【API】rename div to divide, add floor_divide, remainder (#26434)

paddle/fluid/operators/elementwise/elementwise_mod_op.cc

@@ -25,14 +25,14 @@ class ElementwiseModOpMaker : public ElementwiseOpMaker {
 
   void AddInputX() override {
     AddInput("X",
-             "(Variable), Tensor or LoDTensor of any dimensions. Its dtype "
-             "should be int32, int64.");
+             "(Tensor), Tensor of any dimensions. Its dtype "
+             "should be int32, int64, float32 or float64.");
   }
 
   void AddInputY() override {
     AddInput("Y",
-             "(Variable), Tensor or LoDTensor of any dimensions. Its dtype "
-             "should be int32, int64.");
+             "(Tensor), Tensor of any dimensions. Its dtype "
+             "should be int32, int64, float32 or float64.");
   }
 
   std::string GetOpFuntionality() const override {

python/paddle/__init__.py

@@ -176,7 +176,11 @@ from .tensor.math import maximum  #DEFINE_ALIAS
 from .tensor.math import min  #DEFINE_ALIAS
 from .tensor.math import minimum  #DEFINE_ALIAS
 from .tensor.math import mm  #DEFINE_ALIAS
-from .tensor.math import div  #DEFINE_ALIAS
+from .tensor.math import divide  #DEFINE_ALIAS
+from .tensor.math import floor_divide  #DEFINE_ALIAS
+from .tensor.math import remainder  #DEFINE_ALIAS
+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 atan  #DEFINE_ALIAS

python/paddle/fluid/layers/nn.py

@@ -11484,6 +11484,7 @@ Examples:
     return _elementwise_op(LayerHelper('elementwise_add', **locals()))
 
 
+@deprecated(since="2.0.0", update_to="paddle.divide")
 def elementwise_div(x, y, axis=-1, act=None, name=None):
     """
     :alias_main: paddle.elementwise_div
@@ -11907,6 +11908,7 @@ Examples:
     return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
 
 
+@deprecated(since="2.0.0", update_to="paddle.remainder")
 def elementwise_mod(x, y, axis=-1, act=None, name=None):
     """
     :alias_main: paddle.elementwise_mod
@@ -11944,6 +11946,7 @@ Examples:
     return _elementwise_op(LayerHelper('elementwise_mod', **locals()))
 
 
+@deprecated(since="2.0.0", update_to="paddle.floor_divide")
 def elementwise_floordiv(x, y, axis=-1, act=None, name=None):
     """
     :alias_main: paddle.elementwise_floordiv

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

@@ -240,22 +240,22 @@ class TestElementwiseDivBroadcast(unittest.TestCase):
             self.assertEqual((out_result == (2 / x)).all(), True)
 
 
-class TestDivOp(unittest.TestCase):
+class TestDivideOp(unittest.TestCase):
     def test_name(self):
         with fluid.program_guard(fluid.Program()):
             x = fluid.data(name="x", shape=[2, 3], dtype="float32")
             y = fluid.data(name='y', shape=[2, 3], dtype='float32')
 
-            y_1 = paddle.div(x, y, name='div_res')
+            y_1 = paddle.divide(x, y, name='div_res')
             self.assertEqual(('div_res' in y_1.name), True)
 
     def test_dygraph(self):
         with fluid.dygraph.guard():
             np_x = np.array([2, 3, 4]).astype('float64')
             np_y = np.array([1, 5, 2]).astype('float64')
-            x = fluid.dygraph.to_variable(np_x)
-            y = fluid.dygraph.to_variable(np_y)
-            z = paddle.div(x, y)
+            x = paddle.to_tensor(np_x)
+            y = paddle.to_tensor(np_y)
+            z = paddle.divide(x, y)
             np_z = z.numpy()
             z_expected = np.array([2., 0.6, 2.])
             self.assertEqual((np_z == z_expected).all(), True)

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

@@ -15,6 +15,8 @@
 from __future__ import print_function
 import unittest
 import numpy as np
+import paddle
+import paddle.fluid as fluid
 import paddle.fluid.core as core
 from op_test import OpTest
 
@@ -65,5 +67,26 @@ class TestElementwiseModOp_scalar(TestElementwiseModOp):
         self.out = np.floor_divide(self.x, self.y)
 
 
+class TestFloorDivideOp(unittest.TestCase):
+    def test_name(self):
+        with fluid.program_guard(fluid.Program()):
+            x = fluid.data(name="x", shape=[2, 3], dtype="int64")
+            y = fluid.data(name='y', shape=[2, 3], dtype='int64')
+
+            y_1 = paddle.floor_divide(x, y, name='div_res')
+            self.assertEqual(('div_res' in y_1.name), True)
+
+    def test_dygraph(self):
+        with fluid.dygraph.guard():
+            np_x = np.array([2, 3, 8, 7]).astype('int64')
+            np_y = np.array([1, 5, 3, 3]).astype('int64')
+            x = paddle.to_tensor(np_x)
+            y = paddle.to_tensor(np_y)
+            z = paddle.floor_divide(x, y)
+            np_z = z.numpy()
+            z_expected = np.array([2, 0, 2, 2])
+            self.assertEqual((np_z == z_expected).all(), True)
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -15,6 +15,8 @@
 from __future__ import print_function
 import unittest
 import numpy as np
+import paddle
+import paddle.fluid as fluid
 import paddle.fluid.core as core
 from op_test import OpTest
 
@@ -82,5 +84,26 @@ class TestElementwiseModOpDouble(TestElementwiseModOpFloat):
         self.dtype = np.float64
 
 
+class TestRemainderOp(unittest.TestCase):
+    def test_name(self):
+        with fluid.program_guard(fluid.Program()):
+            x = fluid.data(name="x", shape=[2, 3], dtype="int64")
+            y = fluid.data(name='y', shape=[2, 3], dtype='int64')
+
+            y_1 = paddle.remainder(x, y, name='div_res')
+            self.assertEqual(('div_res' in y_1.name), True)
+
+    def test_dygraph(self):
+        with fluid.dygraph.guard():
+            np_x = np.array([2, 3, 8, 7]).astype('int64')
+            np_y = np.array([1, 5, 3, 3]).astype('int64')
+            x = paddle.to_tensor(np_x)
+            y = paddle.to_tensor(np_y)
+            z = paddle.remainder(x, y)
+            np_z = z.numpy()
+            z_expected = np.array([0, 3, 2, 1])
+            self.assertEqual((np_z == z_expected).all(), True)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/tensor/__init__.py

@@ -144,7 +144,11 @@ from .math import maximum  #DEFINE_ALIAS
 from .math import min  #DEFINE_ALIAS
 from .math import minimum  #DEFINE_ALIAS
 from .math import mm  #DEFINE_ALIAS
-from .math import div  #DEFINE_ALIAS
+from .math import divide  #DEFINE_ALIAS
+from .math import floor_divide  #DEFINE_ALIAS
+from .math import remainder  #DEFINE_ALIAS
+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 atan  #DEFINE_ALIAS

python/paddle/tensor/math.py

@@ -111,7 +111,11 @@ __all__ = [
         'min',
         'minimum',
         'mm',
-        'div',
+        'divide',
+        'floor_divide',
+        'remainder',
+        'mod',
+        'floor_mod',
         'multiply',
         'add',
         'atan',
@@ -263,114 +267,143 @@ Examples:
     return _elementwise_op(LayerHelper(op_type, **locals()))
 
 
-def div(x, y, name=None):
+def divide(x, y, name=None):
     """
-Examples:
-
-    .. code-block:: python
-
-        import paddle
-        import paddle.fluid as fluid
-        import numpy as np
+    Divide two tensors element-wise. The equation is:
 
-        def gen_data():
-            return {
-                "x": np.array([2, 3, 4]).astype('float32'),
-                "y": np.array([1, 5, 2]).astype('float32')
-            }
+    .. math::
+        out = x / y
 
-        x = fluid.data(name="x", shape=[3], dtype='float32')
-        y = fluid.data(name="y", shape=[3], dtype='float32')
-        z = paddle.div(x, y)
-        # z = x / y
+    **Note**:
+    ``paddle.divide`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
 
-        place = fluid.CPUPlace()
-        exe = fluid.Executor(place)
-        z_value = exe.run(feed=gen_data(),
-                            fetch_list=[z.name])
+    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`.
 
-        print(z_value) # [2., 0.6, 2.]
+    Returns:
+        N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
 
+    Examples:
 
         ..  code-block:: python
 
             import paddle
-        import paddle.fluid as fluid
             import numpy as np
 
-        def gen_data():
-            return {
-                "x": np.ones((2, 3, 4, 5)).astype('float32'),
-                "y": np.zeros((4, 5)).astype('float32')
-            }
+            paddle.disable_static()
 
-        x = fluid.data(name="x", shape=[2, 3, 4, 5], dtype='float32')
-        y = fluid.data(name="y", shape=[4, 5], dtype='float32')
-        z = paddle.div(x, y, name='z')
-        # z = x / y
+            np_x = np.array([2, 3, 4]).astype('float64')
+            np_y = np.array([1, 5, 2]).astype('float64')
+            x = paddle.to_tensor(np_x)
+            y = paddle.to_tensor(np_y)
+            z = paddle.divide(x, y)
+            print(z.numpy())  # [2., 0.6, 2.]
+
+    """
+    op_type = 'elementwise_div'
+    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()))
 
-        place = fluid.CPUPlace()
-        exe = fluid.Executor(place)
 
-        z_value = exe.run(feed=gen_data(),
-                            fetch_list=[z.name])
+def floor_divide(x, y, name=None):
+    """
+    Floor divide two tensors element-wise. The equation is:
+
+    .. math::
+        out = x // y
+
+    **Note**:
+    ``paddle.floor_divide`` 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 int32, int64.
+        y (Tensor): the input tensor, it's data type should be int32, int64.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
-        print(z_value[0])
-        print(z_value[0].shape) # z.shape=[2,3,4,5]
+    Returns:
+        N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
 
+    Examples:
 
         ..  code-block:: python
 
             import paddle
-        import paddle.fluid as fluid
             import numpy as np
 
-        def gen_data():
-            return {
-                "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
-                "y": np.random.randint(1, 5, size=[5]).astype('float32')
-            }
+            paddle.disable_static()
 
-        x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
-        y = fluid.data(name="y", shape=[5], dtype='float32')
-        z = paddle.div(x, y)
-        # z = x / y
+            np_x = np.array([2, 3, 8, 7])
+            np_y = np.array([1, 5, 3, 3])
+            x = paddle.to_tensor(np_x)
+            y = paddle.to_tensor(np_y)
+            z = paddle.floor_divide(x, y)
+            print(z.numpy())  # [2, 0, 2, 2]
 
-        place = fluid.CPUPlace()
-        exe = fluid.Executor(place)
+    """
+    op_type = 'elementwise_floordiv'
+    axis = -1
+    if in_dygraph_mode():
+        return _elementwise_op_in_dygraph(
+            x, y, axis=axis, op_name=op_type)
 
-        z_value = exe.run(feed=gen_data(),
-                            fetch_list=[z.name])
-        print(z_value[0])
-        print(z_value[0].shape) # z.shape=[2,3,4,5]
+    return _elementwise_op(LayerHelper(op_type, **locals()))
 
 
+def remainder(x, y, name=None):
+    """
+    Mod two tensors element-wise. The equation is:
+
+    .. math::
+        out = x \% y
+
+    **Note**:
+    ``paddle.remainder`` 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 int32, int64.
+        y (Tensor): the input tensor, it's data type should be 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. It's dimension equals with $x$.
+
+    Examples:
+
         ..  code-block:: python
 
             import paddle
-        import paddle.fluid as fluid
             import numpy as np
 
-        with fluid.dygraph.guard(fluid.CPUPlace()):
-            np_x = np.array([2, 3, 4]).astype('float64')
-            np_y = np.array([1, 5, 2]).astype('float64')
-            x = fluid.dygraph.to_variable(np_x)
-            y = fluid.dygraph.to_variable(np_y)
-            z = paddle.div(x, y)
-            np_z = z.numpy()
-            print(np_z)  # [2., 0.6, 2.]
+            paddle.disable_static()
+
+            np_x = np.array([2, 3, 8, 7])
+            np_y = np.array([1, 5, 3, 3])
+            x = paddle.to_tensor(np_x)
+            y = paddle.to_tensor(np_y)
+            z = paddle.remainder(x, y)
+            print(z.numpy())  # [0, 3, 2, 1]
 
     """
-    op_type = 'elementwise_div'
+    op_type = 'elementwise_mod'
     axis = -1
-    act = None
     if in_dygraph_mode():
         return _elementwise_op_in_dygraph(
-            x, y, axis=axis, act=act, op_name=op_type)
+            x, y, axis=axis, op_name=op_type)
 
     return _elementwise_op(LayerHelper(op_type, **locals()))
 
 
+mod = remainder  #DEFINE_ALIAS
+floor_mod = remainder  #DEFINE_ALIAS
+
+
 def multiply(x, y, axis=-1, name=None):
     """
 	:alias_main: paddle.multiply
@@ -512,7 +545,6 @@ Examples:
 
 for func in [
         add,
-        div,
         maximum,
         minimum,
         multiply