/
sequence_lod.py
1485 lines (1180 loc) · 56.1 KB
/
sequence_lod.py
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# Copyright (c) 2022 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.
import paddle
from paddle.base.core import VarDesc
from paddle.base.data_feeder import check_type, check_variable_and_dtype
from paddle.base.framework import Variable, in_dygraph_mode
from paddle.base.layer_helper import LayerHelper
from paddle.base.layers.layer_function_generator import templatedoc
__all__ = []
@templatedoc()
def sequence_conv(
input,
num_filters,
filter_size=3,
filter_stride=1,
padding=True,
padding_start=None,
bias_attr=None,
param_attr=None,
act=None,
name=None,
):
r"""
Note:
Only receives Tensor as input. If your input is Tensor, please use conv2d Op.(base.layers.** :ref:`api_paddle_nn_functional_conv2d` ).
This operator receives input sequences with variable length and other convolutional
configuration parameters(num_filters, filter_size) to apply the convolution operation.
It fills all-zero padding data on both sides of the sequence by default to ensure that
the output is the same length as the input. You can customize the padding behavior by
configuring the parameter :attr:`padding\_start` .
**Warning:** the parameter :attr:`padding` take no effect and will be deprecated in the future.
.. code-block:: text
Here we will illustrate the details of the padding operation:
For a mini-batch of 2 variable lengths sentences, containing 3, and 1 time-steps:
Assumed input (X) is a [4, N] float Tensor, and for the sake of simplicity, we assume N=2.
input.data = [[1, 1],
[2, 2],
[3, 3],
[4, 4]]
This is to say that input (X) has 4 words and the dimension of each word
representation is 2.
* Case1:
If padding_start is -1 and filter_size is 3.
The length of padding data is calculated as follows:
up_pad_len = max(0, -padding_start) = 1
down_pad_len = max(0, filter_size + padding_start - 1) = 1
The output of the input sequence after padding is:
data_aftet_padding = [[0, 0, 1, 1, 2, 2],
[1, 1, 2, 2, 3, 3],
[2, 2, 3, 3, 0, 0],
[0, 0, 4, 4, 0, 0]]
It will be multiplied by the filter weight to get the final output.
Assume num_filters = 3
output.data = [[ 0.3234, -0.2334, 0.7433],
[ 0.5646, 0.9464, -0.1223],
[-0.1343, 0.5653, 0.4555],
[ 0.9954, -0.1234, -0.1234]]
output.shape = [4, 3] # 3 = num_filters
output.lod = [[0, 3, 4]] # Remain the same
Args:
input (Tensor): Tensor with shape :math:`(M, K)`, where M is the total time-step of mini-batch
and K is hidden_size of input. Only lod_level of 1 is supported. The data type should be float32 or
float64.
num_filters (int): the number of filters.
filter_size (int): the height of filter. Specified filter width is not supported, the width is
hidden_size by default. Default: 3.
filter_stride (int, optional): stride of the filter. Currently only supports :attr:`stride` = 1.
padding (bool, optional): the parameter :attr:`padding` take no effect and will be discarded in the
future. Currently, it will always pad input to make sure the length of the output is
the same as input whether :attr:`padding` is set true or false. Because the length of
input sequence may be shorter than :attr:`filter\_size`, which will cause the convolution
result to not be computed correctly. These padding data will not be trainable or updated
while training. Default: True.
padding_start (int): It is used to indicate the start index for padding the input
sequence, which can be negative. The negative number means to pad
:attr:`|padding_start|` time-steps of all-zero data at the beginning of each instance.
The positive number means to skip :attr:`padding_start` time-steps of each instance,
and it will pad :math:`filter\_size + padding\_start - 1` time-steps of all-zero data
at the end of the sequence to ensure that the output is the same length as the input.
If set None, the same length :math:`\\frac{filter\_size}{2}` of data will be filled
on both sides of the sequence. If set 0, the length of :math:`filter\_size - 1` data
is padded at the end of each input sequence. Default: None.
bias_attr (ParamAttr): To specify the bias parameter property. Default: None, which means the
default bias parameter property is used. See usage for details in :ref:`api_paddle_ParamAttr` .
param_attr (ParamAttr): To specify the weight parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in :ref:`api_paddle_ParamAttr` .
act (str): Activation to be applied to the output of this layer, such as tanh, softmax,
sigmoid, relu. For more information, please refer to :ref:`api_guide_activations_en` . Default: None.
name (str, 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: Tensor with the same length as input. The data type is float32 or float64, which is same as input.
Examples:
.. code-block:: python
>>> import paddle
>>> paddle.enable_static()
>>> x = paddle.static.data(name='x', shape=[-1, 10], dtype='float32', lod_level=1)
>>> x_conved = paddle.static.nn.sequence_conv(input=x, num_filters=2, filter_size=3, padding_start=-1)
"""
assert (
not in_dygraph_mode()
), "sequence layer is not supported in dygraph mode yet."
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'sequence_conv'
)
helper = LayerHelper('sequence_conv', **locals())
dtype = helper.input_dtype()
filter_shape = [filter_size * input.shape[1], num_filters]
filter_param = helper.create_parameter(
attr=helper.param_attr, shape=filter_shape, dtype=dtype
)
pre_bias = helper.create_variable_for_type_inference(dtype)
if padding_start is None:
padding_start = -int(filter_size // 2)
helper.append_op(
type='sequence_conv',
inputs={
'X': [input],
'Filter': [filter_param],
},
outputs={"Out": pre_bias},
attrs={
'contextStride': filter_stride,
'contextStart': padding_start,
'contextLength': filter_size,
},
)
pre_act = helper.append_bias_op(pre_bias)
return helper.append_activation(pre_act)
def sequence_softmax(input, use_cudnn=False, name=None):
r"""
Note:
The input type of the OP must be Tensor. For Tensor, use:** :ref:`api_paddle_nn_functional_softmax`
A LoD-tensor can be regarded as several sequences, and this op apply softmax algo on each sequence.
The shape of input Tensor can be :math:`[N, 1]` or :math:`[N]`, where :math:`N`
is the sum of the length of all sequences. Recommended usage: :math:`[N]`.
For i-th sequence in a mini-batch:
.. math::
Out(X[lod[i]:lod[i+1]], :) = \\frac{\exp(X[lod[i]:lod[i+1], :])}{\sum(\exp(X[lod[i]:lod[i+1], :]))}
For example, for a LoD-Tensor with 6 sequences ([3, 2, 4, 1, 2, 3] - sequence length list in order),
the lod in the runtime is [[0, 3, 5, 9, 10, 12, 15]],
then softmax will be computed among :math:`X[0:3,:],X[3:5,:],X[5:9,:],X[9:10,:],X[10:12,:],X[12:15,:]`,
and :math:`N` turns out to be 15.
.. code-block:: text
*Case 1:
Given:
input.data = [0.7, 1, 0.6,
1.5, 1.1,
1.2, 0.2, 0.6, 1.9,
3.1,
2.5, 0.8,
0.1, 2.4, 1.3]
input.lod = [[0, 3, 5, 9, 10, 12, 15]]
then:
output.data = [0.30724832, 0.41474187, 0.2780098,
0.59868765, 0.40131235,
0.2544242, 0.09359743, 0.13963096, 0.5123474,
1.,
0.84553474, 0.15446526,
0.06995796, 0.69777346, 0.23226859]
output.lod = [[0, 3, 5, 9, 10, 12, 15]]
Args:
input (Tensor):A Tensor with shape of :math:`[N, 1]` or :math:`[N]`, Recommended usage: :math:`[N]`.
Supported data types: float32, float64.
use_cudnn (bool, optional): Use cudnn kernel or not. Effective only when the cudnn version of the paddle
library is installed and GPU is used for training or reasoning. Default: False.
name (str, 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: A LoD-Tensor which has the same shape and data type with input.
Examples:
.. code-block:: python
>>> import paddle
>>> paddle.enable_static()
>>> x = paddle.static.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
>>> x_sequence_softmax_1 = paddle.static.nn.sequence_softmax(input=x)
>>> y = paddle.static.data(name='y', shape=[7],
... dtype='float32', lod_level=1)
>>> x_sequence_softmax_2 = paddle.static.nn.sequence_softmax(input=y)
"""
assert (
not in_dygraph_mode()
), "sequence layer is not supported in dygraph mode yet."
helper = LayerHelper('sequence_softmax', **locals())
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'sequence_softmax'
)
dtype = helper.input_dtype()
softmax_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="sequence_softmax",
inputs={"X": input},
outputs={"Out": softmax_out},
attrs={"use_cudnn": use_cudnn},
)
return softmax_out
def sequence_pool(input, pool_type, is_test=False, pad_value=0.0):
r"""
Note:
Only receives Tensor as input. If your input is Tensor, please use pool2d Op.(static.nn.** :ref:`api_paddle_nn_functional_avg_pool2d` or :ref:`api_paddle_nn_functional_max_pool2d` ).
This operator only supports Tensor as input. It will apply specified pooling
operation on the input Tensor. It pools features of all time-steps of each
sequence at the last lod_level using :attr:`pool_type` mentioned in the parameters,
such as sum, average, sqrt, etc.
It supports six pool_type:
- average: :math:`Out[i] = \\frac{\sum_i X_i}{N}`
- sum: :math:`Out[i] = \sum_jX_{ij}`
- sqrt: :math:`Out[i] = \\frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}`
- max: :math:`Out[i] = max(X_i)`
- last: :math:`Out[i] = X_{N_i}`
- first: :math:`Out[i]` = X_0
where :math:`N_i` is the length of i-th input sequence.
.. code-block:: text
Case 1:
input is a 1-level Tensor and pad_value = 0.0:
input.lod = [[0, 2, 5, 7, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
output is Tensor:
out.shape = [4, 1]
with condition out.shape[0] == len(x.lod[-1]) == 4
for different pool_type:
average: out.data = [[2.], [4.], [3.], [0.0]], where 2.=(1. + 3.)/2, 4.=(2. + 4. + 6.)/3, 3.=(5. + 1.)/2
sum : out.data = [[4.], [12.], [6.], [0.0]], where 4.=1. + 3., 12.=2. + 4. + 6., 6.=5. + 1.
sqrt : out.data = [[2.82], [6.93], [4.24], [0.0]], where 2.82=(1. + 3.)/sqrt(2), 6.93=(2. + 4. + 6.)/sqrt(3), 4.24=(5. + 1.)/sqrt(2)
max : out.data = [[3.], [6.], [5.], [0.0]], where 3.=max(1., 3.), 6.=max(2., 4., 6.), 5.=max(5., 1.)
last : out.data = [[3.], [6.], [1.], [0.0]], where 3.=last(1., 3.), 6.=last(2., 4., 6.), 1.=last(5., 1.)
first : out.data = [[1.], [2.], [5.], [0.0]], where 1.=first(1., 3.), 2.=first(2., 4., 6.), 5.=first(5., 1.)
and all above [0.0] at last of out.data is padding data.
Case 2:
input is a 2-level Tensor containing 3 sequences with length info [2, 0, 3],
where 0 means empty sequence.
The first sequence contains 2 subsequence with length info [1, 2];
The last sequence contains 3 subsequence with length info [1, 0, 3].
input.lod = [[0, 2, 2, 5], [0, 1, 3, 4, 4, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
If pool_typ = sum, it will apply pooling on last lod_level [0, 1, 3, 4, 4, 7]. pad_value = 0.0
output is Tensor:
out.shape= [5, 1]
out.lod = [[0, 2, 2, 5]]
where out.shape[0] == len(x.lod[-1]) == 5
sum: out.data = [[1.], [5.], [4.], [0.0], [12.]]
where 1.=1., 5.=3. + 2., 4.=4., 0.0=pad_value, 12.=6. + 5. + 1.
Args:
input (variable): Tensor with lod_level no more than 2. The data type should be float32 or float64.
pool_type (str): The pooling type that supports average, sum, sqrt, max, last or first.
is_test (bool): Only works when :attr:`pool_type` is max. If set False, a temporary Tenosr maxIndex is
created to record the index information corresponding to the maximum value, which is used for backward
gradient calculation in the training phase. Default: False.
pad_value (float): Used to pad the pooling result for empty input sequence. Default: 0.0
Returns:
Tensor: Tensor after pooling with data type float32 or float64.
Examples:
.. code-block:: python
>>> import paddle
>>> paddle.enable_static()
>>> x = paddle.static.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
>>> avg_x = paddle.static.nn.sequence_pool(input=x, pool_type='average')
>>> sum_x = paddle.static.nn.sequence_pool(input=x, pool_type='sum')
>>> sqrt_x = paddle.static.nn.sequence_pool(input=x, pool_type='sqrt')
>>> max_x = paddle.static.nn.sequence_pool(input=x, pool_type='max')
>>> last_x = paddle.static.nn.sequence_pool(input=x, pool_type='last')
>>> first_x = paddle.static.nn.sequence_pool(input=x, pool_type='first')
"""
assert (
not in_dygraph_mode()
), "sequence layer is not supported in dygraph mode yet."
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'sequence_pool'
)
helper = LayerHelper('sequence_pool', **locals())
dtype = helper.input_dtype()
pool_out = helper.create_variable_for_type_inference(dtype)
max_index = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="sequence_pool",
inputs={"X": input},
outputs={"Out": pool_out, "MaxIndex": max_index},
attrs={
"pooltype": pool_type.upper(),
"is_test": is_test,
"pad_value": pad_value,
},
)
# when pool_type is max, variable max_index is initialized,
# so we stop the gradient explicitly here
if pool_type == 'max':
max_index.stop_gradient = True
return pool_out
@templatedoc()
def sequence_concat(input, name=None):
"""
Note:
Only receives Tensor as input. If your input is Tensor, please use concat Op.(static.nn.** :ref:`api_paddle_concat` ).
This operator only supports Tensor as input. It concatenates the multiple Tensor from input by the LoD information,
and outputs the concatenated Tensor.
.. code-block:: text
input is a list of Tensor:
input = [x1, x2]
where:
x1.lod = [[0, 3, 5]]
x1.data = [[1], [2], [3], [4], [5]]
x1.shape = [5, 1]
x2.lod = [[0, 2, 4]]
x2.data = [[6], [7], [8], [9]]
x2.shape = [4, 1]
and should satisfy: len(x1.lod[0]) == len(x2.lod[0])
output is Tensor:
out.lod = [[0, 3+2, 5+4]]
out.data = [[1], [2], [3], [6], [7], [4], [5], [8], [9]]
out.shape = [9, 1]
Args:
input(list of Tensor): List of Tensor to be concatenated. The length of each Tensor should be same.
The data type can be float32, float64 or int64.
name(str, 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: Output the concatenated Tensor. The data type is same as input.
Examples:
.. code-block:: python
>>> import paddle
>>> paddle.enable_static()
>>> x = paddle.static.data(name='x', shape=[-1, 10], dtype='float32', lod_level=1)
>>> y = paddle.static.data(name='y', shape=[-1, 10], dtype='float32', lod_level=1)
>>> out = paddle.static.nn.sequence_concat(input=[x, y])
"""
assert (
not in_dygraph_mode()
), "sequence layer is not supported in dygraph mode yet."
helper = LayerHelper('sequence_concat', **locals())
check_type(
input, 'input', list, 'paddle.static.nn.sequence_lod.sequence_concat'
)
for i, input_x in enumerate(input):
check_variable_and_dtype(
input_x,
'input[' + str(i) + ']',
['int64', 'float32', 'float64'],
'paddle.static.nn.sequence_lod.sequence_concat',
)
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='sequence_concat', inputs={'X': input}, outputs={'Out': [out]}
)
return out
def sequence_first_step(input):
"""
Only supports Tensor as input. Given the input Tensor, it will
select first time-step feature of each sequence as output.
.. code-block:: text
Case 1:
input is 1-level Tensor:
input.lod = [[0, 2, 5, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
output is a Tensor:
out.shape = [3, 1]
out.shape[0] == len(x.lod[-1]) == 3
out.data = [[1.], [2.], [5.]], where 1.=first(1., 3.), 2.=first(2., 4., 6.), 5.=first(5., 1.)
Case 2:
input is a 2-level Tensor containing 3 sequences with length info [2, 0, 3],
where 0 means empty sequence.
The first sequence contains 2 subsequence with length info [1, 2];
The last sequence contains 3 subsequence with length info [1, 0, 3].
input.lod = [[0, 2, 2, 5], [0, 1, 3, 4, 4, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
It will apply pooling on last lod_level [0, 1, 3, 4, 4, 7]. pad_value = 0.0
output is a Tensor:
out.shape= [5, 1]
out.lod = [[0, 2, 2, 5]]
out.shape[0] == len(x.lod[-1]) == 5
out.data = [[1.], [3.], [4.], [0.0], [6.]]
where 1.=first(1.), 3.=first(3., 2.), 4.=first(4.), 0.0 = pad_value, 6.=first(6., 5., 1.)
Args:
input(Tensor): Tensor with lod_level no more than 2. The data type should be float32 or float64.
Returns:
Tensor: Tensor consist of the sequence's first step vector. The data type is float32 or float64.
Examples:
.. code-block:: python
>>> import paddle
>>> paddle.enable_static()
>>> x = paddle.static.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
>>> x_first_step = paddle.static.nn.sequence_first_step(input=x)
"""
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'sequence_first_step'
)
return sequence_pool(input=input, pool_type="first")
def sequence_last_step(input):
"""
Only supports Tensor as input. Given the input Tensor, it will
select last time-step feature of each sequence as output.
.. code-block:: text
Case 1:
input is 1-level Tensor:
input.lod = [[0, 2, 5, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
output is a Tensor:
out.shape = [3, 1]
out.shape[0] == len(x.lod[-1]) == 3
out.data = [[3.], [6.], [1.]], where 3.=last(1., 3.), 6.=last(2., 4., 6.), 1.=last(5., 1.)
Case 2:
input is a 2-level Tensor containing 3 sequences with length info [2, 0, 3],
where 0 means empty sequence.
The first sequence contains 2 subsequence with length info [1, 2];
The last sequence contains 3 subsequence with length info [1, 0, 3].
input.lod = [[0, 2, 2, 5], [0, 1, 3, 4, 4, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
It will apply pooling on last lod_level [0, 1, 3, 4, 4, 7]. pad_value = 0.0
output is a Tensor:
out.shape= [5, 1]
out.lod = [[0, 2, 2, 5]]
out.shape[0] == len(x.lod[-1]) == 5
out.data = [[1.], [2.], [4.], [0.0], [1.]]
where 1.=last(1.), 2.=last(3., 2.), 4.=last(4.), 0.0 = pad_value, 1=last(6., 5., 1.)
Args:
input(Tensor): Tensor with lod_level no more than 2. The data type should be float32.
Returns:
Tensor: Tensor consist of the sequence's last step vector. The data type is float32.
Examples:
.. code-block:: python
>>> import paddle
>>> paddle.enable_static()
>>> x = paddle.static.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
>>> x_last_step = paddle.static.nn.sequence_last_step(input=x)
"""
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'sequence_last_step'
)
return sequence_pool(input=input, pool_type="last")
def sequence_slice(input, offset, length, name=None):
"""
**Sequence Slice Layer**
The layer crops a subsequence from given sequence with given start
offset and subsequence length.
It only supports sequence data (Tensor with lod_level equal to 1).
.. code-block:: text
- Case:
Given the input Tensor **input**:
input.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]],
input.lod = [[3, 2]],
input.dims = (5, 2),
with offset.data = [[0], [1]] and length.data = [[2], [1]],
the output Tensor will be
out.data = [[a1, a2], [b1, b2], [e1, e2]],
out.lod = [[2, 1]],
out.dims = (3, 2).
Note:
The first dimension size of **input**, **offset** and **length**
should be equal. The **offset** should start from 0.
Args:
input(Tensor): Tensor, The input Tensor which consists of the complete
sequences.The data type can be float32, float64, int32 or int64
offset(Tensor): Tensor, The offset to slice each sequence. The data
type is int32 or int64.
length(Tensor): Tensor, The length of each subsequence. The data
type is int32 or int64.
name(str|None): 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: The output subsequences.
Examples:
.. code-block:: python
>>> import paddle
>>> paddle.enable_static()
>>> import numpy as np
>>> seqs = paddle.static.data(name='x', shape=[10, 5],
... dtype='float32', lod_level=1)
>>> offset = paddle.assign(np.array([[0, 1]]).astype("int32"))
>>> length = paddle.assign(np.array([[2, 1]]).astype("int32"))
... subseqs = paddle.static.nn.sequence_slice(input=seqs, offset=offset,
... length=length)
"""
assert (
not in_dygraph_mode()
), "sequence layer is not supported in dygraph mode yet."
helper = LayerHelper("sequence_slice", **locals())
check_variable_and_dtype(
input,
'input',
['float32', 'float64', 'int32', 'int64'],
'sequence_slice',
)
check_variable_and_dtype(
offset, 'offset', ['int32', 'int64'], 'sequence_slice'
)
check_variable_and_dtype(
length, 'length', ['int32', 'int64'], 'sequence_slice'
)
dtype = helper.input_dtype()
out = helper.create_variable_for_type_inference(dtype)
offset.stop_gradient = True
length.stop_gradient = True
helper.append_op(
type="sequence_slice",
inputs={"X": input, "Offset": offset, "Length": length},
outputs={"Out": out},
)
return out
def sequence_expand(x, y, ref_level=-1, name=None):
r"""
Sequence Expand Layer. This layer will expand the input variable ``x`` \
according to specified level ``ref_level`` lod of ``y``. Please note that \
the lod level of ``x`` is at most 1. If the lod level of ``x`` is 1, than \
the size of lod of ``x`` must be equal to the length of ``ref_level`` lod \
of ``y``. If the lod level of ``x`` is 0, then the first dim of ``x`` should \
be equal to the size of ``ref_level`` of ``y``. The rank of **x** is at least 2. \
When rank of ``x`` is greater than 2, then it would be viewed as a 2-D tensor.
Note:
Please note that the input ``x`` should be Tensor or Tensor, \
and input ``y`` must be Tensor.
**Following examples will explain how sequence_expand works:**
.. code-block:: text
Case 1
Consider 2 sequences [a][b] and [c][d], now we want to expand them to [a][b], [a][b], [c][d] and [c][d].
Sequence [a][b] expand twice and [c][d] expands twice, so the lod which according to is [2, 2].
Input x is a 1-level Tensor:
x.lod = [[2, 2]] #lod based on length may be easier to understand
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]
input y is a Tensor:
y.lod = [[2, 2], #the 0th level lod, according to this level
[3, 3, 1, 1]] #the 1st level lod, it has nothing to do with this level
ref_level: 0
then output is a 1-level Tensor out:
out.lod = [[2, 2, 2, 2]] #lod based on offset
out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
out.dims = [8, 1]
Case 2
Consider 3 sequences [a], [b], [c], now we want to expand them to [a][a], [c][c][c].
It's obvious that the lod info of expanded sequences is [2, 0, 3].
x is a Tensor:
x.data = [[a], [b], [c]]
x.dims = [3, 1]
y is a Tensor:
y.lod = [[2, 0, 3]]
ref_level: -1
then output is a 1-level Tensor:
out.data = [[a], [a], [c], [c], [c]]
out.dims = [5, 1]
Args:
x (Tensor): The input variable which is a Tensor or Tensor, with the \
dims ``[M, K]``. The lod level is at most 1. The data type should be \
float32, float64, int32 or int64.
y (Tensor): The input variable which is a Tensor, the lod level is \
at least 1.
ref_level (int): Lod level of ``y`` to be referred by ``x``. If set to -1, \
refer the last level of lod.
name(str, optional): For detailed information, please refer \
to :ref:`api_guide_Name`. Usually name is no need to set and \
None by default.
Returns:
Tensor, The expanded variable which is a Tensor, with dims ``[N, K]``. \
``N`` depends on the lod info of ``x`` and ``y``. \
The data type is same as input.
Examples:
.. code-block:: python
>>> import paddle
>>> from paddle import base
>>> paddle.enable_static()
>>> import numpy as np
>>> x = paddle.static.data(name='x', shape=[4, 1], dtype='float32')
>>> y = paddle.static.data(name='y', shape=[8, 1],
... dtype='float32', lod_level=1)
>>> out = paddle.static.nn.sequence_expand(x=x, y=y, ref_level=0)
>>> exe = paddle.static.Executor(base.CPUPlace())
>>> place = paddle.CPUPlace()
>>> np_data = np.array([[1], [2], [3], [4]]).astype('float32')
>>> x_lod_tensor = base.create_lod_tensor(np_data, [[2, 2]], place)
>>> print(x_lod_tensor)
- lod: {{0, 2, 4}}
- place: Place(cpu)
- shape: [4, 1]
- layout: NCHW
- dtype: float32
- data: [1 2 3 4]
>>> np_data = np.array([[1], [2], [3], [4], [5], [6], [7], [8]]).astype('float32')
>>> y_lod_tensor = base.create_lod_tensor(np_data, [[2, 2], [3,3,1,1]], place)
>>> print(y_lod_tensor)
- lod: {{0, 2, 4}{0, 3, 6, 7, 8}}
- place: Place(cpu)
- shape: [8, 1]
- layout: NCHW
- dtype: float32
- data: [1 2 3 4 5 6 7 8]
>>> out_main = exe.run(base.default_main_program(),
... feed={'x': x_lod_tensor, 'y': y_lod_tensor},
... fetch_list=[out], return_numpy=False)
>>> print(out_main[0])
- lod: {{0, 2, 4, 6, 8}}
- place: Place(cpu)
- shape: [8, 1]
- layout: NCHW
- dtype: float32
- data: [1 2 1 2 3 4 3 4]
"""
assert (
not in_dygraph_mode()
), "sequence layer is not supported in dygraph mode yet."
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'sequence_expand'
)
helper = LayerHelper('sequence_expand', **locals())
dtype = helper.input_dtype(input_param_name='x')
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sequence_expand',
inputs={'X': x, 'Y': y},
outputs={'Out': tmp},
attrs={'ref_level': ref_level},
)
return tmp
def sequence_expand_as(x, y, name=None):
r"""
Sequence Expand As Layer. This OP will expand the input variable ``x`` \
according to the zeroth level lod of ``y``. Current implementation requires \
the level number of ``y``'s lod must be 1, and the first dimension of \
``x`` should be equal to the size of ``y``'s zeroth level lod, thus \
the expanded Tensor has the same lod info as ``y``. The expanded result \
has nothing to do with ``x``'s lod, so the lod of Input(X) is not considered.
Note:
Please note that the input ``x`` should be Tensor or Tensor, \
and input ``y`` must be Tensor.
Following examples will explain how sequence_expand_as works:
.. code-block:: text
Case 1:
Consider 4 sequences [a], [b], [c], [d], now we want to expand them to [a][a][a], [b][b][b], [c] and [d].
It's obvious that the lod info of expanded sequences is [0, 3, 6, 7, 8].
Given a 1-level Tensor ``x``:
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]
and input ``y``
y.lod = [[3, 3, 1, 1]] #lod based on length may be easier to understand
then we get 1-level Tensor out:
Out.lod = [[0, 3, 6, 7, 8]] #based on offset
Out.data = [[a], [a], [a], [b], [b], [b], [c], [d]]
Out.dims = [8, 1]
Case 2:
Given a common Tensor ``x``:
x.data = [[a, b], [c, d], [e, f]]
x.dims = [3, 2]
and input ``y``:
y.lod = [[0, 2, 3, 6]]
then we get a 1-level Tensor:
out.lod = [[0, 2, 3, 6]]
out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
out.dims = [6, 2]
Args:
x (Tensor): The input variable which is a Tensor or Tensor, with the \
dims ``[M, K]``. The data type should be float32, float64, int32 \
or int64.
y (Tensor): The input variable which is a Tensor with 1-level lod.
name (str, optional): For detailed information, please refer \
to :ref:`api_guide_Name`. Usually name is no need to set and \
None by default.
Returns:
Tensor, The expanded variable which is a Tensor with the dims ``[N, K]``. \
``N`` depends on the lod of ``y``, and the lod level must be 1. \
The data type is same as input.
Examples:
.. code-block:: python
>>> import paddle
>>> import paddle.base as base
>>> paddle.enable_static()
>>> import numpy as np
>>> x = paddle.static.data(name='x', shape=[4, 1], dtype='float32')
>>> y = paddle.static.data(name='y', shape=[8, 1], dtype='float32', lod_level=1)
>>> out = paddle.static.nn.sequence_expand_as(x=x, y=y)
>>> exe = base.Executor(base.CPUPlace())
>>> place = base.CPUPlace()
>>> np_data = np.array([[1], [2], [3], [4]]).astype('float32')
>>> x_lod_tensor = base.create_lod_tensor(np_data, [[2, 2]], place)
>>> print(x_lod_tensor)
- lod: {{0, 2, 4}}
- place: Place(cpu)
- shape: [4, 1]
- layout: NCHW
- dtype: float32
- data: [1 2 3 4]
>>> np_data = np.array([[1], [2], [3], [4], [5], [6], [7], [8]]).astype('float32')
>>> y_lod_tensor = base.create_lod_tensor(np_data, [[3,3,1,1]], place)
>>> print(y_lod_tensor)
- lod: {{0, 3, 6, 7, 8}}
- place: Place(cpu)
- shape: [8, 1]
- layout: NCHW
- dtype: float32
- data: [1 2 3 4 5 6 7 8]
>>> out_main = exe.run(base.default_main_program(),
... feed={'x': x_lod_tensor, 'y': y_lod_tensor},
... fetch_list=[out], return_numpy=False)
>>> print(out_main[0])
- lod: {{0, 3, 6, 7, 8}}
- place: Place(cpu)
- shape: [8, 1]
- layout: NCHW
- dtype: float32
- data: [1 1 1 2 2 2 3 4]
"""
assert (
not in_dygraph_mode()
), "sequence layer is not supported in dygraph mode yet."
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'sequence_expand_as'
)
check_type(y, 'y', Variable, 'sequence_expand_as')
helper = LayerHelper('sequence_expand_as', **locals())
dtype = helper.input_dtype(input_param_name='x')
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sequence_expand_as', inputs={'X': x, 'Y': y}, outputs={'Out': tmp}
)
return tmp
def sequence_pad(x, pad_value, maxlen=None, name=None):
r"""
This layer padding the sequences in a same batch to a common length (according
to ``maxlen``). The padding value is defined by ``pad_value``, and will be
appended to the tail of sequences. The result is a Python tuple ``(Out, Length)``:
the Tensor ``Out`` is the padded sequences, and Tensor ``Length`` is
the length information of input sequences. For removing padding data (unpadding operation), See :ref:`api_paddle_static_nn_sequence_unpad`.
Note:
Please note that the input ``x`` should be Tensor.
.. code-block:: text
Case 1:
Given input 1-level Tensor x:
x.lod = [[0, 2, 5]]
x.data = [[a],[b],[c],[d],[e]]
pad_value:
pad_value.data = [0]
maxlen = 4
the output tuple (Out, Length):
Out.data = [[[a],[b],[0],[0]],[[c],[d],[e],[0]]]
Length.data = [2, 3] #Original sequences length
Case 2:
Given input 1-level Tensor x:
x.lod = [[0, 2, 5]]
x.data = [[a1,a2],[b1,b2],[c1,c2],[d1,d2],[e1,e2]]
pad_value:
pad_value.data = [0]
default maxlen = None, (the virtual value is 3, according to the shape of x)
the output tuple (Out, Length):
Out.data = [[[a1,a2],[b1,b2],[0,0]],[[c1,c2],[d1,d2],[e1,e2]]]
Length.data = [2, 3]
Case 3:
Given input 1-level Tensor x:
x.lod = [[0, 2, 5]]
x.data = [[a1,a2],[b1,b2],[c1,c2],[d1,d2],[e1,e2]]
pad_value:
pad_value.data = [p1,p2]
default maxlen = None, (the virtual value is 3)
get tuple (Out, Length):
Out.data = [[[a1,a2],[b1,b2],[p1,p2]],[[c1,c2],[d1,d2],[e1,e2]]]
Length.data = [2, 3]
Args:
x (Tensor): Input 1-level Tensor with dims ``[M, K]``. The batch \
size is described by lod infor (the number of sequences ). \
The data type should be float32, float64, int8, int32 or int64.
pad_value (Tensor): Padding value. It can be a scalar or a 1D tensor \
with length ``K``. If it's a scalar, it will be automatically broadcasted \
to a Tensor. The data type should be as same as ``x``.
maxlen (int, optional): The length of padded sequences, None by default. \
When it is None, all sequences will be padded up to the length of the \
longest one among them; when it a certain positive value, it must be \
greater than the length of the longest original sequence.
name (str, optional): For detailed information, please refer \
to :ref:`api_guide_Name`. Usually name is no need to set and \
None by default.
Returns:
tuple, A Python tuple (Out, Length): the 1st is a 0 level Tensor \
``Out``, with the shape ``[batch_size, maxlen, K]``; the second is the original \
sequences length infor ``Length``, which should be a 0-level 1D Tensor. \
The size of ``Length`` is equal to batch size, and the data type is int64.
Examples:
.. code-block:: python
>>> import paddle
>>> paddle.enable_static()
>>> import paddle.base as base
>>> import numpy
>>> x = paddle.static.data(name='x', shape=[10, 5], dtype='float32', lod_level=1)
>>> pad_value = paddle.assign(
... numpy.array([0.0], dtype=numpy.float32))