diff --git a/test/spmd/test_xla_sharding.py b/test/spmd/test_xla_sharding.py
index 06dbab66fc7d..6fccfe328004 100644
--- a/test/spmd/test_xla_sharding.py
+++ b/test/spmd/test_xla_sharding.py
@@ -714,7 +714,7 @@ def test_xla_sharded_hlo_dump(self):
                             partition_spec)
     xst2 = xst1 + 5
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([xst2.global_tensor])
-    self.assertIn('%p1.4 = f32[1,8]{1,0} parameter(1), sharding', hlo)
+    self.assertIn('%p1.3 = f32[1,8]{1,0} parameter(1), sharding', hlo)
     # scalar 5 should be implicitly replicated, so the pre-optimization HLO
     # shouldn't mark it with sharding.
     self.assertNotIn('%p0.2 = f32[] parameter(0), sharding={replicated}', hlo)
@@ -831,7 +831,7 @@ def test_mark_sharding_ir(self):
     actual += 0
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([actual.global_tensor])
     self.assertIn(
-        '%add.12 = f32[1,128]{1,0} add(f32[1,128]{1,0} %custom-call.10, f32[1,128]{1,0} %broadcast.11)',
+        '%add.12 = f32[1,128]{1,0} add(f32[1,128]{1,0} %custom-call.9, f32[1,128]{1,0} %broadcast.11)',
         hlo)
 
     self.assertTrue(torch.allclose(expected, actual.cpu()))
diff --git a/torch_xla/csrc/elementwise.cpp b/torch_xla/csrc/elementwise.cpp
index 24beecc2d2c8..f3320eef49f7 100644
--- a/torch_xla/csrc/elementwise.cpp
+++ b/torch_xla/csrc/elementwise.cpp
@@ -500,4 +500,28 @@ xla::XlaOp BuildSub(xla::XlaOp input, xla::XlaOp other, xla::XlaOp alpha) {
   return sub_result;
 }
 
+xla::XlaOp BuildAdd(xla::XlaOp input, xla::XlaOp other, xla::XlaOp alpha) {
+  // Three-way shape and value promotion
+  std::tie(input, other) = XlaHelpers::Promote(input, other);
+  std::tie(input, alpha) = XlaHelpers::Promote(input, alpha);
+  std::tie(input, other) = XlaHelpers::Promote(input, other);
+
+  xla::XlaOp multiplied =
+      xla::Mul(other, alpha, XlaHelpers::getBroadcastDimensions(other, alpha));
+  xla::XlaOp add_result = xla::Add(
+      input, multiplied, XlaHelpers::getBroadcastDimensions(input, multiplied));
+
+  return add_result;
+}
+
+xla::XlaOp BuildMul(xla::XlaOp input, xla::XlaOp other) {
+  // Shape and value promotion
+  std::tie(input, other) = XlaHelpers::Promote(input, other);
+
+  xla::XlaOp mul_result =
+      xla::Mul(input, other, XlaHelpers::getBroadcastDimensions(input, other));
+
+  return mul_result;
+}
+
 }  // namespace torch_xla
diff --git a/torch_xla/csrc/elementwise.h b/torch_xla/csrc/elementwise.h
index 9262ea248817..0db3ffd79e9e 100644
--- a/torch_xla/csrc/elementwise.h
+++ b/torch_xla/csrc/elementwise.h
@@ -117,14 +117,22 @@ xla::XlaOp BuildEluBackward(xla::XlaOp grad_output, xla::XlaOp output,
 // based on a scalar or tensor weight and returns the resulting out tensor.
 xla::XlaOp BuildLerp(xla::XlaOp start, xla::XlaOp end, xla::XlaOp weight);
 
-// Compuate the rsub function. Subtracts input, scaled by alpha, from other.
+// Computes the rsub function. Subtracts input, scaled by alpha, from other.
 // out = other − alpha * input
 xla::XlaOp BuildRsub(xla::XlaOp input, xla::XlaOp other, xla::XlaOp alpha);
 
-// Compuate the sub function. Subtracts other, scaled by alpha, from input.
+// Computes the sub function. Subtracts other, scaled by alpha, from input.
 // out = input − alpha * other
 xla::XlaOp BuildSub(xla::XlaOp input, xla::XlaOp other, xla::XlaOp alpha);
 
+// Computes the add function. Adds other, scaled by alpha, from input.
+// out = input + alpha * other
+xla::XlaOp BuildAdd(xla::XlaOp input, xla::XlaOp other, xla::XlaOp alpha);
+
+// Computes the mul function.
+// out = input * other
+xla::XlaOp BuildMul(xla::XlaOp input, xla::XlaOp other);
+
 }  // namespace torch_xla
 
 #endif  // XLA_TORCH_XLA_CSRC_ELEMENTWISE_H_
\ No newline at end of file
diff --git a/torch_xla/csrc/ops/ops.cpp b/torch_xla/csrc/ops/ops.cpp
index d9b9085542a9..aef9b825e087 100644
--- a/torch_xla/csrc/ops/ops.cpp
+++ b/torch_xla/csrc/ops/ops.cpp
@@ -1017,4 +1017,55 @@ torch::lazy::NodePtr Sub(const torch::lazy::Value& input,
       std::move(lower_fn));
 }
 
+torch::lazy::NodePtr Add(const torch::lazy::Value& input,
+                         const torch::lazy::Value& other,
+                         const torch::lazy::Value& alpha) {
+  torch::lazy::ScopePusher ir_scope(at::aten::add.toQualString());
+  auto lower_fn = [](const XlaNode& node,
+                     LoweringContext* loctx) -> XlaOpVector {
+    xla::XlaOp xla_input = loctx->GetOutputOp(node.operand(0));
+    xla::XlaOp xla_other = loctx->GetOutputOp(node.operand(1));
+    xla::XlaOp xla_alpha = loctx->GetOutputOp(node.operand(2));
+    xla::XlaOp xla_output = BuildAdd(xla_input, xla_other, xla_alpha);
+    return node.ReturnOp(xla_output, loctx);
+  };
+  auto lower_for_shape_fn =
+      [](absl::Span<const xla::XlaOp> operands) -> xla::XlaOp {
+    XLA_CHECK_EQ(operands.size(), 3) << "Unexpected number of operands";
+    return BuildAdd(operands[0], operands[1], operands[2]);
+  };
+  return GenericOp(
+      torch::lazy::OpKind(at::aten::add), {input, other, alpha},
+      [&]() {
+        return InferOutputShape(
+            {GetXlaShape(input), GetXlaShape(other), GetXlaShape(alpha)},
+            lower_for_shape_fn);
+      },
+      std::move(lower_fn));
+}
+
+torch::lazy::NodePtr Mul(const torch::lazy::Value& input,
+                         const torch::lazy::Value& other) {
+  torch::lazy::ScopePusher ir_scope(at::aten::mul.toQualString());
+  auto lower_fn = [](const XlaNode& node,
+                     LoweringContext* loctx) -> XlaOpVector {
+    xla::XlaOp xla_input = loctx->GetOutputOp(node.operand(0));
+    xla::XlaOp xla_other = loctx->GetOutputOp(node.operand(1));
+    xla::XlaOp xla_output = BuildMul(xla_input, xla_other);
+    return node.ReturnOp(xla_output, loctx);
+  };
+  auto lower_for_shape_fn =
+      [](absl::Span<const xla::XlaOp> operands) -> xla::XlaOp {
+    XLA_CHECK_EQ(operands.size(), 2) << "Unexpected number of operands";
+    return BuildMul(operands[0], operands[1]);
+  };
+  return GenericOp(
+      torch::lazy::OpKind(at::aten::mul), {input, other},
+      [&]() {
+        return InferOutputShape({GetXlaShape(input), GetXlaShape(other)},
+                                lower_for_shape_fn);
+      },
+      std::move(lower_fn));
+}
+
 }  // namespace torch_xla
diff --git a/torch_xla/csrc/ops/ops.h b/torch_xla/csrc/ops/ops.h
index 99efd7f87e1d..adef3edc117d 100644
--- a/torch_xla/csrc/ops/ops.h
+++ b/torch_xla/csrc/ops/ops.h
@@ -253,6 +253,13 @@ torch::lazy::NodePtr Sub(const torch::lazy::Value& input,
                          const torch::lazy::Value& other,
                          const torch::lazy::Value& alpha);
 
+torch::lazy::NodePtr Add(const torch::lazy::Value& input,
+                         const torch::lazy::Value& other,
+                         const torch::lazy::Value& alpha);
+
+torch::lazy::NodePtr Mul(const torch::lazy::Value& input,
+                         const torch::lazy::Value& other);
+
 }  // namespace torch_xla
 
 #endif  // XLA_TORCH_XLA_CSRC_OPS_OPS_H_
diff --git a/torch_xla/csrc/tensor_methods.cpp b/torch_xla/csrc/tensor_methods.cpp
index 4a22afefe03b..fb6d0c17e33e 100644
--- a/torch_xla/csrc/tensor_methods.cpp
+++ b/torch_xla/csrc/tensor_methods.cpp
@@ -767,8 +767,9 @@ XLATensorPtr add(const XLATensorPtr& input, const XLATensorPtr& other,
         sym_int_elements, logical_element_type, device);
   }
 
-  return input->CreateFrom(input->GetIrValue() + other->GetIrValue() * constant,
-                           logical_element_type);
+  return input->CreateFrom(
+      Add(input->GetIrValue(), other->GetIrValue(), constant),
+      logical_element_type);
 }
 
 XLATensorPtr add(const XLATensorPtr& input, const at::Scalar& other,
@@ -787,8 +788,9 @@ XLATensorPtr add(const XLATensorPtr& input, const at::Scalar& other,
           xla::ShapeUtil::MakeScalarShape(
               MakeXlaPrimitiveType(input->dtype(), &device)),
           logical_element_type, device);
+
   return input->CreateFrom(
-      input->GetIrValue() + other_constant * alpha_constant,
+      Add(input->GetIrValue(), other_constant, alpha_constant),
       logical_element_type);
 }
 
@@ -1878,7 +1880,7 @@ XLATensorPtr mse_loss_backward(const XLATensorPtr& grad_output,
 
 XLATensorPtr mul(const XLATensorPtr& input, const XLATensorPtr& other,
                  c10::optional<at::ScalarType> logical_element_type) {
-  return input->CreateFrom(input->GetIrValue() * other->GetIrValue(),
+  return input->CreateFrom(Mul(input->GetIrValue(), other->GetIrValue()),
                            logical_element_type);
 }
 
@@ -1890,7 +1892,7 @@ XLATensorPtr mul(const XLATensorPtr& input, const at::Scalar& other,
       xla::ShapeUtil::MakeScalarShape(
           MakeXlaPrimitiveType(input->dtype(), &device)),
       logical_element_type, device);
-  return input->CreateFrom(input->GetIrValue() * constant,
+  return input->CreateFrom(Mul(input->GetIrValue(), constant),
                            logical_element_type);
 }