add paddle.subtract, optimize paddle.maximum and paddle.minimum

add paddle.subtract, optimize paddle.maximum and paddle.minimum 

python/paddle/__init__.py

@@ -186,6 +186,7 @@ from .tensor.math import mod  #DEFINE_ALIAS
 from .tensor.math import floor_mod  #DEFINE_ALIAS
 from .tensor.math import multiply  #DEFINE_ALIAS
 from .tensor.math import add  #DEFINE_ALIAS
+from .tensor.math import subtract  #DEFINE_ALIAS
 from .tensor.math import atan  #DEFINE_ALIAS
 from .tensor.math import logsumexp  #DEFINE_ALIAS
 from .tensor.math import inverse  #DEFINE_ALIAS

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

@@ -16,7 +16,6 @@ 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.core as core
 
@@ -31,6 +30,14 @@ class ApiMaximumTest(unittest.TestCase):
         self.input_x = np.random.rand(10, 15).astype("float32")
         self.input_y = np.random.rand(10, 15).astype("float32")
         self.input_z = np.random.rand(15).astype("float32")
+        self.input_a = np.array([0, np.nan, np.nan]).astype('int64')
+        self.input_b = np.array([2, np.inf, -np.inf]).astype('int64')
+        self.input_c = np.array([4, 1, 3]).astype('int64')
+
+        self.np_expected1 = np.maximum(self.input_x, self.input_y)
+        self.np_expected2 = np.maximum(self.input_x, self.input_z)
+        self.np_expected3 = np.maximum(self.input_a, self.input_c)
+        self.np_expected4 = np.maximum(self.input_b, self.input_c)
 
     def test_static_api(self):
         paddle.enable_static()
@@ -43,38 +50,64 @@ class ApiMaximumTest(unittest.TestCase):
             res, = exe.run(feed={"x": self.input_x,
                                  "y": self.input_y},
                            fetch_list=[result_max])
-        self.assertEqual((res == np.maximum(self.input_x, self.input_y)).all(),
-                         True)
+        self.assertTrue(np.allclose(res, self.np_expected1))
 
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
             data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
             data_z = paddle.static.data("z", shape=[15], dtype="float32")
-            result_max = paddle.maximum(data_x, data_z, axis=1)
+            result_max = paddle.maximum(data_x, data_z)
             exe = paddle.static.Executor(self.place)
             res, = exe.run(feed={"x": self.input_x,
                                  "z": self.input_z},
                            fetch_list=[result_max])
-        self.assertEqual((res == np.maximum(self.input_x, self.input_z)).all(),
-                         True)
+        self.assertTrue(np.allclose(res, self.np_expected2))
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_a = paddle.static.data("a", shape=[3], dtype="int64")
+            data_c = paddle.static.data("c", shape=[3], dtype="int64")
+            result_max = paddle.maximum(data_a, data_c)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"a": self.input_a,
+                                 "c": self.input_c},
+                           fetch_list=[result_max])
+        self.assertTrue(np.allclose(res, self.np_expected3))
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_b = paddle.static.data("b", shape=[3], dtype="int64")
+            data_c = paddle.static.data("c", shape=[3], dtype="int64")
+            result_max = paddle.maximum(data_b, data_c)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"b": self.input_b,
+                                 "c": self.input_c},
+                           fetch_list=[result_max])
+        self.assertTrue(np.allclose(res, self.np_expected4))
 
     def test_dynamic_api(self):
         paddle.disable_static()
-        np_x = np.array([10, 10]).astype('float64')
         x = paddle.to_tensor(self.input_x)
         y = paddle.to_tensor(self.input_y)
-        z = paddle.maximum(x, y)
-        np_z = z.numpy()
-        z_expected = np.array(np.maximum(self.input_x, self.input_y))
-        self.assertEqual((np_z == z_expected).all(), True)
+        z = paddle.to_tensor(self.input_z)
 
-    def test_broadcast_axis(self):
-        paddle.disable_static()
-        np_x = np.random.rand(5, 4, 3, 2).astype("float64")
-        np_y = np.random.rand(4, 3).astype("float64")
+        a = paddle.to_tensor(self.input_a)
+        b = paddle.to_tensor(self.input_b)
+        c = paddle.to_tensor(self.input_c)
 
-        x = paddle.to_tensor(self.input_x)
-        y = paddle.to_tensor(self.input_y)
-        result_1 = paddle.maximum(x, y, axis=1)
-        result_2 = paddle.maximum(x, y, axis=-2)
-        self.assertEqual((result_1.numpy() == result_2.numpy()).all(), True)
+        res = paddle.maximum(x, y)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected1))
+
+        # test broadcast
+        res = paddle.maximum(x, z)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected2))
+
+        res = paddle.maximum(a, c)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected3))
+
+        res = paddle.maximum(b, c)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected4))

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

@@ -16,7 +16,6 @@ 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.core as core
 
@@ -31,6 +30,14 @@ class ApiMinimumTest(unittest.TestCase):
         self.input_x = np.random.rand(10, 15).astype("float32")
         self.input_y = np.random.rand(10, 15).astype("float32")
         self.input_z = np.random.rand(15).astype("float32")
+        self.input_a = np.array([0, np.nan, np.nan]).astype('int64')
+        self.input_b = np.array([2, np.inf, -np.inf]).astype('int64')
+        self.input_c = np.array([4, 1, 3]).astype('int64')
+
+        self.np_expected1 = np.minimum(self.input_x, self.input_y)
+        self.np_expected2 = np.minimum(self.input_x, self.input_z)
+        self.np_expected3 = np.minimum(self.input_a, self.input_c)
+        self.np_expected4 = np.minimum(self.input_b, self.input_c)
 
     def test_static_api(self):
         paddle.enable_static()
@@ -38,43 +45,69 @@ class ApiMinimumTest(unittest.TestCase):
                                          paddle.static.Program()):
             data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
             data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
-            result_min = paddle.minimum(data_x, data_y)
+            result_max = paddle.minimum(data_x, data_y)
             exe = paddle.static.Executor(self.place)
             res, = exe.run(feed={"x": self.input_x,
                                  "y": self.input_y},
-                           fetch_list=[result_min])
-        self.assertEqual((res == np.minimum(self.input_x, self.input_y)).all(),
-                         True)
+                           fetch_list=[result_max])
+        self.assertTrue(np.allclose(res, self.np_expected1))
 
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
             data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
             data_z = paddle.static.data("z", shape=[15], dtype="float32")
-            result_min = paddle.minimum(data_x, data_z, axis=1)
+            result_max = paddle.minimum(data_x, data_z)
             exe = paddle.static.Executor(self.place)
             res, = exe.run(feed={"x": self.input_x,
                                  "z": self.input_z},
-                           fetch_list=[result_min])
-        self.assertEqual((res == np.minimum(self.input_x, self.input_z)).all(),
-                         True)
+                           fetch_list=[result_max])
+        self.assertTrue(np.allclose(res, self.np_expected2))
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_a = paddle.static.data("a", shape=[3], dtype="int64")
+            data_c = paddle.static.data("c", shape=[3], dtype="int64")
+            result_max = paddle.minimum(data_a, data_c)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"a": self.input_a,
+                                 "c": self.input_c},
+                           fetch_list=[result_max])
+        self.assertTrue(np.allclose(res, self.np_expected3))
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_b = paddle.static.data("b", shape=[3], dtype="int64")
+            data_c = paddle.static.data("c", shape=[3], dtype="int64")
+            result_max = paddle.minimum(data_b, data_c)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"b": self.input_b,
+                                 "c": self.input_c},
+                           fetch_list=[result_max])
+        self.assertTrue(np.allclose(res, self.np_expected4))
 
     def test_dynamic_api(self):
         paddle.disable_static()
-        np_x = np.array([10, 10]).astype('float64')
         x = paddle.to_tensor(self.input_x)
         y = paddle.to_tensor(self.input_y)
-        z = paddle.minimum(x, y)
-        np_z = z.numpy()
-        z_expected = np.array(np.minimum(self.input_x, self.input_y))
-        self.assertEqual((np_z == z_expected).all(), True)
+        z = paddle.to_tensor(self.input_z)
 
-    def test_broadcast_axis(self):
-        paddle.disable_static()
-        np_x = np.random.rand(5, 4, 3, 2).astype("float64")
-        np_y = np.random.rand(4, 3).astype("float64")
+        a = paddle.to_tensor(self.input_a)
+        b = paddle.to_tensor(self.input_b)
+        c = paddle.to_tensor(self.input_c)
 
-        x = paddle.to_tensor(self.input_x)
-        y = paddle.to_tensor(self.input_y)
-        result_1 = paddle.minimum(x, y, axis=1)
-        result_2 = paddle.minimum(x, y, axis=-2)
-        self.assertEqual((result_1.numpy() == result_2.numpy()).all(), True)
+        res = paddle.minimum(x, y)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected1))
+
+        # test broadcast
+        res = paddle.minimum(x, z)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected2))
+
+        res = paddle.minimum(a, c)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected3))
+
+        res = paddle.minimum(b, c)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected4))

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

+# Copyright (c) 2020 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid.core as core
+
+
+class ApiSubtractTest(unittest.TestCase):
+    def setUp(self):
+        if core.is_compiled_with_cuda():
+            self.place = core.CUDAPlace(0)
+        else:
+            self.place = core.CPUPlace()
+
+        self.input_x = np.random.rand(10, 15).astype("float32")
+        self.input_y = np.random.rand(10, 15).astype("float32")
+        self.input_z = np.random.rand(15).astype("float32")
+        self.input_a = np.array([0, np.nan, np.nan]).astype('int64')
+        self.input_b = np.array([2, np.inf, -np.inf]).astype('int64')
+        self.input_c = np.array([4, 1, 3]).astype('int64')
+
+        self.np_expected1 = np.subtract(self.input_x, self.input_y)
+        self.np_expected2 = np.subtract(self.input_x, self.input_z)
+        self.np_expected3 = np.subtract(self.input_a, self.input_c)
+        self.np_expected4 = np.subtract(self.input_b, self.input_c)
+
+    def test_static_api(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
+            data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
+            result_max = paddle.subtract(data_x, data_y)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"x": self.input_x,
+                                 "y": self.input_y},
+                           fetch_list=[result_max])
+        self.assertTrue(np.allclose(res, self.np_expected1))
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
+            data_z = paddle.static.data("z", shape=[15], dtype="float32")
+            result_max = paddle.subtract(data_x, data_z)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"x": self.input_x,
+                                 "z": self.input_z},
+                           fetch_list=[result_max])
+        self.assertTrue(np.allclose(res, self.np_expected2))
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_a = paddle.static.data("a", shape=[3], dtype="int64")
+            data_c = paddle.static.data("c", shape=[3], dtype="int64")
+            result_max = paddle.subtract(data_a, data_c)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"a": self.input_a,
+                                 "c": self.input_c},
+                           fetch_list=[result_max])
+        self.assertTrue(np.allclose(res, self.np_expected3))
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_b = paddle.static.data("b", shape=[3], dtype="int64")
+            data_c = paddle.static.data("c", shape=[3], dtype="int64")
+            result_max = paddle.subtract(data_b, data_c)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"b": self.input_b,
+                                 "c": self.input_c},
+                           fetch_list=[result_max])
+        self.assertTrue(np.allclose(res, self.np_expected4))
+
+    def test_dynamic_api(self):
+        paddle.disable_static()
+        x = paddle.to_tensor(self.input_x)
+        y = paddle.to_tensor(self.input_y)
+        z = paddle.to_tensor(self.input_z)
+
+        a = paddle.to_tensor(self.input_a)
+        b = paddle.to_tensor(self.input_b)
+        c = paddle.to_tensor(self.input_c)
+
+        res = paddle.subtract(x, y)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected1))
+
+        # test broadcast
+        res = paddle.subtract(x, z)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected2))
+
+        res = paddle.subtract(a, c)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected3))
+
+        res = paddle.subtract(b, c)
+        res = res.numpy()
+        self.assertTrue(np.allclose(res, self.np_expected4))

python/paddle/tensor/__init__.py

@@ -148,6 +148,7 @@ from .math import mod  #DEFINE_ALIAS
 from .math import floor_mod  #DEFINE_ALIAS
 from .math import multiply  #DEFINE_ALIAS
 from .math import add  #DEFINE_ALIAS
+from .math import subtract  #DEFINE_ALIAS
 from .math import atan  #DEFINE_ALIAS
 from .math import logsumexp  #DEFINE_ALIAS
 from .math import inverse  #DEFINE_ALIAS

python/paddle/tensor/math.py

@@ -111,6 +111,7 @@ __all__ = [
         'floor_mod',
         'multiply',
         'add',
+        'subtract',
         'atan',
         'logsumexp',
         'inverse',
@@ -286,6 +287,67 @@ def add(x, y, name=None):
     return _elementwise_op(LayerHelper(op_type, **locals()))
 
 
+def subtract(x, y, name=None):
+    """
+    Substract two tensors element-wise. The equation is: 
+
+    .. math::
+        out = x - y
+
+    **Note**:
+    ``paddle.subtract`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+
+    Args:
+        x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
+        y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        N-D Tensor. A location into which the result is stored. If x, 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
+        
+            import numpy as np
+            import paddle
+
+            x = paddle.to_tensor([[1, 2], [7, 8]])
+            y = paddle.to_tensor([[5, 6], [3, 4]])
+            res = paddle.subtract(x, y)
+            print(res)
+            #       [[-4, -4],
+            #        [4, 4]]
+
+            x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]])
+            y = paddle.to_tensor([1, 0, 4])
+            res = paddle.subtract(x, y)
+            print(res)
+            #       [[[ 0,  2, -1],
+            #         [ 0,  2, -1]]]
+
+            x = paddle.to_tensor([2, np.nan, 5], dtype='float32')
+            y = paddle.to_tensor([1, 4, np.nan], dtype='float32')
+            res = paddle.subtract(x, y)
+            print(res)
+            #       [ 1., nan, nan]
+
+            x = paddle.to_tensor([5, np.inf, -np.inf], dtype='float64')
+            y = paddle.to_tensor([1, 4, 5], dtype='float64')
+            res = paddle.subtract(x, y)
+            print(res)
+            #       [   4.,  inf., -inf.]
+
+    """
+    op_type = 'elementwise_sub'
+    axis = -1
+    act = None
+    if in_dygraph_mode():
+        return _elementwise_op_in_dygraph(
+            x, y, axis=axis, act=act, op_name=op_type)
+    return _elementwise_op(LayerHelper(op_type, **locals()))
+
+
 def divide(x, y, name=None):
     """
     Divide two tensors element-wise. The equation is:
@@ -302,7 +364,7 @@ def divide(x, y, name=None):
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
+        N-D Tensor. A location into which the result is stored. If x, 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:
 
@@ -382,7 +444,7 @@ def remainder(x, y, name=None):
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
+        N-D Tensor. A location into which the result is stored. If x, 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:
 
@@ -425,7 +487,7 @@ def multiply(x, y, name=None):
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        N-D Tensor. A location into which the result is stored. Its dimension equals with $x$.
+        N-D Tensor. A location into which the result is stored. If x, 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:
 
@@ -463,84 +525,118 @@ def multiply(x, y, name=None):
             x, y, axis=axis, act=act, op_name=op_type)
     return _elementwise_op(LayerHelper(op_type, **locals()))
 
-def maximum(x, y, axis=-1, name=None):
+def maximum(x, y, name=None):
     """
-Examples:
+    Compare two tensors and returns a new tensor containing the element-wise maxima. The equation is: 
 
-    .. code-block:: python
+    .. math::
+        out = max(x, y)
 
-        import paddle
-        import numpy as np
-
-        x = paddle.to_tensor([[1, 2], [3, 4]])
-        y = paddle.to_tensor([[5, 6], [7, 8]])
-        res = paddle.maximum(x, y)
-        print(res)
-        #[[5. 6.]
-        # [7. 8.]]
-
-        x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]])
-        y = paddle.to_tensor([1, 2])
-        res = paddle.maximum(x, y, axis=1)
-        print(res)
-        #[[[1. 2. 3.]
-        #  [2. 2. 3.]]]
-
-        x = paddle.to_tensor([2, 3, 5], dtype='float32')
-        y = paddle.to_tensor([1, 4, np.nan], dtype='float32')
-        res = paddle.maximum(x, y)
-        print(res)
-        #[ 2.  4. nan]
-
-        x = paddle.to_tensor([5, 3, np.inf], dtype='float32')
-        y = paddle.to_tensor([1, 4, 5], dtype='float32')
-        res = paddle.maximum(x, y)
-        print(res)
-        #[ 5.  4. inf]
+    **Note**:
+    ``paddle.maximum`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+
+    Args:
+        x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
+        y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        N-D Tensor. A location into which the result is stored. If x, 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
+
+            import numpy as np
+            import paddle
+
+            x = paddle.to_tensor([[1, 2], [7, 8]])
+            y = paddle.to_tensor([[3, 4], [5, 6]])
+            res = paddle.maximum(x, y)
+            print(res)
+            #    [[3, 4],
+            #     [7, 8]]
+
+            x = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
+            y = paddle.to_tensor([3, 0, 4])
+            res = paddle.maximum(x, y)
+            print(res)
+            #    [[3, 2, 4],
+            #     [3, 2, 4]]
+
+            x = paddle.to_tensor([2, 3, 5], dtype='float32')
+            y = paddle.to_tensor([1, np.nan, np.nan], dtype='float32')
+            res = paddle.maximum(x, y)
+            print(res)
+            #    [ 2., nan, nan]
+
+            x = paddle.to_tensor([5, 3, np.inf], dtype='float32')
+            y = paddle.to_tensor([1, -np.inf, 5], dtype='float32')
+            res = paddle.maximum(x, y)
+            print(res)
+            #    [  5.,   3., inf.]
     """
     op_type = 'elementwise_max'
+    axis = -1
     act = None
     if in_dygraph_mode():
         return _elementwise_op_in_dygraph(
             x, y, axis=axis, act=act, op_name=op_type)
     return _elementwise_op(LayerHelper(op_type, **locals()))
 
-def minimum(x, y, axis=-1, name=None):
+def minimum(x, y, name=None):
     """
-Examples:
+    Compare two tensors and returns a new tensor containing the element-wise minima. The equation is: 
 
-    .. code-block:: python
+    .. math::
+        out = min(x, y)
 
-        import paddle
-        import numpy as np
-  
-        x = paddle.to_tensor([[1, 2], [3, 4]], dtype='float32')
-        y = paddle.to_tensor([[5, 6], [7, 8]], dtype='float32')
-        res = paddle.minimum(x, y)
-        print(res)
-        #[[1. 2.]
-        # [3. 4.]]
-
-        x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]], dtype='float32')
-        y = paddle.to_tensor([1, 2], dtype='float32')
-        res = paddle.minimum(x, y, axis=1)
-        print(res)
-        #[[[1. 1. 1.]
-        #  [2. 2. 2.]]]
-
-        x = paddle.to_tensor([2, 3, 5], dtype='float32')
-        y = paddle.to_tensor([1, 4, np.nan], dtype='float32')
-        res = paddle.minimum(x, y)
-        print(res)
-        #[ 1.  3. nan]
-
-        x = paddle.to_tensor([5, 3, np.inf], dtype='float32')
-        y = paddle.to_tensor([1, 4, 5], dtype='float32')
-        res = paddle.minimum(x, y)
-        print(res)
-        #[1. 3. 5.]
+    **Note**:
+    ``paddle.minimum`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+
+    Args:
+        x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
+        y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        N-D Tensor. A location into which the result is stored. If x, 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
+
+            import numpy as np
+            import paddle
+
+            x = paddle.to_tensor([[1, 2], [7, 8]])
+            y = paddle.to_tensor([[3, 4], [5, 6]])
+            res = paddle.minimum(x, y)
+            print(res)
+            #       [[1, 2],
+            #        [5, 6]]
+
+            x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]])
+            y = paddle.to_tensor([3, 0, 4])
+            res = paddle.minimum(x, y)
+            print(res)
+            #       [[[1, 0, 3],
+            #         [1, 0, 3]]]
+
+            x = paddle.to_tensor([2, 3, 5], dtype='float32')
+            y = paddle.to_tensor([1, np.nan, np.nan], dtype='float32')
+            res = paddle.minimum(x, y)
+            print(res)
+            #       [ 1., nan, nan]
+
+            x = paddle.to_tensor([5, 3, np.inf], dtype='float64')
+            y = paddle.to_tensor([1, -np.inf, 5], dtype='float64')
+            res = paddle.minimum(x, y)
+            print(res)
+            #       [   1., -inf.,    5.]
     """
     op_type = 'elementwise_min'
+    axis = -1
     act = None
     if in_dygraph_mode():
         return _elementwise_op_in_dygraph(
@@ -549,11 +645,9 @@ Examples:
 
 for func in [
         add,
-        maximum,
-        minimum,
         multiply
 ]:
-    proto_dict = {'add': 'elementwise_add', 'div': 'elementwise_div', 'maximum': 'elementwise_max', 'minimum': 'elementwise_min', 'multiply': 'elementwise_mul'}
+    proto_dict = {'add': 'elementwise_add', 'multiply': 'elementwise_mul'}
     op_proto = OpProtoHolder.instance().get_op_proto(proto_dict[func.__name__])
 
     additional_args_lines = [