Add support of sparse_binary_vector as input for fm layer

Author: will-am

paddle/gserver/layers/FactorizationMachineLayer.cpp

@@ -96,15 +96,20 @@ void FactorizationMachineLayer::backward(const UpdateCallback& callback) {
 
   /* Calculate the gradients of the latentVectors_ matrix */
   if (latentVectors_->getWGrad()) {
-    MatrixPtr tmpInput = inputV->clone(0, 0, useGpu_);
     if (dynamic_cast<CpuSparseMatrix*>(inputV.get())) {
+      Matrix::resizeOrCreateSparseMatrix(tmpInput_,
+                                         inputV->getHeight(),
+                                         inputV->getWidth(),
+                                         inputV->getElementCnt());
+
       CpuSparseMatrix* sparseInputV =
           dynamic_cast<CpuSparseMatrix*>(inputV.get());
       CpuSparseMatrix* sparseInputSquare =
           dynamic_cast<CpuSparseMatrix*>(inputSquare_.get());
       CpuSparseMatrix* sparseTmpInput =
-          dynamic_cast<CpuSparseMatrix*>(tmpInput.get());
+          dynamic_cast<CpuSparseMatrix*>(tmpInput_.get());
       sparseTmpInput->copyFrom(*sparseInputV);
+
       sparseTmpInput->rowScale(0, *sparseInputV, *oGrad);
       latentVectors_->getWGrad()->mul(
           *sparseTmpInput->getTranspose(), *inputMulFactor_, 1, 1);
@@ -115,12 +120,15 @@ void FactorizationMachineLayer::backward(const UpdateCallback& callback) {
       negOnes_->add(-1);
       tmpSum_->mul(*negOnes_, *sparseTmpInput, 1, 0);
     } else {
-      tmpInput->rowScale(0, *inputV, *oGrad);
+      Matrix::resizeOrCreate(
+          tmpInput_, inputV->getHeight(), inputV->getWidth(), false, useGpu_);
+
+      tmpInput_->rowScale(0, *inputV, *oGrad);
       latentVectors_->getWGrad()->mul(
-          *tmpInput->getTranspose(), *inputMulFactor_, 1, 1);
-      tmpInput->rowScale(0, *inputSquare_, *oGrad);
+          *tmpInput_->getTranspose(), *inputMulFactor_, 1, 1);
+      tmpInput_->rowScale(0, *inputSquare_, *oGrad);
 
-      tmpSum_->sumCols(*tmpInput, -1, 0);
+      tmpSum_->sumCols(*tmpInput_, -1, 0);
     }
 
     latentVectors_->getWGrad()->addRowScale(

paddle/gserver/layers/FactorizationMachineLayer.h

@@ -61,6 +61,7 @@ class FactorizationMachineLayer : public Layer {
   // Store temporary calculation result
   MatrixPtr tmpOut_;
   MatrixPtr tmpSum_;
+  MatrixPtr tmpInput_;
   // Negative identity matrix
   MatrixPtr negOnes_;
 

paddle/math/CpuSparseMatrix.cpp

@@ -266,13 +266,25 @@ void CpuSparseMatrix::rowScale(size_t cCol, CpuSparseMatrix& b, Matrix& c) {
   CHECK_EQ(width_, b.getWidth());
   real* A = getValue();
   real* B = b.getValue();
-  for (size_t i = 0; i < height_; i++) {
-    size_t start = getRowStartIdx(i);
-    size_t end = getRowStartIdx(i + 1);
-    CHECK_EQ(start, b.getRowStartIdx(i));
-    CHECK_EQ(end, b.getRowStartIdx(i + 1));
-    for (size_t j = start; j < end; j++) {
-      A[j] = B[j] * c.getElement(i, cCol);
+  if (b.getValueType() == FLOAT_VALUE) {
+    for (size_t i = 0; i < height_; i++) {
+      size_t start = getRowStartIdx(i);
+      size_t end = getRowStartIdx(i + 1);
+      CHECK_EQ(start, b.getRowStartIdx(i));
+      CHECK_EQ(end, b.getRowStartIdx(i + 1));
+      for (size_t j = start; j < end; j++) {
+        A[j] = B[j] * c.getElement(i, cCol);
+      }
+    }
+  } else if (b.getValueType() == NO_VALUE) {
+    for (size_t i = 0; i < height_; i++) {
+      size_t start = getRowStartIdx(i);
+      size_t end = getRowStartIdx(i + 1);
+      CHECK_EQ(start, b.getRowStartIdx(i));
+      CHECK_EQ(end, b.getRowStartIdx(i + 1));
+      for (size_t j = start; j < end; j++) {
+        A[j] = c.getElement(i, cCol);
+      }
     }
   }
 }