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

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* Add new API: paddle.prod

test=develop
revert-24895-update_cub
guofei 5 years ago committed by GitHub
parent 40d193ed17
commit bb7fd0974f
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5 changed files with 230 additions and 3 deletions
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1
python/paddle/__init__.py
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@ -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

15
python/paddle/fluid/layers/nn.py
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@ -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):
dim = [dim]
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},

132
python/paddle/fluid/tests/unittests/test_prod_op.py
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@ -0,0 +1,132 @@
# 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()

1
python/paddle/tensor/__init__.py
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@ -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

84
python/paddle/tensor/math.py
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@ -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)

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