diff --git a/test/test_operations_hlo.py b/test/test_operations_hlo.py
index dc59a87a324c..b9c728332d03 100644
--- a/test/test_operations_hlo.py
+++ b/test/test_operations_hlo.py
@@ -35,6 +35,21 @@ def test_expand(self):
     hlo_text = torch_xla._XLAC._get_xla_tensors_text([b])
     assert 'aten::expand' in hlo_text
 
+  def test_special_scalars_addcdiv_addcmul(self):
+    a = torch.rand(5, 5).to(xm.xla_device())
+    b = torch.rand(5, 5).to(xm.xla_device())
+    c = torch.rand(5, 5).to(xm.xla_device())
+    for op in [torch.addcdiv, torch.addcmul]:
+      out = op(a, b, c, value=1.0)
+      hlo_text = torch_xla._XLAC._get_xla_tensors_text([out])
+      instructions = hlo_text.split('\n')
+      const_hlo = instructions[1]
+      root_hlo = instructions[5]
+      assert 'prim::Constant()' in const_hlo
+      assert 'xla::device_data()' not in const_hlo
+      assert 'f32' in root_hlo
+      assert 'f64' not in root_hlo
+
 
 if __name__ == '__main__':
   torch.set_default_tensor_type('torch.FloatTensor')
diff --git a/torch_xla/csrc/init_python_bindings.cpp b/torch_xla/csrc/init_python_bindings.cpp
index 60365ff324f9..40fed350b00d 100644
--- a/torch_xla/csrc/init_python_bindings.cpp
+++ b/torch_xla/csrc/init_python_bindings.cpp
@@ -746,6 +746,9 @@ absl::flat_hash_map<std::string, absl::variant<int>> ConvertDictToMap(
 void MapXlaEnvVarsToLazy() {
   static bool wants_frames = xla::sys_util::GetEnvBool("XLA_IR_DEBUG", false);
   FLAGS_torch_lazy_ir_debug = wants_frames;
+  static bool no_scalars =
+      xla::sys_util::GetEnvBool("XLA_NO_SPECIAL_SCALARS", false);
+  FLAGS_torch_lazy_handle_special_scalars = !no_scalars;
 }
 
 std::string GetPyTypeString(py::handle obj) {
diff --git a/torch_xla/csrc/xla_lower_util.cpp b/torch_xla/csrc/xla_lower_util.cpp
index 01299aa08905..33d2c089e808 100644
--- a/torch_xla/csrc/xla_lower_util.cpp
+++ b/torch_xla/csrc/xla_lower_util.cpp
@@ -1014,6 +1014,7 @@ xla::XlaOp BuildRoll(xla::XlaOp input, absl::Span<const int64_t> shifts,
 
 xla::XlaOp BuildAddcdiv(xla::XlaOp input, xla::XlaOp t1, xla::XlaOp t2,
                         xla::XlaOp val) {
+  val = MaybeConvertTo(val, XlaHelpers::ShapeOfXlaOp(t1).element_type());
   return XlaHelpers::PromotedAdd(
       input, XlaHelpers::PromotedMul(XlaHelpers::PromotedDiv(t1, t2), val));
 }