change grad elementwise_mul for complex types (#29757)

* add conj op for complex types

* add conj for complex types

* add more test case

* add conj_op test

* modify conj api and impl

* add complex type for fill_constant_op xpu

* add setConstant for complex type

* remove complex conj test file

* user define grad for test_conj_op

* add test case for static mode of conj api

* modify conj doc

* change input args name to x

* remove useless codes

* conj support real types

* add conj test case for real number

* delete no need to calculate inputs in dygraph op_test

* delete no need to calculate inputs in dygraph op_test

* modify grad of mul for complex types

* fix the grads of inputs args order not match bug

paddle/fluid/operators/elementwise/elementwise_mul_op.cu

@@ -75,6 +75,36 @@ static __global__ void SimpleElemwiseMulGradCUDAKernel(const T* x, const T* y,
   }
 }
 
+template <>
+__global__ void SimpleElemwiseMulGradCUDAKernel<plat::complex64>(
+    const plat::complex64* x, const plat::complex64* y,
+    const plat::complex64* out, const plat::complex64* dout, int64_t size,
+    plat::complex64* dx, plat::complex64* dy) {
+  int col = blockIdx.x * blockDim.x + threadIdx.x;
+
+  while (col < size) {
+    plat::complex64 o = dout[col];
+    dx[col] = plat::complex64(y[col].real, -y[col].imag) * o;
+    dy[col] = plat::complex64(x[col].real, -x[col].imag) * o;
+    col += blockDim.x * gridDim.x;
+  }
+}
+
+template <>
+__global__ void SimpleElemwiseMulGradCUDAKernel<plat::complex128>(
+    const plat::complex128* x, const plat::complex128* y,
+    const plat::complex128* out, const plat::complex128* dout, int64_t size,
+    plat::complex128* dx, plat::complex128* dy) {
+  int col = blockIdx.x * blockDim.x + threadIdx.x;
+
+  while (col < size) {
+    plat::complex128 o = dout[col];
+    dx[col] = plat::complex128(y[col].real, -y[col].imag) * o;
+    dy[col] = plat::complex128(x[col].real, -x[col].imag) * o;
+    col += blockDim.x * gridDim.x;
+  }
+}
+
 template <typename DeviceContext, typename T>
 typename std::enable_if<
     std::is_same<DeviceContext, plat::CUDADeviceContext>::value>::type

paddle/fluid/operators/elementwise/elementwise_mul_op.h

@@ -132,11 +132,53 @@ struct MulGradDX {
   HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return dout * y; }
 };
 
+template <>
+struct MulGradDX<paddle::platform::complex64> {
+  HOSTDEVICE paddle::platform::complex64 operator()(
+      paddle::platform::complex64 x, paddle::platform::complex64 y,
+      paddle::platform::complex64 out, paddle::platform::complex64 dout) const {
+    paddle::platform::complex64 y_conj(y.real, -y.imag);
+    return dout * y_conj;
+  }
+};
+
+template <>
+struct MulGradDX<paddle::platform::complex128> {
+  HOSTDEVICE paddle::platform::complex128 operator()(
+      paddle::platform::complex128 x, paddle::platform::complex128 y,
+      paddle::platform::complex128 out,
+      paddle::platform::complex128 dout) const {
+    paddle::platform::complex128 y_conj(y.real, -y.imag);
+    return dout * y_conj;
+  }
+};
+
 template <typename T>
 struct MulGradDY {
   HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return dout * x; }
 };
 
+template <>
+struct MulGradDY<paddle::platform::complex64> {
+  HOSTDEVICE paddle::platform::complex64 operator()(
+      paddle::platform::complex64 x, paddle::platform::complex64 y,
+      paddle::platform::complex64 out, paddle::platform::complex64 dout) const {
+    paddle::platform::complex64 x_conj(x.real, -x.imag);
+    return dout * x_conj;
+  }
+};
+
+template <>
+struct MulGradDY<paddle::platform::complex128> {
+  HOSTDEVICE paddle::platform::complex128 operator()(
+      paddle::platform::complex128 x, paddle::platform::complex128 y,
+      paddle::platform::complex128 out,
+      paddle::platform::complex128 dout) const {
+    paddle::platform::complex128 x_conj(x.real, -x.imag);
+    return dout * x_conj;
+  }
+};
+
 template <typename DeviceContext, typename T>
 typename std::enable_if<
     std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type

python/paddle/fluid/tests/unittests/test_elementwise_mul_op.py

@@ -13,13 +13,17 @@
 # limitations under the License.
 
 from __future__ import print_function
+
 import unittest
+
 import numpy as np
-from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.fluid as fluid
 import paddle.fluid.core as core
+from paddle.fluid import Program, compiler, program_guard
 from paddle.fluid.op import Operator
-import paddle.fluid as fluid
-from paddle.fluid import compiler, Program, program_guard
+
+from op_test import OpTest, skip_check_grad_ci
 
 
 class ElementwiseMulOp(OpTest):
@@ -241,5 +245,65 @@ class TestElementwiseMulOpError(unittest.TestCase):
             self.assertRaises(TypeError, fluid.layers.elementwise_mul, x2, y2)
 
 
+class TestComplexElementwiseMulOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_mul"
+        self.init_base_dtype()
+        self.init_input_output()
+        self.init_grad_input_output()
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
+        }
+        self.attrs = {'axis': -1, 'use_mkldnn': False}
+        self.outputs = {'Out': self.out}
+
+    def init_base_dtype(self):
+        self.dtype = np.float64
+
+    def init_input_output(self):
+        self.x = np.random.random(
+            (2, 3, 4, 5)).astype(self.dtype) + 1J * np.random.random(
+                (2, 3, 4, 5)).astype(self.dtype)
+        self.y = np.random.random(
+            (2, 3, 4, 5)).astype(self.dtype) + 1J * np.random.random(
+                (2, 3, 4, 5)).astype(self.dtype)
+        self.out = self.x * self.y
+
+    def init_grad_input_output(self):
+        self.grad_out = np.ones((2, 3, 4, 5), self.dtype) + 1J * np.ones(
+            (2, 3, 4, 5), self.dtype)
+        self.grad_x = self.grad_out * np.conj(self.y)
+        self.grad_y = self.grad_out * np.conj(self.x)
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(
+            ['X', 'Y'],
+            'Out',
+            user_defined_grads=[self.grad_x, self.grad_y],
+            user_defined_grad_outputs=[self.grad_out])
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'],
+            'Out',
+            no_grad_set=set("X"),
+            user_defined_grads=[self.grad_y],
+            user_defined_grad_outputs=[self.grad_out])
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'],
+            'Out',
+            no_grad_set=set('Y'),
+            user_defined_grads=[self.grad_x],
+            user_defined_grad_outputs=[self.grad_out])
+
+
 if __name__ == '__main__':
+    paddle.enable_static()
     unittest.main()