TensorFlow的c++實踐及各種坑！

作者：全球人工智慧

前言

Tensorflow當前官網僅包含python、C、Java、Go的發佈包， 並無C++ release包， 並且tensorflow官網也注明了並不保證除python以外庫的穩定性， 在功能方面python也是最完善的。 眾所周知， python在開發效率、易用性上有著巨大的優勢， 但作為一個解釋性語言， 在性能方面還是存在比較大的缺陷， 在各類AI服務化過程中， 採用python作為模型快速構建工具， 使用高階語言(如C++， java)作為服務化程式實現是大勢所趨。 本文重點介紹tensorflow C++服務化過程中實現方式及遇到的各種問題。

實現方案

對於tensorflow c++庫的使用， 有兩種方法：

(1) 最佳方式當然是直接用C++構建graph， 但是當前c++tensorflow庫並不像python api那樣full-featured。

可參照builds a small graph in c++ here, C++ tensorflow api中還包含cpu和gpu的數字內核實現的類， 可用以添加新的op。 可參照https://www.tensorflow.org/extend/adding_an_op

(2) 常用的方式， c++調用python生成好的graph。 本文主要介紹該方案。

實現步驟

(1) 編譯tensorflow源碼C++ so

(2) 模型訓練輸出結果

(3) 模型固化

(4) 模型載入及運行

(5) 運行問題

(1) 源碼編譯

環境要求： 公司tlinux2.2版本， GCC版本 >= 4.8.5

安裝組件： protobuf 3.3.0 bazel 0.5.0 python 2.7 java8

機器要求： 4GB記憶體

a. 安裝java8

yum install java

b. 安裝protobuf 3.3.0

下載 下載https://github.com/google/protobuf/archive/v3.3.0.zip

./configure && make && make install

c. 安裝bazel

download https://github.com/bazelbuild/bazel/releases

sh bazel-0.5.0-installer-linux-x86_64.sh

d. 編譯源碼

最好採用最新release版本：https://github.com/tensorflow/tensorflow/releases

bazel build //tensorflow:libtensorflow_cc.so

編譯過程中可能遇到的問題：

問題一： fatal error: unsupported/Eigen/CXX11/Tensor: No such file or directory

安裝Eigen3.3或以上版本

問題二： java.io.IOException: Cannot run program "patch"

yum install patch

問題三： 記憶體不夠

(2) 模型訓練與輸出

模型訓練輸出可參照改用例去實踐https://blog.metaflow.fr/tensorflow-saving-restoring-and-mixing-multiple-models-c4c94d5d7125 ， google上也很多， 模型訓練保存好得到下面檔：

(3) 模型固化

模型固化方式有三種：

a. freeze_graph 工具

bazel build tensorflow/python/tools:freeze_graph && bazel-bin/tensorflow/python/tools/freeze_graph --input_graph=graph.pb --input_checkpoint=checkpoint --output_graph=./frozen_graph.pb --output_node_names=output/output/scores

b. 利用freeze_graph.py工具

# We save out the graph to disk, and then call the const conversion# routine.checkpoint_state_name = "checkpoint"input_graph_name = "graph.pb"output_graph_name = "frozen_graph.pb"input_graph_path = os.path.join(FLAGS.model_dir, input_graph_name)input_saver_def_path = ""input_binary = Falseinput_checkpoint_path = os.path.join(FLAGS.checkpoint_dir, 'saved_checkpoint') + "-0"# Note that we this normally should be only "output_node"!!!output_node_names = "output/output/scores" restore_op_name = "save/restore_all"filename_tensor_name = "save/Const:0"output_graph_path = os.path.join(FLAGS.model_dir, output_graph_name)clear_devices = Falsefreeze_graph.freeze_graph(input_graph_path, input_saver_def_path, input_binary, input_checkpoint_path, output_node_names, restore_op_name, filename_tensor_name, output_graph_path, clear_devices)

c. 利用tensorflow python

import os, argparseimport tensorflow as tffrom tensorflow.python.framework import graph_util

dir = os.path.dirname(os.path.realpath(__file__))def freeze_graph(model_folder): # We retrieve our checkpoint fullpath checkpoint = tf.train.get_checkpoint_state(model_folder) input_checkpoint = checkpoint.model_checkpoint_path # We precise the file fullname of our freezed graph absolute_model_folder = "/".join(input_checkpoint.split('/')[:-1]) output_graph = absolute_model_folder + "/frozen_model.pb" print output_graph # Before exporting our graph, we need to precise what is our output node # This is how TF decides what part of the Graph he has to keep and what part it can dump # NOTE: this variable is plural, because you can have multiple output nodes output_node_names = "output/output/scores" # We clear devices to allow TensorFlow to control on which device it will load operations clear_devices = True # We import the meta graph and retrieve a Saver saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices) # We retrieve the protobuf graph definition graph = tf.get_default_graph() input_graph_def = graph.as_graph_def() # fix batch norm nodes for node in input_graph_def.node: if node.op == 'RefSwitch': node.op = 'Switch' for index in xrange(len(node.input)): if 'moving_' in node.input[index]: node.input[index] = node.input[index] + '/read' elif node.op == 'AssignSub': node.op = 'Sub' if 'use_locking' in node.attr: del node.attr['use_locking'] # We start a session and restore the graph weights with tf.Session() as sess: saver.restore(sess, input_checkpoint) # We use a built-in TF helper to export variables to constants output_graph_def = graph_util.convert_variables_to_constants( sess, # The session is used to retrieve the weights input_graph_def, # The graph_def is used to retrieve the nodes output_node_names.split(",") # The output node names are used to select the usefull nodes ) # Finally we serialize and dump the output graph to the filesystem with tf.gfile.GFile(output_graph, "wb") as f: f.write(output_graph_def.SerializeToString()) print("%d ops in the final graph." % len(output_graph_def.node))if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("--model_folder", type=str, help="Model folder to export") args = parser.parse_args() freeze_graph(args.model_folder)

坑 BatchNorm bug

在具體實際專案， 用方式一與方式二將生成的模型利用tensorflow c++ api載入， 報以上錯誤，

採用tensorflow python載入模型報同樣錯：

原因是模型中用到了BatchNorm， 修復方式如上面c中給出的方案

(4) 模型載入及運行

構建輸入輸出

模型輸入輸出主要就是構造輸入輸出矩陣， 相比python的numpy庫， tensorflow提供的Tensor和Eigen::Tensor還是非常難用的， 特別是動態矩陣創建， 如果你的編譯器支援C++14， 可以用xTensor庫， 和numpy一樣強大， 並且用法機器類似。 如果是C++11版本就好好看看eigen庫和tensorflow::Tensor文檔吧。 例舉集中簡單的用法：

矩陣賦值：

tensorflow::Tensor four_dim_plane(DT_FLOAT, tensorflow::TensorShape({1, MODEL_X_AXIS_LEN, MODEL_Y_AXIS_LEN, fourth_dim_size}));auto plane_tensor = four_dim_plane.tensor();for (uint32_t k = 0; k < array_plane.size(); ++k){ for (uint32_t j = 0; j < MODEL_Y_AXIS_LEN; ++j) { for (uint32_t i = 0; i < MODEL_X_AXIS_LEN; ++i) { plane_tensor(0, i, j, k) = array_plane[k](i, j); } }}

SOFTMAX:

Eigen::Tensor ModelApp::TensorSoftMax(const Eigen::Tensor& tensor){ Eigen::Tensor max = tensor.maximum(); auto e_x = (tensor - tensor.constant(max())).exp(); Eigen::Tensor e_x_sum = e_x.sum(); return e_x / e_x_sum();}

模型載入及session初始化：

int32_t ModelApp::Init(const std::string& graph_file, Logger *logger){ auto status = NewSession(SessionOptions(), &m_session); if (!status.ok()) { LOG_ERR(logger, "New session failed! %s", status.ToString().c_str()); return Error::ERR_FAILED_NEW_TENSORFLOW_SESSION; } GraphDef graph_def; status = ReadBinaryProto(Env::Default(), graph_file, &graph_def); if (!status.ok()) { LOG_ERR(logger, "Read binary proto failed! %s", status.ToString().c_str()); return Error::ERR_FAILED_READ_BINARY_PROTO; } status = m_session->Create(graph_def); if (!status.ok()) { LOG_ERR(logger, "Session create failed! %s", status.ToString().c_str()); return Error::ERR_FAILED_CREATE_TENSORFLOW_SESSION; } return Error::Success;}

運行：

0.10以上的tensorflow庫是執行緒安全的， 因此可多執行緒調用predict

int32_t ModelApp::Predict(const Action& action, std::vector* info, Logger *logger){ ... auto tensor_x = m_writer->Generate(action, logger); Tensor phase_train(DT_BOOL, TensorShape()); phase_train.scalar()() = false; std::vector> inputs = { {"input_x", tensor_x}, {"phase_train", phase_train} }; std::vector result; auto status = m_session->Run(inputs, {"output/output/scores"}, {}, &result); if (!status.ok()) { LOG_ERR(logger, "Session run failed! %s", status.ToString().c_str()); return Error::ERR_FAILED_TENSORFLOW_EXECUTION; } ... auto scores = result[0].flat() ; ... return Error::SUCCESS;}

(5) 運行問題

問題一：運行告警

2017-08-16 14:11:14.393295: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.2017-08-16 14:11:14.393324: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.2017-08-16 14:11:14.393331: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.2017-08-16 14:11:14.393338: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.

是因為在編譯tensorflow so庫的時候沒有把這些CPU加速指令編譯進去， 因此可以在編譯的時候加入加速指令， 在沒有GPU條件下， 加入這些庫實測可以將CPU計算提高10%左右。

bazel build -c opt --copt=-mavx --copt=-mfma --copt=-mfpmath=both --copt=-msse4.2 -k //tensorflow:libtensorflow_cc.so

需要注意的是並不是所有CPU都支援這些指令，

一定要實機測試， 以免abort。

問題二: C++ libtensorflow和python tensorflow混用

為驗證C++載入模型調用的準確性， 利用swig將c++ api封裝成了python庫供python調用， 在同時import tensorflow as tf和import封裝好的python swig介面時， core dump

該問題tensorflow官方並不打算解決