【complex op】No.33 add complex support for sigmoid

paddle/phi/kernels/cpu/activation_grad_kernel.cc

@@ -423,9 +423,11 @@ PD_REGISTER_KERNEL(cos_triple_grad,
                    phi::dtype::complex<double>) {}
 
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(softsign_grad, SoftsignGradKernel)
-PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_grad, SigmoidGradKernel)
-PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_double_grad, SigmoidDoubleGradKernel)
-PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_triple_grad, SigmoidTripleGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(sigmoid_grad, SigmoidGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(sigmoid_double_grad,
+                                                SigmoidDoubleGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(sigmoid_triple_grad,
+                                                SigmoidTripleGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(hardsigmoid_grad, HardSigmoidGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(logsigmoid_grad,
                                                 LogSigmoidGradKernel)

paddle/phi/kernels/cpu/activation_kernel.cc

@@ -231,7 +231,7 @@ PD_REGISTER_KERNEL(logit, CPU, ALL_LAYOUT, phi::LogitKernel, float, double) {}
 PD_REGISTER_KERNEL(
     square, CPU, ALL_LAYOUT, phi::SquareKernel, float, double, int, int64_t) {}
 PD_REGISTER_ACTIVATION_KERNEL(softsign, SoftsignKernel)
-PD_REGISTER_ACTIVATION_KERNEL(sigmoid, SigmoidKernel)
+PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(sigmoid, SigmoidKernel)
 PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(logsigmoid, LogSigmoidKernel)
 PD_REGISTER_ACTIVATION_KERNEL(hardsigmoid, HardSigmoidKernel)
 PD_REGISTER_ACTIVATION_KERNEL(swish, SwishKernel)

paddle/phi/kernels/funcs/activation_functor.h

@@ -1969,6 +1969,24 @@ struct SigmoidGradFunctor : public BaseActivationFunctor<T> {
   }
 };
 
+template <typename T>
+struct SigmoidGradFunctor<ComplexType<T>>
+    : public BaseActivationFunctor<ComplexType<T>> {
+  template <typename Device,
+            typename X,
+            typename Out,
+            typename dOut,
+            typename dX>
+  void operator()(Device d, X x UNUSED, Out out, dOut dout, dX dx) const {
+    ComplexType<T> one = static_cast<ComplexType<T>>(1);
+    dx.device(d) = dout * (out * (one - out)).unaryExpr(Conj<T>());
+  }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() {
+    return ActBwdOpFwdDeps::kDepOut;
+  }
+};
+
 /*
     Out
     DOut -> SigmoidGradGrad -> DOutNew
@@ -3981,6 +3999,23 @@ struct CudaSigmoidGradFunctor : public BaseActivationFunctor<T> {
   }
 };
 
+template <typename T>
+struct CudaSigmoidGradFunctor<ComplexType<T>>
+    : public BaseActivationFunctor<ComplexType<T>> {
+  using Complex = ComplexType<T>;
+  Complex one = Complex(1.0f);
+  // dx = dout * out * (1 - out)
+  __device__ __forceinline__ Complex operator()(const Complex dout,
+                                                const Complex out) const {
+    Complex y = out * (one - out);
+    return dout * conj(y);
+  }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() {
+    return ActBwdOpFwdDeps::kDepOut;
+  }
+};
+
 template <typename T>
 struct CudaLogSigmoidFunctor : public BaseActivationFunctor<T> {
   using MPType = typename phi::dtype::MPTypeTrait<T>::Type;

paddle/phi/kernels/gpu/activation_grad_kernel.cu

@@ -495,9 +495,11 @@ PD_REGISTER_KERNEL(cos_triple_grad,
                    phi::dtype::complex<double>) {}
 
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(softsign_grad, SoftsignGradKernel)
-PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_grad, SigmoidGradKernel)
-PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_double_grad, SigmoidDoubleGradKernel)
-PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_triple_grad, SigmoidTripleGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(sigmoid_grad, SigmoidGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(sigmoid_double_grad,
+                                                SigmoidDoubleGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(sigmoid_triple_grad,
+                                                SigmoidTripleGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(hardsigmoid_grad, HardSigmoidGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL_WITH_COMPLEX(logsigmoid_grad,
                                                 LogSigmoidGradKernel)

paddle/phi/kernels/gpu/activation_kernel.cu

@@ -293,7 +293,7 @@ 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(softsign, SoftsignKernel)
-PD_REGISTER_ACTIVATION_KERNEL(sigmoid, SigmoidKernel)
+PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(sigmoid, SigmoidKernel)
 PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(logsigmoid, LogSigmoidKernel)
 PD_REGISTER_ACTIVATION_KERNEL(hardsigmoid, HardSigmoidKernel)
 PD_REGISTER_ACTIVATION_KERNEL(hardswish, HardSwishKernel)

python/paddle/tensor/ops.py

@@ -828,7 +828,7 @@ def sigmoid(x, name=None):
        out = \\frac{1}{1 + e^{-x}}
 
     Args:
-        x (Tensor): Input of Sigmoid operator, an N-D Tensor, with data type float32, float64 or float16.
+        x (Tensor): Input of Sigmoid operator, an N-D Tensor, with data type float16, float32, float64, complex64 or complex128.
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
@@ -850,7 +850,17 @@ def sigmoid(x, name=None):
         return _C_ops.sigmoid(x)
     else:
         check_variable_and_dtype(
-            x, 'x', ['float16', 'float32', 'float64', 'uint16'], 'sigmoid'
+            x,
+            'x',
+            [
+                'float16',
+                'float32',
+                'float64',
+                'uint16',
+                'complex64',
+                'complex128',
+            ],
+            'sigmoid',
         )
         helper = LayerHelper('sigmoid', **locals())
         out = helper.create_variable_for_type_inference(dtype=x.dtype)

test/legacy_test/test_activation_op.py

@@ -356,6 +356,11 @@ def setUp(self):
         self.if_enable_cinn()
         np.random.seed(1024)
         x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        if self.dtype == np.complex64 or self.dtype == np.complex128:
+            x = (
+                np.random.uniform(-1, 1, self.shape)
+                + 1j * np.random.uniform(-1, 1, self.shape)
+            ).astype(self.dtype)
         out = 1 / (1 + np.exp(-x))
 
         self.inputs = {'X': OpTest.np_dtype_to_base_dtype(x)}
@@ -375,6 +380,34 @@ def test_check_grad(self):
         self.check_grad(['X'], 'Out', max_relative_error=0.01, check_prim=True)
 
 
+class TestSigmoid_Complex64(TestSigmoid):
+    def init_dtype(self):
+        self.dtype = np.complex64
+
+    def test_check_output(self):
+        self.check_output(check_prim=False)
+
+    def test_check_grad(self):
+        self.check_grad(
+            ['X'],
+            'Out',
+            max_relative_error=0.006,
+            check_prim=False,
+        )
+
+
+class TestSigmoid_Complex128(TestSigmoid_Complex64):
+    def init_dtype(self):
+        self.dtype = np.complex128
+
+    def test_check_grad(self):
+        self.check_grad(
+            ['X'],
+            'Out',
+            check_prim=False,
+        )
+
+
 class TestSigmoid_ZeroDim(TestSigmoid):
     def init_shape(self):
         self.shape = []