diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 49a4406aa0ff..aa4c3407f405 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -3560,13 +3560,15 @@ def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None):
         if isinstance(rhs, numeric_types):
             return fn_scalar(lhs, rhs)
         else:
+            is_int = isinstance(lhs, integer_types)
             if rfn_scalar is None:
                 # commutative function
-                return lfn_scalar(rhs, float(lhs))
+                return lfn_scalar(rhs, scalar=float(lhs), is_int=is_int)
             else:
-                return rfn_scalar(rhs, float(lhs))
+                return rfn_scalar(rhs, scalar=float(lhs), is_int=is_int)
     elif isinstance(rhs, numeric_types):
-        return lfn_scalar(lhs, float(rhs))
+        is_int = isinstance(rhs, integer_types)
+        return lfn_scalar(lhs, scalar=float(rhs), is_int=is_int)
     elif isinstance(rhs, NDArray):
         return fn_array(lhs, rhs)
     else:
diff --git a/src/operator/contrib/gradient_multiplier_op.cc b/src/operator/contrib/gradient_multiplier_op.cc
index 47f891ef802b..f1664a3eaac4 100644
--- a/src/operator/contrib/gradient_multiplier_op.cc
+++ b/src/operator/contrib/gradient_multiplier_op.cc
@@ -76,9 +76,7 @@ In forward pass it acts as an identity transform. During backpropagation it
 multiplies the gradient from the subsequent level by a scalar factor lambda and passes it to
 the preceding layer.
 )code" ADD_FILELINE)
-.set_attr_parser([](NodeAttrs* attrs) {
-    attrs->parsed = std::stod(attrs->dict["scalar"]);
-  })
+.set_attr_parser(ParamParser<NumpyBinaryScalarParam>)
 .set_attr<FInferStorageType>("FInferStorageType", ElemwiseStorageType<1, 1, false, true, true>)
 .set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
 .set_attr<FComputeEx>("FComputeEx<cpu>", UnaryOp::IdentityComputeEx<cpu>)
@@ -87,7 +85,7 @@ the preceding layer.
   [](const NodeAttrs& attrs){
     return std::vector<bool>{true};
   })
-.add_argument("scalar", "float", "lambda multiplier");
+.add_arguments(NumpyBinaryScalarParam::__FIELDS__());
 
 MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_contrib_backward_gradientmultiplier)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
diff --git a/src/operator/numpy/np_elemwise_broadcast_logic_op.cc b/src/operator/numpy/np_elemwise_broadcast_logic_op.cc
index b44a22d65856..8c8320de30c6 100644
--- a/src/operator/numpy/np_elemwise_broadcast_logic_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_logic_op.cc
@@ -223,7 +223,8 @@ struct TVMBinaryBroadcastScalarCompute {
 
     // scalar param
     type_codes[1] = kDLFloat;
-    values[1].v_float64 = nnvm::get<double>(attrs.parsed);
+    const NumpyBinaryScalarParam& param = nnvm::get<NumpyBinaryScalarParam>(attrs.parsed);
+    values[1].v_float64 = param.scalar;
 
     // output tensor
     type_codes[2] = kTVMDLTensorHandle;
diff --git a/src/operator/numpy/np_true_divide-inl.h b/src/operator/numpy/np_true_divide-inl.h
index c0edc8cad47a..d466af716d53 100644
--- a/src/operator/numpy/np_true_divide-inl.h
+++ b/src/operator/numpy/np_true_divide-inl.h
@@ -47,7 +47,8 @@ void TrueDivideScalarCompute(const nnvm::NodeAttrs &attrs,
   using namespace mxnet_op;
   using namespace mshadow::expr;
   Stream<xpu> *s = ctx.get_stream<xpu>();
-  const double alpha = nnvm::get<double>(attrs.parsed);
+  const NumpyBinaryScalarParam& param = nnvm::get<NumpyBinaryScalarParam>(attrs.parsed);
+  const double alpha = param.scalar;
   const TBlob& data = inputs[0];
   const TBlob& out = outputs[0];
   if (out.type_flag_ == data.type_flag_) {
diff --git a/src/operator/numpy/np_true_divide.cc b/src/operator/numpy/np_true_divide.cc
index bee8a80bab7f..1e87cff3c145 100644
--- a/src/operator/numpy/np_true_divide.cc
+++ b/src/operator/numpy/np_true_divide.cc
@@ -103,9 +103,7 @@ NNVM_REGISTER_OP(_backward_npi_broadcast_div)
 NNVM_REGISTER_OP(_npi_true_divide_scalar)
 .set_num_inputs(1)
 .set_num_outputs(1)
-.set_attr_parser([](NodeAttrs* attrs) {
-    attrs->parsed = std::stod(attrs->dict["scalar"]);
-  })
+.set_attr_parser(ParamParser<NumpyBinaryScalarParam>)
 .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
 .set_attr<nnvm::FInferType>("FInferType", TrueDivideType<1>)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption",
@@ -121,14 +119,12 @@ NNVM_REGISTER_OP(_npi_true_divide_scalar)
 .set_attr<FCompute>("FCompute<cpu>", TrueDivideScalarCompute<cpu, op::mshadow_op::true_divide>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_div_scalar"})
 .add_argument("data", "NDArray-or-Symbol", "source input")
-.add_argument("scalar", "float", "scalar input");
+.add_arguments(NumpyBinaryScalarParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_npi_rtrue_divide_scalar)
 .set_num_inputs(1)
 .set_num_outputs(1)
-.set_attr_parser([](NodeAttrs* attrs) {
-  attrs->parsed = std::stod(attrs->dict["scalar"]);
-  })
+.set_attr_parser(ParamParser<NumpyBinaryScalarParam>)
 .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
 .set_attr<nnvm::FInferType>("FInferType", TrueDivideType<1>)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption",
@@ -144,7 +140,7 @@ NNVM_REGISTER_OP(_npi_rtrue_divide_scalar)
 .set_attr<FCompute>("FCompute<cpu>", TrueDivideScalarCompute<cpu, mshadow_op::rtrue_divide>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rdiv_scalar"})
 .add_argument("data", "NDArray-or-Symbol", "source input")
-.add_argument("scalar", "float", "scalar input");
+.add_arguments(NumpyBinaryScalarParam::__FIELDS__());
 
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/tensor/elemwise_binary_scalar_op.h b/src/operator/tensor/elemwise_binary_scalar_op.h
index 3e3a1e3bdad4..c09e41867f46 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op.h
+++ b/src/operator/tensor/elemwise_binary_scalar_op.h
@@ -44,7 +44,7 @@ struct NumpyBinaryScalarParam : public dmlc::Parameter<NumpyBinaryScalarParam> {
     .set_default(1)
     .describe("Scalar input value");
     DMLC_DECLARE_FIELD(is_int)
-    .set_default(false)
+    .set_default(true)
     .describe("Indicate whether scalar input is int type");
   }