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2.0rc api add all any (#28199)
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* reduce trt warning message (#28011)

add paddle.enable_static() on sample code

alias recude_all-->all, reduce_any-->any

add import reduce_all and reduce_any in python/paddle/tensor/math.py

import all and any in python/paddle/tensor/__init__.py

remove all and any OP in python/paddle/tensor/logic.py, add all and any OP in python/paddle/tensor/math.py

fix import error

remove TestAllAPI temporary

* fix doc of recdue_all and reduce_any, test=document_fix

* fix typo

* fix unittest for all and any API

Co-authored-by: Pei Yang <peiyang@baidu.com>
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@cryoco
cnn and cryoco authored Oct 22, 2020
1 parent 4ccc171 commit 97227e6
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Showing 6 changed files with 381 additions and 22 deletions.
4 changes: 2 additions & 2 deletions python/paddle/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,8 +103,6 @@
from .tensor.logic import logical_or #DEFINE_ALIAS
from .tensor.logic import logical_xor #DEFINE_ALIAS
from .tensor.logic import not_equal #DEFINE_ALIAS
# from .tensor.logic import reduce_all #DEFINE_ALIAS
# from .tensor.logic import reduce_any #DEFINE_ALIAS
from .tensor.logic import allclose #DEFINE_ALIAS
from .tensor.logic import equal_all #DEFINE_ALIAS
# from .tensor.logic import isnan #DEFINE_ALIAS
Expand Down Expand Up @@ -161,6 +159,8 @@
# from .tensor.math import reduce_min #DEFINE_ALIAS
# from .tensor.math import reduce_prod #DEFINE_ALIAS
# from .tensor.math import reduce_sum #DEFINE_ALIAS
from .tensor.math import all #DEFINE_ALIAS
from .tensor.math import any #DEFINE_ALIAS
from .tensor.math import round #DEFINE_ALIAS
from .tensor.math import rsqrt #DEFINE_ALIAS
from .tensor.math import scale #DEFINE_ALIAS
Expand Down
79 changes: 62 additions & 17 deletions python/paddle/fluid/layers/nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -315,6 +315,8 @@ def fc(input,
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()
# when input is single tensor
data = fluid.data(name="data", shape=[-1, 32], dtype="float32")
fc = fluid.layers.fc(input=data, size=1000, act="tanh")
Expand Down Expand Up @@ -468,6 +470,9 @@ def embedding(input,

import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()

data = fluid.data(name='x', shape=[None, 1], dtype='int64')

# example 1
Expand Down Expand Up @@ -731,6 +736,8 @@ def linear_chain_crf(input, label, param_attr=None, length=None):

import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()

#define net structure, using LodTensor
train_program = fluid.Program()
Expand Down Expand Up @@ -855,6 +862,8 @@ def crf_decoding(input, param_attr, label=None, length=None):
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()

# LoDTensor-based example
num_labels = 10
Expand Down Expand Up @@ -1458,6 +1467,9 @@ def conv2d(input,
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()

data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32')
conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
"""
Expand Down Expand Up @@ -1728,6 +1740,8 @@ def conv3d(input,
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32')
conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu")
"""
Expand Down Expand Up @@ -2377,6 +2391,7 @@ def adaptive_pool2d(input,
# output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
#
import paddle
paddle.enable_static()
data = paddle.rand(shape=[1,3,32,32])
pool_out = paddle.fluid.layers.adaptive_pool2d(
input=data,
Expand Down Expand Up @@ -2531,6 +2546,7 @@ def adaptive_pool3d(input,
#

import paddle
paddle.enable_static()
data = paddle.rand(shape=[1,3,32,32,32])
pool_out = paddle.fluid.layers.adaptive_pool3d(
input=data,
Expand Down Expand Up @@ -2726,6 +2742,8 @@ def batch_norm(input,
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()
x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
hidden2 = fluid.layers.batch_norm(input=hidden1)
Expand All @@ -2735,6 +2753,8 @@ def batch_norm(input,
# batch_norm with momentum as Variable
import paddle.fluid as fluid
import paddle.fluid.layers.learning_rate_scheduler as lr_scheduler
import paddle
paddle.enable_static()

def get_decay_momentum(momentum_init, decay_steps, decay_rate):
global_step = lr_scheduler._decay_step_counter()
Expand Down Expand Up @@ -3134,6 +3154,8 @@ def instance_norm(input,
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()
x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
hidden2 = fluid.layers.instance_norm(input=hidden1)
Expand Down Expand Up @@ -3269,6 +3291,7 @@ def data_norm(input,
.. code-block:: python

import paddle
paddle.enable_static()

x = paddle.randn(shape=[32,100])
hidden2 = paddle.static.nn.data_norm(input=x)
Expand Down Expand Up @@ -3451,6 +3474,8 @@ def layer_norm(input,

import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
x = fluid.data(name='x', shape=[-1, 32, 32], dtype='float32')
hidden1 = fluid.layers.layer_norm(input=x, begin_norm_axis=1)
place = fluid.CPUPlace()
Expand Down Expand Up @@ -3566,6 +3591,9 @@ def group_norm(input,
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()

data = fluid.data(name='data', shape=[None, 8, 32, 32], dtype='float32')
x = fluid.layers.group_norm(input=data, groups=4)
"""
Expand Down Expand Up @@ -3887,6 +3915,8 @@ def conv2d_transpose(input,
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3)
"""
Expand Down Expand Up @@ -4177,6 +4207,8 @@ def conv3d_transpose(input,
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32')
conv3d_transpose = fluid.layers.conv3d_transpose(input=data, num_filters=2, filter_size=3)
"""
Expand Down Expand Up @@ -4659,7 +4691,7 @@ def reduce_all(input, dim=None, keep_dim=False, name=None):
This OP computes the ``logical and`` of tensor elements over the given dimension, and output the result.

Args:
input (Variable): The input variable which is a Tensor or LoDTensor, the input data type should be `bool`.
input (Tensor): the input tensor, it's data type should be `bool`.
dim (list|int|optional): The dimension along which the logical and is computed.
If :attr:`None`, compute the logical and over all elements of
:attr:`input` and return a Tensor variable with a single element,
Expand All @@ -4672,27 +4704,28 @@ def reduce_all(input, dim=None, keep_dim=False, name=None):
will be named automatically. The default value is None.

Returns:
Variable, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims.
Tensor, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims.

Examples:
.. code-block:: python

import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np

# x is a bool Tensor variable with following elements:
# [[True, False]
# [True, True]]
x = layers.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
x = layers.cast(x, 'bool')
x = paddle.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
x = paddle.cast(x, 'bool')

out = layers.reduce_all(x) # False
out = layers.reduce_all(x, dim=0) # [True, False]
out = layers.reduce_all(x, dim=-1) # [False, True]
out = paddle.reduce_all(x) # False
out = paddle.reduce_all(x, dim=0) # [True, False]
out = paddle.reduce_all(x, dim=-1) # [False, True]
# keep_dim=False, x.shape=(2,2), out.shape=(2,)

out = layers.reduce_all(x, dim=1, keep_dim=True) # [[False], [True]]
out = paddle.reduce_all(x, dim=1, keep_dim=True) # [[False], [True]]
# keep_dim=True, x.shape=(2,2), out.shape=(2,1)

"""
Expand All @@ -4719,7 +4752,7 @@ def reduce_any(input, dim=None, keep_dim=False, name=None):
This OP computes the ``logical or`` of tensor elements over the given dimension, and output the result.

Args:
input (Variable): The input variable which is a Tensor or LoDTensor, the input data type should be `bool`.
input (Tensor): the input tensor, it's data type should be `bool`.
dim (list|int|optional): The dimension along which the logical and is computed.
If :attr:`None`, compute the logical and over all elements of
:attr:`input` and return a Tensor variable with a single element,
Expand All @@ -4728,30 +4761,31 @@ def reduce_any(input, dim=None, keep_dim=False, name=None):
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true. The default value is False.
name(str|None): A name for this layer(optional). If set None, the layer
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

Returns:
Variable, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims.
Tensor, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims.

Examples:
.. code-block:: python

import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np

# x is a bool Tensor variable with following elements:
# [[True, False]
# [False, False]]
x = layers.assign(np.array([[1, 0], [0, 0]], dtype='int32'))
x = layers.cast(x, 'bool')
x = paddle.assign(np.array([[1, 0], [0, 0]], dtype='int32'))
x = paddle.cast(x, 'bool')

out = layers.reduce_any(x) # True
out = layers.reduce_any(x, dim=0) # [True, False]
out = layers.reduce_any(x, dim=-1) # [True, False]
out = paddle.reduce_any(x) # True
out = paddle.reduce_any(x, dim=0) # [True, False]
out = paddle.reduce_any(x, dim=-1) # [True, False]
# keep_dim=False, x.shape=(2,2), out.shape=(2,)

out = layers.reduce_any(x, dim=1,
out = paddle.reduce_any(x, dim=1,
keep_dim=True) # [[True], [False]]
# keep_dim=True, x.shape=(2,2), out.shape=(2,1)

Expand Down Expand Up @@ -5613,6 +5647,8 @@ def im2sequence(input,
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 3, 32, 32],
dtype='float32')
output = fluid.layers.im2sequence(
Expand Down Expand Up @@ -5669,6 +5705,8 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
Examples:
>>> # for LodTensor inputs
>>> import paddle.fluid as fluid
>>> import paddle
>>> paddle.enable_static()
>>> x = fluid.data(name='x', shape=[9, 16],
>>> dtype='float32', lod_level=1)
>>> out = fluid.layers.row_conv(input=x, future_context_size=2)
Expand Down Expand Up @@ -5982,6 +6020,8 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()
global_step = fluid.layers.autoincreased_step_counter(
counter_name='@LR_DECAY_COUNTER@', begin=0, step=1)
"""
Expand Down Expand Up @@ -9730,6 +9770,8 @@ def prelu(x, mode, param_attr=None, name=None):
.. code-block:: python

import paddle.fluid as fluid
import paddle
paddle.enable_static()
from paddle.fluid.param_attr import ParamAttr
x = fluid.data(name="x", shape=[None,5,10,10], dtype="float32")
mode = 'channel'
Expand Down Expand Up @@ -14307,6 +14349,9 @@ def deformable_conv(input,
#deformable conv v2:

import paddle.fluid as fluid
import paddle
paddle.enable_static()

C_in, H_in, W_in = 3, 32, 32
filter_size, deformable_groups = 3, 1
data = fluid.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32')
Expand Down
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✅Congratulation! Your pull request passed all required CI. You could ask reviewer(s) to approve and merge. 🎉

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✅Congratulation! Your pull request passed all required CI. You could ask reviewer(s) to approve and merge. 🎉

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