform::CUDAPlace(),
-                              memory::Allocator::kDefault, size);
-  }
-  LOG(FATAL) << "not valid target " << TargetToStr(target);
-  return nullptr;
-}
-#endif
-
-static int product(const DDim &dims, int start, int end) {
-  int res = 1;
-  for (int i = start; i < end; i++) {
-    res *= dims[i];
-  }
-  return res;
-}
-
-static DDim SliceDims(const DDim &dims, int begin, int end) {
-#ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
-  return DDim(dims[0] + begin, dims.begin() + end - 1);
+using DDim = lite::DDimLite;
+using Tensor = lite::TensorLite;
 #else
-  auto vec = framework::vectorize(dims);
-  return DDim(&vec[0] + begin, end - begin);
+using DDim = lite::DDimHvy;
+using Tensor = lite::TensorHvy;
 #endif
-}
-
-static std::vector<int64_t> DDimVectorize(const DDim &x) {
-#ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
-  return x;
-#else
-  return framework::vectorize(x);
-#endif
-}
-
-#ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
-static int product(const DDim &dims) {
-  return std::accumulate(dims.begin(), dims.end(), 1,
-                         [](int a, int b) { return a * b; });
-}
-#endif
-
-static DDim flatten_to_2d(const DDim &dims, int col) {
-  return DDim({product(SliceDims(dims, 0, col)),
-               product(SliceDims(dims, col, dims.size()))});
-}
 
 }  // namespace lite
 }  // namespace paddle

paddle/fluid/lite/core/hvy_tensor.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/core/hvy_tensor.h"

paddle/fluid/lite/core/hvy_tensor.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.
+
+/*
+ * This file defines the heavy tensor (alias of the LoDTensor in the server
+ * framework). We derive it from the TensorLite interface, so the lite framework
+ * can share much code between the server side and mobile side.
+ */
+
+#pragma once
+#include <vector>
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/lite/core/tensor.h"
+
+namespace paddle {
+namespace lite {
+
+class DDimHvy : public DDimBase<DDimHvy> {
+ public:
+  DDimHvy() = default;
+  explicit DDimHvy(const std::vector<value_type>& x) : DDimBase<DDimHvy>() {
+    ConstructFrom(x);
+  }
+  explicit DDimHvy(const framework::DDim& x) : data_(x) {}
+
+  void ConstructFrom(const std::vector<value_type>& xs) {
+    data_ = framework::DDim(xs.data(), xs.size());
+  }
+
+  value_type operator[](int offset) const { return data_[offset]; }
+
+  std::vector<int64_t> Vectorize() const { return framework::vectorize(data_); }
+
+  const framework::DDim& data() const { return data_; }
+
+  size_t size() const { return data_.size(); }
+  bool empty() const { return data_.size() == 0; }
+
+ private:
+  framework::DDim data_;
+};
+
+class TensorHvy : public TensorBase<TensorHvy> {
+ public:
+  using DDimT = DDimHvy;
+  using LoDT = framework::LoD;
+
+  TargetType target() const {
+    if (platform::is_gpu_place(data_.place())) {
+      return TARGET(kCUDA);
+    } else if (platform::is_cpu_place(data_.place())) {
+      return TARGET(kX86);
+    }
+    LOG(FATAL) << "Unknown place";
+    return TARGET(kUnk);
+  }
+
+  template <typename T>
+  T* mutable_data() {
+    return data_.mutable_data<T>(data_.dims(), platform::CPUPlace());
+  }
+  template <typename T>
+  T* mutable_data(TargetType target) {
+    if (target == TARGET(kCUDA)) {
+      return data_.mutable_data<T>(data_.dims(), platform::CUDAPlace());
+    }
+    return data_.mutable_data<T>(data_.dims(), platform::CPUPlace());
+  }
+
+  template <typename T>
+  const T* data() const {
+    return data_.data<T>();
+  }
+
+  template <typename DimT>
+  void Resize(const DimT& dims) {
+    LOG(INFO) << "dims.size " << dims.size();
+    data_.Resize(framework::make_ddim(dims.Vectorize()));
+  }
+
+  void ShareDataWith(const TensorHvy& other) {
+    data_.ShareDataWith(other.data_);
+  }
+  void CopyDataFrom(const TensorHvy& other) {
+    data_.ShareDataWith(other.data_);
+  }
+
+  DDimT dims() const { return DDimT(framework::vectorize(data_.dims())); }
+
+  const framework::LoD& lod() const { return data_.lod(); }
+  framework::LoD* mutable_lod() { return data_.mutable_lod(); }
+
+ private:
+  framework::LoDTensor data_;
+};
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/core/kernel.h

@@ -52,7 +52,7 @@ class KernelBase {
   }
   template <typename P>
   P& Param() const {
-    return param_.get<P>();
+    return *param_.get_mutable<P>();
   }
 
   // This is used in the kernels that takes 'kAny' places and inference the

paddle/fluid/lite/core/lite_gtest_main.cc

@@ -12,10 +12,12 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <gflags/gflags.h>
 #include <gtest/gtest.h>
 
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
+  google::ParseCommandLineFlags(&argc, &argv, false);
 
   return RUN_ALL_TESTS();
 }

paddle/fluid/lite/core/lite_tensor.cc

@@ -17,31 +17,7 @@
 namespace paddle {
 namespace lite {
 
-std::ostream &operator<<(std::ostream &os, const DDim &dims) {
-  if (dims.empty()) {
-    os << "[]";
-    return os;
-  }
-
-  os << "[";
-  for (size_t i = 0; i < dims.size() - 1; i++) {
-    os << dims[i] << " ";
-  }
-  os << dims.back() << "]";
-  return os;
-}
-
-std::ostream &operator<<(std::ostream &os, const Tensor &tensor) {
-  os << "Tensor:" << '\n';
-  os << "dim: " << tensor.dims() << '\n';
-  for (int i = 0; i < product(tensor.dims()); i++) {
-    os << tensor.data<float>()[i] << " ";
-  }
-  os << "\n";
-  return os;
-}
-
-void Tensor::ShareDataWith(const Tensor &other) {
+void TensorLite::ShareDataWith(const TensorLite &other) {
   buffer_ = other.buffer_;
   dims_ = other.dims_;
   target_ = other.target_;
@@ -49,17 +25,17 @@ void Tensor::ShareDataWith(const Tensor &other) {
   memory_size_ = other.memory_size_;
 }
 
-void *Tensor::mutable_data(size_t memory_size) {
+void *TensorLite::mutable_data(size_t memory_size) {
   buffer_->ResetLazy(target_, memory_size);
   return buffer_->data();
 }
 
-void *Tensor::mutable_data(TargetType target, size_t memory_size) {
+void *TensorLite::mutable_data(TargetType target, size_t memory_size) {
   target_ = target;
   return mutable_data(memory_size);
 }
 
-void Tensor::CopyDataFrom(const Tensor &other) {
+void TensorLite::CopyDataFrom(const TensorLite &other) {
   dims_ = other.dims_;
   target_ = other.target_;
   lod_ = other.lod_;

paddle/fluid/lite/core/lite_tensor.h

@@ -20,28 +20,49 @@
 
 #include "paddle/fluid/lite/core/memory.h"
 #include "paddle/fluid/lite/core/target_wrapper.h"
+#include "paddle/fluid/lite/core/tensor.h"
 
 namespace paddle {
 namespace lite {
-namespace details {
 
-using DDim = std::vector<int64_t>;
+class DDimLite : public DDimBase<DDimLite> {
+ public:
+  DDimLite() = default;
+
+  DDimLite(const std::vector<value_type> &x) : DDimBase<DDimLite>() {
+    ConstructFrom(x);
+  }
+
+  void ConstructFrom(const std::vector<value_type> &x) { data_ = x; }
+
+  value_type operator[](int offset) const { return data_[offset]; }
+  std::vector<int64_t> Vectorize() { return data_; }
+
+  size_t size() const { return data_.size(); }
+  bool empty() const { return data_.empty(); }
+  const std::vector<value_type> &data() const { return data_; }
+
+ private:
+  std::vector<value_type> data_;
+};
 
 using LoD = std::vector<std::vector<size_t>>;
 
 // A light-weight tensor implementation.
-class Tensor {
+class TensorLite : public TensorBase<TensorLite> {
  public:
-  Tensor() : buffer_(std::make_shared<Buffer>()) {}
+  using DDimT = DDimLite;
+
+  TensorLite() : buffer_(std::make_shared<Buffer>()) {}
 
   template <typename T>
   const T *data() const {
     return static_cast<const T *>(buffer_->data());
   }
 
-  void Resize(const DDim &ddim) { dims_ = ddim; }
+  void Resize(const DDimLite &ddim) { dims_ = ddim; }
 
-  const DDim &dims() const { return dims_; }
+  const DDimLite &dims() const { return dims_; }
 
   const LoD &lod() const { return lod_; }
   LoD *mutable_lod() { return &lod_; }
@@ -58,38 +79,34 @@ class Tensor {
   bool IsInitialized() const { return buffer_->data(); }
 
   // Other share data to this.
-  void ShareDataWith(const Tensor &other);
+  void ShareDataWith(const TensorLite &other);
 
-  void CopyDataFrom(const Tensor &other);
+  void CopyDataFrom(const TensorLite &other);
 
   TargetType target() const { return target_; }
 
  private:
   TargetType target_{TargetType::kHost};
-  DDim dims_;
+  DDimLite dims_;
   std::shared_ptr<Buffer> buffer_;
   LoD lod_;
   size_t memory_size_{};
 };
 
 template <typename T>
-T *Tensor::mutable_data() {
-  memory_size_ = product(dims_) * sizeof(T);
+T *TensorLite::mutable_data() {
+  memory_size_ = dims_.production() * sizeof(T);
   buffer_->ResetLazy(target_, memory_size_);
   return static_cast<T *>(buffer_->data());
 }
 
 template <typename T>
-T *Tensor::mutable_data(TargetType target) {
+T *TensorLite::mutable_data(TargetType target) {
   target_ = target;
-  memory_size_ = product(dims_) * sizeof(T);
+  memory_size_ = dims_.production() * sizeof(T);
   buffer_->ResetLazy(target, memory_size());
   return static_cast<T *>(buffer_->data());
 }
 
-std::ostream &operator<<(std::ostream &os, const DDim &dims);
-std::ostream &operator<<(std::ostream &os, const Tensor &tensor);
-
-}  // namespace details
 }  // namespace lite
 }  // namespace paddle

paddle/fluid/lite/core/op_executor_test.cc

@@ -39,11 +39,11 @@ TEST(executor, test) {
   op_desc.SetAttr("in_num_col_dims", static_cast<int>(1));
   program.Flush();
 
-  auto* w = scope->Var("w")->GetMutable<Tensor>();
+  auto* w = scope->Var("w")->GetMutable<TensorBase>();
   w->Resize({20, 20});
-  auto* x = scope->Var("x")->GetMutable<Tensor>();
+  auto* x = scope->Var("x")->GetMutable<TensorBase>();
   x->Resize({1, 10, 20});
-  auto* bias = scope->Var("bias")->GetMutable<Tensor>();
+  auto* bias = scope->Var("bias")->GetMutable<TensorBase>();
   bias->Resize({1, 20});
 
   bias->mutable_data<float>();

paddle/fluid/lite/core/program.h

@@ -81,8 +81,8 @@ struct Program {
     CHECK(!exec_scope) << "Duplicate PrepareWorkspace found";
     exec_scope = &scope->NewScope();
     // Create Feed and Fetch var.
-    scope->Var("feed")->GetMutable<std::vector<Tensor>>();
-    scope->Var("fetch")->GetMutable<std::vector<Tensor>>();
+    scope->Var("feed")->GetMutable<std::vector<lite::Tensor>>();
+    scope->Var("fetch")->GetMutable<std::vector<lite::Tensor>>();
 
     tmp_vars.push_back("feed");
     tmp_vars.push_back("fetch");

paddle/fluid/lite/core/program_fake_utils.h

@@ -28,9 +28,9 @@ Program FakeProgram() {
     std::string w1 = "w" + std::to_string(id);
     std::string b1 = "b" + std::to_string(id);
     std::string out1 = "out" + std::to_string(id);
-    auto w1v = program.scope->Var(w1)->GetMutable<Tensor>();
-    auto b1v = program.scope->Var(b1)->GetMutable<Tensor>();
-    auto out1v = program.scope->Var(out1)->GetMutable<Tensor>();
+    auto w1v = program.scope->Var(w1)->GetMutable<TensorBase>();
+    auto b1v = program.scope->Var(b1)->GetMutable<TensorBase>();
+    auto out1v = program.scope->Var(out1)->GetMutable<TensorBase>();
 
     lite::OpDesc desc;
     desc.SetInput("Input", {x});
@@ -60,7 +60,7 @@ Program FakeProgram() {
 
   std::string x = "x";
   program.tmp_vars.push_back(x);
-  auto* xv = program.scope->Var(x)->GetMutable<Tensor>();
+  auto* xv = program.scope->Var(x)->GetMutable<TensorBase>();
   xv->Resize({100, 100});
 
   for (int i = 0; i < 3; i++) {
@@ -81,7 +81,7 @@ class ProgramFaker {
   void CreateVars(lite::Scope* scope) {
     for (auto& var : tmp_vars_) {
       auto* x = scope->Var(var);
-      x->GetMutable<lite::Tensor>();
+      x->GetMutable<lite::TensorBase>();
     }
 
     for (auto& x : tmp_vars_) {

paddle/fluid/lite/core/tensor.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
+
+/*
+ * This file defines the general interface for DDim and Tensor, which is used in
+ * server and mobile framework, to make the framework on the two devices share
+ * the same code, we clear up the methods and make the different implementations
+ * looks the same.
+ */
+
+#include <vector>
+#include "paddle/fluid/lite/core/target_wrapper.h"
+
+namespace paddle {
+namespace lite {
+
+/*
+ * This class defines the basic interfaces of the DDims for server and mobile.
+ * For the DDims's implementation is too tedious, we add a simple implementation
+ * for mobile, and use this interface to share the framework both for mobile and
+ * server.
+ *
+ * The derived should implement following interfaces:
+ * ConstructFrom
+ * operator[]
+ * Vectorize
+ * size
+ */
+template <typename DDimT>
+class DDimBase {
+ public:
+  using value_type = int64_t;
+
+  DDimBase() = default;
+
+  explicit DDimBase(const std::vector<int64_t> &x) { self()->ConstructFrom(x); }
+  value_type operator[](int offset) const { return (*self())[offset]; }
+  std::vector<int64_t> Vectorize() { return self()->Vectorize(); }
+  size_t size() const { return const_self()->size(); }
+  bool empty() const { return const_self()->empty(); }
+
+  value_type production() const {
+    value_type res = 1;
+    for (int i = 0; i < const_self()->size(); i++) {
+      res *= (*const_self())[i];
+    }
+    return res;
+  }
+
+  DDimT Slice(int start, int end) const {
+    std::vector<value_type> vec;
+    for (int i = start; i < end; i++) {
+      vec.push_back((*const_self())[i]);
+    }
+    return DDimT(vec);
+  }
+
+  DDimT Flattern2D(int col) const {
+    return DDimT(std::vector<value_type>(
+        {Slice(0, col).production(), Slice(col, size()).production()}));
+  }
+
+  friend std::ostream &operator<<(std::ostream &os, const DDimT &dims) {
+    if (dims.empty()) {
+      os << "[]";
+      return os;
+    }
+
+    os << "[";
+    for (size_t i = 0; i < dims.size() - 1; i++) {
+      os << dims[i] << " ";
+    }
+    if (!dims.empty()) os << dims[dims.size() - 1];
+    os << "]";
+    return os;
+  }
+
+ private:
+  DDimT *self() { return static_cast<DDimT *>(this); }
+  const DDimT *const_self() const { return static_cast<const DDimT *>(this); }
+};
+
+/*
+ * This class defines the basic interfaces of the tensors implemented for
+ * server and mobile. It use the CRTR technology to accelerate the runtime
+ * performance.
+ */
+template <typename TensorT>
+class TensorBase {
+ public:
+  TensorBase() = default;
+  TargetType target() const { return self()->target(); }
+
+  template <typename T>
+  T *mutable_data() {
+    return self()->template mutable_data<T>();
+  }
+
+  template <typename T>
+  T *mutable_data(TargetType target) {
+    return self()->template mutable_data<T>(target);
+  }
+
+  template <typename T>
+  const T *data() {
+    return self()->template data<T>();
+  }
+
+  template <typename DimT>
+  void Resize(const DimT &dims) {
+    self()->Resize(dims);
+  }
+
+  template <typename DDimT>
+  DDimT dims() {
+    return self()->dims();
+  }
+
+  template <typename LoDT>
+  const LoDT &lod() const {
+    return const_self()->lod();
+  }
+  template <typename LoDT>
+  LoDT *mutable_lod() {
+    return self()->mutable_lod();
+  }
+  template <typename T>
+  const T &data() const {
+    return const_self()->data();
+  }
+
+  size_t data_size() const { return const_self()->dims().production(); }
+
+  void ShareDataWith(const TensorBase &other) { self()->ShareDataWith(other); }
+  void CopyDataFrom(const TensorBase &other) { self()->CopyDataFrom(other); }
+
+  friend std::ostream &operator<<(std::ostream &os, const TensorT &tensor) {
+    os << "Tensor:" << '\n';
+    os << "dim: " << tensor.dims() << '\n';
+    for (int i = 0; i < tensor.dims().production(); i++) {
+      os << tensor.template data<float>()[i] << " ";
+    }
+    os << "\n";
+    return os;
+  }
+
+ private:
+  TensorT *self() { return static_cast<TensorT *>(this); }
+  const TensorT *const_self() const {
+    return static_cast<const TensorT *>(this);
+  }
+};
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/core/tensor_test.cc

@@ -19,7 +19,7 @@ namespace paddle {
 namespace lite {
 
 TEST(tensor, test) {
-  Tensor tensor;
+  TensorBase tensor;
   tensor.Resize({1, 8});
 
   for (int i = 0; i < 8; i++) {

paddle/fluid/lite/core/type_system_test.cc

@@ -19,7 +19,7 @@ namespace paddle {
 namespace lite {
 
 TEST(TypeSystem, test) {
-  ASSERT_TRUE(TypeSystem::Global().Contains<lite::Tensor>());
+  ASSERT_TRUE(TypeSystem::Global().Contains<lite::TensorBase>());
 }
 
 TEST(TypeSystem, register_new) {

paddle/fluid/lite/core/variable.h

@@ -29,7 +29,7 @@ class Variable {
   template <typename T>
   T* GetMutable() {
     if (!blob_.is<T>()) blob_.set<T>();
-    return &blob_.get<T>();
+    return blob_.get_mutable<T>();
   }
 
   template <typename T>
@@ -38,7 +38,7 @@ class Variable {
   }
 
  private:
-  variant<int, float, std::string, Tensor> blob_;
+  variant<int, float, std::string, lite::Tensor> blob_;
 };
 
 }  // namespace lite

paddle/fluid/lite/kernels/CMakeLists.txt

-set(lite_kernel_deps type_system kernel_lite op_registry_lite)
+set(lite_kernel_deps type_system kernel_lite op_lite op_registry_lite ${tensor_lite})
 add_subdirectory(host)
 add_subdirectory(arm)
 add_subdirectory(cuda)

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

@@ -2,7 +2,7 @@ if(NOT LITE_WITH_CUDA)
     return()
 endif()
 
-nv_library(mul_compute_cuda SRCS mul_compute.cc DEPS tensor_lite)
-cc_library(io_copy_compute_cuda SRCS io_copy_compute.cc DEPS tensor_lite)
+nv_library(mul_compute_cuda SRCS mul_compute.cc DEPS ${tensor_lite})
+cc_library(io_copy_compute_cuda SRCS io_copy_compute.cc DEPS ${tensor_lite})
 
 nv_library(kernels_cuda DEPS mul_compute_cuda io_copy_compute_cuda cuda_blas_lite)

paddle/fluid/lite/kernels/cuda/io_copy_compute.cc

@@ -46,12 +46,11 @@ class IoCopyHostToCudaCompute
  public:
   void Run() override {
     auto& param = Param<operators::IoCopyParam>();
-    CHECK(TensorGetTarget(*param.x) == TARGET(kHost) ||
-          TensorGetTarget(*param.x) == TARGET(kX86));
-    LOG(INFO) << "copy size " << param.x->memory_size();
-    auto* data = TensorMutableData<int8_t>(param.y, TARGET(kCUDA),
-                                           param.x->memory_size());
-    CopyFromHostSync(data, param.x->data<int8_t>(), param.x->memory_size());
+    CHECK(param.x->target() == TARGET(kHost) ||
+          param.x->target() == TARGET(kX86));
+    LOG(INFO) << "copy size " << param.x->data_size();
+    auto* data = param.y->mutable_data<int8_t>(TARGET(kCUDA));
+    CopyFromHostSync(data, param.x->data<int8_t>(), param.x->data_size());
   }
 
   std::unique_ptr<type_infer_handler_t> GetTypeInferHandler() override {
@@ -82,11 +81,10 @@ class IoCopyCudaToHostCompute
  public:
   void Run() override {
     auto& param = Param<operators::IoCopyParam>();
-    CHECK(TensorGetTarget(*param.x) == TARGET(kCUDA));
-    auto* data = TensorMutableData<int8_t>(param.y, TARGET(kHost),
-                                           param.x->memory_size());
-    LOG(INFO) << "copy size " << param.x->memory_size();
-    CopyToHostSync(data, param.x->data<void>(), param.x->memory_size());
+    CHECK(param.x->target() == TARGET(kCUDA));
+    auto* data = param.y->mutable_data<float>();
+    LOG(INFO) << "copy size " << param.x->data_size();
+    CopyToHostSync(data, param.x->data<void>(), param.x->data_size());
   }
 
   std::string doc() const override { return "Copy IO from CUDA to HOST"; }

paddle/fluid/lite/kernels/cuda/mul_compute.h

@@ -51,9 +51,8 @@ class MulCompute : public KernelLite<TARGET(kCUDA), PRECISION(kFloat)> {
      */
 
     const auto& param = Param<operators::MulParam>();
-    TensorMutableData<float>(param.output, TARGET(kCUDA),
-                             product(param.output->dims()));
-    LOG(INFO) << "mul output memory size " << param.output->memory_size();
+    param.output->mutable_data<float>(TARGET(kCUDA));
+    LOG(INFO) << "mul output memory size " << param.output->data_size();
 
     // mul_compute<float>(blas, x, x_h, x_w, y, y_h, y_w, out);
   }

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

@@ -29,16 +29,17 @@ void FcCompute::Run() {
   CHECK_GE(param.input->dims().size(), 2UL);
   CHECK_EQ(param.output->dims().size(), 2UL);
 
-  fc_compute_eigen(param.input->data<float>(),  // x
-                   product(param.input->dims(), 0, param.in_num_col_dims),
-                   product(param.input->dims(), param.in_num_col_dims,
-                           param.input->dims().size()),
+  fc_compute_eigen(
+      param.input->data<float>(),  // x
+      param.input->dims().Slice(0, param.in_num_col_dims).production(),
+      param.input->dims()
+          .Slice(param.in_num_col_dims, param.input->dims().size())
+          .production(),
       param.w->data<float>(),     // w
       param.w->dims()[1],         // w_w
       param.w->dims()[0],         // w_h
       param.bias->data<float>(),  // b
-                   TensorMutableData<float>(param.output, TARGET(kHost),
-                                            product(param.output->dims())));
+      param.output->mutable_data<float>());
 }
 
 // TargetType FcCompute::target() const { return TARGET(kHost); }

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

@@ -23,7 +23,7 @@ namespace kernels {
 namespace host {
 
 TEST(fc_compute_naive, test) {
-  Tensor x, w, b, out, out1;
+  TensorBase x, w, b, out, out1;
   const int batch_size = 2;
   x.Resize({batch_size, 3});
   w.Resize({4, 3});
@@ -79,10 +79,10 @@ TEST(fc_host, compute) {
   FcCompute fc;
   operators::FcParam param;
 
-  Tensor x;
-  Tensor w;
-  Tensor bias;
-  Tensor output;
+  TensorBase x;
+  TensorBase w;
+  TensorBase bias;
+  TensorBase output;
 
   x.Resize({1, 10, 20});
   w.Resize({20, 20});

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

@@ -27,7 +27,9 @@ class FeedCompute
 
   void Run() override {
     auto &param = Param<operators::FeedParam>();
-    const Tensor &feed_item = param.feed_list->at(param.col);
+    LOG(INFO) << "feed_list.size: " << param.feed_list->size();
+    LOG(INFO) << "col " << param.col;
+    const lite::Tensor &feed_item = (*param.feed_list)[0];
     param.out->ShareDataWith(feed_item);
     LOG(INFO) << "FEED input " << feed_item << " col " << param.col;
     LOG(INFO) << "FEED output " << *param.out;

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

@@ -41,18 +41,24 @@ class MulCompute : public KernelLite<TARGET(kHost), PRECISION(kFloat)> {
 
   void Run() override {
     auto& param = Param<operators::MulParam>();
-    core::dim2 x_shape({product(param.x->dims(), 0, param.x_num_col_dims),
-                        product(param.x->dims(), param.x_num_col_dims,
-                                param.x->dims().size())});
-
-    core::dim2 y_shape({product(param.y->dims(), 0, param.y_num_col_dims),
-                        product(param.y->dims(), param.y_num_col_dims,
-                                param.y->dims().size())});
+    core::dim2 x_shape(
+        {static_cast<int>(
+             param.x->dims().Slice(0, param.x_num_col_dims).production()),
+         static_cast<int>(
+             param.x->dims()
+                 .Slice(param.x_num_col_dims, param.x->dims().size())
+                 .production())});
+    core::dim2 y_shape(
+        {static_cast<int>(
+             param.y->dims().Slice(0, param.y_num_col_dims).production()),
+         static_cast<int>(
+             param.y->dims()
+                 .Slice(param.y_num_col_dims, param.y->dims().size())
+                 .production())});
 
     mul_compute_eigen(param.x->data<float>(), x_shape.x, x_shape.y,  //
                       param.y->data<float>(), y_shape.x, y_shape.y,  //
-                      TensorMutableData<float>(param.output, TARGET(kHost),
-                                               product(param.output->dims())));
+                      param.output->mutable_data<float>());
     LOG(INFO) << "MUL x " << *param.x;
     LOG(INFO) << "MUL W " << *param.y;
     LOG(INFO) << "MUL out " << *param.output;

paddle/fluid/lite/kernels/host/relu_compute.h

@@ -25,10 +25,9 @@ class ReluCompute : public KernelLite<TARGET(kHost), PRECISION(kFloat)> {
  public:
   void Run() override {
     auto& param = Param<operators::ReluParam>();
-    auto n = product(param.input->dims());
+    auto n = param.input->dims().production();
     const float* input = param.input->data<float>();
-    float* output = TensorMutableData<float>(param.output, TARGET(kHost),
-                                             product(param.output->dims()));
+    float* output = param.output->mutable_data<float>();
     for (int i = 0; i < n; i++) {
       output[i] = std::max(0.f, input[i]);
     }

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

@@ -37,10 +37,8 @@ class ScaleCompute : public KernelLite<TARGET(kHost), PRECISION(kFloat)> {
 
   void Run() override {
     auto& param = Param<operators::ScaleParam>();
-    scale_compute(param.x->data<float>(),
-                  TensorMutableData<float>(param.output, TARGET(kHost),
-                                           product(param.output->dims())),
-                  product(param.x->dims()), param.scale, param.bias,
+    scale_compute(param.x->data<float>(), param.output->mutable_data<float>(),
+                  param.x->dims().production(), param.scale, param.bias,
                   param.bias_after_scale);
   }
 

paddle/fluid/lite/model_parser/CMakeLists.txt

@@ -6,7 +6,7 @@ else()
     cc_library(compatible_pb_lite SRCS compatible_pb.cc DEPS framework_proto proto_desc)
 endif(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
 
-set(model_parser_deps variable_lite scope_lite tensor_lite scope_lite
+set(model_parser_deps variable_lite scope_lite ${tensor_lite} scope_lite
                       target_wrapper_host
                       compatible_pb_lite
                       )

paddle/fluid/lite/model_parser/model_parser.cc

@@ -58,19 +58,20 @@ void TensorFromStream(std::istream &is, lite::Tensor *tensor) {
   }
 
   // read tensor
-  std::vector<int64_t> dims;
-  std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
-  tensor->Resize(lite::DDim(&dims[0], dims.size()));
+  std::vector<int64_t> dims_vec;
+  std::copy(desc.dims().begin(), desc.dims().end(),
+            std::back_inserter(dims_vec));
+  lite::DDim dims(dims_vec);
+  tensor->Resize(dims);
   void *buf;
-  size_t size = product(tensor->dims()) * SizeOfType(desc.data_type());
+  size_t size = tensor->dims().production() * SizeOfType(desc.data_type());
   // alllocate memory
   switch (static_cast<int>(desc.data_type())) {
 #define DO(desc, type)                  \
   case Type::VarType_Type_##desc:       \
-    buf = TensorMutableData<type>(tensor, TensorGetTarget(*tensor), \
-                                  product(tensor->dims()));
+    buf = tensor->mutable_data<type>(); \
     break;
-    DO(BOOL, bool);
+    // DO(BOOL, bool);
     DO(FP32, float);
     DO(INT8, int8_t);
     DO(INT16, int16_t);
@@ -198,7 +199,7 @@ void TensorToStream(std::ostream &os, const lite::Tensor &tensor) {
     auto dims = tensor.dims();
     auto *pb_dims = desc.mutable_dims();
     pb_dims->Resize(static_cast<int>(dims.size()), 0);
-    auto dims_vec = DDimVectorize(dims);
+    auto dims_vec = dims.Vectorize();
     std::copy(dims_vec.begin(), dims_vec.end(), pb_dims->begin());
     int32_t size = desc.ByteSize();
     os.write(reinterpret_cast<const char *>(&size), sizeof(size));
@@ -206,15 +207,15 @@ void TensorToStream(std::ostream &os, const lite::Tensor &tensor) {
     os.write(out.data(), size);
   }
   {  // the 3rd field, tensor data
-    uint64_t size = tensor.memory_size();
+    uint64_t size = tensor.data_size();
     CHECK_LT(size, std::numeric_limits<std::streamsize>::max())
         << "Index overflow when writing tensor";
 
 #ifdef LITE_WITH_CUDA
-    if (TensorGetTarget(tensor) == TARGET(kCUDA)) {
+    if (tensor.target() == TARGET(kCUDA)) {
       std::unique_ptr<char> tmp_buffer(new char[size]);
       TargetWrapperCuda::MemcpySync(tmp_buffer.get(), tensor.data<float>(),
-                                    tensor.memory_size(), IoDirection::DtoH);
+                                    tensor.data_size(), IoDirection::DtoH);
       os.write(static_cast<const char *>(tmp_buffer.get()),
                static_cast<std::streamsize>(size));
     } else

paddle/fluid/lite/model_parser/model_parser_test.cc

@@ -28,7 +28,7 @@ TEST(ModelParser, LoadParam) {
   Scope scope;
   auto* v = scope.Var("xxx");
   LoadParam("/home/chunwei/project2/models/fc/fluid_checkpoint/b1", v);
-  const auto& t = v->Get<Tensor>();
+  const auto& t = v->Get<TensorBase>();
   LOG(INFO) << "loaded\n";
   LOG(INFO) << t;
 }

paddle/fluid/lite/operators/CMakeLists.txt

-cc_library(fc_op_lite SRCS fc_op.cc DEPS op_lite op_params_lite tensor_lite)
-cc_library(relu_op_lite SRCS relu_op.cc DEPS op_lite)
-cc_library(mul_op_lite SRCS mul_op.cc DEPS op_lite)
-cc_library(scale_op_lite SRCS scale_op.cc DEPS op_lite)
-cc_library(feed_op_lite SRCS feed_op.cc DEPS op_lite)
-cc_library(fetch_op_lite SRCS fetch_op.cc DEPS op_lite)
-cc_library(io_copy_op_lite SRCS io_copy_op.cc DEPS op_lite)
+set(op_DEPS ${tensor_lite} op_lite op_params_lite)
+cc_library(fc_op_lite SRCS fc_op.cc DEPS ${op_DEPS})
+cc_library(relu_op_lite SRCS relu_op.cc DEPS ${op_DEPS})
+cc_library(mul_op_lite SRCS mul_op.cc DEPS ${op_DEPS})
+cc_library(scale_op_lite SRCS scale_op.cc DEPS ${op_DEPS})
+cc_library(feed_op_lite SRCS feed_op.cc DEPS ${op_DEPS})
+cc_library(fetch_op_lite SRCS fetch_op.cc DEPS ${op_DEPS})
+cc_library(io_copy_op_lite SRCS io_copy_op.cc DEPS ${op_DEPS})
 
-cc_library(op_params_lite SRCS op_params.cc DEPS tensor_lite)
+cc_library(op_params_lite SRCS op_params.cc DEPS ${tensor_lite})
 cc_library(ops_lite DEPS
         fc_op_lite
         relu_op_lite

paddle/fluid/lite/operators/fc_op.cc

@@ -42,7 +42,7 @@ bool FcOpLite::CheckShape() const {
   CHECK_GT_OR_FALSE(input_dims.size(),
                     static_cast<size_t>(param_.in_num_col_dims));
 
-  param_.in_mat_dims = lite::flatten_to_2d(input_dims, param_.in_num_col_dims);
+  param_.in_mat_dims = input_dims.Flattern2D(param_.in_num_col_dims);
   // CHECK_EQ_OR_FALSE(param_.in_mat_dims[1], w_dims[0]);
 
   return true;
@@ -58,7 +58,7 @@ bool FcOpLite::InferShape() const {
     output_dims[i] = input_dims[i];
   }
   output_dims.back() = w_dims[1];
-  param_.output->Resize(DDim(&output_dims[0], output_dims.size()));
+  param_.output->Resize(lite::DDim(output_dims));
 
   // share LoD
   // param_.output->set_lod(param_.input->lod());

paddle/fluid/lite/operators/fc_op.h

@@ -52,11 +52,11 @@ class FcOpLite : public OpLite {
     auto bias = op_desc.Input("Bias").front();
     auto out = op_desc.Output("Out").front();
 
-    param_.input = scope->FindVar(input)->GetMutable<Tensor>();
-    param_.w = scope->FindVar(W)->GetMutable<Tensor>();
-    param_.bias = scope->FindVar(bias)->GetMutable<Tensor>();
+    param_.input = scope->FindVar(input)->GetMutable<lite::Tensor>();
+    param_.w = scope->FindVar(W)->GetMutable<lite::Tensor>();
+    param_.bias = scope->FindVar(bias)->GetMutable<lite::Tensor>();
     CHECK(scope->FindVar(out));
-    param_.output = scope->FindVar(out)->GetMutable<Tensor>();
+    param_.output = scope->FindVar(out)->GetMutable<lite::Tensor>();
     param_.in_num_col_dims = GetAttr<int>(op_desc.GetAttr("in_num_col_dims"));
 
     CHECK(kernel_);

paddle/fluid/lite/operators/fc_op_test.cc

@@ -24,10 +24,10 @@ TEST(fc_op_lite, test) {
   LOG(INFO) << "\n" << KernelRegistry::Global().DebugString();
   // prepare variables
   Scope scope;
-  auto* x = scope.Var("x")->GetMutable<Tensor>();
-  auto* w = scope.Var("w")->GetMutable<Tensor>();
-  auto* bias = scope.Var("bias")->GetMutable<Tensor>();
-  auto* output = scope.Var("output")->GetMutable<Tensor>();
+  auto* x = scope.Var("x")->GetMutable<TensorBase>();
+  auto* w = scope.Var("w")->GetMutable<TensorBase>();
+  auto* bias = scope.Var("bias")->GetMutable<TensorBase>();
+  auto* output = scope.Var("output")->GetMutable<TensorBase>();
   x->Resize({1, 10, 20});
   w->Resize({20, 20});
   bias->Resize({1, 10});

paddle/fluid/lite/operators/feed_op.cc

@@ -39,13 +39,13 @@ class FeedOp : public OpLite {
     auto feed_var_name = opdesc.Input("X").front();
     auto* feed_var = scope->FindVar(feed_var_name);
     CHECK(feed_var);
-    auto& feed_tensor_list = feed_var->Get<std::vector<Tensor>>();
+    auto& feed_tensor_list = feed_var->Get<std::vector<lite::Tensor>>();
     param_.feed_list = &feed_tensor_list;
 
     auto out_name = opdesc.Output("Out").front();
     auto* out_var = scope->FindVar(out_name);
     CHECK(out_var);
-    param_.out = out_var->GetMutable<Tensor>();
+    param_.out = out_var->GetMutable<lite::Tensor>();
 
     // NOTE need boost here
     // TODO(Superjomn) drop the need of framework::op_desc

paddle/fluid/lite/operators/fetch_op.cc

@@ -37,7 +37,7 @@ class FetchOp : public OpLite {
     auto _x = opdesc.Input("X").front();
     auto* x = scope->FindVar(_x);
     CHECK(x);
-    param_.input = &x->Get<Tensor>();
+    param_.input = &x->Get<lite::Tensor>();
 
     auto _out = opdesc.Output("Out").front();
     auto* out = scope->FindVar(_out);

paddle/fluid/lite/operators/mul_op.cc

@@ -45,7 +45,7 @@ bool MulOpLite::InferShape() const {
   }
   out_dims.back() = y_dims[1];
 
-  param_.output->Resize(DDim(&out_dims[0], out_dims.size()));
+  param_.output->Resize(lite::DDim(out_dims));
 
   // share LoD
   // param_.output->set_lod(param_.input->lod());

paddle/fluid/lite/operators/op_params.h

@@ -25,36 +25,36 @@ namespace lite {
 namespace operators {
 
 struct FeedParam {
-  const std::vector<Tensor>* feed_list{};
-  Tensor* out{};
+  const std::vector<lite::Tensor>* feed_list{};
+  lite::Tensor* out{};
   int col;
 };
 
 struct FetchParam {
-  const Tensor* input{};
-  std::vector<Tensor>* fetch_list{};
+  const lite::Tensor* input{};
+  std::vector<lite::Tensor>* fetch_list{};
   int col;
 };
 
 struct FcParam {
-  Tensor* input{};
-  Tensor* w{};
-  Tensor* bias{};
-  Tensor* output{};
-  DDim in_mat_dims;
+  lite::Tensor* input{};
+  lite::Tensor* w{};
+  lite::Tensor* bias{};
+  lite::Tensor* output{};
+  lite::DDim in_mat_dims;
   int in_num_col_dims{1};
 };
 
 struct ReluParam {
-  Tensor* input{};
-  Tensor* output{};
+  lite::Tensor* input{};
+  lite::Tensor* output{};
 };
 
 // For Mul Op
 struct MulParam {
-  Tensor* x{};
-  Tensor* y{};
-  Tensor* output{};
+  lite::Tensor* x{};
+  lite::Tensor* y{};
+  lite::Tensor* output{};
 
   int x_num_col_dims{1};
   int y_num_col_dims{1};
@@ -62,8 +62,8 @@ struct MulParam {
 
 // For Scale Op
 struct ScaleParam {
-  Tensor* x{};
-  Tensor* output{};
+  lite::Tensor* x{};
+  lite::Tensor* output{};
 
   float scale{1.};
   float bias{};
@@ -71,8 +71,8 @@ struct ScaleParam {
 };
 
 struct IoCopyParam {
-  const Tensor* x{};
-  Tensor* y{};
+  const lite::Tensor* x{};
+  lite::Tensor* y{};
 };
 
 using param_t = variant<FeedParam, FetchParam, FcParam, ReluParam, MulParam,

paddle/fluid/lite/operators/relu_op.cc

@@ -32,10 +32,10 @@ bool ReluOp::InferShape() const {
 }
 
 bool ReluOp::AttachImpl(const OpDesc &opdesc, lite::Scope *scope) {
-  param_.input = const_cast<Tensor *>(
-      &scope->FindVar(opdesc.Input("Input").front())->Get<Tensor>());
+  param_.input = const_cast<lite::Tensor *>(
+      &scope->FindVar(opdesc.Input("Input").front())->Get<lite::Tensor>());
   param_.output =
-      scope->FindVar(opdesc.Output("Out").front())->GetMutable<Tensor>();
+      scope->FindVar(opdesc.Output("Out").front())->GetMutable<lite::Tensor>();
   CHECK(param_.input);
   CHECK(param_.output);
   kernel_->SetParam(param_);

paddle/fluid/lite/utils/varient.h

@@ -109,10 +109,21 @@ struct variant {
     type_id = typeid(T).hash_code();
   }
   template <typename T>
-  T& get() {
+  const T& get() const {
     // It is a dynamic_cast-like behaviour
     if (type_id == typeid(T).hash_code())
-      return *reinterpret_cast<T*>(&data);
+      return *reinterpret_cast<const T*>(&data);
+    else
+      LOG(FATAL) << "unmatched type get, should be " << type_id << " but get "
+                 << typeid(T).name();
+    return *reinterpret_cast<const T*>(&data);
+  }
+
+  template <typename T>
+  T* get_mutable() {
+    // It is a dynamic_cast-like behaviour
+    if (type_id == typeid(T).hash_code())
+      return reinterpret_cast<T*>(&data);
     else
       LOG(FATAL) << "unmatched type get, should be " << type_id << " but get "
                  << typeid(T).name();