【PaddlePaddle Hackathon 2】18、为 Paddle 新增 paddle.heaviside 和 paddle.Tensor.heaviside API (#41872)

* Create elementwise_heaviside_op.cc

* add ElementwiseHeavisideFunctor

* Create test_elementwise_heaviside_op.py

* 增加heaviside的python接口

* add heaviside in white list

* 增加heaviside的签名

* 增加heaviside的核函数

* 增加heaviside梯度的核函数

* 增加heaviside梯度的注册

* 调整代码格式

* Update elementwise_sig.cc

* add heaviside in __all__

* Update heaviside docs

* Update math.py

* Update math.py

* Update math.py

paddle/fluid/operators/elementwise/elementwise_heaviside_op.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <string>
+#include "paddle/fluid/operators/elementwise/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+
+class ElementwiseHeavisideOpMaker : public ElementwiseOpMaker {
+ protected:
+  std::string GetName() const override { return "Heaviside"; }
+  std::string GetEquation() const override { return "Out = Heaviside(X, Y)"; }
+
+  void AddInputX() override {
+    AddInput("X",
+             "(Tensor), The input tensor of Heaviside step function. "
+             "Its dtype can be int32, int64, float32 and float64");
+  }
+
+  void AddInputY() override {
+    AddInput("Y",
+             "(Tensor), The tensor determining a Heaviside step function, "
+             "which is the value when X = 0. Its dtype should be same as X.");
+  }
+
+  std::string GetOpFuntionality() const override {
+    return "Computes the Heaviside step function determined by Y "
+           "for each element in X.";
+  }
+};
+
+template <typename T>
+class ElementwiseHeavisideGradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("elementwise_heaviside_grad");
+    op->SetInput("X", this->Input("X"));
+    op->SetInput("Y", this->Input("Y"));
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+    op->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
+    op->SetAttrMap(this->Attrs());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(
+    elementwise_heaviside, ops::ElementwiseOp, ops::ElementwiseHeavisideOpMaker,
+    ops::ElementwiseHeavisideGradOpMaker<paddle::framework::OpDesc>,
+    ops::ElementwiseHeavisideGradOpMaker<paddle::imperative::OpBase>);
+
+REGISTER_OPERATOR(elementwise_heaviside_grad, ops::ElementwiseOpGrad);

paddle/phi/kernels/cpu/elementwise_grad_kernel.cc

@@ -88,6 +88,16 @@ PD_REGISTER_KERNEL(minimum_grad,
                    int,
                    int64_t,
                    phi::dtype::bfloat16) {}
+
+PD_REGISTER_KERNEL(elementwise_heaviside_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::ElementwiseHeavisideGradKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}
+
 PD_REGISTER_KERNEL(elementwise_pow_grad,
                    CPU,
                    ALL_LAYOUT,

paddle/phi/kernels/cpu/elementwise_kernel.cc

@@ -95,6 +95,18 @@ void ElementwisePowRawKernel(const Context& dev_ctx,
       dev_ctx, x, y, axis, funcs::ElementwisePowFunctor<T>(), out);
 }
 
+template <typename T, typename Context>
+void ElementwiseHeavisideRawKernel(const Context& dev_ctx,
+                                   const DenseTensor& x,
+                                   const DenseTensor& y,
+                                   int axis,
+                                   DenseTensor* out) {
+  // allocate memory for out
+  dev_ctx.template Alloc<T>(out);
+  funcs::ElementwiseCompute<funcs::ElementwiseHeavisideFunctor<T>, T>(
+      dev_ctx, x, y, axis, funcs::ElementwiseHeavisideFunctor<T>(), out);
+}
+
 }  // namespace phi
 
 using complex64 = ::phi::dtype::complex<float>;
@@ -149,3 +161,11 @@ PD_REGISTER_KERNEL(elementwise_pow_raw,
                    double,
                    int,
                    int64_t) {}
+PD_REGISTER_KERNEL(elementwise_heaviside_raw,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::ElementwiseHeavisideRawKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}

paddle/phi/kernels/elementwise_grad_kernel.h

@@ -55,6 +55,15 @@ void MinimumGradKernel(const Context& dev_ctx,
                        DenseTensor* dx,
                        DenseTensor* dy);
 
+template <typename T, typename Context>
+void ElementwiseHeavisideGradKernel(const Context& dev_ctx,
+                                    const DenseTensor& x,
+                                    const DenseTensor& y,
+                                    const DenseTensor& dout,
+                                    int axis,
+                                    DenseTensor* dx,
+                                    DenseTensor* dy);
+
 template <typename T, typename Context>
 void ElementwisePowGradKernel(const Context& dev_ctx,
                               const DenseTensor& x,

paddle/phi/kernels/elementwise_kernel.cc

@@ -64,6 +64,15 @@ void ElementwisePowKernel(const Context& dev_ctx,
   ElementwisePowRawKernel<T>(dev_ctx, x, y, axis, out);
 }
 
+template <typename T, typename Context>
+void ElementwiseHeavisideKernel(const Context& dev_ctx,
+                                const DenseTensor& x,
+                                const DenseTensor& y,
+                                DenseTensor* out) {
+  int axis = -1;
+  ElementwiseHeavisideRawKernel<T>(dev_ctx, x, y, axis, out);
+}
+
 }  // namespace phi
 
 using complex64 = ::phi::dtype::complex<float>;
@@ -91,6 +100,14 @@ PD_REGISTER_KERNEL(
     modulo, CPU, ALL_LAYOUT, phi::ModuloKernel, float, double, int, int64_t) {}
 PD_REGISTER_KERNEL(
     floor_divide, CPU, ALL_LAYOUT, phi::FloorDivideKernel, int, int64_t) {}
+PD_REGISTER_KERNEL(elementwise_heaviside,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::ElementwiseHeavisideKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}
 PD_REGISTER_KERNEL(elementwise_pow,
                    CPU,
                    ALL_LAYOUT,
@@ -126,6 +143,14 @@ PD_REGISTER_KERNEL(
     modulo, GPU, ALL_LAYOUT, phi::ModuloKernel, float, double, int, int64_t) {}
 PD_REGISTER_KERNEL(
     floor_divide, KPS, ALL_LAYOUT, phi::FloorDivideKernel, int, int64_t) {}
+PD_REGISTER_KERNEL(elementwise_heaviside,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::ElementwiseHeavisideKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}
 PD_REGISTER_KERNEL(elementwise_pow,
                    KPS,
                    ALL_LAYOUT,

paddle/phi/kernels/elementwise_kernel.h

@@ -98,6 +98,19 @@ void ElementwisePowKernel(const Context& dev_ctx,
                           const DenseTensor& y,
                           DenseTensor* out);
 
+template <typename T, typename Context>
+void ElementwiseHeavisideRawKernel(const Context& dev_ctx,
+                                   const DenseTensor& x,
+                                   const DenseTensor& y,
+                                   int axis,
+                                   DenseTensor* out);
+
+template <typename T, typename Context>
+void ElementwiseHeavisideKernel(const Context& dev_ctx,
+                                const DenseTensor& x,
+                                const DenseTensor& y,
+                                DenseTensor* out);
+
 template <typename T, typename Context>
 DenseTensor Maximum(const Context& dev_ctx,
                     const DenseTensor& x,
@@ -142,6 +155,17 @@ DenseTensor FloorDivide(const Context& dev_ctx,
   return dense_out;
 }
 
+template <typename T, typename Context>
+DenseTensor ElementwiseHeaviside(const Context& dev_ctx,
+                                 const DenseTensor& x,
+                                 const DenseTensor& y) {
+  DenseTensor dense_out;
+  MetaTensor meta_out(&dense_out);
+  ElementwiseInferMeta(x, y, &meta_out);
+  ElementwiseHeavisideKernel<T, Context>(dev_ctx, x, y, &dense_out);
+  return dense_out;
+}
+
 template <typename T, typename Context>
 DenseTensor ElementwisePow(const Context& dev_ctx,
                            const DenseTensor& x,

paddle/phi/kernels/funcs/elementwise_functor.h

@@ -543,6 +543,13 @@ struct InverseModuloFunctor<
   }
 };
 
+template <typename T>
+struct ElementwiseHeavisideFunctor {
+  inline HOSTDEVICE T operator()(const T a, const T b) const {
+    return a == static_cast<T>(0) ? b : static_cast<T>(a > 0);
+  }
+};
+
 template <typename T>
 struct FloorDivideFunctor {
   inline HOSTDEVICE T operator()(const T a, const T b) const {

paddle/phi/kernels/gpu/elementwise_grad_kernel.cu

@@ -128,6 +128,16 @@ PD_REGISTER_KERNEL(minimum_grad,
                    int64_t,
                    phi::dtype::float16,
                    phi::dtype::bfloat16) {}
+
+PD_REGISTER_KERNEL(elementwise_heaviside_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::ElementwiseHeavisideGradKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}
+
 PD_REGISTER_KERNEL(elementwise_pow_grad,
                    GPU,
                    ALL_LAYOUT,

paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h

@@ -683,6 +683,43 @@ struct MinGradDy {
   }
 };
 
+template <typename T>
+struct HeavisideGradDx {
+  HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
+    return dout * static_cast<T>(0);
+  }
+};
+
+template <typename T>
+struct HeavisideGradDy {
+  HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
+    return dout * static_cast<T>(x == static_cast<T>(0));
+  }
+};
+
+template <typename T, typename Context>
+void ElementwiseHeavisideGradKernel(const Context& dev_ctx,
+                                    const DenseTensor& x,
+                                    const DenseTensor& y,
+                                    const DenseTensor& dout,
+                                    int axis,
+                                    DenseTensor* dx,
+                                    DenseTensor* dy) {
+  funcs::ElementwiseGradPreProcess(dout, dx);
+  phi::funcs::
+      ElemwiseGradCompute<Context, T, HeavisideGradDx<T>, HeavisideGradDy<T>>(
+          dev_ctx,
+          x,
+          y,
+          dout,
+          dout,
+          axis,
+          dx,
+          dy,
+          HeavisideGradDx<T>(),
+          HeavisideGradDy<T>());
+}
+
 template <typename T>
 struct PowGradDX {
   HOSTDEVICE T operator()(T x, T y, T out, T dout) const {

paddle/phi/kernels/kps/elementwise_kernel.cu

@@ -54,6 +54,8 @@ void FloorDivideKernel(const Context& dev_ctx,
   int axis = -1;
   FloorDivideRawKernel<T>(dev_ctx, x, y, axis, out);
 }
+// Create the definition of Heaviside
+DEFINE_CUDA_ELEMENTWISE_OP(ElementwiseHeaviside)
 // Create the definition of Pow
 DEFINE_CUDA_ELEMENTWISE_OP(ElementwisePow)
 template <typename T, typename Context>
@@ -130,6 +132,14 @@ PD_REGISTER_KERNEL(floor_divide_raw,
                    phi::FloorDivideRawKernel,
                    int,
                    int64_t) {}
+PD_REGISTER_KERNEL(elementwise_heaviside_raw,
+                   KPS,
+                   ALL_LAYOUT,
+                   phi::ElementwiseHeavisideRawKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}
 PD_REGISTER_KERNEL(elementwise_pow_raw,
                    KPS,
                    ALL_LAYOUT,

paddle/phi/ops/compat/elementwise_sig.cc

@@ -95,6 +95,16 @@ KernelSignature ElementwiseFloorDivOpArgumentMapping(
   return KernelSignature("floor_divide_raw", {"X", "Y"}, {"axis"}, {"Out"});
 }
 
+KernelSignature ElementwiseHeavisideOpArgumentMapping(
+    const ArgumentMappingContext& ctx) {
+  int axis = paddle::any_cast<int>(ctx.Attr("axis"));
+  if (axis == -1) {
+    return KernelSignature("elementwise_heaviside", {"X", "Y"}, {}, {"Out"});
+  }
+  return KernelSignature(
+      "elementwise_heaviside_raw", {"X", "Y"}, {"axis"}, {"Out"});
+}
+
 KernelSignature ElementwisePowOpArgumentMapping(
     const ArgumentMappingContext& ctx) {
   int axis = paddle::any_cast<int>(ctx.Attr("axis"));
@@ -208,6 +218,15 @@ KernelSignature ElementwiseMinGradOpArgumentMapping(
   return KernelSignature(
       "minimum_grad", {"X", "Y", "Out@GRAD"}, {"axis"}, {"X@GRAD", "Y@GRAD"});
 }
+
+KernelSignature ElementwiseHeavisideGradOpArgumentMapping(
+    const ArgumentMappingContext& ctx) {
+  return KernelSignature("elementwise_heaviside_grad",
+                         {"X", "Y", "Out@GRAD"},
+                         {"axis"},
+                         {"X@GRAD", "Y@GRAD"});
+}
+
 KernelSignature ElementwisePowGradOpArgumentMapping(
     const ArgumentMappingContext& ctx) {
   return KernelSignature("elementwise_pow_grad",
@@ -258,6 +277,8 @@ PD_REGISTER_ARG_MAPPING_FN(elementwise_mod,
                            phi::ElementwiseModOpArgumentMapping);
 PD_REGISTER_ARG_MAPPING_FN(elementwise_floordiv,
                            phi::ElementwiseFloorDivOpArgumentMapping);
+PD_REGISTER_ARG_MAPPING_FN(elementwise_heaviside,
+                           phi::ElementwiseHeavisideOpArgumentMapping);
 PD_REGISTER_ARG_MAPPING_FN(elementwise_pow,
                            phi::ElementwisePowOpArgumentMapping);
 PD_REGISTER_ARG_MAPPING_FN(elementwise_add_grad,
@@ -292,5 +313,7 @@ PD_REGISTER_ARG_MAPPING_FN(elementwise_max_grad,
                            phi::ElementwiseMaxGradOpArgumentMapping);
 PD_REGISTER_ARG_MAPPING_FN(elementwise_min_grad,
                            phi::ElementwiseMinGradOpArgumentMapping);
+PD_REGISTER_ARG_MAPPING_FN(elementwise_heaviside_grad,
+                           phi::ElementwiseHeavisideGradOpArgumentMapping);
 PD_REGISTER_ARG_MAPPING_FN(elementwise_pow_grad,
                            phi::ElementwisePowGradOpArgumentMapping);

python/paddle/__init__.py

@@ -269,6 +269,7 @@ from .tensor.math import fmax  # noqa: F401
 from .tensor.math import fmin  # noqa: F401
 from .tensor.math import inner  # noqa: F401
 from .tensor.math import outer  # noqa: F401
+from .tensor.math import heaviside  # noqa: F401
 from .tensor.math import frac  # noqa: F401
 
 from .tensor.random import bernoulli  # noqa: F401
@@ -635,4 +636,5 @@ __all__ = [  # noqa
            'renorm',
            'take_along_axis',
            'put_along_axis',
+           'heaviside',
 ]

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

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle
+
+
+class TestElementwiseOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_heaviside"
+        x = np.random.random((13, 17)).astype("float64")
+        y = np.random.random((13, 17)).astype("float64")
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out')
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(['Y'], 'Out', no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(['X'], 'Out', no_grad_set=set('Y'))
+
+
+class TestHeavisideBroadcast(unittest.TestCase):
+    def setUp(self):
+        self.input_1 = np.random.rand(2, 100, 13, 17).astype("float32")
+        self.input_2 = np.random.rand(100, 13, 17).astype("float32")
+        self.input_3 = np.random.rand(100, 13, 1).astype("float32")
+        self.input_4 = np.random.rand(13, 17).astype("float32")
+        self.input_5 = np.random.rand(1).astype("float32")
+
+        self.np_expected1 = np.heaviside(self.input_1, self.input_2)
+        self.np_expected2 = np.heaviside(self.input_2, self.input_3)
+        self.np_expected3 = np.heaviside(self.input_2, self.input_4)
+        self.np_expected4 = np.heaviside(self.input_4, self.input_5)
+
+    def test_broadcast(self):
+        paddle.disable_static()
+        self.tensor_1 = paddle.to_tensor(self.input_1)
+        self.tensor_2 = paddle.to_tensor(self.input_2)
+        self.tensor_3 = paddle.to_tensor(self.input_3)
+        self.tensor_4 = paddle.to_tensor(self.input_4)
+        self.tensor_5 = paddle.to_tensor(self.input_5)
+
+        res = paddle.heaviside(self.tensor_1, self.tensor_2)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected1))
+
+        res = paddle.heaviside(self.tensor_2, self.tensor_3)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected2))
+
+        res = paddle.heaviside(self.tensor_2, self.tensor_4)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected3))
+
+        res = paddle.heaviside(self.tensor_4, self.tensor_5)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected4))
+
+
+class TestHeavisideAPI_float64(unittest.TestCase):
+    def setUp(self):
+        self.x_np = np.random.random((13, 17)).astype("float64")
+        self.y_np = np.random.random((13, 17)).astype("float64")
+        self.out_np = np.heaviside(self.x_np, self.y_np)
+        self.dtype = "float64"
+
+    def test_static(self):
+        for use_cuda in ([False, True]
+                         if paddle.device.is_compiled_with_cuda() else [False]):
+            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+
+            paddle.enable_static()
+            prog = paddle.static.Program()
+            with paddle.static.program_guard(prog):
+                x = paddle.static.data(
+                    name=f"x_{self.dtype}", shape=[13, 17], dtype=self.dtype)
+                y = paddle.static.data(
+                    name=f"y_{self.dtype}", shape=[13, 17], dtype=self.dtype)
+                out = paddle.heaviside(x, y)
+
+            exe = paddle.static.Executor(place=place)
+            res = exe.run(prog,
+                          feed={
+                              f"x_{self.dtype}": self.x_np,
+                              f"y_{self.dtype}": self.y_np
+                          },
+                          fetch_list=out,
+                          use_prune=True)
+
+            self.assertTrue(np.allclose(res, self.out_np))
+
+    def test_dygraph(self):
+        for use_cuda in ([False, True]
+                         if paddle.device.is_compiled_with_cuda() else [False]):
+            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+            paddle.disable_static(place=place)
+            result = paddle.heaviside(
+                paddle.to_tensor(self.x_np), paddle.to_tensor(self.y_np))
+
+            self.assertTrue(np.allclose(result.numpy(), self.out_np))
+
+
+class TestHeavisideAPI_float32(TestHeavisideAPI_float64):
+    def setUp(self):
+        self.x_np = np.random.random((13, 17)).astype("float32")
+        self.y_np = np.random.random((13, 17)).astype("float32")
+        self.out_np = np.heaviside(self.x_np, self.y_np)
+        self.dtype = "float32"
+
+
+class TestHeavisideAPI_int64(TestHeavisideAPI_float64):
+    def setUp(self):
+        self.x_np = np.random.random((13, 17)).astype("int64")
+        self.y_np = np.random.random((13, 17)).astype("int64")
+        self.out_np = np.heaviside(self.x_np, self.y_np)
+        self.dtype = "int64"
+
+
+class TestHeavisideAPI_int32(TestHeavisideAPI_float64):
+    def setUp(self):
+        self.x_np = np.random.random((13, 17)).astype("int32")
+        self.y_np = np.random.random((13, 17)).astype("int32")
+        self.out_np = np.heaviside(self.x_np, self.y_np)
+        self.dtype = "int32"
+
+
+class TestHeavisideError(unittest.TestCase):
+    def test_input(self):
+        paddle.disable_static()
+
+        def test_input_x():
+            paddle.heaviside(1, paddle.randn([100]))
+
+        self.assertRaises(ValueError, test_input_x)
+
+        def test_input_y():
+            paddle.heaviside(paddle.randn([100]), 1)
+
+        self.assertRaises(ValueError, test_input_y)
+
+        def test_input_xy():
+            paddle.heaviside(
+                paddle.randn([100], 'float32'), paddle.randn([100], 'float64'))
+
+        self.assertRaises(ValueError, test_input_xy)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/white_list/no_grad_set_white_list.py

@@ -37,6 +37,7 @@ NEED_TO_FIX_OP_LIST = [
     'dot',
     'elementwise_add',
     'elementwise_div',
+    'elementwise_heaviside',
     'elementwise_max',
     'elementwise_min',
     'elementwise_mul',

python/paddle/tensor/__init__.py

@@ -229,6 +229,7 @@ from .math import fmax  # noqa: F401
 from .math import fmin  # noqa: F401
 from .math import inner  # noqa: F401
 from .math import outer  # noqa: F401
+from .math import heaviside  # noqa: F401
 from .math import frac  # noqa: F401
 
 from .random import multinomial  # noqa: F401
@@ -495,6 +496,7 @@ tensor_method_func  = [ #noqa
            'put_along_axis',
            'put_along_axis_',
            'exponential_',
+           'heaviside',
 ]
 
 #this list used in math_op_patch.py for magic_method bind

python/paddle/tensor/math.py

@@ -4381,6 +4381,54 @@ def angle(x, name=None):
     helper.append_op(type=op_type, inputs=inputs, outputs=outputs)
     return out
 
+def heaviside(x, y, name=None):
+    """
+    Computes the Heaviside step function determined by corresponding element in y for each element in x. The equation is
+
+    .. math::
+        heaviside(x, y)=
+            \left\{
+                \\begin{array}{lcl}
+                0,& &\\text{if} \ x < 0, \\\\
+                y,& &\\text{if} \ x = 0, \\\\
+                1,& &\\text{if} \ x > 0.
+                \end{array}
+            \\right.
+
+    Notes:
+        ``paddle.heaviside`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`.
+
+    Args:
+        x (Tensor): The input tensor of Heaviside step function, it's data type should be float32, float64, int32 or int64.
+        y (Tensor): The tensor that determines a Heaviside step function, it's data type should be float32, float64, int32 or int64.
+        name (str, optional): Name for the operation (optional, default is None). Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        N-D Tensor. A location into which the result is stored. If x and y have different shapes and are broadcastable, the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y.
+
+    Examples:
+        .. code-block:: python
+            :name: heaviside-example
+
+            import paddle
+            x = paddle.to_tensor([-0.5, 0, 0.5])
+            y = paddle.to_tensor([0.1])
+            paddle.heaviside(x, y)
+            #    [0.        , 0.10000000, 1.        ]
+            x = paddle.to_tensor([[-0.5, 0, 0.5], [-0.5, 0.5, 0]])
+            y = paddle.to_tensor([0.1, 0.2, 0.3])
+            paddle.heaviside(x, y)
+            #    [[0.        , 0.20000000, 1.        ],
+            #     [0.        , 1.        , 0.30000001]]
+     """
+    op_type = 'elementwise_heaviside'
+    axis = -1
+    act = None
+    if _non_static_mode():
+        return _elementwise_op_in_dygraph(
+            x, y, axis=axis, act=act, op_name=op_type)
+    return _elementwise_op(LayerHelper(op_type, **locals()))
+
 def frac(x, name=None):
     """
     This API is used to return the fractional portion of each element in input.

tools/static_mode_white_list.py

@@ -170,6 +170,7 @@ STATIC_MODE_TESTING_LIST = [
     'test_elementwise_div_op',
     'test_elementwise_floordiv_op',
     'test_elementwise_gradient_op',
+    'test_elementwise_heaviside_op',
     'test_elementwise_max_op',
     'test_elementwise_min_op',
     'test_elementwise_mod_op',