diff --git a/torch_xla/csrc/tensor_methods.cpp b/torch_xla/csrc/tensor_methods.cpp
index 48c876d322f..d2e69b7afb8 100644
--- a/torch_xla/csrc/tensor_methods.cpp
+++ b/torch_xla/csrc/tensor_methods.cpp
@@ -2127,21 +2127,21 @@ XLATensorPtr permute(const XLATensorPtr& input,
 XLATensorPtr pow(const XLATensorPtr& input, const at::Scalar& exponent) {
   // We want to pass exponent_node as a constant to give XLA more room to
   // optimize
-  torch::lazy::Value exponent_node =
-      XLAGraphExecutor::Get()->GetIrValueForConstant(exponent);
-  torch::lazy::NodePtr node = Pow(input->GetIrValue(), exponent_node);
-  auto* xla_node = dynamic_cast<XlaNode*>(node.get());
-  at::ScalarType dtype =
-      TorchTypeFromXlaType(xla_node->xla_shape().element_type());
-  return input->CreateFrom(node, dtype);
+  const torch::lazy::BackendDevice& device = input->GetDevice();
+  auto xla_type = MakeXlaPrimitiveType(GetScalarType(exponent), &device);
+  // Float scalar literal in Python defaults to F64. But we want to produce
+  // F32 as this is the default Pytorch behavior.
+  if (xla_type == xla::PrimitiveType::F64) {
+    xla_type = xla::PrimitiveType::F32;
+  }
+  torch::lazy::Value exp_node = ScalarOp(exponent, xla_type);
+  torch::lazy::NodePtr node = Pow(input->GetIrValue(), exp_node);
+  return input->CreateFrom(node, /*logical_element_type=*/c10::nullopt);
 }
 
 XLATensorPtr pow(const XLATensorPtr& input, const XLATensorPtr& exponent) {
   torch::lazy::NodePtr node = Pow(input->GetIrValue(), exponent->GetIrValue());
-  auto* xla_node = dynamic_cast<XlaNode*>(node.get());
-  at::ScalarType dtype =
-      TorchTypeFromXlaType(xla_node->xla_shape().element_type());
-  return input->CreateFrom(node, dtype);
+  return input->CreateFrom(node, /*logical_element_type=*/c10::nullopt);
 }
 
 XLATensorPtr pow(const at::Scalar& input, const XLATensorPtr& exponent) {
diff --git a/torch_xla/csrc/xla_graph_executor.cpp b/torch_xla/csrc/xla_graph_executor.cpp
index 7e32cbe921a..40ee5f7a642 100644
--- a/torch_xla/csrc/xla_graph_executor.cpp
+++ b/torch_xla/csrc/xla_graph_executor.cpp
@@ -259,13 +259,6 @@ torch::lazy::Value XLAGraphExecutor::GetDeviceDataIrValue(
   return torch::lazy::MakeNode<DeviceData>(std::move(data));
 }
 
-torch::lazy::Value XLAGraphExecutor::GetIrValueForConstant(
-    const at::Scalar& value) {
-  torch::lazy::Value ir_value =
-      ScalarOp(std::move(value), XlaTypeFromTorchType(value.type()));
-  return ir_value;
-}
-
 torch::lazy::Value XLAGraphExecutor::GetIrValueForScalar(
     const at::Scalar& value, xla::PrimitiveType type,
     const torch::lazy::BackendDevice& device) {
diff --git a/torch_xla/csrc/xla_graph_executor.h b/torch_xla/csrc/xla_graph_executor.h
index c7b80f3bf38..371f962925d 100644
--- a/torch_xla/csrc/xla_graph_executor.h
+++ b/torch_xla/csrc/xla_graph_executor.h
@@ -63,9 +63,6 @@ class XLAGraphExecutor : public torch::lazy::LazyGraphExecutor {
   torch::lazy::Value GetDeviceDataIrValue(
       const at::Scalar& value, xla::PrimitiveType type,
       const torch::lazy::BackendDevice& device);
-  // Use with caution, constant will cause more frequent recompilation
-  // compared to the device_data.
-  torch::lazy::Value GetIrValueForConstant(const at::Scalar& value);
   torch::lazy::Value GetIrValueForScalar(
       const at::Scalar& value, xla::PrimitiveType type,
       const torch::lazy::BackendDevice& device);