Add paddle.tensor.math.prod (#26351)

* Add new API: paddle.prod

test=develop

python/paddle/__init__.py

@@ -183,6 +183,7 @@ from .tensor.math import addmm  #DEFINE_ALIAS
 from .tensor.math import clamp  #DEFINE_ALIAS
 from .tensor.math import trace  #DEFINE_ALIAS
 from .tensor.math import kron  #DEFINE_ALIAS
+from .tensor.math import prod  #DEFINE_ALIAS
 # from .tensor.random import gaussin        #DEFINE_ALIAS
 # from .tensor.random import uniform        #DEFINE_ALIAS
 from .tensor.random import shuffle  #DEFINE_ALIAS

python/paddle/fluid/layers/nn.py

@@ -4595,7 +4595,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
     Args:
         input (Variable): The input variable which is a Tensor, the data type is float32,
             float64, int32, int64.
-        dim (list|int, optional): The dimensions along which the product is performed. If
+        dim (int|list|tuple, optional): The dimensions along which the product is performed. If
             :attr:`None`, multiply all elements of :attr:`input` and return a
             Tensor variable with a single element, otherwise must be in the
             range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
@@ -4635,9 +4635,18 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
             fluid.layers.reduce_prod(y, dim=[0, 1]) # [105.0, 384.0]
     """
     helper = LayerHelper('reduce_prod', **locals())
-    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     if dim is not None and not isinstance(dim, list):
+        if isinstance(dim, tuple):
+            dim = list(dim)
+        elif isinstance(dim, int):
             dim = [dim]
+        else:
+            raise TypeError(
+                "The type of axis must be int, list or tuple, but received {}".
+                format(type(dim)))
+    check_variable_and_dtype(
+        input, 'input', ['float32', 'float64', 'int32', 'int64'], 'reduce_prod')
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     helper.append_op(
         type='reduce_prod',
         inputs={'X': input},

python/paddle/fluid/tests/unittests/test_prod_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 paddle
+import unittest
+import numpy as np
+
+
+class TestProdOp(unittest.TestCase):
+    def setUp(self):
+        self.input = np.random.random(size=(10, 10, 5)).astype(np.float32)
+
+    def run_imperative(self):
+        input = paddle.to_tensor(self.input)
+        dy_result = paddle.prod(input)
+        expected_result = np.prod(self.input)
+        self.assertTrue(np.allclose(dy_result.numpy(), expected_result))
+
+        dy_result = paddle.prod(input, axis=1)
+        expected_result = np.prod(self.input, axis=1)
+        self.assertTrue(np.allclose(dy_result.numpy(), expected_result))
+
+        dy_result = paddle.prod(input, axis=-1)
+        expected_result = np.prod(self.input, axis=-1)
+        self.assertTrue(np.allclose(dy_result.numpy(), expected_result))
+
+        dy_result = paddle.prod(input, axis=[0, 1])
+        expected_result = np.prod(self.input, axis=(0, 1))
+        self.assertTrue(np.allclose(dy_result.numpy(), expected_result))
+
+        dy_result = paddle.prod(input, axis=1, keepdim=True)
+        expected_result = np.prod(self.input, axis=1, keepdims=True)
+        self.assertTrue(np.allclose(dy_result.numpy(), expected_result))
+
+        dy_result = paddle.prod(input, axis=1, dtype='int64')
+        expected_result = np.prod(self.input, axis=1, dtype=np.int64)
+        self.assertTrue(np.allclose(dy_result.numpy(), expected_result))
+
+        dy_result = paddle.prod(input, axis=1, keepdim=True, dtype='int64')
+        expected_result = np.prod(
+            self.input, axis=1, keepdims=True, dtype=np.int64)
+        self.assertTrue(np.allclose(dy_result.numpy(), expected_result))
+
+    def run_static(self, use_gpu=False):
+        input = paddle.data(name='input', shape=[10, 10, 5], dtype='float32')
+        result0 = paddle.prod(input)
+        result1 = paddle.prod(input, axis=1)
+        result2 = paddle.prod(input, axis=-1)
+        result3 = paddle.prod(input, axis=[0, 1])
+        result4 = paddle.prod(input, axis=1, keepdim=True)
+        result5 = paddle.prod(input, axis=1, dtype='int64')
+        result6 = paddle.prod(input, axis=1, keepdim=True, dtype='int64')
+
+        place = paddle.CUDAPlace(0) if use_gpu else paddle.CPUPlace()
+        exe = paddle.static.Executor(place)
+        exe.run(paddle.static.default_startup_program())
+        static_result = exe.run(feed={"input": self.input},
+                                fetch_list=[
+                                    result0, result1, result2, result3, result4,
+                                    result5, result6
+                                ])
+
+        expected_result = np.prod(self.input)
+        self.assertTrue(np.allclose(static_result[0], expected_result))
+        expected_result = np.prod(self.input, axis=1)
+        self.assertTrue(np.allclose(static_result[1], expected_result))
+        expected_result = np.prod(self.input, axis=-1)
+        self.assertTrue(np.allclose(static_result[2], expected_result))
+        expected_result = np.prod(self.input, axis=(0, 1))
+        self.assertTrue(np.allclose(static_result[3], expected_result))
+        expected_result = np.prod(self.input, axis=1, keepdims=True)
+        self.assertTrue(np.allclose(static_result[4], expected_result))
+        expected_result = np.prod(self.input, axis=1, dtype=np.int64)
+        self.assertTrue(np.allclose(static_result[5], expected_result))
+        expected_result = np.prod(
+            self.input, axis=1, keepdims=True, dtype=np.int64)
+        self.assertTrue(np.allclose(static_result[6], expected_result))
+
+    def test_cpu(self):
+        paddle.disable_static(place=paddle.CPUPlace())
+        self.run_imperative()
+        paddle.enable_static()
+
+        with paddle.static.program_guard(paddle.static.Program()):
+            self.run_static()
+
+    def test_gpu(self):
+        if not paddle.fluid.core.is_compiled_with_cuda():
+            return
+
+        paddle.disable_static(place=paddle.CUDAPlace(0))
+        self.run_imperative()
+        paddle.enable_static()
+
+        with paddle.static.program_guard(paddle.static.Program()):
+            self.run_static(use_gpu=True)
+
+
+class TestProdOpError(unittest.TestCase):
+    def test_error(self):
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            x = paddle.data(name='x', shape=[2, 2, 4], dtype='float32')
+            bool_x = paddle.data(name='bool_x', shape=[2, 2, 4], dtype='bool')
+            # The argument x shoule be a Tensor
+            self.assertRaises(TypeError, paddle.prod, [1])
+
+            # The data type of x should be float32, float64, int32, int64
+            self.assertRaises(TypeError, paddle.prod, bool_x)
+
+            # The argument axis's type shoule be int ,list or tuple
+            self.assertRaises(TypeError, paddle.prod, x, 1.5)
+
+            # The argument dtype of prod_op should be float32, float64, int32 or int64.
+            self.assertRaises(TypeError, paddle.prod, x, 'bool')
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -157,6 +157,7 @@ from .math import addmm  #DEFINE_ALIAS
 from .math import clamp  #DEFINE_ALIAS
 from .math import trace  #DEFINE_ALIAS
 from .math import kron  #DEFINE_ALIAS
+from .math import prod  #DEFINE_ALIAS
 # from .random import gaussin        #DEFINE_ALIAS
 # from .random import uniform        #DEFINE_ALIAS
 from .random import shuffle  #DEFINE_ALIAS

python/paddle/tensor/math.py

@@ -63,6 +63,7 @@ from ..fluid.layers import tanh    #DEFINE_ALIAS
 from ..fluid.layers import increment    #DEFINE_ALIAS
 from ..fluid.layers import multiplex    #DEFINE_ALIAS
 from ..fluid.layers import sums    #DEFINE_ALIAS
+from ..fluid import layers
 
 __all__ = [
         'abs',
@@ -85,6 +86,7 @@ __all__ = [
         'log',
         'mul',
         'multiplex',
+        'prod',
         'pow',
         'reciprocal',
         'reduce_max',
@@ -1632,3 +1634,85 @@ def cumsum(x, axis=None, dtype=None, name=None):
             kwargs[name] = val
     _cum_sum_ = generate_layer_fn('cumsum')
     return _cum_sum_(**kwargs)
+
+def prod(x, axis=None, keepdim=False, dtype=None, name=None):
+    """
+    Compute the product of tensor elements over the given axis.
+
+    Args:
+        x(Tensor): An N-D Tensor, the data type is float32, float64, int32 or int64.
+        axis(int|list|tuple, optional): The axis along which the product is computed. If :attr:`None`, 
+            multiply all elements of `x` and return a Tensor with a single element, 
+            otherwise must be in the range :math:`[-x.ndim, x.ndim)`. If :math:`axis[i]<0`, 
+            the axis to reduce is :math:`x.ndim + axis[i]`. Default is None.
+        dtype(str|np.dtype, optional): The desired date type of returned tensor, can be float32, float64, 
+            int32, int64. If specified, the input tensor is casted to dtype before operator performed. 
+            This is very useful for avoiding data type overflows. The default value is None, the dtype 
+            of output is the same as input Tensor `x`.
+        keepdim(bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result 
+            tensor will have one fewer dimension than the input unless keep_dim is true. Default is False.
+        name(string, optional): The default value is None. Normally there is no need for user to set this property.
+            For more information, please refer to :ref:`api_guide_Name` .
+
+    Returns:
+        Tensor, result of product on the specified dim of input tensor.
+
+    Raises:
+        ValueError: The :attr:`dtype` must be float32, float64, int32 or int64.
+        TypeError: The type of :attr:`axis` must be int, list or tuple.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import numpy as np
+
+            paddle.disable_static()
+
+            # the axis is a int element
+            data_x = np.array([[0.2, 0.3, 0.5, 0.9],
+                         [0.1, 0.2, 0.6, 0.7]]).astype(np.float32)
+            x = paddle.to_tensor(data_x)
+            out1 = paddle.prod(x)
+            print(out1.numpy())
+            # [0.0002268]
+
+            out2 = paddle.prod(x, -1)
+            print(out2.numpy())
+            # [0.027  0.0084]
+
+            out3 = paddle.prod(x, 0)
+            print(out3.numpy())
+            # [0.02 0.06 0.3  0.63]
+            print(out3.numpy().dtype)
+            # float32
+
+            out4 = paddle.prod(x, 0, keepdim=True)
+            print(out4.numpy())
+            # [[0.02 0.06 0.3  0.63]]
+
+            out5 = paddle.prod(x, 0, dtype='int64')
+            print(out5.numpy())
+            # [0 0 0 0]
+            print(out5.numpy().dtype)
+            # int64
+
+            # the axis is list
+            data_y = np.array([[[1.0, 2.0], [3.0, 4.0]],
+                               [[5.0, 6.0], [7.0, 8.0]]])
+            y = paddle.to_tensor(data_y)
+            out6 = paddle.prod(y, [0, 1])
+            print(out6.numpy())
+            # [105. 384.]
+
+            out7 = paddle.prod(y, (1, 2))
+            print(out7.numpy())
+            # [  24. 1680.]
+
+    """
+    if dtype is not None:
+        check_dtype(dtype, 'dtype', ['float32', 'float64', 'int32', 'int64'], 'prod')
+        if x.dtype != convert_np_dtype_to_dtype_(dtype):
+            x = layers.cast(x, dtype)
+
+    return layers.reduce_prod(input=x, dim=axis, keep_dim=keepdim, name=name)