refine code

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -285,32 +285,34 @@ void MultiGradientMachine::forwardBackward(const std::vector<Argument>& inArgs,
 MatrixPtr MultiGradientMachine::getLayerOutput(const std::string& layerName) {
   // each thread has the same neuro network
   auto nn = threads_[0]->getGradientMachine();
-
   size_t height = 0;
   size_t width = nn->getLayerOutput(layerName)->getWidth();
+  std::vector<MatrixPtr> mats;
+  mats.reserve(threads_.size());
   for (auto& thread : threads_) {
-    auto out = thread->getGradientMachine()->getLayerOutput(layerName);
+    MatrixPtr out = thread->getGradientMachine()->getLayerOutput(layerName);
+    mats.push_back(out);
     height += out->getHeight();
     CHECK_EQ(width, out->getWidth());
   }
 
-  MatrixPtr dst;
-  Matrix::resizeOrCreate(dst, height, width, false, useGpu_);
+  MatrixPtr layerOutput;
+  Matrix::resizeOrCreate(layerOutput, height, width, false, useGpu_);
 
   // copy one layer output from one trainer thread at each time
   size_t startRow = 0;
-  for (auto& thread : threads_) {
-    auto src = thread->getGradientMachine()->getLayerOutput(layerName);
-    auto tmpMatrix = dst->subMatrix(startRow, src->getHeight());
-    tmpMatrix->copyFrom(*src, HPPL_STREAM_DEFAULT);
-    startRow += src->getHeight();
+
+  for (size_t i = 0; i < threads_.size(); i++) {
+    auto tmpMatrix = layerOutput->subMatrix(startRow, mats[i]->getHeight());
+    tmpMatrix->copyFrom(*mats[i], HPPL_STREAM_DEFAULT);
+    startRow += mats[i]->getHeight();
   }
 
   if (useGpu_) {
     hl_stream_synchronize(HPPL_STREAM_DEFAULT);
   }
 
-  return dst;
+  return layerOutput;
 }
 
 void MultiGradientMachine::backwardImp(const UpdateCallback& callback) {