use existed sgd updater function

paddle/math/Vector.h

@@ -92,28 +92,6 @@ public:
   const T* getData() const { return this->data_; }
   T* getData() { return this->data_; }
 
-#ifdef PADDLE_USE_MKLDNN
-  /**
-   * sgd update with openmp to speedup
-   */
-  void sgdUpdateWithOMP(VectorT& gradVec,
-                        VectorT& momVec,
-                        T learningRate,
-                        T momentum,
-                        T decayRate) {
-    size_t size = this->getSize();
-    T* val = this->getData();
-    T* grd = gradVec.getData();
-    T* mom = momVec.getData();
-    decayRate *= learningRate;
-#pragma omp parallel for
-    for (size_t i = 0; i < size; ++i) {
-      mom[i] = momentum * mom[i] - learningRate * grd[i] - decayRate * val[i];
-      val[i] += mom[i];
-    }
-  }
-#endif
-
   virtual void zeroMem() = 0;
   // set all elements to value
   virtual void reset(const T& value) = 0;

paddle/parameter/FirstOrderOptimizer.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include "ParameterOptimizer.h"
+#include "ParameterUpdateFunctions.h"
 #include "Regularizer.h"
 
 namespace paddle {
@@ -38,13 +39,13 @@ public:
                                   ? 1.0 - paraConfig.momentum()
                                   : 1.0;
 #ifdef PADDLE_USE_MKLDNN
-    vecs[PARAMETER_VALUE]->sgdUpdateWithOMP(
-        *vecs[PARAMETER_GRADIENT],
-        *vecs[PARAMETER_MOMENTUM],
-        learningRate_ * paraConfig.learning_rate() *
-            (firstTime_ ? 1.0 : torch_learningRate),
-        paraConfig.momentum(),
-        applyDecay_ ? paraConfig.decay_rate() : 0);
+    sgdUpdate(learningRate_ * paraConfig.learning_rate() *
+                  (firstTime_ ? 1.0 : torch_learningRate),
+              paraConfig.momentum(),
+              applyDecay_ ? paraConfig.decay_rate() : 0,
+              vecs[PARAMETER_VALUE].get(),
+              vecs[PARAMETER_GRADIENT].get(),
+              vecs[PARAMETER_MOMENTUM].get());
 #else
     vecs[PARAMETER_VALUE]->sgdUpdate(
         *vecs[PARAMETER_GRADIENT],