Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into batch_norm

paddle/gserver/tests/test_ConvTrans.cpp

@@ -206,8 +206,8 @@ TEST(Layer, convTransLayerFwd2) {
                  /* filter_size */ 5,
                  result);
 
-  float resultData[] = {1, 2, 2, 2, 1, 2, 4, 4, 4, 2, 2, 4, 4,
-                        4, 2, 2, 4, 4, 4, 2, 1, 2, 2, 2, 1};
+  real resultData[] = {1, 2, 2, 2, 1, 2, 4, 4, 4, 2, 2, 4, 4,
+                       4, 2, 2, 4, 4, 4, 2, 1, 2, 2, 2, 1};
   result->setData(resultData);
   doOneConvtTest(/* imgSize */ 5,
                  /* output_x */ 2,
@@ -216,8 +216,8 @@ TEST(Layer, convTransLayerFwd2) {
                  /* filter_size */ 4,
                  result);
 
-  float resultData2[] = {1, 2, 2, 2, 1, 2, 4, 4, 4, 2, 2, 4, 4,
-                         4, 2, 2, 4, 4, 4, 2, 1, 2, 2, 2, 1};
+  real resultData2[] = {1, 2, 2, 2, 1, 2, 4, 4, 4, 2, 2, 4, 4,
+                        4, 2, 2, 4, 4, 4, 2, 1, 2, 2, 2, 1};
   result->setData(resultData2);
   doOneConvtTest(/* imgSize */ 5,
                  /* output_x */ 2,
@@ -226,8 +226,8 @@ TEST(Layer, convTransLayerFwd2) {
                  /* filter_size */ 5,
                  result);
 
-  float resultData3[] = {1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 4,
-                         2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1};
+  real resultData3[] = {1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 4,
+                        2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1};
   result->setData(resultData3);
   doOneConvtTest(/* imgSize */ 5,
                  /* output_x */ 2,

paddle/gserver/tests/test_ConvUnify.cpp

@@ -106,8 +106,8 @@ TEST(Layer, convParaUnified) {
 #ifndef PADDLE_ONLY_CPU
   MatrixPtr input, resultCpu, resultGpu;
   input = Matrix::create(1, 4 * 4, false, false);
-  float inputData[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
-  float param[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 8, 7, 6, 5, 4, 3, 2, 1};
+  real inputData[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
+  real param[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 8, 7, 6, 5, 4, 3, 2, 1};
 
   input->setData(inputData);
 
@@ -137,26 +137,9 @@ TEST(Layer, convParaUnified) {
   checkMatrixEqual(resultCpu, resultGpu);
 
   input = Matrix::create(1, 3 * 3 * 2, false, false);
-  float inputData2[] = {1,
-                        2,
-                        3,
-                        4,
-                        5,
-                        6,
-                        7,
-                        8,
-                        9,
-
-                        10,
-                        11,
-                        12,
-                        13,
-                        14,
-                        15,
-                        16,
-                        17,
-                        18};
-  float param2[] = {1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1};
+  real inputData2[] = {
+      1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18};
+  real param2[] = {1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1};
 
   input->setData(inputData2);
 
@@ -185,7 +168,7 @@ TEST(Layer, convParaUnified) {
                             true);
   checkMatrixEqual(resultCpu, resultGpu);
 
-  float param3[] = {1, 2, 3, 4, 4, 3, 2, 1};
+  real param3[] = {1, 2, 3, 4, 4, 3, 2, 1};
 
   resultCpu = doOneConvTest(/* imgSize */ 3,
                             /* output_x */ 2,

paddle/parameter/ParameterUpdaterBase.h

@@ -38,7 +38,7 @@ public:
   virtual void startPass() {}
 
   // called by Trainer then finishing a pass, ruturn true if pass accepted
-  virtual bool finishPass(real cost = 0) { return true; }
+  virtual bool finishPass() { return true; }
 
   // called by Trainer before backward() of a batch
   // Return the type of pass it needs. This pass type will be passed
@@ -112,9 +112,9 @@ public:
         [&](int tid, size_t numThreads) { updaters_[tid]->startPass(); });
   }
 
-  virtual bool finishPass(real cost = 0) {
+  virtual bool finishPass() {
     syncThreadPool_->execPlusOwner(
-        [&](int tid, size_t numThreads) { updaters_[tid]->finishPass(cost); });
+        [&](int tid, size_t numThreads) { updaters_[tid]->finishPass(); });
     return true;
   }
 

paddle/trainer/ParameterUpdater.h

@@ -102,9 +102,9 @@ public:
    * @param cost sum cost during one pass.
    * @return true if accept (used for owlqn).
    */
-  virtual bool finishPass(real cost) {
+  virtual bool finishPass() {
     optimizer_->finishPass();
-    return ParameterUpdater::finishPass(cost);
+    return ParameterUpdater::finishPass();
   }
 
   /**
@@ -220,9 +220,9 @@ public:
     averager_->startPass();
     SgdLocalUpdater::startPass();
   }
-  virtual bool finishPass(real cost) {
+  virtual bool finishPass() {
     averager_->finishPass();
-    return SgdLocalUpdater::finishPass(cost);
+    return SgdLocalUpdater::finishPass();
   }
 
   /// apply the averaged parameter to PARAMETER_VALUE

paddle/trainer/RemoteParameterUpdater.cpp

@@ -309,7 +309,7 @@ void RemoteParameterUpdater::startPass() {
   }
 }
 
-bool RemoteParameterUpdater::finishPass(real cost) {
+bool RemoteParameterUpdater::finishPass() {
   if (localUpdater_) {
     localUpdater_->finishPass();
   }
@@ -712,7 +712,7 @@ void SparseRemoteParameterUpdater::startPass() {
   }
 }
 
-bool SparseRemoteParameterUpdater::finishPass(real cost) {
+bool SparseRemoteParameterUpdater::finishPass() {
   if (config_.algorithm() == TrainAlgorithm::SGD) {
     parameterClient_->waitPassFinish();
   } else {

paddle/trainer/RemoteParameterUpdater.h

@@ -90,7 +90,7 @@ public:
    */
   virtual void finishBatch(real cost);
   virtual void startPass();
-  virtual bool finishPass(real cost);
+  virtual bool finishPass();
 
 #ifndef PADDLE_DISABLE_TIMER
   virtual void setForwardbackwardTime(uint64_t delta) {
@@ -281,7 +281,7 @@ public:
   /// send all sparse related parameters to all pservers
   virtual void finishBatch(real cost);
   virtual void startPass();
-  virtual bool finishPass(real cost);
+  virtual bool finishPass();
 
   virtual void apply();
   virtual void restore();

paddle/trainer/ThreadParameterUpdater.cpp

@@ -70,7 +70,7 @@ void SgdThreadUpdater::startPass() {
   }
 }
 
-bool SgdThreadUpdater::finishPass(real cost) {
+bool SgdThreadUpdater::finishPass() {
   catchUpWith();
 
   for (auto& para : parameters_) {

paddle/trainer/ThreadParameterUpdater.h

@@ -47,7 +47,7 @@ public:
   virtual void startPass();
 
   // Use the finishPass() function of the base optimizer.
-  virtual bool finishPass(real cost);
+  virtual bool finishPass();
 
   virtual void init(const std::vector<ParameterPtr>& parameters);
   virtual PassType startBatch(int64_t batchSize);

paddle/trainer/Trainer.cpp

@@ -537,7 +537,7 @@ void Trainer::trainOnePassBatch(int passId) {
 
   trainerInternal_.getGradientMachine()->onPassEnd();
 
-  bool accepted = trainerInternal_.getParameterUpdater()->finishPass(cost);
+  bool accepted = trainerInternal_.getParameterUpdater()->finishPass();
 
   globalStat.setThreadInfo(true);
   globalStat.printAllStatus();