Codegen for Tanh

torch_xla/csrc/aten_xla_type.cpp

@@ -1533,11 +1533,6 @@ at::Tensor XLANativeFunctions::hardtanh(const at::Tensor& self,
       XLATensor::clamp(bridge::GetXlaTensor(self), min_val, max_val));
 }
 
-at::Tensor XLANativeFunctions::tanh(const at::Tensor& self) {
-  XLA_FN_COUNTER("xla::");
-  return bridge::AtenFromXlaTensor(XLATensor::tanh(bridge::GetXlaTensor(self)));
-}
-
 at::Tensor XLANativeFunctions::hardtanh_backward(const at::Tensor& grad_output,
                                                  const at::Tensor& self,
                                                  const at::Scalar& min_val,

torch_xla/csrc/ops/ops.cpp

@@ -68,7 +68,6 @@ namespace torch_xla {
                      std::move(lower_fn));                                     \
   }
 
-PTXLA_UNARY_OP(Tanh, at::aten::tanh, xla::Tanh);
 PTXLA_UNARY_OP(Neg, at::aten::neg, xla::Neg);
 PTXLA_UNARY_OP(Exp, at::aten::exp, xla::Exp);
 PTXLA_UNARY_OP(Expm1, at::aten::expm1, xla::Expm1);
@@ -867,7 +866,9 @@ torch::lazy::NodePtr TanhGelu(const torch::lazy::Value& input) {
   torch::lazy::NodePtr one = ScalarOp(1, shape);
   torch::lazy::NodePtr half = ScalarOp(0.5, shape);
   torch::lazy::NodePtr inner = beta * (input + kappa * Pow(input, three));
-  return half * input * (one + Tanh(inner));
+  return half * input *
+         (one + torch::lazy::MakeNode<Tanh>(inner,
+                                            std::vector<torch::lazy::Shape>()));
 }
 
 torch::lazy::NodePtr TanhGeluBackward(const torch::lazy::Value& grad,
@@ -883,7 +884,8 @@ torch::lazy::NodePtr TanhGeluBackward(const torch::lazy::Value& grad,
   torch::lazy::NodePtr three = ScalarOp(3, shape);
   torch::lazy::NodePtr half = ScalarOp(0.5, shape);
   torch::lazy::NodePtr inner = beta * (input + kappa * Pow(input, three));
-  torch::lazy::NodePtr tanh_inner = Tanh(inner);
+  torch::lazy::NodePtr tanh_inner =
+      torch::lazy::MakeNode<Tanh>(inner, std::vector<torch::lazy::Shape>());
 
   torch::lazy::NodePtr left = half * input;
   torch::lazy::NodePtr right = one + tanh_inner;

torch_xla/csrc/ops/ops.h

@@ -68,8 +68,6 @@ torch::lazy::NodePtr Atan2(const torch::lazy::Value& input,
 
 torch::lazy::NodePtr Tan(const torch::lazy::Value& input);
 
-torch::lazy::NodePtr Tanh(const torch::lazy::Value& input);
-
 torch::lazy::NodePtr Neg(const torch::lazy::Value& input);
 
 torch::lazy::NodePtr SgnOp(const torch::lazy::Value& input);

torch_xla/csrc/ops/ops_lower_fn.cpp

@@ -139,4 +139,9 @@ torch_xla::XlaOpVector Tan::Lower(LoweringContext* loctx) const {
   return ReturnOp(xla::Tan(xla_input), loctx);
 }
 
+torch_xla::XlaOpVector Tanh::Lower(LoweringContext* loctx) const {
+  xla::XlaOp xla_input = loctx->GetOutputOp(operand(0));
+  return ReturnOp(xla::Tanh(xla_input), loctx);
+}
+
 }  // namespace torch_xla

torch_xla/csrc/ops/ops_xla_shape_fn.cpp

@@ -131,4 +131,8 @@ xla::Shape TanOutputShape(const torch::lazy::Value& input) {
   return GetXlaShape(input);
 }
 
+xla::Shape TanhOutputShape(const torch::lazy::Value& input) {
+  return GetXlaShape(input);
+}
+
 }  // namespace torch_xla

torch_xla/csrc/ops/ops_xla_shape_fn.h

@@ -56,4 +56,6 @@ xla::Shape SinhOutputShape(const torch::lazy::Value& input);
 
 xla::Shape TanOutputShape(const torch::lazy::Value& input);
 
+xla::Shape TanhOutputShape(const torch::lazy::Value& input);
+
 }  // namespace torch_xla

torch_xla/csrc/tensor.h

@@ -1169,8 +1169,6 @@ class XLATensor : public c10::intrusive_ptr_target {
   static XLATensorPtr take(const XLATensorPtr& input,
                            const XLATensorPtr& index);
 
-  static XLATensorPtr tanh(const XLATensorPtr& input);
-
   static XLATensorPtr tanh_backward(const XLATensorPtr& grad_output,
                                     const XLATensorPtr& output);
 

torch_xla/csrc/tensor_methods.cpp

@@ -1924,9 +1924,10 @@ void XLATensor::min_out(XLATensorPtr& min, XLATensorPtr& min_indices,
 }
 
 XLATensorPtr XLATensor::mish(const XLATensorPtr& input) {
-  return input->CreateFrom(
-      input->GetIrValue() *
-      Tanh(tensor_ops::Softplus(input, 1, 20)->GetIrValue()));
+  return input->CreateFrom(input->GetIrValue() *
+                           torch::lazy::MakeNode<Tanh>(
+                               tensor_ops::Softplus(input, 1, 20)->GetIrValue(),
+                               std::vector<torch::lazy::Shape>()));
 }
 
 XLATensorPtr XLATensor::mm(const XLATensorPtr& input,
@@ -2776,10 +2777,6 @@ XLATensorPtr XLATensor::take(const XLATensorPtr& input,
   return input->CreateFrom(Take(input->GetIrValue(), index->GetIrValue()));
 }
 
-XLATensorPtr XLATensor::tanh(const XLATensorPtr& input) {
-  return input->CreateFrom(Tanh(input->GetIrValue()));
-}
-
 XLATensorPtr XLATensor::tanh_backward(const XLATensorPtr& grad_output,
                                       const XLATensorPtr& output) {
   return XLATensor::mul(grad_output,

xla_native_functions.yaml

@@ -25,6 +25,7 @@ full_codegen:
   - sin
   - sinh
   - tan
+  - tanh
 supported:
   - __ilshift__.Scalar
   - __ilshift__.Tensor
@@ -302,7 +303,6 @@ supported:
   - t
   - t_
   - take
-  - tanh
   - tanh_backward
   - threshold
   - threshold_backward