diff --git a/src/common/io.cpp b/src/common/io.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..fc1466237e938a9ded5862d6cc3c7597766197e4
--- /dev/null
+++ b/src/common/io.cpp
@@ -0,0 +1,421 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "io.h"
+#include <fstream>
+#include <vector>
+#include "common/enforce.h"
+#include "common/log.h"
+#include "framework/framework.pb-c.h"
+#include "framework/lod_tensor.h"
+#include "framework/operator.h"
+#include "framework/program/program_desc.h"
+#include "framework/program/var_desc.h"
+#include "framework/scope.h"
+#include "framework/tensor.h"
+
+namespace paddle_mobile {
+using framework::Variable;
+
+void ReadBinaryFile(const std::string &filename, std::string *contents) {
+  std::ifstream fin(filename, std::ios::in | std::ios::binary);
+  PADDLE_MOBILE_ENFORCE(fin.is_open(), "open file: %s failed",
+                        filename.c_str());
+  fin.seekg(0, std::ios::end);
+  contents->clear();
+  contents->resize(fin.tellg());
+  fin.seekg(0, std::ios::beg);
+  fin.read(&(contents->at(0)), contents->size());
+  fin.close();
+}
+
+static size_t ReadBuffer(const char *file_name, uint8_t **out) {
+  printf("%s \n", file_name);
+  FILE *fp;
+  fp = fopen(file_name, "rb");
+  PADDLE_MOBILE_ENFORCE(fp != NULL, " %s open failed !", file_name);
+
+  fseek(fp, 0, SEEK_END);
+  size_t size = ftell(fp);
+  rewind(fp);
+
+  DLOG << "model size: " << size;
+
+  *out = reinterpret_cast<uint8_t *>(malloc(size));
+
+  size_t cur_len = 0;
+  size_t nread;
+  while ((nread = fread(*out + cur_len, 1, size - cur_len, fp)) != 0) {
+    cur_len += nread;
+  }
+  fclose(fp);
+  return cur_len;
+}
+
+template <typename Dtype, Precision P>
+void Loader<Dtype, P>::LoadVar(framework::Variable *variable,
+                               const framework::VarDesc &var_desc,
+                               const std::string &file_path) {
+  auto tensor = variable->GetMutable<framework::LoDTensor>();
+  std::ifstream is(file_path);
+  PADDLE_MOBILE_ENFORCE(is.is_open(), "open file: %s failed",
+                        file_path.c_str());
+
+  std::fpos<mbstate_t> pos;
+  pos = is.tellg();  // save   current   position
+  is.seekg(0, std::ios::end);
+  is.seekg(pos);  // restore   saved   position
+
+  // 1. version
+  uint32_t version;
+  is.read(reinterpret_cast<char *>(&version), sizeof(version));
+
+  // 2 Lod information
+  uint64_t lod_level;
+  is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
+  auto &lod = *tensor->mutable_lod();
+  lod.resize(lod_level);
+  for (uint64_t i = 0; i < lod_level; ++i) {
+    uint64_t size;
+    is.read(reinterpret_cast<char *>(&size), sizeof(size));
+    std::vector<size_t> tmp(size / sizeof(size_t));
+    is.read(reinterpret_cast<char *>(tmp.data()),
+            static_cast<std::streamsize>(size));
+    for (auto j : tmp) {
+      LOG(kLOG_DEBUG1) << "    lod - " << j;
+    }
+    lod[i] = tmp;
+  }
+
+  // 3. tensor version
+  uint32_t tensor_version;
+  is.read(reinterpret_cast<char *>(&tensor_version), sizeof(tensor_version));
+
+  // 4. tensor desc
+  int32_t size;
+  is.read(reinterpret_cast<char *>(&size), sizeof(size));
+  std::unique_ptr<char[]> buf(new char[size]);
+  is.read(reinterpret_cast<char *>(buf.get()), size);
+
+  const framework::TensorDesc &desc = var_desc.Tensor_desc();
+
+  PaddleMobile__Framework__Proto__VarType__TensorDesc *tensor_desc = NULL;
+  //  void *v;
+  //  PaddleMobile__Framework__Proto__VarType__TensorDesc_Closure()(tensor_desc,
+  //  buf.get());
+
+  //  DLOG << "PaddleMobile__Framework__Proto__VarType__TensorDesc_Closure- " <<
+  //  tensor_desc;
+
+  //  framework::TensorDesc &tensor_desc = variable->
+  //  PaddleMobile__Framework__Proto__ProgramDesc *c_program;
+  //  uint8_t *proto_buf = NULL;
+  //  size_t read_size = ReadBuffer(file_path.c_str(), &proto_buf);
+  //  c_program = paddle_mobile__framework__proto__program_desc__unpack(NULL,
+  //  read_size, buf);
+
+  //  paddle_mobile__framework__proto__var_type__tensor_desc__init()
+
+  int memory_size = 1;
+  for (auto l : desc.Dims()) {
+    memory_size *= l;
+  }
+
+  tensor->Resize(framework::make_ddim(desc.Dims()));
+
+  void *memory = tensor;
+  int type_size = 0;
+  switch (desc.DataType()) {
+    case framework::VARTYPE_TYPE_FP16:
+      type_size = 2;
+      break;
+    case framework::VARTYPE_TYPE_FP32:
+      type_size = 4;
+      memory = tensor->mutable_data<float>();
+      break;
+    case framework::VARTYPE_TYPE_FP64:
+      type_size = 8;
+      break;
+    case framework::VARTYPE_TYPE_INT32:
+      type_size = 4;
+      break;
+    case framework::VARTYPE_TYPE_INT64:
+      type_size = 8;
+      break;
+    case framework::VARTYPE_TYPE_BOOL:
+      type_size = 1;
+      break;
+    default:
+      break;
+  }
+
+  is.read(static_cast<char *>(memory), memory_size * type_size);
+  is.close();
+}
+
+template <typename Dtype, Precision P>
+const framework::Program<Dtype, P> Loader<Dtype, P>::Load(
+    const std::string &dirname) {
+  std::string model_filename = dirname + "/__model__";
+  PaddleMobile__Framework__Proto__ProgramDesc *c_program;
+  uint8_t *buf = NULL;
+  size_t read_size = ReadBuffer(model_filename.c_str(), &buf);
+
+  PADDLE_MOBILE_ENFORCE(buf != NULL, "read from __model__ is null");
+
+  c_program = paddle_mobile__framework__proto__program_desc__unpack(
+      NULL, read_size, buf);
+  //
+  PADDLE_MOBILE_ENFORCE(c_program != NULL, "program is null");
+  //
+  DLOG << "n_ops: " << (*c_program->blocks)->n_ops;
+  //
+  std::shared_ptr<framework::ProgramDesc> originProgramDesc =
+      std::make_shared<framework::ProgramDesc>(c_program);
+
+  framework::Program<Dtype, P> program;
+  program.model_path = dirname;
+  program.originProgram = originProgramDesc;
+
+  std::shared_ptr<framework::Scope> scope =
+      std::make_shared<framework::Scope>();
+  program.scope = scope;
+  originProgramDesc->Block(0);
+
+  for (const auto &block : originProgramDesc->Blocks()) {
+    for (int i = 0; i < block->Vars().size(); ++i) {
+      std::shared_ptr<framework::VarDesc> var_desc = block->Vars()[i];
+      //      DLOG << "var name-- " << var_desc->Name();
+      auto var = scope->Var(var_desc->Name());
+
+      if (var_desc->Type() == framework::VARTYPE_TYPE_LOD_TENSOR) {
+        if (var_desc->Persistable() &&
+            var_desc->Type() != framework::VARTYPE_TYPE_FEED_MINIBATCH &&
+            var_desc->Type() != framework::VARTYPE_TYPE_FETCH_LIST) {
+          //          DLOG << "to load var ";
+          auto dim = var_desc->Tensor_desc().Dims();
+          auto tensor = var->GetMutable<framework::LoDTensor>();
+          tensor->Resize(framework::make_ddim(dim));
+        } else {
+          auto dim = var_desc->Tensor_desc().Dims();
+          PADDLE_MOBILE_ENFORCE(dim.size() > 0, "dim size is 0");
+          dim[0] = 1;
+          auto tensor = var->GetMutable<framework::LoDTensor>();
+          tensor->Resize(framework::make_ddim(dim));
+        }
+      } else {
+        // TODO(codeWorm): some.
+      }
+    }
+  }
+
+  originProgramDesc->Description("program: ");
+
+  paddle_mobile__framework__proto__program_desc__free_unpacked(c_program, NULL);
+  return program;
+}
+
+template class Loader<CPU, Precision::FP32>;
+
+#pragma mark - executor
+
+template <typename Dtype, Precision P>
+Executor<Dtype, P>::Executor(const framework::Program<Dtype> p) : program_(p) {
+  if (use_optimize_) {
+    to_predict_program_ = program_.optimizeProgram;
+  } else {
+    to_predict_program_ = program_.originProgram;
+  }
+
+  const std::vector<std::shared_ptr<framework::BlockDesc>> blocks =
+      to_predict_program_->Blocks();
+  for (int i = 0; i < blocks.size(); ++i) {
+    std::shared_ptr<framework::BlockDesc> block_desc = blocks[i];
+    std::vector<std::shared_ptr<framework::OpDesc>> ops = block_desc->Ops();
+    for (int j = 0; j < ops.size(); ++j) {
+      std::shared_ptr<framework::OpDesc> op = ops[j];
+      auto op_base = framework::OpRegistry<Dtype>::CreateOp(
+          op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(),
+          program_.scope);
+      op_base->InferShape();
+      ops_of_block_[*block_desc.get()].push_back(op_base);
+    }
+  }
+  InitMemory();
+}
+
+template <typename Dtype, Precision P>
+Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size)
+    : program_(p), batch_size_(batch_size) {
+  if (use_optimize_) {
+    to_predict_program_ = program_.optimizeProgram;
+  } else {
+    to_predict_program_ = program_.originProgram;
+  }
+  Variable *variable_ptr = program_.scope->Var("batch_size");
+  variable_ptr[0].SetValue<int>(batch_size);
+  const std::vector<std::shared_ptr<framework::BlockDesc>> blocks =
+      to_predict_program_->Blocks();
+  for (int i = 0; i < blocks.size(); ++i) {
+    std::shared_ptr<framework::BlockDesc> block_desc = blocks[i];
+    std::vector<std::shared_ptr<framework::OpDesc>> ops = block_desc->Ops();
+    for (int j = 0; j < ops.size(); ++j) {
+      std::shared_ptr<framework::OpDesc> op = ops[j];
+      auto op_base = framework::OpRegistry<Dtype>::CreateOp(
+          op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(),
+          program_.scope);
+      op_base->InferShape();
+
+      ops_of_block_[*block_desc.get()].push_back(op_base);
+    }
+  }
+  InitMemory();
+}
+
+template <typename Dtype, Precision P>
+void Executor<Dtype, P>::LoadMemory(const framework::VarDesc var_desc,
+                                    framework::LoDTensor *tensor,
+                                    const std::string &file_path) {
+  std::ifstream is(file_path);
+  PADDLE_MOBILE_ENFORCE(is.is_open(), "open file: %s failed",
+                        file_path.c_str());
+  std::fpos<mbstate_t> pos;
+  pos = is.tellg();  // save   current   position
+  is.seekg(0, std::ios::end);
+  is.seekg(pos);  // restore   saved   position
+
+  // 1. version
+  uint32_t version;
+  is.read(reinterpret_cast<char *>(&version), sizeof(version));
+
+  // 2 Lod information
+  uint64_t lod_level;
+  is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
+  auto &lod = *tensor->mutable_lod();
+  lod.resize(lod_level);
+  for (uint64_t i = 0; i < lod_level; ++i) {
+    uint64_t size;
+    is.read(reinterpret_cast<char *>(&size), sizeof(size));
+    std::vector<size_t> tmp(size / sizeof(size_t));
+    is.read(reinterpret_cast<char *>(tmp.data()),
+            static_cast<std::streamsize>(size));
+    for (auto j : tmp) {
+      LOG(kLOG_DEBUG1) << "    lod - " << j;
+    }
+    lod[i] = tmp;
+  }
+
+  // 3. tensor version
+  uint32_t tensor_version;
+  is.read(reinterpret_cast<char *>(&tensor_version), sizeof(tensor_version));
+
+  // 4. tensor desc
+  int32_t size;
+  is.read(reinterpret_cast<char *>(&size), sizeof(size));
+  std::unique_ptr<char[]> buf(new char[size]);
+  is.read(reinterpret_cast<char *>(buf.get()), size);
+
+  const framework::TensorDesc &desc = var_desc.Tensor_desc();
+
+  int memory_size = 1;
+  for (auto l : desc.Dims()) {
+    memory_size *= l;
+  }
+
+  tensor->Resize(framework::make_ddim(desc.Dims()));
+
+  void *memory = tensor;
+  int type_size = 0;
+  switch (desc.DataType()) {
+    case framework::VARTYPE_TYPE_FP16:
+      type_size = 2;
+      break;
+    case framework::VARTYPE_TYPE_FP32:
+      type_size = 4;
+      memory = tensor->mutable_data<float>();
+      break;
+    case framework::VARTYPE_TYPE_FP64:
+      type_size = 8;
+      break;
+    case framework::VARTYPE_TYPE_INT32:
+      type_size = 4;
+      break;
+    case framework::VARTYPE_TYPE_INT64:
+      type_size = 8;
+      break;
+    case framework::VARTYPE_TYPE_BOOL:
+      type_size = 1;
+      break;
+    default:
+      break;
+  }
+
+  is.read(static_cast<char *>(memory), memory_size * type_size);
+  is.close();
+}
+
+template <typename Dtype, Precision P>
+void Executor<Dtype, P>::InitMemory() {
+  for (const auto &block : to_predict_program_->Blocks()) {
+    for (const auto &var_desc : block->Vars()) {
+      auto var = program_.scope->Var(var_desc->Name());
+      if (var_desc->Persistable()) {
+        auto tensor = var->template GetMutable<framework::LoDTensor>();
+        if (var_desc->Name() == "feed" || var_desc->Name() == "fetch") {
+          continue;
+        }
+        LoadMemory(*var_desc, tensor,
+                   program_.model_path + "/" + var_desc->Name());
+      } else {
+        if (var_desc->Type() == framework::VARTYPE_TYPE_LOD_TENSOR) {
+          auto tensor = var->template GetMutable<framework::LoDTensor>();
+
+          tensor->template mutable_data<Ptype>();
+        }
+      }
+    }
+  }
+}
+
+template <typename Dtype, Precision P>
+void Executor<Dtype, P>::predict(const framework::Tensor &t, int block_id) {
+  framework::Variable *g_feed_value = program_.scope->Var("feed");
+  framework::Tensor *feed_tensor =
+      g_feed_value->GetMutable<framework::LoDTensor>();
+  feed_tensor->Resize(t.dims());
+  feed_tensor->ShareDataWith(t);
+  std::shared_ptr<framework::BlockDesc> to_predict_block =
+      to_predict_program_->Block(block_id);
+  for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
+    auto op = ops_of_block_[*to_predict_block.get()][j];
+    op->Run();
+  }
+}
+
+template <typename Dtype, Precision P>
+std::vector<typename Executor<Dtype, P>::Ptype> Executor<Dtype, P>::predict(
+    const std::vector<Ptype> &input, const std::vector<int64_t> &dims) {
+  framework::Tensor tensor(input, framework::make_ddim(dims));
+
+  predict(tensor, 0);
+
+  framework::Variable *g_feed_value = program_.scope->Var("col");
+  auto feed_tensor = g_feed_value->GetMutable<framework::Tensor>();
+
+  return {};
+}
+
+template class Executor<CPU, Precision::FP32>;
+
+}  // namespace paddle_mobile
diff --git a/src/common/io.h b/src/common/io.h
new file mode 100644
index 0000000000000000000000000000000000000000..678441a9e05dacf4e1f6a41705c1499c3ea99238
--- /dev/null
+++ b/src/common/io.h
@@ -0,0 +1,71 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <memory.h>
+#include <string>
+#include <vector>
+
+#include "common/types.h"
+#include "framework/lod_tensor.h"
+#include "framework/operator.h"
+#include "framework/paddle_mobile_object.h"
+#include "framework/program/program.h"
+#include "framework/tensor.h"
+
+namespace paddle_mobile {
+
+template <typename Dtype, Precision P = Precision::FP32>
+class Loader : PaddleMobileObject {
+ public:
+  const framework::Program<Dtype, P> Load(const std::string &dirname);
+
+ private:
+  void LoadVar(framework::Variable *variable,
+               const framework::VarDesc &var_desc,
+               const std::string &file_path);
+};
+
+template <typename Dtype, Precision P = Precision::FP32>
+class Executor {
+ public:
+  typedef typename PrecisionTrait<P>::ptype Ptype;
+
+  Executor() = default;
+
+  Executor(const framework::Program<Dtype> p);
+
+  Executor(const framework::Program<Dtype> p, int batch_size);
+
+  std::shared_ptr<framework::Tensor> predict(framework::Tensor &t);
+
+  std::vector<Ptype> predict(const std::vector<Ptype> &input,
+                             const std::vector<int64_t> &dims);
+
+ protected:
+  void InitMemory();
+  void LoadMemory(const framework::VarDesc var_desc,
+                  framework::LoDTensor *tensor, const std::string &file_path);
+  framework::Program<Dtype> program_;
+  int batch_size_ = 1;
+  std::shared_ptr<framework::ProgramDesc> to_predict_program_;
+  void predict(const framework::Tensor &t, int block_id);
+  std::map<framework::BlockDesc,
+           std::vector<std::shared_ptr<framework::OperatorBase<Dtype>>>>
+      ops_of_block_;
+  bool use_optimize_ = false;
+};
+
+}  // namespace paddle_mobile