[Cherry-pick]Promote int to float for tanh operation (consistent with Pytorch) (#6166)

test/cpp/test_aten_xla_tensor_2.cpp

@@ -2425,6 +2425,20 @@ TEST_F(AtenXlaTensorTest, TestTanh) {
  });
 }
 
+// In torch, tanh works with integer inputs. The same should be true for
+// torch_xla
+TEST_F(AtenXlaTensorTest, TestTanhWithInt) {
+ torch::Tensor a = torch::rand({2, 2});
+ torch::Tensor b = torch::tanh(a);
+ ForEachDevice([&](const torch::Device& device) {
+ torch::Tensor xla_a = CopyToDevice(a, device);
+ torch::Tensor xla_b = torch::tanh(xla_a);
+ AllClose(b, xla_b, /*rtol=*/1e-3, /*atol=*/1e-5);
+ });
+ ExpectCounterNotChanged("aten::.*", cpp_test::GetIgnoredCounters());
+ ExpectCounterChanged("xla::tanh", cpp_test::GetIgnoredCounters());
+}
+
 TEST_F(AtenXlaTensorTest, TestClampMinMax) {
  torch::Tensor a = torch::rand({2, 2}, torch::TensorOptions(torch::kFloat));
  torch::Scalar min_val(0.311);

test/test_core_aten_ops.py

@@ -4413,7 +4413,6 @@ def test_aten_tanh_1(self):
  kwargs = dict()
  run_export_and_compare(self, torch.ops.aten.tanh, args, kwargs)
 
- @unittest.skip
  def test_aten_tanh_2(self):
  args = (torch.randint(0, 10, (10, 10)).to(torch.int32),)
  kwargs = dict()

torch_xla/csrc/ops/ops_lower_fn.cpp

@@ -718,6 +718,11 @@ torch_xla::XlaOpVector Tan::Lower(LoweringContext* loctx) const {
 
 torch_xla::XlaOpVector Tanh::Lower(LoweringContext* loctx) const {
  xla::XlaOp xla_input = loctx->GetOutputOp(operand(0));
+ if (xla::primitive_util::IsIntegralType(XlaHelpers::TypeOfXlaOp(xla_input))) {
+ xla::PrimitiveType input_type = XlaHelpers::TypeOfXlaOp(xla_input);
+ xla_input = ConvertTo(xla_input, input_type, xla::PrimitiveType::F32,
+ /*device=*/nullptr);
+ }
  return ReturnOp(xla::Tanh(xla_input), loctx);
 }
 

torch_xla/csrc/ops/ops_xla_shape_fn.cpp

@@ -803,7 +803,11 @@ xla::Shape TakeOutputShape(const torch::lazy::Value& input,
 }
 
 xla::Shape TanhOutputShape(const torch::lazy::Value& input) {
- return GetXlaShape(input);
+ xla::Shape result_shape = GetXlaShape(input);
+ if (xla::primitive_util::IsIntegralType(result_shape.element_type())) {
+ result_shape.set_element_type(xla::PrimitiveType::F32);
+ }
+ return result_shape;
 }
 
 xla::Shape TrilOutputShape(const torch::lazy::Value& input) {