【Complex OP】No.30 complex stanh op

paddle/phi/kernels/cpu/activation_grad_kernel.cc

@@ -303,7 +303,7 @@ PD_REGISTER_ACTIVATION_GRAD_KERNEL(tanh_shrink_grad, TanhShrinkGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(elu_grad, EluGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(silu_grad, SiluGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(mish_grad, MishGradKernel)
-PD_REGISTER_ACTIVATION_GRAD_KERNEL(stanh_grad, STanhGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(stanh_grad, STanhGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(reciprocal_grad, ReciprocalGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(sqrt_grad, SqrtGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(rsqrt_grad, RsqrtGradKernel)

paddle/phi/kernels/cpu/activation_kernel.cc

@@ -197,7 +197,7 @@ PD_REGISTER_ACTIVATION_KERNEL(tanh_shrink, TanhShrinkKernel)
 PD_REGISTER_ACTIVATION_KERNEL(elu, EluKernel)
 PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(silu, SiluKernel)
 PD_REGISTER_ACTIVATION_KERNEL(mish, MishKernel)
-PD_REGISTER_ACTIVATION_KERNEL(stanh, STanhKernel)
+PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(stanh, STanhKernel)
 PD_REGISTER_ACTIVATION_KERNEL(reciprocal, ReciprocalKernel)
 PD_REGISTER_ACTIVATION_KERNEL(sqrt, SqrtKernel)
 PD_REGISTER_ACTIVATION_KERNEL(rsqrt, RsqrtKernel)

paddle/phi/kernels/funcs/activation_functor.h

@@ -599,6 +599,32 @@ struct STanhGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
 };
 
+template <typename T>
+struct STanhGradFunctor<ComplexType<T>>
+    : public BaseActivationFunctor<ComplexType<T>> {
+  float scale_a;
+  float scale_b;
+  typename BaseActivationFunctor<ComplexType<T>>::AttrPair GetAttrs() {
+    return {{"scale_a", &scale_a}, {"scale_b", &scale_b}};
+  }
+
+  template <typename Device,
+            typename X,
+            typename Out,
+            typename dOut,
+            typename dX>
+  void operator()(Device d, X x, Out out UNUSED, dOut dout, dX dx) const {
+    auto a = static_cast<ComplexType<T>>(scale_a);  // NOLINT
+    auto b = static_cast<ComplexType<T>>(scale_b);
+    auto temp = (a * x).tanh() * (a * x).tanh();
+    dx.device(d) =
+        dout *
+        (a * b * (static_cast<ComplexType<T>>(1) - temp)).unaryExpr(Conj<T>());
+  }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
+};
+
 template <typename T>
 struct Tangent {
   HOSTDEVICE T operator()(const T& val) const { return tan(val); }
@@ -3560,6 +3586,32 @@ struct CudaSTanhGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
 };
 
+template <typename T>
+struct CudaSTanhGradFunctor<ComplexType<T>>
+    : public BaseActivationFunctor<ComplexType<T>> {
+  ComplexType<T> one = static_cast<ComplexType<T>>(1.0f);
+  float scale_a;
+  float scale_b;
+
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"scale_a", &scale_a}, {"scale_b", &scale_b}};
+  }
+
+  // dx = dout * a * b * (1 - tanh(a * x) * tanh(a * x))
+  __device__ __forceinline__ ComplexType<T> operator()(
+      const ComplexType<T> arg_dout, const ComplexType<T> arg_x) const {
+    ComplexType<T> dout = static_cast<ComplexType<T>>(arg_dout);
+    ComplexType<T> x = static_cast<ComplexType<T>>(arg_x);
+    ComplexType<T> a = static_cast<ComplexType<T>>(scale_a);
+    ComplexType<T> b = static_cast<ComplexType<T>>(scale_b);
+    ComplexType<T> temp = tanh(a * x);
+    return static_cast<ComplexType<T>>(dout *
+                                       conj(a * b * (one - temp * temp)));
+  }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
+};
+
 template <typename T>
 struct CudaSoftplusFunctor : public BaseActivationFunctor<T> {
   using MPType = typename phi::dtype::MPTypeTrait<T>::Type;

paddle/phi/kernels/gpu/activation_grad_kernel.cu

@@ -379,7 +379,7 @@ PD_REGISTER_ACTIVATION_GRAD_KERNEL(thresholded_relu_grad,
                                    ThresholdedReluGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(relu6_grad, Relu6GradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(mish_grad, MishGradKernel)
-PD_REGISTER_ACTIVATION_GRAD_KERNEL(stanh_grad, STanhGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(stanh_grad, STanhGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(reciprocal_grad, ReciprocalGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(softplus_grad, SoftplusGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(softplus_double_grad,

paddle/phi/kernels/gpu/activation_kernel.cu

@@ -246,7 +246,7 @@ PD_REGISTER_ACTIVATION_KERNEL(thresholded_relu, ThresholdedReluKernel)
 PD_REGISTER_ACTIVATION_KERNEL(relu6, Relu6Kernel)
 PD_REGISTER_ACTIVATION_KERNEL(leaky_relu, LeakyReluKernel)
 PD_REGISTER_ACTIVATION_KERNEL(mish, MishKernel)
-PD_REGISTER_ACTIVATION_KERNEL(stanh, StanhKernel)
+PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(stanh, StanhKernel)
 PD_REGISTER_ACTIVATION_KERNEL(reciprocal, ReciprocalKernel)
 PD_REGISTER_ACTIVATION_KERNEL(sqrt, SqrtKernel)
 PD_REGISTER_ACTIVATION_KERNEL(rsqrt, RsqrtKernel)

test/legacy_test/test_activation_op.py

@@ -3634,7 +3634,13 @@ def setUp(self):
         scale_b = self.get_scale_b()
 
         np.random.seed(1024)
-        x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+        if self.dtype is np.complex64 or self.dtype is np.complex128:
+            x = (
+                np.random.uniform(0.1, 1, self.shape)
+                + 1j * np.random.uniform(0.1, 1, self.shape)
+            ).astype(self.dtype)
+        else:
+            x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
         # The same reason with TestAbs
         out = ref_stanh(x, scale_a, scale_b)
 
@@ -3664,6 +3670,16 @@ def init_shape(self):
         self.shape = []
 
 
+class TestSTanhComplex64(TestSTanh):
+    def init_dtype(self):
+        self.dtype = np.complex64
+
+
+class TestSTanhComplex128(TestSTanh):
+    def init_dtype(self):
+        self.dtype = np.complex128
+
+
 class TestSTanhAPI(unittest.TestCase):
     # test paddle.nn.stanh
     def get_scale_a(self):