Add sparse matrix support in factorization machine layer

paddle/gserver/layers/FactorizationMachineLayer.cpp

@@ -62,7 +62,12 @@ void FactorizationMachineLayer::forward(PassType passType) {
   outV->sumRows(*tmpOut_, 0.5, 0);
 
   x2_ = inputV->clone(0, 0, useGpu_);
-  inputV->square2(*x2_);
+  if (dynamic_cast<CpuSparseMatrix*>(x2_.get())) {
+    x2_->copyFrom(*inputV);
+    (dynamic_cast<CpuSparseMatrix*>(x2_.get()))->square2();
+  } else {
+    inputV->square2(*x2_);
+  }
   latentVectors_->getW()->square2(*v2_);
   tmpOut_->mul(*x2_, *v2_);
   outV->sumRows(*tmpOut_, -0.5, 1.0);
@@ -93,11 +98,20 @@ void FactorizationMachineLayer::backward(const UpdateCallback& callback) {
   /* Calculate the gradients of the latentVectors_ matrix */
   if (latentVectors_->getWGrad()) {
     MatrixPtr tmpIn = inputV->clone(0, 0, useGpu_);
-    tmpIn->rowScale(0, *inputV, *oGrad);
-
-    latentVectors_->getWGrad()->mul(*tmpIn->getTranspose(), *tmpMul_, 1, 1);
+    if (dynamic_cast<CpuSparseMatrix*>(inputV.get())) {
+      CpuSparseMatrix* inputV_s = dynamic_cast<CpuSparseMatrix*>(inputV.get());
+      CpuSparseMatrix* x2_s = dynamic_cast<CpuSparseMatrix*>(x2_.get());
+      CpuSparseMatrix* tmpIn_s = dynamic_cast<CpuSparseMatrix*>(tmpIn.get());
+      tmpIn_s->copyFrom(*inputV_s);
+      tmpIn_s->rowScale(0, *inputV_s, *oGrad);
+      latentVectors_->getWGrad()->mul(*tmpIn->getTranspose(), *tmpMul_, 1, 1);
+      tmpIn_s->rowScale(0, *x2_s, *oGrad);
+    } else {
+      tmpIn->rowScale(0, *inputV, *oGrad);
+      latentVectors_->getWGrad()->mul(*tmpIn->getTranspose(), *tmpMul_, 1, 1);
+      tmpIn->rowScale(0, *x2_, *oGrad);
+    }
 
-    tmpIn->rowScale(0, *x2_, *oGrad);
     tmpSum->sumCols(*tmpIn, -1, 0);
     latentVectors_->getWGrad()->addRowScale(
         0, *latentVectors_->getW(), *tmpSum_T);