Add a simple example for fluid to do inference in C++ code.

CMakeLists.txt

@@ -20,8 +20,10 @@ set(PADDLE_BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR})
 include(system)
 
 project(paddle CXX C Go)
-message(STATUS "CXX compiler: " ${CMAKE_CXX_COMPILER} ", version: " ${CMAKE_CXX_COMPILER_VERSION})
-message(STATUS "C compiler: " ${CMAKE_C_COMPILER} ", version: " ${CMAKE_C_COMPILER_VERSION})
+message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: "
+        "${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}")
+message(STATUS "C compiler: ${CMAKE_C_COMPILER}, version: "
+        "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
 
 find_package(Sphinx)
 if(NOT CMAKE_CROSSCOMPILING)

cmake/external/eigen.cmake

@@ -19,7 +19,7 @@ ExternalProject_Add(
 
 if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
     set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/eigen3_dummy.c)
-    file(WRITE ${dummyfile} "const char * dummy_eigen3 = \"${dummyfile}\";")
+    file(WRITE ${dummyfile} "const char *dummy_eigen3 = \"${dummyfile}\";")
     add_library(eigen3 STATIC ${dummyfile})
 else()
     add_library(eigen3 INTERFACE)

cmake/external/openblas.cmake

@@ -113,7 +113,7 @@ INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
 # FIXME(gangliao): generate cblas target to track all high performance
 # linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
 SET(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/cblas_dummy.c)
-FILE(WRITE ${dummyfile} "const char * dummy = \"${dummyfile}\";")
+FILE(WRITE ${dummyfile} "const char *dummy_cblas = \"${dummyfile}\";")
 ADD_LIBRARY(cblas STATIC ${dummyfile})
 TARGET_LINK_LIBRARIES(cblas ${CBLAS_LIBRARIES})
 

cmake/generic.cmake

@@ -120,7 +120,7 @@ function(merge_static_libs TARGET_NAME)
       DEPENDS ${libs})
 
     # Generate dummy staic lib
-    file(WRITE ${target_SRCS} "const char *dummy = \"${target_SRCS}\";")
+    file(WRITE ${target_SRCS} "const char *dummy_${TARGET_NAME} = \"${target_SRCS}\";")
     add_library(${TARGET_NAME} STATIC ${target_SRCS})
     target_link_libraries(${TARGET_NAME} ${libs_deps})
 
@@ -160,7 +160,7 @@ function(merge_static_libs TARGET_NAME)
       DEPENDS ${libs} ${target_OBJS})
 
     # Generate dummy staic lib
-    file(WRITE ${target_SRCS} "const char *dummy = \"${target_SRCS}\";")
+    file(WRITE ${target_SRCS} "const char *dummy_${TARGET_NAME} = \"${target_SRCS}\";")
     add_library(${TARGET_NAME} STATIC ${target_SRCS})
     target_link_libraries(${TARGET_NAME} ${libs_deps})
 
@@ -324,7 +324,7 @@ function(go_library TARGET_NAME)
     )
 
   # Add dummy code to support `make target_name` under Terminal Command
-  file(WRITE ${dummyfile} "const char * dummy = \"${dummyfile}\";")
+  file(WRITE ${dummyfile} "const char *dummy_${TARGET_NAME} = \"${dummyfile}\";")
   if (go_library_SHARED OR go_library_shared)
     add_library(${TARGET_NAME} SHARED ${dummyfile})
   else()

paddle/CMakeLists.txt

@@ -24,6 +24,7 @@ else()
     add_subdirectory(framework)
     add_subdirectory(operators)
     add_subdirectory(pybind)
+    add_subdirectory(inference)
   endif()
 
   if(WITH_SWIG_PY)

paddle/framework/op_desc.cc

@@ -64,7 +64,7 @@ class CompileTimeInferShapeContext : public InferShapeContext {
     PADDLE_ENFORCE_EQ(in_var->GetType(), proto::VarDesc::LOD_TENSOR,
                       "The %d-th output of Output(%s) must be LoDTensor.", j,
                       out);
-    out_var->SetLoDLevel(in_var->GetLodLevel());
+    out_var->SetLoDLevel(in_var->GetLoDLevel());
   }
   bool IsRuntime() const override;
 

paddle/framework/var_desc.cc

@@ -52,7 +52,7 @@ void VarDesc::SetLoDLevel(int32_t lod_level) {
   }
 }
 
-int32_t VarDesc::GetLodLevel() const {
+int32_t VarDesc::GetLoDLevel() const {
   switch (desc_.type()) {
     case proto::VarDesc::LOD_TENSOR:
       return desc_.lod_tensor().lod_level();

paddle/framework/var_desc.h

@@ -76,7 +76,7 @@ class VarDesc {
 
   void SetLoDLevel(int32_t lod_level);
 
-  int32_t GetLodLevel() const;
+  int32_t GetLoDLevel() const;
 
   proto::VarDesc::VarType GetType() const;
 

paddle/inference/CMakeLists.txt

+set(FLUID_CORE_MODULES
+    backward proto_desc paddle_memory executor prune init ${GLOB_OP_LIB})
+
+cc_library(paddle_fluid_api
+    SRCS inference.cc
+    DEPS ${FLUID_CORE_MODULES})
+
+# Merge all modules into a simgle static library
+cc_library(paddle_fluid DEPS paddle_fluid_api ${FLUID_CORE_MODULES})
+
+# ptools
+# just for testing, we may need to change the storing format for inference_model
+# and move the dependent of pickle.
+# download from http://www.picklingtools.com/
+# build in the C++ sub-directory, using command
+#     make -f Makefile.Linux libptools.so
+set(PTOOLS_LIB)
+set(PTOOLS_ROOT $ENV{PTOOLS_ROOT} CACHE PATH "Folder contains PicklingTools")
+find_path(PTOOLS_INC_DIR chooseser.h PATHS ${PTOOLS_ROOT}/C++)
+find_library(PTOOLS_SHARED_LIB NAMES ptools PATHS ${PTOOLS_ROOT}/C++)
+if(PTOOLS_INC_DIR AND PTOOLS_SHARED_LIB)
+  add_definitions(-DPADDLE_USE_PTOOLS)
+  set(PTOOLS_LIB ptools)
+  message(STATUS "Found PicklingTools: ${PTOOLS_SHARED_LIB}")
+  add_library(${PTOOLS_LIB} SHARED IMPORTED GLOBAL)
+  set_property(TARGET ${PTOOLS_LIB} PROPERTY IMPORTED_LOCATION ${PTOOLS_SHARED_LIB})
+  include_directories(${PTOOLS_ROOT}/C++)
+  include_directories(${PTOOLS_ROOT}/C++/opencontainers_1_8_5/include)
+  add_definitions(-DOC_NEW_STYLE_INCLUDES) # used in ptools
+endif()
+
+add_executable(example example.cc)
+target_link_libraries(example
+    -Wl,--start-group -Wl,--whole-archive paddle_fluid
+    -Wl,--no-whole-archive -Wl,--end-group
+    ${PTOOLS_LIB})

paddle/inference/example.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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 <time.h>
+#include <iostream>
+#include "paddle/inference/inference.h"
+
+int main(int argc, char* argv[]) {
+  std::string dirname =
+      "/home/work/liuyiqun/PaddlePaddle/Paddle/paddle/inference/"
+      "recognize_digits_mlp.inference.model";
+  std::vector<std::string> feed_var_names = {"x"};
+  std::vector<std::string> fetch_var_names = {"fc_2.tmp_2"};
+  paddle::InferenceEngine* desc = new paddle::InferenceEngine();
+  desc->LoadInferenceModel(dirname, feed_var_names, fetch_var_names);
+
+  paddle::framework::LoDTensor input;
+  srand(time(0));
+  float* input_ptr =
+      input.mutable_data<float>({1, 784}, paddle::platform::CPUPlace());
+  for (int i = 0; i < 784; ++i) {
+    input_ptr[i] = rand() / (static_cast<float>(RAND_MAX));
+  }
+
+  std::vector<paddle::framework::LoDTensor> feeds;
+  feeds.push_back(input);
+  std::vector<paddle::framework::LoDTensor> fetchs;
+  desc->Execute(feeds, fetchs);
+
+  for (size_t i = 0; i < fetchs.size(); ++i) {
+    auto dims_i = fetchs[i].dims();
+    std::cout << "dims_i:";
+    for (int j = 0; j < dims_i.size(); ++j) {
+      std::cout << " " << dims_i[j];
+    }
+    std::cout << std::endl;
+    std::cout << "result:";
+    float* output_ptr = fetchs[i].data<float>();
+    for (int j = 0; j < paddle::framework::product(dims_i); ++j) {
+      std::cout << " " << output_ptr[j];
+    }
+    std::cout << std::endl;
+  }
+  return 0;
+}

paddle/inference/inference.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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 "inference.h"
+#include <fstream>
+#include "paddle/framework/executor.h"
+#include "paddle/framework/feed_fetch_method.h"
+#include "paddle/framework/init.h"
+#include "paddle/framework/scope.h"
+
+#ifdef PADDLE_USE_PTOOLS
+#include "chooseser.h"
+#endif
+
+namespace paddle {
+
+void InferenceEngine::LoadInferenceModel(
+    const std::string& dirname,
+    const std::vector<std::string>& feed_var_names,
+    const std::vector<std::string>& fetch_var_names) {
+#ifdef PADDLE_USE_PTOOLS
+  std::string model_filename = dirname + "/__model__";
+  LOG(INFO) << "Using PicklingTools, loading model from " << model_filename;
+  Val v;
+  LoadValFromFile(model_filename.c_str(), v, SERIALIZE_P0);
+  std::string program_desc_str = v["program_desc_str"];
+  LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
+// PicklingTools cannot parse the vector of strings correctly.
+#else
+  // program_desc_str
+  // the inference.model is stored by following python codes:
+  //  inference_program = fluid.io.get_inference_program(predict)
+  //  model_filename = "recognize_digits_mlp.inference.model/inference.model"
+  //  with open(model_filename, "w") as f:
+  //      program_str = inference_program.desc.serialize_to_string()
+  //          f.write(struct.pack('q', len(program_str)))
+  //          f.write(program_str)
+  std::string model_filename = dirname + "/inference.model";
+  LOG(INFO) << "loading model from " << model_filename;
+  std::ifstream fs(model_filename, std::ios_base::binary);
+  int64_t size = 0;
+  fs.read(reinterpret_cast<char*>(&size), sizeof(int64_t));
+  LOG(INFO) << "program_desc_str's size: " << size;
+  std::string program_desc_str;
+  program_desc_str.resize(size);
+  fs.read(&program_desc_str[0], size);
+#endif
+  program_ = new framework::ProgramDesc(program_desc_str);
+  GenerateLoadProgram(dirname);
+
+  if (feed_var_names.empty() || fetch_var_names.empty()) {
+    LOG(FATAL) << "Please specify the feed_var_names and fetch_var_names.";
+  }
+  feed_var_names_ = feed_var_names;
+  fetch_var_names_ = fetch_var_names;
+  PrependFeedOp();
+  AppendFetchOp();
+}
+
+bool InferenceEngine::IsParameter(const framework::VarDesc* var) {
+  if (var->Persistable()) {
+    // There are many unreachable variables in the program
+    for (size_t i = 0; i < program_->Size(); ++i) {
+      const framework::BlockDesc& block = program_->Block(i);
+      for (auto* op : block.AllOps()) {
+        for (auto input_argument_name : op->InputArgumentNames()) {
+          if (input_argument_name == var->Name()) {
+            return true;
+          }
+        }
+      }
+    }
+  }
+  return false;
+}
+
+void InferenceEngine::GenerateLoadProgram(const std::string& dirname) {
+  framework::BlockDesc* global_block = program_->MutableBlock(0);
+
+  load_program_ = new framework::ProgramDesc();
+  framework::BlockDesc* load_block = load_program_->MutableBlock(0);
+  for (auto* var : global_block->AllVars()) {
+    if (IsParameter(var)) {
+      LOG(INFO) << "parameter's name: " << var->Name();
+
+      // framework::VarDesc new_var = *var;
+      framework::VarDesc* new_var = load_block->Var(var->Name());
+      new_var->SetShape(var->Shape());
+      new_var->SetDataType(var->GetDataType());
+      new_var->SetType(var->GetType());
+      new_var->SetLoDLevel(var->GetLoDLevel());
+      new_var->SetPersistable(true);
+
+      // append_op
+      framework::OpDesc* op = load_block->AppendOp();
+      op->SetType("load");
+      op->SetOutput("Out", {new_var->Name()});
+      op->SetAttr("file_path", {dirname + "/" + new_var->Name()});
+      op->CheckAttrs();
+    }
+  }
+}
+
+void InferenceEngine::PrependFeedOp() {
+  if (!program_) {
+    LOG(FATAL) << "Please initialize the program_ first.";
+  }
+
+  framework::BlockDesc* global_block = program_->MutableBlock(0);
+
+  // create_var
+  framework::VarDesc* feed_var = global_block->Var("feed");
+  feed_var->SetType(framework::proto::VarDesc::FEED_MINIBATCH);
+  feed_var->SetPersistable(true);
+
+  // prepend feed_op
+  for (size_t i = 0; i < feed_var_names_.size(); ++i) {
+    std::string var_name = feed_var_names_[i];
+    LOG(INFO) << "feed var's name: " << var_name;
+
+    // prepend_op
+    framework::OpDesc* op = global_block->PrependOp();
+    op->SetType("feed");
+    op->SetInput("X", {"feed"});
+    op->SetOutput("Out", {var_name});
+    op->SetAttr("col", {static_cast<int>(i)});
+    op->CheckAttrs();
+  }
+}
+
+void InferenceEngine::AppendFetchOp() {
+  if (!program_) {
+    LOG(FATAL) << "Please initialize the program_ first.";
+  }
+
+  framework::BlockDesc* global_block = program_->MutableBlock(0);
+
+  // create_var
+  framework::VarDesc* fetch_var = global_block->Var("fetch");
+  fetch_var->SetType(framework::proto::VarDesc::FETCH_LIST);
+  fetch_var->SetPersistable(true);
+
+  // append fetch_op
+  for (size_t i = 0; i < fetch_var_names_.size(); ++i) {
+    std::string var_name = fetch_var_names_[i];
+    LOG(INFO) << "fetch var's name: " << var_name;
+
+    // append_op
+    framework::OpDesc* op = global_block->AppendOp();
+    op->SetType("fetch");
+    op->SetInput("X", {var_name});
+    op->SetOutput("Out", {"fetch"});
+    op->SetAttr("col", {static_cast<int>(i)});
+    op->CheckAttrs();
+  }
+}
+
+void InferenceEngine::Execute(const std::vector<framework::LoDTensor>& feeds,
+                              std::vector<framework::LoDTensor>& fetchs) {
+  if (!program_ || !load_program_) {
+    LOG(FATAL) << "Please initialize the program_ and load_program_ first.";
+  }
+
+  if (feeds.size() < feed_var_names_.size()) {
+    LOG(FATAL) << "Please feed " << feed_var_names_.size() << " input Tensors.";
+  }
+
+  auto* place = new platform::CPUPlace();
+  framework::InitDevices({"CPU"});
+  framework::Executor* executor = new framework::Executor(*place);
+  framework::Scope* scope = new framework::Scope();
+
+  executor->Run(*load_program_, scope, 0, true, true);
+
+  // set_feed_variable
+  for (size_t i = 0; i < feed_var_names_.size(); ++i) {
+    framework::SetFeedVariable(scope, feeds[i], "feed", i);
+  }
+
+  executor->Run(*program_, scope, 0, true, true);
+
+  // get_fetch_variable
+  fetchs.resize(fetch_var_names_.size());
+  for (size_t i = 0; i < fetch_var_names_.size(); ++i) {
+    fetchs[i] = framework::GetFetchVariable(*scope, "fetch", i);
+  }
+
+  delete place;
+  delete scope;
+  delete executor;
+}
+}  // namespace paddle

paddle/inference/inference.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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 "paddle/framework/block_desc.h"
+#include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/program_desc.h"
+
+namespace paddle {
+
+class InferenceEngine {
+public:
+  InferenceEngine() : program_(nullptr), load_program_(nullptr) {}
+  ~InferenceEngine() {
+    delete program_;
+    delete load_program_;
+  }
+
+  void LoadInferenceModel(const std::string& dirname,
+                          const std::vector<std::string>& feed_var_names,
+                          const std::vector<std::string>& fetch_var_names);
+  void Execute(const std::vector<framework::LoDTensor>& feeds,
+               std::vector<framework::LoDTensor>& fetchs);
+
+private:
+  bool IsParameter(const framework::VarDesc* var);
+  void GenerateLoadProgram(const std::string& dirname);
+  void PrependFeedOp();
+  void AppendFetchOp();
+
+private:
+  framework::ProgramDesc* program_;
+  framework::ProgramDesc* load_program_;
+  std::vector<std::string> feed_var_names_;
+  std::vector<std::string> fetch_var_names_;
+};
+
+}  // namespace paddle

paddle/pybind/protobuf.cc

@@ -216,7 +216,7 @@ void BindVarDsec(py::module &m) {
       .def("set_dtype", &VarDesc::SetDataType)
       .def("shape", &VarDesc::Shape, py::return_value_policy::reference)
       .def("dtype", &VarDesc::GetDataType, py::return_value_policy::reference)
-      .def("lod_level", &VarDesc::GetLodLevel)
+      .def("lod_level", &VarDesc::GetLoDLevel)
       .def("set_lod_level", &VarDesc::SetLoDLevel)
       .def("type", &VarDesc::GetType)
       .def("set_type", &VarDesc::SetType)

python/paddle/v2/fluid/io.py

@@ -36,7 +36,7 @@ def save_vars(executor, dirname, main_program=None, vars=None, predicate=None):
     :param executor: executor that save variable
     :param dirname: directory path
     :param main_program: program. If vars is None, then filter all variables in this
-    program which fit `predicate`. Default g_program.
+    program which fit `predicate`. Default default_main_program.
     :param predicate: The Predicate describes a callable that returns a variable
     as a bool. If it returns true, the variables will be saved.
     :param vars: variables need to be saved. If specify vars, program & predicate

python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py

@@ -14,6 +14,7 @@ hidden1 = fluid.layers.fc(input=image,
                           param_attr=fluid.ParamAttr(
                               regularizer=regularizer,
                               clip=fluid.clip.ClipByValue(10)))
+
 hidden2 = fluid.layers.fc(input=hidden1,
                           size=64,
                           act='relu',
@@ -73,5 +74,9 @@ for pass_id in range(PASS_NUM):
               + " test_acc=" + str(test_pass_acc))
 
         if test_pass_acc > 0.7:
+            fluid.io.save_inference_model(
+                "./recognize_digits_mlp.inference.model/", ["x"], [predict],
+                exe)
             exit(0)
+
 exit(1)