Merge branch 'develop' of https://github.com/baidu/Paddle into cn_doc

doc_cn/concepts/use_concepts.rst

@@ -54,7 +54,7 @@ trainer进程会调用DataProvider函数，将用户的原始数据转换成系
 ..  literalinclude:: trainer_config.py
     :linenos:
 
-文件开头 ``from paddle.trainer_config_helpers import *`` ，是因为PaddlePaddle配置文件与C++模块通信的最基础协议是protobuf。为了避免用户直接写复杂的protobuf string，我们为用户定以Python接口来配置网络，该Python代码可以生成protobuf包，这就是的作用`trainer_config_helpers`_的作用。因此，在文件的开始，需要import这些函数。 这个包里面包含了模型配置需要的各个模块。
+文件开头 ``from paddle.trainer_config_helpers import *`` ，是因为PaddlePaddle配置文件与C++模块通信的最基础协议是protobuf，为了避免用户直接写复杂的protobuf string，我们为用户定以Python接口来配置网络，该Python代码可以生成protobuf包，这就是`trainer_config_helpers`_的作用。因此，在文件的开始，需要import这些函数。 这个包里面包含了模型配置需要的各个模块。
 
 下面分别介绍DataConfig、OptimizationConfig、ModelConfig这三部分该概念。
 

paddle/gserver/evaluators/CTCErrorEvaluator.cpp

@@ -240,7 +240,7 @@ public:
     seqClassficationError_ = 0;
   }
 
-  virtual void printStats(std::ostream& os) {
+  virtual void printStats(std::ostream& os) const {
     os << config_.name() << "="
        << (numSequences_ ? totalScore_ / numSequences_ : 0);
     os << "  deletions error"

paddle/gserver/evaluators/ChunkEvaluator.cpp

@@ -114,7 +114,7 @@ public:
     numCorrect_ = 0;
   }
 
-  virtual void printStats(std::ostream& os) {
+  virtual void printStats(std::ostream& os) const {
     double precision = (double)numCorrect_ / numOutputSegments_;
     double recall = (double)numCorrect_ / numLabelSegments_;
     double f1 =

paddle/gserver/evaluators/Evaluator.cpp

@@ -315,7 +315,7 @@ public:
     return 0;
   }
 
-  virtual void printStats(std::ostream& os) {
+  virtual void printStats(std::ostream& os) const {
     CHECK(colIdx_ + (int32_t)colNum_ >= 0 && colIdx_ - (int32_t)colNum_ < 0)
         << "column index [" << colIdx_ << "] out of range [-" << colNum_ << ", "
         << colNum_ << ")";
@@ -421,7 +421,7 @@ void AucEvaluator::distributeEval(ParameterClient2* client) {
   client->reduce(statNeg_, statNeg_, kBinNum_ + 1, FLAGS_trainer_id, 0);
 }
 
-double AucEvaluator::calcAuc() {
+double AucEvaluator::calcAuc() const {
   double totPos = 0.0;
   double totNeg = 0.0;
   double totPosPrev = 0.0;
@@ -584,7 +584,7 @@ real PrecisionRecallEvaluator::evalImp(std::vector<Argument>& arguments) {
   return 0;
 }
 
-void PrecisionRecallEvaluator::printStats(std::ostream& os) {
+void PrecisionRecallEvaluator::printStats(std::ostream& os) const {
   int label = config_.positive_label();
   if (label != -1) {
     CHECK(label >= 0 && label < (int)statsInfo_.size())

paddle/gserver/evaluators/Evaluator.h

@@ -99,19 +99,19 @@ public:
    * @brief print the statistics of evaluate result
    * @note finish() should be called before printStats
    */
-  virtual void printStats(std::ostream& os) {
+  virtual void printStats(std::ostream& os) const {
     os << config_.name() << "="
        << (numSamples_ ? totalScore_ / numSamples_ : 0);
   }
 
   friend std::ostream& operator<<(std::ostream& os,
-                                  Evaluator& evaluator) {
+                                  const Evaluator& evaluator) {
     evaluator.printStats(os);
     return os;
   }
 
   friend std::ostream&& operator<<(std::ostream&& os,    // NOLINT
-                                   Evaluator& evaluator) {
+                                   const Evaluator& evaluator) {
     evaluator.printStats(os);
     return std::move(os);
   }
@@ -135,7 +135,7 @@ public:
     return -1;
   }
   virtual void finish() {}
-  virtual void printStats(std::ostream&) {}
+  virtual void printStats(std::ostream&) const {}
 };
 /**
  * @brief evaluate AUC using colIdx-th column as prediction.
@@ -165,7 +165,7 @@ public:
 
   virtual real evalImp(std::vector<Argument>& arguments);
 
-  virtual void printStats(std::ostream& os) {
+  virtual void printStats(std::ostream& os) const {
     os << config_.name() << "=" << calcAuc();
   }
 
@@ -189,7 +189,7 @@ private:
     return (X1 > X2 ? (X1 - X2) : (X2 - X1)) * (Y1 + Y2) / 2.0;
   }
 
-  double calcAuc();
+  double calcAuc() const;
 };
 
 /**
@@ -244,7 +244,7 @@ public:
 
   virtual real evalImp(std::vector<Argument>& arguments);
 
-  virtual void printStats(std::ostream& os);
+  virtual void printStats(std::ostream& os) const;
 
   virtual void distributeEval(ParameterClient2* client);
 
@@ -339,7 +339,7 @@ public:
 
   virtual void finish() { calc(predictArray_); }
 
-  virtual void printStats(std::ostream& os) {
+  virtual void printStats(std::ostream& os) const {
     os << " pos/neg"
        << "=" << pairArray_[0] / ((pairArray_[1] <= 0) ? 1.0 : pairArray_[1]);
   }

paddle/gserver/gradientmachines/MultiNetwork.cpp

@@ -154,7 +154,7 @@ public:
     return -1;
   }
 
-  virtual void printStats(std::ostream& os) {
+  virtual void printStats(std::ostream& os) const {
     for (auto& evaluator : evaluators_) {
       evaluator->printStats(os);
       os << ' ';

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -325,7 +325,7 @@ public:
     (void)arguments;
     return -1;
   }
-  virtual void printStats(std::ostream& os) {
+  virtual void printStats(std::ostream& os) const {
     for (auto& evaluator : evaluators_) {
       evaluator->printStats(os);
       os << ' ';

paddle/math/BaseMatrix.cu

@@ -1449,8 +1449,8 @@ template<>
 template <class Agg>
 int BaseMatrixT<real>::applyRow(Agg agg, BaseMatrixT& b) {
   MatrixOffset offset(0, 0, 0, 0, 0, 0);
-  int numRows = b.height_;
-  int numCols = b.width_;
+  size_t numRows = b.height_;
+  size_t numCols = b.width_;
   CHECK_EQ(height_, numRows);
   CHECK_EQ(width_, 1UL);
   aggregate(agg, base::unary::identity(), base::binary::second(), b, numRows,
@@ -1463,8 +1463,8 @@ template<>
 template <class Agg, class Saver>
 int BaseMatrixT<real>::applyRow(Agg agg, Saver sv, BaseMatrixT& b) {
   MatrixOffset offset(0, 0, 0, 0, 0, 0);
-  int numRows = b.height_;
-  int numCols = b.width_;
+  size_t numRows = b.height_;
+  size_t numCols = b.width_;
   CHECK_EQ(height_, numRows);
   CHECK_EQ(width_, 1UL);
   aggregate(agg, base::unary::identity(), sv, b, numRows, numCols, offset,
@@ -1493,8 +1493,8 @@ template <class Agg, class Op, class Saver>
 int BaseMatrixT<real>::applyRow(Agg agg, Op op, Saver sv,
                                 BaseMatrixT& b, BaseMatrixT& c) {
   MatrixOffset offset(0, 0, 0, 0, 0, 0);
-  int numRows = b.height_;
-  int numCols = b.width_;
+  size_t numRows = b.height_;
+  size_t numCols = b.width_;
   CHECK_EQ(height_, numRows);
   CHECK_EQ(width_, 1UL);
   CHECK_EQ(c.height_, numRows);
@@ -1524,8 +1524,8 @@ template<>
 template <class Agg>
 int BaseMatrixT<real>::applyCol(Agg agg, BaseMatrixT& b) {
   MatrixOffset offset(0, 0, 0, 0, 0, 0);
-  int numRows = b.height_;
-  int numCols = b.width_;
+  size_t numRows = b.height_;
+  size_t numCols = b.width_;
   CHECK_EQ(width_, numCols);
   CHECK_EQ(height_, 1UL);
   aggregate(agg, base::unary::identity(), base::binary::second(), b, numRows,
@@ -1538,8 +1538,8 @@ template<>
 template <class Agg, class Saver>
 int BaseMatrixT<real>::applyCol(Agg agg, Saver sv, BaseMatrixT& b) {
   MatrixOffset offset(0, 0, 0, 0, 0, 0);
-  int numRows = b.height_;
-  int numCols = b.width_;
+  size_t numRows = b.height_;
+  size_t numCols = b.width_;
   CHECK_EQ(width_, numCols);
   CHECK_EQ(height_, 1UL);
   aggregate(agg, base::unary::identity(), sv, b, numRows, numCols, offset,

paddle/math/Vector.cpp

@@ -82,8 +82,8 @@ MatrixPtr VectorT<real>::toOneHotSparseMatrix(size_t idRange, bool useGpu) {
 
 template <>
 MatrixPtr VectorT<int>::toOneHotSparseMatrix(size_t idRange, bool useGpu) {
-  int height = getSize();
-  int width = idRange;
+  size_t height = getSize();
+  size_t width = idRange;
   MatrixPtr mat = Matrix::createSparseMatrix(
       height, idRange, height, NO_VALUE, SPARSE_CSR, false, useGpu);
 
@@ -91,7 +91,7 @@ MatrixPtr VectorT<int>::toOneHotSparseMatrix(size_t idRange, bool useGpu) {
   cpuIds.copyFrom(*this);
   int *idData = cpuIds.getData();
 
-  for (int i = 0; i < height; i ++) {
+  for (decltype(height) i = 0; i < height; i ++) {
     const unsigned int id = idData[i];
     CHECK_LT(id, width);
     mat->setRow(i, 1, &id, nullptr);

paddle/pserver/ParameterServer2.cpp

@@ -1469,7 +1469,6 @@ void ParameterServer2::waitPassFinish(const WaitPassFinishRequest& request,
 
 void ParameterServer2::synchronize(const SynchronizeRequest& request,
                                    ProtoResponseCallback callback) {
-  CHECK_LT(request.sync_object_id(), SyncObject_ARRAYSIZE);
   synchronizeBarriers_[request.sync_object_id()]->wait();
   dataSize_ = 0;
   callback(SynchronizeResponse());
@@ -1477,7 +1476,6 @@ void ParameterServer2::synchronize(const SynchronizeRequest& request,
 
 void ParameterServer2::asyncFinishPass(const SynchronizeRequest& request,
                                        ProtoResponseCallback callback) {
-  CHECK_LT(request.sync_object_id(), SyncObject_ARRAYSIZE);
   synchronizeBarriers_[request.sync_object_id()]->wait();
   callback(SynchronizeResponse());
 

paddle/utils/BarrierStat.cpp

@@ -29,10 +29,10 @@ P_DEFINE_bool(log_barrier_show_log, false,  // for performance tuning insight
 
 namespace paddle {
 
-std::ostream &operator<<(std::ostream &output, BarrierStatBase &stat) {
+std::ostream &operator<<(std::ostream &output,
+                         const BarrierStatBase &stat) {
   if (FLAGS_log_barrier_abstract) {
-    std::lock_guard<std::mutex> guard(
-        const_cast<BarrierStatBase &>(stat).lock_);
+    std::lock_guard<std::mutex> guard(stat.lock_);
     stat.showAbstract(output);
   }
   return output;
@@ -136,7 +136,7 @@ void BarrierEndStat::reset(bool clearRawData) {
   totAbstract_.minDelta = UINT64_MAX;
 }
 
-void BarrierEndStat::showAbstract(std::ostream &output) {
+void BarrierEndStat::showAbstract(std::ostream &output) const {
   // do not support the case "<=2 pserver"
   if (numConnThreads_ <= 2 || !totSamples_) {
     return;
@@ -272,7 +272,7 @@ void BarrierDeltaStat::reset(bool clearRawData) {
   totAbstract_.minDelta = UINT64_MAX;
 }
 
-void BarrierDeltaStat::showAbstract(std::ostream &output) {
+void BarrierDeltaStat::showAbstract(std::ostream &output) const {
   // do not support the case "<=2 pserver"
   if (numConnThreads_ <= 2 || !totSamples_) {
     return;

paddle/utils/BarrierStat.h

@@ -218,11 +218,12 @@ public:
   }
 
 protected:
-  virtual void showAbstract(std::ostream &output) {}
-  friend std::ostream &operator<<(std::ostream &output, BarrierStatBase &stat);
+  virtual void showAbstract(std::ostream &output) const {}
+  friend std::ostream &operator<<(std::ostream &output,
+                                  const BarrierStatBase &stat);
 
 protected:
-  std::mutex lock_;
+  mutable std::mutex lock_;
   std::mutex abstractLock_;  // see note on updaterStat
   // each freqency for each barrier trainer
   std::vector<struct Abstract> abstract_;
@@ -262,7 +263,7 @@ protected:
    * log_barrier_abstract, log_barrier_lowest_nodes, log_barrier_threshold
    * control details.
    */
-  virtual void showAbstract(std::ostream &output);
+  virtual void showAbstract(std::ostream &output) const;
 
 private:
   std::unique_ptr<TimeVectorEnd> timeVector_;
@@ -286,7 +287,7 @@ public:
   virtual bool checkPassBarrier() { return timeVector_->empty(); }
 
 protected:
-  virtual void showAbstract(std::ostream &outPut);
+  virtual void showAbstract(std::ostream &outPut) const;
 
 private:
   // store delta time in uint64_t, eg BP time of all trainers

paddle/utils/CompilerMacros.h

+/* Copyright (c) 2016 Baidu, Inc. 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
+
+#define ATTR_NORETURN __attribute__((noreturn))

paddle/utils/Logging.cpp

@@ -134,7 +134,7 @@ static void initializeLogFds(char* argv0) {
   gLogInited = true;
 }
 
-static void (*gFailureFunctionPtr)() __attribute__((noreturn)) = abort;
+static void (*gFailureFunctionPtr)() ATTR_NORETURN = abort;
 
 LogMessage::LogMessage(const char* fname, int line, int severity)
     : fname_(fname), line_(line), severity_(severity) {}
@@ -171,7 +171,7 @@ void setMinLogLevel(int level) {
   paddle::internal::gMinLogLevel = level;
 }
 
-void installFailureFunction(void (*callback)()) {
+void installFailureFunction(void (*callback)() ATTR_NORETURN) {
   paddle::internal::gFailureFunctionPtr = callback;
 }
 

paddle/utils/Logging.h

@@ -23,6 +23,7 @@ limitations under the License. */
 #include <string>
 
 #ifndef PADDLE_USE_GLOG
+#include "CompilerMacros.h"
 
 //! TODO(yuyang18): Move this utility macro into some global header.
 #define PP_CAT(a, b) PP_CAT_I(a, b)
@@ -168,7 +169,7 @@ void setMinLogLevel(int level);
  * @brief Install Log(Fatal) failure function. Default is abort();
  * @param callback: The failure function.
  */
-void installFailureFunction(void (*callback)());
+void installFailureFunction(void (*callback)() ATTR_NORETURN);
 
 /**
  * @brief installFailureWriter