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Paddle/python/paddle/fluid/layers/rnn.py

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# Copyright (c) 2019 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 sys
from functools import partial, reduce
from . import nn
from . import tensor
from . import control_flow
from . import utils
from . import sequence_lod
from .utils import *
from .. import core
from ..framework import default_main_program
from ..data_feeder import convert_dtype
from ..layer_helper import LayerHelper
from ..framework import in_dygraph_mode
from ..param_attr import ParamAttr
__all__ = [
'RNNCell',
'GRUCell',
'LSTMCell',
'Decoder',
'BeamSearchDecoder',
'rnn',
'dynamic_decode',
'DecodeHelper',
'TrainingHelper',
'GreedyEmbeddingHelper',
'SampleEmbeddingHelper',
'BasicDecoder',
'dynamic_lstm',
'dynamic_lstmp',
'dynamic_gru',
'gru_unit',
'lstm_unit',
'lstm',
'beam_search',
'beam_search_decode',
]
class RNNCell(object):
"""
RNNCell is the base class for abstraction representing the calculations
mapping the input and state to the output and new state. It is suitable to
and mostly used in RNN.
"""
def call(self, inputs, states, **kwargs):
"""
Every cell must implement this method to do the calculations mapping the
inputs and states to the output and new states.
To be more flexible, both inputs and states can be a tensor variable or
a nested structure (list|tuple|namedtuple|dict) of tensor variable, that
is, a (possibly nested structure of) tensor variable[s].
Parameters:
inputs: A (possibly nested structure of) tensor variable[s].
states: A (possibly nested structure of) tensor variable[s].
**kwargs: Additional keyword arguments, provided by the caller.
Returns:
tuple: outputs and new_states pair. outputs and new_states both \
can be nested structure of tensor variables. new_states must \
have the same structure with states.
"""
raise NotImplementedError("RNNCell must implent the call function.")
def __call__(self, inputs, states, **kwargs):
return self.call(inputs, states, **kwargs)
def get_initial_states(self,
batch_ref,
shape=None,
dtype=None,
init_value=0,
batch_dim_idx=0):
"""
Generate initialized states according to provided shape, data type and
value.
Parameters:
batch_ref: A (possibly nested structure of) tensor variable[s].
The first dimension of the tensor will be used as batch size to
initialize states.
shape: A (possibly nested structure of) shape[s], where a shape is
represented as a list/tuple of integer). -1(for batch size) will
beautomatically inserted if shape is not started with it. If None,
property `state_shape` will be used. The default value is None.
dtype: A (possibly nested structure of) data type[s]. The structure
must be same as that of `shape`, except when all tensors' in states
has the same data type, a single data type can be used. If None and
property `cell.state_shape` is not available, float32 will be used
as the data type. The default value is None.
init_value: A float value used to initialize states.
batch_dim_idx: An integer indicating which dimension of the tensor in
inputs represents batch size. The default value is 0.
Returns:
Variable: tensor variable[s] packed in the same structure provided \
by shape, representing the initialized states.
"""
# TODO: use inputs and batch_size
batch_ref = flatten(batch_ref)[0]
def _is_shape_sequence(seq):
if sys.version_info < (3, ):
integer_types = (
int,
long, )
else:
integer_types = (int, )
"""For shape, list/tuple of integer is the finest-grained objection"""
if (isinstance(seq, list) or isinstance(seq, tuple)):
if reduce(lambda flag, x: isinstance(x, integer_types) and flag,
seq, True):
return False
# TODO: Add check for the illegal
if isinstance(seq, dict):
return True
return (isinstance(seq, collections.Sequence) and
not isinstance(seq, six.string_types))
class Shape(object):
def __init__(self, shape):
self.shape = shape if shape[0] == -1 else ([-1] + list(shape))
# nested structure of shapes
states_shapes = self.state_shape if shape is None else shape
is_sequence_ori = utils.is_sequence
utils.is_sequence = _is_shape_sequence
states_shapes = map_structure(lambda shape: Shape(shape), states_shapes)
utils.is_sequence = is_sequence_ori
# nested structure of dtypes
try:
states_dtypes = self.state_dtype if dtype is None else dtype
except NotImplementedError: # use fp32 as default
states_dtypes = "float32"
if len(flatten(states_dtypes)) == 1:
dtype = flatten(states_dtypes)[0]
states_dtypes = map_structure(lambda shape: dtype, states_shapes)
init_states = map_structure(
lambda shape, dtype: tensor.fill_constant_batch_size_like(
input=batch_ref,
shape=shape.shape,
dtype=dtype,
value=init_value,
input_dim_idx=batch_dim_idx), states_shapes, states_dtypes)
return init_states
@property
def state_shape(self):
"""
Abstract method (property).
Used to initialize states.
A (possibly nested structure of) shape[s], where a shape is represented
as a list/tuple of integers (-1 for batch size would be automatically
inserted into a shape if shape is not started with it).
Not necessary to be implemented if states are not initialized by
`get_initial_states` or the `shape` argument is provided when using
`get_initial_states`.
"""
raise NotImplementedError(
"Please add implementaion for `state_shape` in the used cell.")
@property
def state_dtype(self):
"""
Abstract method (property).
Used to initialize states.
A (possibly nested structure of) data types[s]. The structure must be
same as that of `shape`, except when all tensors' in states has the same
data type, a single data type can be used.
Not necessary to be implemented if states are not initialized
by `get_initial_states` or the `dtype` argument is provided when using
`get_initial_states`.
"""
raise NotImplementedError(
"Please add implementaion for `state_dtype` in the used cell.")
class GRUCell(RNNCell):
"""
Gated Recurrent Unit cell. It is a wrapper for
`fluid.contrib.layers.rnn_impl.BasicGRUUnit` to make it adapt to RNNCell.
The formula used is as follow:
.. math::
u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
h_t & = u_t \odot h_{t-1} + (1-u_t) \odot \\tilde{h_t}
For more details, please refer to `Learning Phrase Representations using
RNN Encoder Decoder for Statistical Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_
Examples:
.. code-block:: python
import paddle.fluid.layers as layers
cell = layers.GRUCell(hidden_size=256)
"""
def __init__(self,
hidden_size,
param_attr=None,
bias_attr=None,
gate_activation=None,
activation=None,
dtype="float32",
name="GRUCell"):
"""
Constructor of GRUCell.
Parameters:
hidden_size (int): The hidden size in the GRU cell.
param_attr(ParamAttr, optional): The parameter attribute for the learnable
weight matrix. Default: None.
bias_attr (ParamAttr, optional): The parameter attribute for the bias
of GRU. Default: None.
gate_activation (function, optional): The activation function for :math:`act_g`.
Default: `fluid.layers.sigmoid`.
activation (function, optional): The activation function for :math:`act_c`.
Default: `fluid.layers.tanh`.
dtype(string, optional): The data type used in this cell. Default float32.
name(string, optional) : The name scope used to identify parameters and biases.
"""
self.hidden_size = hidden_size
from .. import contrib # TODO: resolve recurrent import
self.gru_unit = contrib.layers.rnn_impl.BasicGRUUnit(
name, hidden_size, param_attr, bias_attr, gate_activation,
activation, dtype)
def call(self, inputs, states):
"""
Perform calculations of GRU.
Parameters:
inputs(Variable): A tensor with shape `[batch_size, input_size]`,
corresponding to :math:`x_t` in the formula. The data type
should be float32.
states(Variable): A tensor with shape `[batch_size, hidden_size]`.
corresponding to :math:`h_{t-1}` in the formula. The data type
should be float32.
Returns:
tuple: A tuple( :code:`(outputs, new_states)` ), where `outputs` and \
`new_states` is the same tensor shaped `[batch_size, hidden_size]`, \
corresponding to :math:`h_t` in the formula. The data type of the \
tensor is same as that of `states`.
"""
new_hidden = self.gru_unit(inputs, states)
return new_hidden, new_hidden
@property
def state_shape(self):
"""
The `state_shape` of GRUCell is a shape `[hidden_size]` (-1 for batch
size would be automatically inserted into shape). The shape corresponds
to :math:`h_{t-1}`.
"""
return [self.hidden_size]
class LSTMCell(RNNCell):
"""
Long-Short Term Memory cell. It is a wrapper for
`fluid.contrib.layers.rnn_impl.BasicLSTMUnit` to make it adapt to RNNCell.
The formula used is as follow:
.. math::
i_{t} & = act_g(W_{x_{i}}x_{t} + W_{h_{i}}h_{t-1} + b_{i})
f_{t} & = act_g(W_{x_{f}}x_{t} + W_{h_{f}}h_{t-1} + b_{f} + forget\\_bias)
c_{t} & = f_{t}c_{t-1} + i_{t} act_c (W_{x_{c}}x_{t} + W_{h_{c}}h_{t-1} + b_{c})
o_{t} & = act_g(W_{x_{o}}x_{t} + W_{h_{o}}h_{t-1} + b_{o})
h_{t} & = o_{t} act_c (c_{t})
For more details, please refer to `RECURRENT NEURAL NETWORK REGULARIZATION <http://arxiv.org/abs/1409.2329>`_
Examples:
.. code-block:: python
import paddle.fluid.layers as layers
cell = layers.LSTMCell(hidden_size=256)
"""
def __init__(self,
hidden_size,
param_attr=None,
bias_attr=None,
gate_activation=None,
activation=None,
forget_bias=1.0,
dtype="float32",
name="LSTMCell"):
"""
Constructor of LSTMCell.
Parameters:
hidden_size (int): The hidden size in the LSTM cell.
param_attr(ParamAttr, optional): The parameter attribute for the learnable
weight matrix. Default: None.
bias_attr (ParamAttr, optional): The parameter attribute for the bias
of LSTM. Default: None.
gate_activation (function, optional): The activation function for :math:`act_g`.
Default: 'fluid.layers.sigmoid'.
activation (function, optional): The activation function for :math:`act_h`.
Default: 'fluid.layers.tanh'.
forget_bias(float, optional): forget bias used when computing forget gate.
Default 1.0
dtype(string, optional): The data type used in this cell. Default float32.
name(string, optional) : The name scope used to identify parameters and biases.
"""
self.hidden_size = hidden_size
from .. import contrib # TODO: resolve recurrent import
self.lstm_unit = contrib.layers.rnn_impl.BasicLSTMUnit(
name, hidden_size, param_attr, bias_attr, gate_activation,
activation, forget_bias, dtype)
def call(self, inputs, states):
"""
Perform calculations of LSTM.
Parameters:
inputs(Variable): A tensor with shape `[batch_size, input_size]`,
corresponding to :math:`x_t` in the formula. The data type
should be float32.
states(Variable): A list of containing two tensors, each shaped
`[batch_size, hidden_size]`, corresponding to :math:`h_{t-1}, c_{t-1}`
in the formula. The data type should be float32.
Returns:
tuple: A tuple( :code:`(outputs, new_states)` ), where `outputs` is \
a tensor with shape `[batch_size, hidden_size]`, corresponding \
to :math:`h_{t}` in the formula; `new_states` is a list containing \
two tenser variables shaped `[batch_size, hidden_size]`, corresponding \
to :math:`h_{t}, c_{t}` in the formula. The data type of these \
tensors all is same as that of `states`.
"""
pre_hidden, pre_cell = states
new_hidden, new_cell = self.lstm_unit(inputs, pre_hidden, pre_cell)
return new_hidden, [new_hidden, new_cell]
@property
def state_shape(self):
"""
The `state_shape` of LSTMCell is a list with two shapes: `[[hidden_size], [hidden_size]]`
(-1 for batch size would be automatically inserted into shape). These two
shapes correspond to :math:`h_{t-1}` and :math:`c_{t-1}` separately.
"""
return [[self.hidden_size], [self.hidden_size]]
def rnn(cell,
inputs,
initial_states=None,
sequence_length=None,
time_major=False,
is_reverse=False,
**kwargs):
"""
rnn creates a recurrent neural network specified by RNNCell `cell`,
which performs :code:`cell.call()` repeatedly until reaches to the maximum
length of `inputs`.
Parameters:
cell(RNNCell): An instance of `RNNCell`.
inputs(Variable): A (possibly nested structure of) tensor variable[s].
The shape of tensor should be `[batch_size, sequence_length, ...]`
for `time_major == False` or `[sequence_length, batch_size, ...]`
for `time_major == True`. It represents the inputs to be unrolled
in RNN.
initial_states(Variable, optional): A (possibly nested structure of)
tensor variable[s], representing the initial state for RNN.
If not provided, `cell.get_initial_states` would be used to produce
the initial state. Default None.
sequence_length(Variable, optional): A tensor with shape `[batch_size]`.
It stores real length of each instance, thus enables users to extract
the last valid state when past a batch element's sequence length for
correctness. If not provided, the paddings would be treated same as
non-padding inputs. Default None.
time_major(bool, optional): Indicate the data layout of Tensor included
in `input` and `output` tensors. If `False`, the data layout would
be batch major with shape `[batch_size, sequence_length, ...]`. If
`True`, the data layout would be time major with shape
`[sequence_length, batch_size, ...]`. Default: `False`.
is_reverse(bool, optional): Indicate whether to calculate in the reverse
order of input sequences. Default: `False`.
**kwargs: Additional keyword arguments. Arguments passed to `cell.call`.
Returns:
tuple: A tuple( :code:`(final_outputs, final_states)` ) including the final \
outputs and states, both are Tensor or nested structure of Tensor. \
`final_outputs` has the same structure and data types as \
the returned `outputs` of :code:`cell.call` , and each Tenser in `final_outputs` \
stacks all time steps' counterpart in `outputs` thus has shape `[batch_size, sequence_length, ...]` \
for `time_major == False` or `[sequence_length, batch_size, ...]` for `time_major == True`. \
`final_states` is the counterpart at last time step of initial states, \
thus has the same structure with it and has tensors with same shapes \
and data types.
Examples:
.. code-block:: python
import paddle.fluid as fluid
inputs = fluid.data(name="inputs",
shape=[-1, 32, 128],
dtype="float32")
cell = fluid.layers.GRUCell(hidden_size=128)
outputs = fluid.layers.rnn(cell=cell, inputs=inputs)
"""
def _maybe_copy(state, new_state, step_mask):
# TODO: use where_op
new_state = nn.elementwise_mul(
new_state, step_mask, axis=0) - nn.elementwise_mul(
state, (step_mask - 1), axis=0)
return new_state
def _transpose_batch_time(x):
return nn.transpose(x, [1, 0] + list(range(2, len(x.shape))))
def _switch_grad(x, stop=False):
x.stop_gradient = stop
return x
if initial_states is None:
initial_states = cell.get_initial_states(
batch_ref=inputs, batch_dim_idx=1 if time_major else 0)
initial_states = map_structure(_switch_grad, initial_states)
if not time_major:
inputs = map_structure(_transpose_batch_time, inputs)
if sequence_length:
max_seq_len = nn.shape(flatten(inputs)[0])[0]
mask = sequence_lod.sequence_mask(
sequence_length,
maxlen=max_seq_len,
dtype=flatten(initial_states)[0].dtype)
mask = nn.transpose(mask, [1, 0])
if is_reverse:
inputs = map_structure(lambda x: tensor.reverse(x, axis=[0]), inputs)
mask = tensor.reverse(mask, axis=[0]) if sequence_length else None
# StaticRNN
rnn = control_flow.StaticRNN()
with rnn.step():
inputs = map_structure(rnn.step_input, inputs)
states = map_structure(rnn.memory, initial_states)
copy_states = map_structure(lambda x: x, states)
outputs, new_states = cell.call(inputs, copy_states, **kwargs)
assert_same_structure(states, new_states)
if sequence_length:
step_mask = rnn.step_input(mask)
new_states = map_structure(
partial(
_maybe_copy, step_mask=step_mask), states, new_states)
map_structure(rnn.update_memory, states, new_states)
flat_outputs = flatten(outputs)
map_structure(rnn.step_output, outputs)
map_structure(rnn.step_output, new_states)
rnn_out = rnn()
final_outputs = rnn_out[:len(flat_outputs)]
final_outputs = pack_sequence_as(outputs, final_outputs)
final_states = map_structure(lambda x: x[-1], rnn_out[len(flat_outputs):])
final_states = pack_sequence_as(new_states, final_states)
if is_reverse:
final_outputs = map_structure(lambda x: tensor.reverse(x, axis=[0]),
final_outputs)
if not time_major:
final_outputs = map_structure(_transpose_batch_time, final_outputs)
return (final_outputs, final_states)
class Decoder(object):
"""
Decoder is the base class for any decoder instance used in `dynamic_decode`.
It provides interface for output generation for one time step, which can be
used to generate sequences.
The key abstraction provided by Decoder is:
1. :code:`(initial_input, initial_state, finished) = initialize(inits)` ,
which generates the input and state for the first decoding step, and gives the
initial status telling whether each sequence in the batch is finished.
It would be called once before the decoding iterations.
2. :code:`(output, next_state, next_input, finished) = step(time, input, state)` ,
which transforms the input and state to the output and new state, generates
input for the next decoding step, and emits the flag indicating finished status.
It is the main part for each decoding iteration.
3. :code:`(final_outputs, final_state) = finalize(outputs, final_state, sequence_lengths)` ,
which revises the outputs(stack of all time steps' output) and final state(state from the
last decoding step) to get the counterpart for special usage.
Not necessary to be implemented if no need to revise the stacked outputs and
state from the last decoding step. If implemented, it would be called after
the decoding iterations.
Decoder is more general compared to RNNCell, since the returned `next_input`
and `finished` make it can determine the input and when to finish by itself
when used in dynamic decoding. Decoder always wraps a RNNCell instance though
not necessary.
"""
def initialize(self, inits):
"""
Called once before the decoding iterations.
Parameters:
inits: Argument provided by the caller.
Returns:
tuple: A tuple( :code:`(initial_inputs, initial_states, finished)` ). \
`initial_inputs` and `initial_states` both are a (possibly nested \
structure of) tensor variable[s], and `finished` is a tensor with \
bool data type.
"""
raise NotImplementedError
def step(self, time, inputs, states, **kwargs):
"""
Called per step of decoding.
Parameters:
time(Variable): A Tensor with shape :math:`[1]` provided by the caller.
The data type is int64.
inputs(Variable): A (possibly nested structure of) tensor variable[s].
states(Variable): A (possibly nested structure of) tensor variable[s].
**kwargs: Additional keyword arguments, provided by the caller.
Returns:
tuple: A tuple( :code:(outputs, next_states, next_inputs, finished)` ). \
`next_inputs` and `next_states` both are a (possibly nested \
structure of) tensor variable[s], and the structure, shape and \
data type must be same as the counterpart from input arguments. \
`outputs` is a (possibly nested structure of) tensor variable[s]. \
`finished` is a Tensor with bool data type.
"""
raise NotImplementedError
def finalize(self, outputs, final_states, sequence_lengths):
"""
Called once after the decoding iterations if implemented.
Parameters:
outputs(Variable): A (possibly nested structure of) tensor variable[s].
The structure and data type is same as `output_dtype`.
The tensor stacks all time steps' output thus has shape
:math:`[time\_step, batch\_size, ...]` , which is done by the caller.
final_states(Variable): A (possibly nested structure of) tensor variable[s].
It is the `next_states` returned by `decoder.step` at last decoding step,
thus has the same structure, shape and data type with states at any time
step.
Returns:
tuple: A tuple( :code:`(final_outputs, final_states)` ). \
`final_outputs` and `final_states` both are a (possibly nested \
structure of) tensor variable[s].
"""
raise NotImplementedError
class BeamSearchDecoder(Decoder):
"""
Decoder with beam search decoding strategy. It wraps a cell to get probabilities,
and follows a beam search step to calculate scores and select candidate
token ids for each decoding step.
Please refer to `Beam search <https://en.wikipedia.org/wiki/Beam_search>`_
for more details.
**NOTE** When decoding with beam search, the `inputs` and `states` of cell
would be tiled to `beam_size` (unsqueeze and tile), resulting to shapes like
`[batch_size * beam_size, ...]` , which is built into `BeamSearchDecoder` and
done automatically. Thus any other tensor with shape `[batch_size, ...]` used
in `cell.call` needs to be tiled manually first, which can be completed by using
:code:`BeamSearchDecoder.tile_beam_merge_with_batch` . The most common case
for this is the encoder output in attention mechanism.
Examples:
.. code-block:: python
import paddle.fluid as fluid
from paddle.fluid.layers import GRUCell, BeamSearchDecoder
trg_embeder = lambda x: fluid.embedding(
x, size=[10000, 128], param_attr=fluid.ParamAttr(name="trg_embedding"))
output_layer = lambda x: layers.fc(x,
size=10000,
num_flatten_dims=len(x.shape) - 1,
param_attr=fluid.ParamAttr(name=
"output_w"),
bias_attr=False)
decoder_cell = GRUCell(hidden_size=128)
decoder = BeamSearchDecoder(decoder_cell,
start_token=0,
end_token=1,
beam_size=4,
embedding_fn=trg_embeder,
output_fn=output_layer)
"""
def __init__(self,
cell,
start_token,
end_token,
beam_size,
embedding_fn=None,
output_fn=None):
"""
Constructor of BeamSearchDecoder.
Parameters:
cell(RNNCell): An instance of `RNNCell` or object with the same interface.
start_token(int): The start token id.
end_token(int): The end token id.
beam_size(int): The beam width used in beam search.
embedding_fn(optional): A callable to apply to selected candidate ids.
Mostly it is an embedding layer to transform ids to embeddings,
and the returned value acts as the `input` argument for `cell.call`.
**Note that fluid.embedding should be used here rather than
fluid.layers.embedding, since shape of ids is [batch_size, beam_size].
when using fluid.layers.embedding, must unsqueeze in embedding_fn.**
If not provided, the id to embedding transformation must be built into
`cell.call`. Default None.
output_fn(optional): A callable to apply to the cell's output prior to
calculate scores and select candidate token ids. Default None.
"""
self.cell = cell
self.embedding_fn = embedding_fn
self.output_fn = output_fn
self.start_token = start_token
self.end_token = end_token
self.beam_size = beam_size
@staticmethod
def tile_beam_merge_with_batch(x, beam_size):
"""
Tile the batch dimension of a tensor. Specifically, this function takes
a tensor t shaped `[batch_size, s0, s1, ...]` composed of minibatch
entries `t[0], ..., t[batch_size - 1]` and tiles it to have a shape
`[batch_size * beam_size, s0, s1, ...]` composed of minibatch entries
`t[0], t[0], ..., t[1], t[1], ...` where each minibatch entry is repeated
`beam_size` times.
Parameters:
x(Variable): A tensor with shape `[batch_size, ...]`. The data type
should be float32, float64, int32, int64 or bool.
beam_size(int): The beam width used in beam search.
Returns:
Variable: A tensor with shape `[batch_size * beam_size, ...]`, whose \
data type is same as `x`.
"""
x = nn.unsqueeze(x, [1]) # [batch_size, 1, ...]
expand_times = [1] * len(x.shape)
expand_times[1] = beam_size
x = nn.expand(x, expand_times) # [batch_size, beam_size, ...]
x = nn.transpose(x, list(range(2, len(x.shape))) +
[0, 1]) # [..., batch_size, beam_size]
# use 0 to copy to avoid wrong shape
x = nn.reshape(
x, shape=[0] *
(len(x.shape) - 2) + [-1]) # [..., batch_size * beam_size]
x = nn.transpose(
x, [len(x.shape) - 1] +
list(range(0, len(x.shape) - 1))) # [batch_size * beam_size, ...]
return x
def _split_batch_beams(self, x):
"""
Reshape a tensor with shape `[batch_size * beam_size, ...]` to a new
tensor with shape `[batch_size, beam_size, ...]`.
Parameters:
x(Variable): A tensor with shape `[batch_size * beam_size, ...]`. The
data type should be float32, float64, int32, int64 or bool.
Returns:
Variable: A tensor with shape `[batch_size, beam_size, ...]`, whose \
data type is same as `x`.
"""
# TODO: avoid fake shape in compile-time like tile_beam_merge_with_batch
return nn.reshape(x, shape=(-1, self.beam_size) + x.shape[1:])
def _merge_batch_beams(self, x):
"""
Reshape a tensor with shape `[batch_size, beam_size, ...]` to a new
tensor with shape `[batch_size * beam_size, ...]`.
Parameters:
x(Variable): A tensor with shape `[batch_size, beam_size, ...]`. The
data type should be float32, float64, int32, int64 or bool.
Returns:
Variable: A tensor with shape `[batch_size * beam_size, ...]`, whose \
data type is same as `x`.
"""
# TODO: avoid fake shape in compile-time like tile_beam_merge_with_batch
return nn.reshape(x, shape=(-1, ) + x.shape[2:])
def _expand_to_beam_size(self, x):
"""
This function takes a tensor t shaped `[batch_size, s0, s1, ...]` composed
of minibatch entries `t[0], ..., t[batch_size - 1]` and tiles it to have a
shape `[batch_size, beam_size, s0, s1, ...]` composed of minibatch entries
`t[0], t[0], ..., t[1], t[1], ...` where each minibatch entry is repeated
`beam_size` times.
Parameters:
probs(Variable): A tensor with shape `[batch_size, ...]`, representing
the log probabilities. Its data type should be float32.
finished(Variable): A tensor with shape `[batch_size, beam_size]`,
representing the finished status for all beams. Its data type
should be bool.
Returns:
Variable: A tensor with shape `[batch_size, beam_size, ...]`, whose \
data type is same as `x`.
"""
x = nn.unsqueeze(x, [1])
expand_times = [1] * len(x.shape)
expand_times[1] = self.beam_size
x = nn.expand(x, expand_times)
return x
def _mask_probs(self, probs, finished):
"""
Mask log probabilities. It forces finished beams to allocate all probability
mass to eos and unfinished beams to remain unchanged.
Parameters:
probs(Variable): A tensor with shape `[batch_size, beam_size, vocab_size]`,
representing the log probabilities. Its data type should be float32.
finished(Variable): A tensor with shape `[batch_size, beam_size]`,
representing the finished status for all beams. Its data type
should be bool.
Returns:
Variable: A tensor with the same shape and data type as `x`, \
where unfinished beams stay unchanged and finished beams are \
replaced with a tensor with all probability on the EOS token.
"""
# TODO: use where_op
finished = tensor.cast(finished, dtype=probs.dtype)
probs = nn.elementwise_mul(
nn.expand(nn.unsqueeze(finished, [2]), [1, 1, self.vocab_size]),
self.noend_mask_tensor,
axis=-1) - nn.elementwise_mul(
probs, (finished - 1), axis=0)
return probs
def _gather(self, x, indices, batch_size):
"""
Gather from the tensor `x` using `indices`.
Parameters:
x(Variable): A tensor with shape `[batch_size, beam_size, ...]`.
indices(Variable): A `int64` tensor with shape `[batch_size, beam_size]`,
representing the indices that we use to gather.
batch_size(Variable): A tensor with shape `[1]`. Its data type should
be int32 or int64.
Returns:
Variable: A tensor with the same shape and data type as `x`, \
representing the gathered tensor.
"""
# TODO: compatibility of int32 and int64
batch_size = tensor.cast(
batch_size,
indices.dtype) if batch_size.dtype != indices.dtype else batch_size
batch_size.stop_gradient = True # TODO: remove this
batch_pos = nn.expand(
nn.unsqueeze(
tensor.range(
0, batch_size, 1, dtype=indices.dtype), [1]),
[1, self.beam_size])
topk_coordinates = nn.stack([batch_pos, indices], axis=2)
topk_coordinates.stop_gradient = True
return nn.gather_nd(x, topk_coordinates)
class OutputWrapper(
collections.namedtuple("OutputWrapper",
("scores", "predicted_ids", "parent_ids"))):
"""
The structure for the returned value `outputs` of `decoder.step`.
A namedtuple includes scores, predicted_ids, parent_ids as fields.
"""
pass
class StateWrapper(
collections.namedtuple(
"StateWrapper",
("cell_states", "log_probs", "finished", "lengths"))):
"""
The structure for the argument `states` of `decoder.step`.
A namedtuple includes cell_states, log_probs, finished, lengths as fields.
"""
pass
def initialize(self, initial_cell_states):
"""
Initialize the BeamSearchDecoder.
Parameters:
initial_cell_states(Variable): A (possibly nested structure of)
tensor variable[s]. An argument provided by the caller.
Returns:
tuple: A tuple( :code:`(initial_inputs, initial_states, finished)` ). \
`initial_inputs` is a tensor t filled by `start_token` with shape \
`[batch_size, beam_size]` when `embedding_fn` is None, or the \
returned value of `embedding_fn(t)` when `embedding_fn` is provided. \
`initial_states` is a nested structure(namedtuple including cell_states, \
log_probs, finished, lengths as fields) of tensor variables, where \
`log_probs, finished, lengths` all has a tensor value shaped \
`[batch_size, beam_size]` with data type `float32, bool, int64`. \
cell_states has a value with the same structure as the input \
argument `initial_cell_states` but with tiled shape `[batch_size, beam_size, ...]`. \
`finished` is a `bool` tensor filled by False with shape `[batch_size, beam_size]`.
"""
self.kinf = 1e9
state = flatten(initial_cell_states)[0]
self.batch_size = nn.shape(state)[0]
self.start_token_tensor = tensor.fill_constant(
shape=[1], dtype="int64", value=self.start_token)
self.end_token_tensor = tensor.fill_constant(
shape=[1], dtype="int64", value=self.end_token)
init_cell_states = map_structure(self._expand_to_beam_size,
initial_cell_states)
# TODO: use fill_constant when support variable shape
init_inputs = nn.expand(
nn.unsqueeze(
nn.expand(self.start_token_tensor, [self.batch_size]), [1]),
[1, self.beam_size])
log_probs = nn.expand(
tensor.assign(
np.array(
[[0.] + [-self.kinf] * (self.beam_size - 1)],
dtype="float32")), [self.batch_size, 1])
# TODO: remove the restriction of force_cpu
init_finished = tensor.fill_constant_batch_size_like(
input=state,
shape=[-1, self.beam_size],
dtype="bool",
value=False,
force_cpu=True)
init_lengths = tensor.zeros_like(init_inputs)
init_inputs = self.embedding_fn(
init_inputs) if self.embedding_fn else init_inputs
return init_inputs, self.StateWrapper(init_cell_states, log_probs,
init_finished,
init_lengths), init_finished
def _beam_search_step(self, time, logits, next_cell_states, beam_state):
"""
Calculate scores and select candidate token ids.
Parameters:
time(Variable): An `int64` tensor with shape `[1]` provided by the caller,
representing the current time step number of decoding.
logits(Variable): A tensor with shape `[batch_size, beam_size, vocab_size]`,
representing the logits at the current time step. Its data type is float32.
next_cell_states(Variable): A (possibly nested structure of) tensor variable[s].
It has the same structure, shape and data type as the `cell_states` of
`initial_states` returned by `initialize()`. It represents the next state
from the cell.
beam_state(Variable): A structure of tensor variables.
It is same as the `initial_states` returned by `initialize()` for
the first decoding step and `beam_search_state` returned by
`step()` for the others.
Returns:
tuple: A tuple( :code:`(beam_search_output, beam_search_state)` ). \
`beam_search_output` is a namedtuple(including scores, predicted_ids, \
parent_ids as fields) of tensor variables, where \
`scores, predicted_ids, parent_ids` all has a tensor value shaped \
`[batch_size, beam_size]` with data type `float32, int64, int64`.
`beam_search_state` has the same structure, shape and data type \
as the input argument `beam_state`.
"""
self.vocab_size = logits.shape[-1]
self.vocab_size_tensor = tensor.fill_constant(
shape=[1], dtype="int64", value=self.vocab_size)
noend_array = [-self.kinf] * self.vocab_size
noend_array[self.end_token] = 0
self.noend_mask_tensor = tensor.assign(np.array(noend_array, "float32"))
step_log_probs = nn.log(nn.softmax(logits))
step_log_probs = self._mask_probs(step_log_probs, beam_state.finished)
log_probs = nn.elementwise_add(
x=step_log_probs, y=beam_state.log_probs, axis=0)
# TODO: length penalty
scores = log_probs
scores = nn.reshape(scores, [-1, self.beam_size * self.vocab_size])
# TODO: add grad for topk then this beam search can be used to train
topk_scores, topk_indices = nn.topk(input=scores, k=self.beam_size)
beam_indices = nn.elementwise_floordiv(topk_indices,
self.vocab_size_tensor)
token_indices = nn.elementwise_mod(topk_indices, self.vocab_size_tensor)
next_log_probs = self._gather(
nn.reshape(log_probs, [-1, self.beam_size * self.vocab_size]),
topk_indices, self.batch_size)
next_cell_states = map_structure(
lambda x: self._gather(x, beam_indices, self.batch_size),
next_cell_states)
next_finished = self._gather(beam_state.finished, beam_indices,
self.batch_size)
next_lengths = self._gather(beam_state.lengths, beam_indices,
self.batch_size)
next_lengths = next_lengths + tensor.cast(
nn.logical_not(next_finished), beam_state.lengths.dtype)
next_finished = control_flow.logical_or(
next_finished,
control_flow.equal(token_indices, self.end_token_tensor))
beam_search_output = self.OutputWrapper(topk_scores, token_indices,
beam_indices)
beam_search_state = self.StateWrapper(next_cell_states, next_log_probs,
next_finished, next_lengths)
return beam_search_output, beam_search_state
def step(self, time, inputs, states, **kwargs):
"""
Perform a beam search decoding step, which uses `cell` to get probabilities,
and follows a beam search step to calculate scores and select candidate
token ids.
Parameters:
time(Variable): An `int64` tensor with shape `[1]` provided by the caller,
representing the current time step number of decoding.
inputs(Variable): A tensor variable. It is same as `initial_inputs`
returned by `initialize()` for the first decoding step and
`next_inputs` returned by `step()` for the others.
states(Variable): A structure of tensor variables.
It is same as the `initial_states` returned by `initialize()` for
the first decoding step and `beam_search_state` returned by
`step()` for the others.
**kwargs: Additional keyword arguments, provided by the caller.
Returns:
tuple: A tuple( :code:`(beam_search_output, beam_search_state, next_inputs, finished)` ). \
`beam_search_state` and `next_inputs` have the same structure, \
shape and data type as the input arguments `states` and `inputs` separately. \
`beam_search_output` is a namedtuple(including scores, predicted_ids, \
parent_ids as fields) of tensor variables, where \
`scores, predicted_ids, parent_ids` all has a tensor value shaped \
`[batch_size, beam_size]` with data type `float32, int64, int64`. \
`finished` is a `bool` tensor with shape `[batch_size, beam_size]`.
"""
inputs = map_structure(self._merge_batch_beams, inputs)
cell_states = map_structure(self._merge_batch_beams, states.cell_states)
cell_outputs, next_cell_states = self.cell(inputs, cell_states,
**kwargs)
cell_outputs = map_structure(self._split_batch_beams, cell_outputs)
next_cell_states = map_structure(self._split_batch_beams,
next_cell_states)
if self.output_fn is not None:
cell_outputs = self.output_fn(cell_outputs)
beam_search_output, beam_search_state = self._beam_search_step(
time=time,
logits=cell_outputs,
next_cell_states=next_cell_states,
beam_state=states)
finished = beam_search_state.finished
sample_ids = beam_search_output.predicted_ids
sample_ids.stop_gradient = True
next_inputs = self.embedding_fn(
sample_ids) if self.embedding_fn else sample_ids
return (beam_search_output, beam_search_state, next_inputs, finished)
def finalize(self, outputs, final_states, sequence_lengths):
"""
Use `gather_tree` to backtrace along the beam search tree and construct
the full predicted sequences.
Parameters:
outputs(Variable): A structure(namedtuple) of tensor variables,
The structure and data type is same as `output_dtype`.
The tensor stacks all time steps' output thus has shape
`[time_step, batch_size, ...]`, which is done by the caller.
final_states(Variable): A structure(namedtuple) of tensor variables.
It is the `next_states` returned by `decoder.step` at last
decoding step, thus has the same structure, shape and data type
with states at any time step.
sequence_lengths(Variable): An `int64` tensor shaped `[batch_size, beam_size]`.
It contains sequence lengths for each beam determined during
decoding.
Returns:
tuple: A tuple( :code:`(predicted_ids, final_states)` ). \
`predicted_ids` is an `int64` tensor shaped \
`[time_step, batch_size, beam_size]`. `final_states` is the same \
as the input argument `final_states`.
"""
predicted_ids = nn.gather_tree(outputs.predicted_ids,
outputs.parent_ids)
# TODO: use FinalBeamSearchDecoderOutput as output
return predicted_ids, final_states
def dynamic_decode(decoder,
inits=None,
max_step_num=None,
output_time_major=False,
impute_finished=False,
is_test=False,
return_length=False,
**kwargs):
"""
Dynamic decoding performs :code:`decoder.step()` repeatedly until the returned
Tensor indicating finished status contains all True values or the number of
decoding step reaches to :attr:`max_step_num`.
:code:`decoder.initialize()` would be called once before the decoding loop.
If the `decoder` has implemented `finalize` method, :code:`decoder.finalize()`
would be called once after the decoding loop.
Parameters:
decoder(Decoder): An instance of `Decoder`.
inits(object, optional): Argument passed to `decoder.initialize`.
Default `None`.
max_step_num(int, optional): The maximum number of steps. If not provided,
decode until the decoder is fully done, or in other words, the returned
Tensor by :code:`decoder.step()` indicating finished status contains
all True. Default `None`.
output_time_major(bool, optional): Indicate the data layout of Tensor included
in the final outputs(the first returned value of this method). If
attr:`False`, the data layout would be batch major with shape
`[batch_size, seq_len, ...]`. If attr:`True`, the data layout would
be time major with shape `[seq_len, batch_size, ...]`. Default: `False`.
impute_finished(bool, optional): If `True`, then states get copied through
for batch entries which are marked as finished, which differs with the
unfinished using the new states returned by :code:`decoder.step()` and
ensures that the final states have the correct values. Otherwise, states
wouldn't be copied through when finished. If the returned `final_states`
is needed, it should be set as True, which causes some slowdown.
Default `False`.
is_test(bool, optional): A flag indicating whether to use test mode. In
test mode, it is more memory saving. Default `False`.
return_length(bool, optional): A flag indicating whether to return an
extra Tensor variable in the output tuple, which stores the actual
lengths of all decoded sequences. Default `False`.
**kwargs: Additional keyword arguments. Arguments passed to `decoder.step`.
Returns:
tuple: A tuple( :code:`(final_outputs, final_states, sequence_lengths)` ) \
when `return_length` is True, otherwise a tuple( :code:`(final_outputs, final_states)` ). \
The final outputs and states, both are Tensor or nested structure of Tensor. \
`final_outputs` has the same structure and data types as the :code:`outputs` \
returned by :code:`decoder.step()` , and each Tenser in `final_outputs` \
is the stacked of all decoding steps' outputs, which might be revised \
by :code:`decoder.finalize()` if the decoder has implemented `finalize`. \
`final_states` is the counterpart at last time step of initial states \
returned by :code:`decoder.initialize()` , thus has the same structure \
with it and has tensors with same shapes and data types. `sequence_lengths` \
is an `int64` tensor with the same shape as `finished` returned \
by :code:`decoder.initialize()` , and it stores the actual lengths of \
all decoded sequences.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.layers import GRUCell, BeamSearchDecoder, dynamic_decode
encoder_output = fluid.data(name="encoder_output",
shape=[-1, 32, 128],
dtype="float32")
trg_embeder = lambda x: fluid.embedding(
x, size=[10000, 128], param_attr=fluid.ParamAttr(name="trg_embedding"))
output_layer = lambda x: layers.fc(x,
size=10000,
num_flatten_dims=len(x.shape) - 1,
param_attr=fluid.ParamAttr(name=
"output_w"),
bias_attr=False)
decoder_cell = GRUCell(hidden_size=128)
decoder = BeamSearchDecoder(decoder_cell,
start_token=0,
end_token=1,
beam_size=4,
embedding_fn=trg_embeder,
output_fn=output_layer)
outputs = dynamic_decode(
decoder=decoder, inits=decoder_cell.get_initial_states(encoder_output))
"""
initial_inputs, initial_states, initial_finished = decoder.initialize(inits)
global_inputs, global_states, global_finished = (
initial_inputs, initial_states, initial_finished)
global_finished.stop_gradient = True
step_idx = tensor.fill_constant(shape=[1], dtype="int64", value=0)
cond = control_flow.logical_not((nn.reduce_all(initial_finished)))
if max_step_num is not None:
max_step_num = tensor.fill_constant(
shape=[1], dtype="int64", value=max_step_num)
while_op = control_flow.While(cond, is_test=is_test)
sequence_lengths = tensor.cast(tensor.zeros_like(initial_finished), "int64")
sequence_lengths.stop_gradient = True
if is_test:
# for test, reuse inputs and states variables to save memory
inputs = map_structure(lambda x: x, initial_inputs)
states = map_structure(lambda x: x, initial_states)
else:
# inputs and states of all steps must be saved for backward and training
inputs_arrays = map_structure(
lambda x: control_flow.array_write(x, step_idx), initial_inputs)
states_arrays = map_structure(
lambda x: control_flow.array_write(x, step_idx), initial_states)
def _maybe_copy(state, new_state, step_mask):
# TODO: use where_op
state_dtype = state.dtype
if convert_dtype(state_dtype) in ["bool"]:
state = tensor.cast(state, dtype="float32")
new_state = tensor.cast(new_state, dtype="float32")
if step_mask.dtype != state.dtype:
step_mask = tensor.cast(step_mask, dtype=state.dtype)
# otherwise, renamed bool gradients of would be summed up leading
# to sum(bool) error.
step_mask.stop_gradient = True
new_state = nn.elementwise_mul(
state, step_mask, axis=0) - nn.elementwise_mul(
new_state, (step_mask - 1), axis=0)
if convert_dtype(state_dtype) in ["bool"]:
new_state = tensor.cast(new_state, dtype=state_dtype)
return new_state
def _transpose_batch_time(x):
return nn.transpose(x, [1, 0] + list(range(2, len(x.shape))))
def _create_array_out_of_while(dtype):
current_block_idx = default_main_program().current_block_idx
default_main_program().current_block_idx = default_main_program(
).current_block().parent_idx
tensor_array = control_flow.create_array(dtype)
default_main_program().current_block_idx = current_block_idx
return tensor_array
# While
with while_op.block():
if not is_test:
inputs = map_structure(
lambda array: control_flow.array_read(array, step_idx),
inputs_arrays)
states = map_structure(
lambda array: control_flow.array_read(array, step_idx),
states_arrays)
(outputs, next_states, next_inputs,
next_finished) = decoder.step(step_idx, inputs, states, **kwargs)
next_finished = control_flow.logical_or(next_finished, global_finished)
next_sequence_lengths = nn.elementwise_add(
sequence_lengths,
tensor.cast(
control_flow.logical_not(global_finished),
sequence_lengths.dtype))
if impute_finished: # rectify the states for the finished.
next_states = map_structure(
lambda x, y: _maybe_copy(x, y, global_finished),
states,
next_states, )
# create tensor array in global block after dtype[s] of outputs can be got
outputs_arrays = map_structure(
lambda x: _create_array_out_of_while(x.dtype), outputs)
map_structure(
lambda x, x_array: control_flow.array_write(
x, i=step_idx, array=x_array), outputs, outputs_arrays)
control_flow.increment(x=step_idx, value=1.0, in_place=True)
if is_test:
map_structure(tensor.assign, next_inputs, global_inputs)
map_structure(tensor.assign, next_states, global_states)
else:
map_structure(
lambda x, x_array: control_flow.array_write(
x, i=step_idx, array=x_array), next_inputs, inputs_arrays)
map_structure(
lambda x, x_array: control_flow.array_write(
x, i=step_idx, array=x_array), next_states, states_arrays)
tensor.assign(next_finished, global_finished)
tensor.assign(next_sequence_lengths, sequence_lengths)
if max_step_num is not None:
control_flow.logical_and(
control_flow.logical_not(nn.reduce_all(global_finished)),
control_flow.less_equal(step_idx, max_step_num), cond)
else:
control_flow.logical_not(nn.reduce_all(global_finished), cond)
final_outputs = map_structure(
lambda array: tensor.tensor_array_to_tensor(
array, axis=0, use_stack=True)[0], outputs_arrays)
if is_test:
final_states = global_states
else:
final_states = map_structure(
lambda array: control_flow.array_read(array, step_idx),
states_arrays)
try:
final_outputs, final_states = decoder.finalize(
final_outputs, final_states, sequence_lengths)
except NotImplementedError:
pass
if not output_time_major:
final_outputs = map_structure(_transpose_batch_time, final_outputs)
return (final_outputs, final_states,
sequence_lengths) if return_length else (final_outputs,
final_states)
class DecodeHelper(object):
"""
DecodeHelper is the base class for any helper instance used in `BasicDecoder`.
It provides interface to implement sampling and produce inputs for the next
time step in dynamic decoding.
"""
def initialize(self):
"""
DecodeHelper initialization to produce inputs for the first decoding step
and give the initial status telling whether each sequence in the batch
is finished. It is the partial of the initialization of `BasicDecoder`.
Returns:
tuple: A tuple( :code:`(initial_inputs, initial_finished)` ). \
`initial_inputs` is a (possibly nested structure of) tensor \
variable[s], and the tensor's shape is `[batch_size, ...]`. \
`initial_finished` is a bool tensor with shape `[batch_size]`.
"""
pass
def sample(self, time, outputs, states):
"""
Perform sampling with some strategies according to `outputs`. It is the
partial of `BasicDecoder.step`.
Parameters:
time(Variable): An `int64` tensor with shape `[1]` provided by the caller,
representing the current time step number of decoding.
outputs(Variable): A tensor variable. Usually it's data type is float32
or float64, and it's shape is `[batch_size, vocabulary_size]`,
representing the predicted logits of current step. It is same as
`outputs` returned by `BasicDecoder.output_fn(BasicDecoder.cell.call())`.
states(Variable): A (possibly nested structure of) tensor variable[s].
It is same as `new_states` returned by `BasicDecoder.cell.call()`.
Returns:
Variable: An `int64` tensor representing the sampled ids.
"""
pass
def next_inputs(self, time, outputs, states, sample_ids):
"""
Produce the inputs and states for next time step and give status telling
whether each minibatch entry is finished. It is called after `sample` in
`BasicDecoder.step`. It is the partial of `BasicDecoder.step`.
Parameters:
time(Variable): An `int64` tensor with shape `[1]` provided by the caller,
representing the current time step number of decoding.
outputs(Variable): A tensor variable. Usually it's data type is float32
or float64, and it's shape is `[batch_size, vocabulary_size]`,
representing the predicted logits of current step. It is same as
`outputs` returned by `BasicDecoder.output_fn(BasicDecoder.cell.call())`.
states(Variable): A (possibly nested structure of) tensor variable[s].
It is same as `new_states` returned by `BasicDecoder.cell.call()`.
sample_ids(Variable): A (possibly nested structure of) tensor variable[s].
It is same as `sample_ids` returned by `sample()`.
Returns:
tuple: A tuple( :code:`(finished, next_inputs, next_states)` ). \
`next_inputs` and `next_states` both are a (possibly nested \
structure of) tensor variable[s], and the structure, shape and \
data type of `next_states` must be same as the input argument \
`states`. `finished` is a bool tensor with shape `[batch_size]`.
"""
pass
class TrainingHelper(DecodeHelper):
"""
TrainingHelper is a subclass of DecodeHelper. It is a decoding helper
slicing from the full sequence inputs as the inputs for corresponding
step. And it uses `argmax` to sample from the outputs of `cell.call()`.
Since the needs of sequence inputs, it is used mostly for teach-forcing MLE
(maximum likelihood) training, and the sampled would not be used.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
trg_emb = fluid.data(name="trg_emb",
shape=[None, None, 128],
dtype="float32")
trg_seq_length = fluid.data(name="trg_seq_length",
shape=[None],
dtype="int64")
helper = layers.TrainingHelper(trg_emb, trg_seq_length)
decoder_cell = layers.GRUCell(hidden_size=128)
decoder = layers.BasicDecoder(decoder_cell, helper)
outputs = layers.dynamic_decode(
decoder,
inits=decoder_cell.get_initial_states(trg_emb),
is_test=False)
"""
def __init__(self, inputs, sequence_length, time_major=False):
"""
Constructor of TrainingHelper.
Parameters:
inputs(Variable): A (possibly nested structure of) tensor variable[s].
The shape of tensor should be `[batch_size, sequence_length, ...]`
for `time_major == False` or `[sequence_length, batch_size, ...]`
for `time_major == True`. It represents the inputs to be sliced
from at every decoding step.
sequence_length(Variable): A tensor with shape `[batch_size]`.
It stores real length of each instance in `inputs`, by which we
can label the finished status of each instance at every decoding
step.
time_major(bool, optional): Indicate the data layout of Tensor included
in `inputs`. If `False`, the data layout would be batch major with
shape `[batch_size, sequence_length, ...]`. If `True`, the data
layout would be time major with shape `[sequence_length, batch_size, ...]`.
Default: `False`.
"""
self.inputs = inputs
self.sequence_length = sequence_length
self.time_major = time_major
# extend inputs to avoid to slice out of range in `next_inputs`
# may be easier and have better performance than condition_op
self.inputs_ = map_structure(
lambda x: nn.pad(x,
paddings=([0, 1] + [0, 0] * (len(x.shape) - 1))
if time_major else ([0, 0, 0, 1] + [0, 0] *
(len(x.shape) - 2))),
self.inputs)
def initialize(self):
"""
TrainingHelper initialization produces inputs for the first decoding
step by slicing at the first time step of full sequence inputs, and it
gives initial status telling whether each sequence in the batch is
finished. It is the partial of the initialization of `BasicDecoder`.
Returns:
tuple: A tuple( :code:`(initial_inputs, initial_finished)` ). \
`initial_inputs` is a (possibly nested structure of) tensor \
variable[s], and the tensor's shape is `[batch_size, ...]`. \
`initial_finished` is a bool tensor with shape `[batch_size]`.
"""
init_finished = control_flow.equal(
self.sequence_length,
tensor.fill_constant(
shape=[1], dtype=self.sequence_length.dtype, value=0))
# TODO: support zero length
init_inputs = map_structure(
lambda x: x[0] if self.time_major else x[:, 0], self.inputs)
return init_inputs, init_finished
def sample(self, time, outputs, states):
"""
Perform sampling by using `argmax` according to the `outputs`. Mostly
the sampled ids would not be used since the inputs for next decoding
step would be got by slicing.
Parameters:
time(Variable): An `int64` tensor with shape `[1]` provided by the
caller, representing the current time step number of decoding.
outputs(Variable): A tensor variable. Usually it's data type is float32
or float64, and it's shape is `[batch_size, vocabulary_size]`,
representing the predicted logits of current step. It is same as
`outputs` returned by `BasicDecoder.output_fn(BasicDecoder.cell.call())`.
states(Variable): A (possibly nested structure of) tensor variable[s].
It is same as `new_states` returned by `BasicDecoder.cell.call()`.
Returns:
Variable: An `int64` tensor with shape `[batch_size]`, representing \
the sampled ids.
"""
sample_ids = tensor.argmax(outputs, axis=-1)
return sample_ids
def next_inputs(self, time, outputs, states, sample_ids):
"""
Generate inputs for the next decoding step by slicing at corresponding
step of the full sequence inputs. Simultaneously, produce the states
for next time step by directly using the input `states` and emit status
telling whether each minibatch entry reaches to the corresponding length.
Parameters:
time(Variable): An `int64` tensor with shape `[1]` provided by the
caller, representing the current time step number of decoding.
outputs(Variable): A tensor variable. Usually it's data type is float32
or float64, and it's shape is `[batch_size, vocabulary_size]`,
representing the predicted logits of current step. It is same as
`outputs` returned by `BasicDecoder.output_fn(BasicDecoder.cell.call())`.
states(Variable): A (possibly nested structure of) tensor variable[s].
It is same as `new_states` returned by `BasicDecoder.cell.call()`.
sample_ids(Variable): An `int64` tensor variable shaped `[batch_size]`.
It is same as `sample_ids` returned by `sample()`.
Returns:
tuple: A tuple( :code:`(finished, next_inputs, next_states)` ). \
`next_inputs` and `next_states` both are a (possibly nested \
structure of) tensor variable[s], and the tensor's shape is \
`[batch_size, ...]`. `next_states` is identical to the input \
argument `states`. `finished` is a `bool` Tensor with \
shape `[batch_size]`.
"""
# TODO: compatibility of int32 and int64
time = tensor.cast(
time,
"int32") if convert_dtype(time.dtype) not in ["int32"] else time
if self.sequence_length.dtype != time.dtype:
self.sequence_length = tensor.cast(self.sequence_length, time.dtype)
next_time = time + 1
finished = control_flow.less_equal(self.sequence_length, next_time)
def _slice(x): # TODO: use Variable.__getitem__
axes = [0 if self.time_major else 1]
return nn.squeeze(
nn.slice(
x, axes=axes, starts=[next_time], ends=[next_time + 1]),
axes=axes)
next_inputs = map_structure(_slice, self.inputs_)
return finished, next_inputs, states
class GreedyEmbeddingHelper(DecodeHelper):
"""
GreedyEmbeddingHelper is a subclass of DecodeHelper. It is a decoding helper
uses the argmax of the output (treated as logits) and passes the results
through an embedding layer to get inputs for the next decoding step.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
trg_emb = fluid.data(name="trg_emb",
shape=[None, None, 128],
dtype="float32")
trg_embeder = lambda x: fluid.embedding(
x, size=[10000, 128], param_attr=fluid.ParamAttr(name="trg_embedding"))
output_layer = lambda x: layers.fc(x,
size=10000,
num_flatten_dims=len(x.shape) - 1,
param_attr=fluid.ParamAttr(name=
"output_w"),
bias_attr=False)
helper = layers.GreedyEmbeddingHelper(trg_embeder, start_tokens=0, end_token=1)
decoder_cell = layers.GRUCell(hidden_size=128)
decoder = layers.BasicDecoder(decoder_cell, helper, output_fn=output_layer)
outputs = layers.dynamic_decode(
decoder=decoder, inits=decoder_cell.get_initial_states(encoder_output))
"""
def __init__(self, embedding_fn, start_tokens, end_token):
"""
Constructor of GreedyEmbeddingHelper.
Parameters:
embedding_fn(callable): A functor to apply on the argmax results.
Mostly it is an embedding layer to transform ids to embeddings.
**Note that fluid.embedding should be used here rather than
fluid.layers.embedding, since shape of ids is [batch_size].
when using fluid.layers.embedding, must unsqueeze in embedding_fn.**
start_tokens(Variable): A `int64` tensor shaped `[batch_size]`,
representing the start tokens.
end_token(int): The end token id.
Returns:
tuple: A tuple( :code:`(initial_inputs, initial_states, finished)` ). \
`initial_inputs` and `initial_states` both are a (possibly nested \
structure of) tensor variable[s], and `finished` is a tensor with \
bool data type.
"""
self.embedding_fn = embedding_fn
self.start_tokens = start_tokens
self.end_token = tensor.fill_constant(
shape=[1], dtype="int64", value=end_token)
def initialize(self):
"""
GreedyEmbeddingHelper initialization produces inputs for the first decoding
step by using `start_tokens` of the constructor, and gives initial
status telling whether each sequence in the batch is finished.
It is the partial of the initialization of `BasicDecoder`.
Returns:
tuple: A tuple( :code:`(initial_inputs, initial_finished)` ). \
`initial_inputs` is same as `start_tokens` of the constructor. \
`initial_finished` is a `bool` tensor filled by False and has \
the same shape as `start_tokens`.
"""
# TODO: remove the restriction of force_cpu
init_finished = tensor.fill_constant_batch_size_like(
input=self.start_tokens,
shape=[-1],
dtype="bool",
value=False,
force_cpu=True)
init_inputs = self.embedding_fn(self.start_tokens)
return init_inputs, init_finished
def sample(self, time, outputs, states):
"""
Perform sampling by using `argmax` according to the `outputs`.
Parameters:
time(Variable): An `int64` tensor with shape `[1]` provided by the
caller, representing the current time step number of decoding.
outputs(Variable): A tensor variable. Usually it's data type is float32
or float64, and it's shape is `[batch_size, vocabulary_size]`,
representing the predicted logits of current step. It is same as
`outputs` returned by `BasicDecoder.output_fn(BasicDecoder.cell.call())`.
states(Variable): A (possibly nested structure of) tensor variable[s].
It is same as `new_states` returned by `BasicDecoder.cell.call()`.
Returns:
Variable: An `int64` tensor with shape `[batch_size]`, representing \
the sampled ids.
"""
sample_ids = tensor.argmax(outputs, axis=-1)
return sample_ids
def next_inputs(self, time, outputs, states, sample_ids):
"""
Generate inputs for the next decoding step by applying `embedding_fn`
to `sample_ids`. Simultaneously, produce the states for next time step
by directly using the input `states` and emit status telling whether
each minibatch entry gets an `end_token` sample.
Parameters:
time(Variable): An `int64` tensor with shape `[1]` provided by the
caller, representing the current time step number of decoding.
outputs(Variable): A tensor variable. Usually it's data type is float32
or float64, and it's shape is `[batch_size, vocabulary_size]`,
representing the predicted logits of current step. It is same as
`outputs` returned by `BasicDecoder.output_fn(BasicDecoder.cell.call())`.
states(Variable): A (possibly nested structure of) tensor variable[s].
It is same as `new_states` returned by `BasicDecoder.cell.call()`.
sample_ids(Variable): An `int64` tensor variable shaped `[batch_size]`.
It is same as `sample_ids` returned by `sample()`.
Returns:
tuple: A tuple( :code:`(finished, next_inputs, next_states)` ). \
`next_inputs` and `next_states` both are a (possibly nested \
structure of) tensor variable[s], and the tensor's shape is \
`[batch_size, ...]`. `next_states` is identical to the input \
argument `states`. `finished` is a `bool` Tensor with \
shape `[batch_size]`.
"""
finished = control_flow.equal(sample_ids, self.end_token)
next_inputs = self.embedding_fn(sample_ids)
return finished, next_inputs, states
class SampleEmbeddingHelper(GreedyEmbeddingHelper):
"""
SampleEmbeddingHelper is a subclass of GreedyEmbeddingHelper. It is a decoding
helper uses sampling (from a distribution) instead of argmax of the output
(treated as logits) and passes the results through an embedding layer to get
inputs for the next decoding step.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
trg_emb = fluid.data(name="trg_emb",
shape=[None, None, 128],
dtype="float32")
trg_embeder = lambda x: fluid.embedding(
x, size=[10000, 128], param_attr=fluid.ParamAttr(name="trg_embedding"))
output_layer = lambda x: layers.fc(x,
size=10000,
num_flatten_dims=len(x.shape) - 1,
param_attr=fluid.ParamAttr(name=
"output_w"),
bias_attr=False)
helper = layers.SampleEmbeddingHelper(trg_embeder, start_tokens=0, end_token=1)
decoder_cell = layers.GRUCell(hidden_size=128)
decoder = layers.BasicDecoder(decoder_cell, helper, output_fn=output_layer)
outputs = layers.dynamic_decode(
decoder=decoder, inits=decoder_cell.get_initial_states(encoder_output))
"""
def __init__(self,
embedding_fn,
start_tokens,
end_token,
softmax_temperature=None,
seed=None):
"""
Constructor of SampleEmbeddingHelper.
Parameters:
embedding_fn(callable): A functor to apply on the argmax results.
Mostly it is an embedding layer to transform ids to embeddings.
**Note that fluid.embedding should be used here rather than
fluid.layers.embedding, since shape of ids is [batch_size].
when using fluid.layers.embedding, must unsqueeze in embedding_fn.**
start_tokens(Variable): A `int64` tensor shaped `[batch_size]`,
representing the start tokens.
end_token(int): The end token id.
softmax_temperature(float, optional): the value to divide the logits
by before computing the softmax. Higher temperatures (above 1.0)
lead to more random, while lower temperatures push the sampling
distribution towards the argmax. It must be strictly greater than
0. Defaults to None, meaning using a temperature valued 1.0.
seed: (int, optional) The sampling seed. Defaults to None, meaning not
to use fixed seed.
Returns:
tuple: A tuple( :code:`(initial_inputs, initial_states, finished)` ). \
`initial_inputs` and `initial_states` both are a (possibly nested \
structure of) tensor variable[s], and `finished` is a tensor with \
bool data type.
"""
super(SampleEmbeddingHelper, self).__init__(embedding_fn, start_tokens,
end_token)
self.softmax_temperature = tensor.fill_constant(
shape=[1], dtype="float32", value=softmax_temperature
) if softmax_temperature is not None else None
self.seed = seed
def sample(self, time, outputs, states):
"""
Perform sampling from a categorical distribution, and the distribution
is computed by `softmax(outputs/softmax_temperature)`.
Parameters:
time(Variable): An `int64` tensor with shape `[1]` provided by the
caller, representing the current time step number of decoding.
outputs(Variable): A tensor variable. Usually it's data type is float32
or float64, and it's shape is `[batch_size, vocabulary_size]`,
representing the predicted logits of current step. It is same as
`outputs` returned by `BasicDecoder.output_fn(BasicDecoder.cell.call())`.
states(Variable): A (possibly nested structure of) tensor variable[s].
It is same as `new_states` returned by `BasicDecoder.cell.call()`.
Returns:
Variable: An `int64` tensor with shape `[batch_size]`, representing \
the sampled ids.
"""
logits = (outputs / self.softmax_temperature
) if self.softmax_temperature is not None else outputs
probs = nn.softmax(logits)
# TODO: remove this stop_gradient. The stop_gradient of sample_ids can
# not pass to probs, since sampling_id op does not have corresponding
# grad op and thus can not pass.
probs.stop_gradient = True
sample_ids = nn.sampling_id(
probs, seed=self.seed, dtype=self.start_tokens.dtype)
return sample_ids
class BasicDecoder(Decoder):
"""
BasicDecoder is a subclass of Decoder and assembles a RNNCell and DecodeHelper
instance as members, where the DecodeHelper helps to implement customed
decoding strategies.. It performs one decoding step as following steps:
1. Perform `cell_outputs, cell_states = cell.call(inputs, states)`
to get outputs and new states from cell.
2. Perform `sample_ids = helper.sample(time, cell_outputs, cell_states)`
to sample ids as decoded results of the current time step.
3. Perform `finished, next_inputs, next_states = helper.next_inputs(time,
cell_outputs, cell_states, sample_ids)` to generate inputs, states and
finished status for the next decoding step.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
trg_emb = fluid.data(name="trg_emb",
shape=[None, None, 128],
dtype="float32")
trg_embeder = lambda x: fluid.embedding(
x, size=[10000, 128], param_attr=fluid.ParamAttr(name="trg_embedding"))
output_layer = lambda x: layers.fc(x,
size=10000,
num_flatten_dims=len(x.shape) - 1,
param_attr=fluid.ParamAttr(name=
"output_w"),
bias_attr=False)
helper = layers.SampleEmbeddingHelper(trg_embeder, start_tokens=0, end_token=1)
decoder_cell = layers.GRUCell(hidden_size=128)
decoder = layers.BasicDecoder(decoder_cell, helper, output_fn=output_layer)
outputs = layers.dynamic_decode(
decoder=decoder, inits=decoder_cell.get_initial_states(encoder_output))
"""
def __init__(self, cell, helper, output_fn=None):
"""
Constructor of BasicDecoder.
Parameters:
cell(RNNCell): An instance of `RNNCell` or object with the same interface.
helper(DecodeHelper): An instance of `DecodeHelper`.
output_fn(optional): A callable to apply to the cell's output prior to
sampling. Default None.
"""
self.cell = cell
self.helper = helper
self.output_fn = output_fn
def initialize(self, initial_cell_states):
"""
BasicDecoder initialization includes helper initialization and cell
initialization, and cell initialization uses `initial_cell_states` as
the result directly.
Parameters:
initial_cell_states(Variable): A (possibly nested structure of)
tensor variable[s]. An argument provided by the caller `dynamic_decode`.
Returns:
tuple: A tuple( :code:(initial_inputs, initial_cell_states, finished)` ). \
`initial_inputs` and `initial_states` both are a (possibly nested \
structure of) tensor variable[s], and `finished` is a tensor with \
bool data type. `initial_inputs` and `finished` are the results \
of `helper.initialize()`, and `initial_cell_states` is same as \
the input argument counterpart.
"""
(initial_inputs, initial_finished) = self.helper.initialize()
return initial_inputs, initial_cell_states, initial_finished
class OutputWrapper(
collections.namedtuple("OutputWrapper",
("cell_outputs", "sample_ids"))):
"""
The structure for the returned value `outputs` of `decoder.step`.
A namedtuple includes cell_outputs, sample_ids as fields.
"""
pass
def step(self, time, inputs, states, **kwargs):
"""
Perform one decoding step as following steps:
1. Perform `cell_outputs, cell_states = cell.call(inputs, states)`
to get outputs and new states from cell.
2. Perform `sample_ids = helper.sample(time, cell_outputs, cell_states)`
to sample ids as decoded results of the current time step.
3. Perform `finished, next_inputs, next_states = helper.next_inputs(time,
cell_outputs, cell_states, sample_ids)` to generate inputs, states and
finished status for the next decoding step.
Parameters:
time(Variable): An `int64` tensor with shape `[1]` provided by the caller,
representing the current time step number of decoding.
inputs(Variable): A tensor variable. It is same as `initial_inputs`
returned by `initialize()` for the first decoding step and
`next_inputs` returned by `step()` for the others.
states(Variable): A structure of tensor variables.
It is same as the `initial_cell_states` returned by `initialize()`
for the first decoding step and `next_states` returned by
`step()` for the others.
**kwargs: Additional keyword arguments, provided by the caller
`dynamic_decode`.
Returns:
tuple: A tuple( :code:`(outputs, next_states, next_inputs, finished)` ). \
`outputs` is a namedtuple(including cell_outputs, sample_ids, \
as fields) of tensor variables, where `cell_outputs` is the result \
fof `cell.call()` and `sample_ids` is the result of `helper.sample()`. \
`next_states` and `next_inputs` have the same structure, shape \
and data type as the input arguments `states` and `inputs` separately. \
`finished` is a `bool` tensor with shape `[batch_size]`.
"""
cell_outputs, cell_states = self.cell(inputs, states, **kwargs)
if self.output_fn is not None:
cell_outputs = self.output_fn(cell_outputs)
sample_ids = self.helper.sample(
time=time, outputs=cell_outputs, states=cell_states)
sample_ids.stop_gradient = True
(finished, next_inputs, next_states) = self.helper.next_inputs(
time=time,
outputs=cell_outputs,
states=cell_states,
sample_ids=sample_ids)
outputs = self.OutputWrapper(cell_outputs, sample_ids)
return (outputs, next_states, next_inputs, finished)
def dynamic_lstm(input,
size,
h_0=None,
c_0=None,
param_attr=None,
bias_attr=None,
use_peepholes=True,
is_reverse=False,
gate_activation='sigmoid',
cell_activation='tanh',
candidate_activation='tanh',
dtype='float32',
name=None):
"""
**Note**:
1. This OP only supports LoDTensor as inputs. If you need to deal with Tensor, please use :ref:`api_fluid_layers_lstm` .
2. In order to improve efficiency, users must first map the input of dimension [T, hidden_size] to input of [T, 4 * hidden_size], and then pass it to this OP.
The implementation of this OP include diagonal/peephole connections.
Please refer to `Gers, F. A., & Schmidhuber, J. (2000) <ftp://ftp.idsia.ch/pub/juergen/TimeCount-IJCNN2000.pdf>`_ .
If you do not need peephole connections, please set use_peepholes to False .
This OP computes each timestep as follows:
.. math::
i_t = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + b_{x_i} + b_{h_i})
.. math::
f_t = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + b_{x_f} + b_{h_f})
.. math::
o_t = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + b_{x_o} + b_{h_o})
.. math::
\widetilde{c_t} = tanh(W_{cx}x_t + W_{ch}h_{t-1} + b{x_c} + b_{h_c})
.. math::
c_t = f_t \odot c_{t-1} + i_t \odot \widetilde{c_t}
.. math::
h_t = o_t \odot tanh(c_t)
The symbolic meanings in the formula are as follows:
- :math:`x_{t}` represents the input at timestep :math:`t`
- :math:`h_{t}` represents the hidden state at timestep :math:`t`
- :math:`h_{t-1}, c_{t-1}` represent the hidden state and cell state at timestep :math:`t-1` , respectively
- :math:`\widetilde{c_t}` represents the candidate cell state
- :math:`i_t` , :math:`f_t` and :math:`o_t` represent input gate, forget gate, output gate, respectively
- :math:`W` represents weight (e.g., :math:`W_{ix}` is the weight of a linear transformation of input :math:`x_{t}` when calculating input gate :math:`i_t` )
- :math:`b` represents bias (e.g., :math:`b_{i}` is the bias of input gate)
- :math:`\sigma` represents nonlinear activation function for gate, default sigmoid
- :math:`\odot` represents the Hadamard product of a matrix, i.e. multiplying the elements of the same position for two matrices with the same dimension to get another matrix with the same dimension
Parameters:
input ( :ref:`api_guide_Variable_en` ): LSTM input tensor, multi-dimensional LODTensor of shape :math:`[T, 4*hidden\_size]` . Data type is float32 or float64.
size (int): must be 4 * hidden_size.
h_0( :ref:`api_guide_Variable_en` , optional): The initial hidden state of the LSTM, multi-dimensional Tensor of shape :math:`[batch\_size, hidden\_size]` .
Data type is float32 or float64. If set to None, it will be a vector of all 0. Default: None.
c_0( :ref:`api_guide_Variable_en` , optional): The initial hidden state of the LSTM, multi-dimensional Tensor of shape :math:`[batch\_size, hidden\_size]` .
Data type is float32 or float64. If set to None, it will be a vector of all 0. `h_0` and `c_0` can be None but only at the same time. Default: None.
param_attr(ParamAttr, optional): Parameter attribute of weight. If it is None, the default weight parameter attribute is used. Please refer to ref:`api_fluid_ParamAttr' .
If the user needs to set this parameter, the dimension must be :math:`[hidden\_size, 4*hidden\_size]` . Default: None.
- Weights = :math:`\{ W_{cr},W_{ir},W_{fr},W_{or} \}` , the shape is [hidden_size, 4*hidden_size].
bias_attr (ParamAttr, optional): The bias attribute for the learnable bias
weights, which contains two parts, input-hidden
bias weights and peephole connections weights if
setting `use_peepholes` to `True`.
Please refer to ref:`api_fluid_ParamAttr' . Default: None.
1. `use_peepholes = False`
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
- The shape is [1, 4*hidden_size].
2. `use_peepholes = True`
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
W_{fc}, W_{oc}`}.
- The shape is [1, 7*hidden_size].
use_peepholes (bool, optional): Whether to use peephole connection or not. Default: True.
is_reverse (bool, optional): Whether to calculate reverse LSTM. Default: False.
gate_activation (str, optional): The activation for input gate, forget gate and output gate. Default: "sigmoid".
cell_activation (str, optional): The activation for cell output. Default: "tanh".
candidate_activation (str, optional): The activation for candidate hidden state. Default: "tanh".
dtype (str, optional): Data type, can be "float32" or "float64". Default: "float32".
name (str, optional): A name for this layer. Please refer to :ref:`api_guide_Name` . Default: None.
Returns:
tuple ( :ref:`api_guide_Variable` , :ref:`api_guide_Variable` ) :
The hidden state and cell state of LSTM
- hidden: LoDTensor with shape of :math:`[T, hidden\_size]` , and its lod and dtype is the same as the input.
- cell: LoDTensor with shape of :math:`[T, hidden\_size]` , and its lod and dtype is the same as the input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
emb_dim = 256
vocab_size = 10000
hidden_dim = 512
data = fluid.data(name='x', shape=[None], dtype='int64', lod_level=1)
emb = fluid.embedding(input=data, size=[vocab_size, emb_dim], is_sparse=True)
forward_proj = fluid.layers.fc(input=emb, size=hidden_dim * 4,
bias_attr=False)
forward, cell = fluid.layers.dynamic_lstm(
input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
forward.shape # (-1, 512)
cell.shape # (-1, 512)
"""
assert in_dygraph_mode(
) is not True, "please use lstm instead of dynamic_lstm in dygraph mode!"
assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
helper = LayerHelper('lstm', **locals())
size = size // 4
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype)
bias_size = [1, 7 * size]
if not use_peepholes:
bias_size[1] = 4 * size
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
hidden = helper.create_variable_for_type_inference(dtype)
cell = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
batch_size = input.shape[0]
if h_0:
assert h_0.shape == (batch_size, size), \
'The shape of h0 should be (batch_size, %d)' % size
inputs['H0'] = h_0
if c_0:
assert c_0.shape == (batch_size, size), \
'The shape of c0 should be (batch_size, %d)' % size
inputs['C0'] = c_0
helper.append_op(
type='lstm',
inputs=inputs,
outputs={
'Hidden': hidden,
'Cell': cell,
'BatchGate': batch_gate,
'BatchCellPreAct': batch_cell_pre_act
},
attrs={
'use_peepholes': use_peepholes,
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'cell_activation': cell_activation,
'candidate_activation': candidate_activation
})
return hidden, cell
def lstm(input,
init_h,
init_c,
max_len,
hidden_size,
num_layers,
dropout_prob=0.0,
is_bidirec=False,
is_test=False,
name=None,
default_initializer=None,
seed=-1):
"""
**Note**:
This OP only supports running on GPU devices.
This OP implements LSTM operation - `Hochreiter, S., & Schmidhuber, J. (1997) <http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf>`_ .
The implementation of this OP does not include diagonal/peephole connections.
Please refer to `Gers, F. A., & Schmidhuber, J. (2000) <ftp://ftp.idsia.ch/pub/juergen/TimeCount-IJCNN2000.pdf>`_ .
If you need peephole connections, please use :ref:`api_fluid_layers_dynamic_lstm` .
This OP computes each timestep as follows:
.. math::
i_t = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + b_{x_i} + b_{h_i})
.. math::
f_t = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + b_{x_f} + b_{h_f})
.. math::
o_t = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + b_{x_o} + b_{h_o})
.. math::
\widetilde{c_t} = tanh(W_{cx}x_t + W_{ch}h_{t-1} + b{x_c} + b_{h_c})
.. math::
c_t = f_t \odot c_{t-1} + i_t \odot \widetilde{c_t}
.. math::
h_t = o_t \odot tanh(c_t)
The symbolic meanings in the formula are as follows:
- :math:`x_{t}` represents the input at timestep :math:`t`
- :math:`h_{t}` represents the hidden state at timestep :math:`t`
- :math:`h_{t-1}, c_{t-1}` represent the hidden state and cell state at timestep :math:`t-1` , respectively
- :math:`\widetilde{c_t}` represents the candidate cell state
- :math:`i_t` , :math:`f_t` and :math:`o_t` represent input gate, forget gate, output gate, respectively
- :math:`W` represents weight (e.g., :math:`W_{ix}` is the weight of a linear transformation of input :math:`x_{t}` when calculating input gate :math:`i_t` )
- :math:`b` represents bias (e.g., :math:`b_{i}` is the bias of input gate)
- :math:`\sigma` represents nonlinear activation function for gate, default sigmoid
- :math:`\odot` represents the Hadamard product of a matrix, i.e. multiplying the elements of the same position for two matrices with the same dimension to get another matrix with the same dimension
Parameters:
input ( :ref:`api_guide_Variable_en` ): LSTM input tensor, 3-D Tensor of shape :math:`[batch\_size, seq\_len, input\_dim]` . Data type is float32 or float64
init_h( :ref:`api_guide_Variable_en` ): The initial hidden state of the LSTM, 3-D Tensor of shape :math:`[num\_layers, batch\_size, hidden\_size]` .
If is_bidirec = True, shape should be :math:`[num\_layers*2, batch\_size, hidden\_size]` . Data type is float32 or float64.
init_c( :ref:`api_guide_Variable_en` ): The initial cell state of the LSTM, 3-D Tensor of shape :math:`[num\_layers, batch\_size, hidden\_size]` .
If is_bidirec = True, shape should be :math:`[num\_layers*2, batch\_size, hidden\_size]` . Data type is float32 or float64.
max_len (int): max length of LSTM. the first dim of input tensor CAN NOT greater than max_len.
hidden_size (int): hidden size of the LSTM.
num_layers (int): total layers number of the LSTM.
dropout_prob(float, optional): dropout prob, dropout ONLY work between rnn layers, NOT between time steps
There is NO dropout work on rnn output of the last RNN layers.
Default: 0.0.
is_bidirec (bool, optional): If it is bidirectional. Default: False.
is_test (bool, optional): If it is in test phrase. Default: False.
name (str, optional): A name for this layer. If set None, the layer
will be named automatically. Default: None.
default_initializer(Initializer, optional): Where use initializer to initialize the Weight
If set None, default initializer will be used. Default: None.
seed(int, optional): Seed for dropout in LSTM, If it's -1, dropout will use random seed. Default: 1.
Returns:
tuple ( :ref:`api_guide_Variable_en` , :ref:`api_guide_Variable_en` , :ref:`api_guide_Variable_en` ) :
Three tensors, rnn_out, last_h, last_c:
- rnn_out is result of LSTM hidden, shape is :math:`[seq\_len, batch\_size, hidden\_size]` \
if is_bidirec set to True, shape will be :math:`[seq\_len, batch\_size, hidden\_size*2]`
- last_h is the hidden state of the last step of LSTM \
shape is :math:`[num\_layers, batch\_size, hidden\_size]` \
if is_bidirec set to True, shape will be :math:`[num\_layers*2, batch\_size, hidden\_size]`
- last_c(Tensor): the cell state of the last step of LSTM \
shape is :math:`[num\_layers, batch\_size, hidden\_size]` \
if is_bidirec set to True, shape will be :math:`[num\_layers*2, batch\_size, hidden\_size]`
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
emb_dim = 256
vocab_size = 10000
data = fluid.data(name='x', shape=[None, 100], dtype='int64')
emb = fluid.embedding(input=data, size=[vocab_size, emb_dim], is_sparse=True)
batch_size = 20
max_len = 100
dropout_prob = 0.2
input_size = 100
hidden_size = 150
num_layers = 1
init_h = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 )
init_c = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 )
rnn_out, last_h, last_c = layers.lstm( emb, init_h, init_c, \
max_len, hidden_size, num_layers, \
dropout_prob=dropout_prob)
rnn_out.shape # (-1, 100, 150)
last_h.shape # (1, 20, 150)
last_c.shape # (1, 20, 150)
"""
helper = LayerHelper('cudnn_lstm', **locals())
dtype = input.dtype
input_shape = list(input.shape)
input_size = input_shape[-1]
weight_size = 0
for i in range(num_layers):
if i == 0:
input_weight_size = (input_size * hidden_size) * 4
else:
if is_bidirec:
input_weight_size = (hidden_size * 2 * hidden_size) * 4
else:
input_weight_size = (hidden_size * hidden_size) * 4
hidden_weight_size = (hidden_size * hidden_size) * 4
if is_bidirec:
weight_size += (input_weight_size + hidden_weight_size) * 2
weight_size += hidden_size * 8 * 2
else:
weight_size += input_weight_size + hidden_weight_size
weight_size += hidden_size * 8
weight = helper.create_parameter(
attr=helper.param_attr,
shape=[weight_size],
dtype=dtype,
default_initializer=default_initializer)
out = helper.create_variable_for_type_inference(dtype)
last_h = helper.create_variable_for_type_inference(dtype)
last_c = helper.create_variable_for_type_inference(dtype)
cache = helper.create_variable(
persistable=True, type=core.VarDesc.VarType.RAW, stop_gradient=True)
helper.append_op(
type='cudnn_lstm',
inputs={
'Input': input,
'InitH': init_h,
'InitC': init_c,
'W': weight,
'Cache': cache,
},
outputs={
'Out': out,
'last_h': last_h,
'last_c': last_c,
},
attrs={
'max_len': max_len,
'is_bidirec': is_bidirec,
'input_size': input_size,
'hidden_size': hidden_size,
'num_layers': num_layers,
'is_test': is_test,
'dropout_prob': dropout_prob,
'seed': seed,
})
return out, last_h, last_c
def dynamic_lstmp(input,
size,
proj_size,
param_attr=None,
bias_attr=None,
use_peepholes=True,
is_reverse=False,
gate_activation='sigmoid',
cell_activation='tanh',
candidate_activation='tanh',
proj_activation='tanh',
dtype='float32',
name=None,
h_0=None,
c_0=None,
cell_clip=None,
proj_clip=None):
"""
**Note**:
1. In order to improve efficiency, users must first map the input of dimension [T, hidden_size] to input of [T, 4 * hidden_size], and then pass it to this OP.
This OP implements the LSTMP (LSTM Projected) layer.
The LSTMP layer has a separate linear mapping layer behind the LSTM layer. -- `Sak, H., Senior, A., & Beaufays, F. (2014) <https://ai.google/research/pubs/pub43905.pdf>`_ .
Compared with the standard LSTM layer, LSTMP has an additional linear mapping layer,
which is used to map from the original hidden state :math:`h_t` to the lower dimensional state :math:`r_t` .
This reduces the total number of parameters and computational complexity, especially when the output unit is relatively large.
The default implementation of the OP contains diagonal/peephole connections,
please refer to `Gers, F. A., & Schmidhuber, J. (2000) <ftp://ftp.idsia.ch/pub/juergen/TimeCount-IJCNN2000.pdf>`_ .
If you need to disable the peephole connections, set use_peepholes to False.
This OP computes each timestep as follows:
.. math::
i_t = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)
.. math::
f_t = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)
.. math::
o_t = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_{t-1} + b_o)
.. math::
\widetilde{c_t} = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)
.. math::
c_t = f_t \odot c_{t-1} + i_t \odot \widetilde{c_t}
.. math::
h_t = o_t \odot act_h(c_t)
.. math::
r_t = \overline{act_h}(W_{rh}h_t)
The symbolic meanings in the formula are as follows:
- :math:`x_{t}` represents the input at timestep :math:`t`
- :math:`h_{t}` represents the hidden state at timestep :math:`t`
- :math:`r_{t}` : represents the state of the projected output of the hidden state :math:`h_{t}`
- :math:`h_{t-1}, c_{t-1}, r_{t-1}` represent the hidden state, cell state and projected output at timestep :math:`t-1` , respectively
- :math:`\widetilde{c_t}` represents the candidate cell state
- :math:`i_t` , :math:`f_t` and :math:`o_t` represent input gate, forget gate, output gate, respectively
- :math:`W` represents weight (e.g., :math:`W_{ix}` is the weight of a linear transformation of input :math:`x_{t}` when calculating input gate :math:`i_t` )
- :math:`b` represents bias (e.g., :math:`b_{i}` is the bias of input gate)
- :math:`\sigma` represents nonlinear activation function for gate, default sigmoid
- :math:`\odot` represents the Hadamard product of a matrix, i.e. multiplying the elements of the same position for two matrices with the same dimension to get another matrix with the same dimension
Parameters:
input( :ref:`api_guide_Variable_en` ): The input of dynamic_lstmp layer, which supports
variable-time length input sequence.
It is a multi-dimensional LODTensor of shape :math:`[T, 4*hidden\_size]` . Data type is float32 or float64.
size(int): must be 4 * hidden_size.
proj_size(int): The size of projection output.
param_attr(ParamAttr, optional): Parameter attribute of weight. If it is None, the default weight parameter attribute is used. Please refer to ref:`api_fluid_ParamAttr' .
If the user needs to set this parameter, the dimension must be :math:`[hidden\_size, 4*hidden\_size]` . Default: None.
- Weights = :math:`\{ W_{cr},W_{ir},W_{fr},W_{or} \}` , the shape is [P, 4*hidden_size] , where P is the projection size.
- Projection weight = :math:`\{ W_{rh} \}` , the shape is [hidden_size, P].
bias_attr (ParamAttr, optional): The bias attribute for the learnable bias
weights, which contains two parts, input-hidden
bias weights and peephole connections weights if
setting `use_peepholes` to `True`.
Please refer to ref:`api_fluid_ParamAttr' . Default: None.
1. `use_peepholes = False`
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
- The shape is [1, 4*hidden_size].
2. `use_peepholes = True`
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
W_{fc}, W_{oc}`}.
- The shape is [1, 7*hidden_size].
use_peepholes (bool, optional): Whether to use peephole connection or not. Default True.
is_reverse (bool, optional): Whether to calculate reverse LSTM. Default False.
gate_activation (str, optional): The activation for input gate, forget gate and output gate. Default "sigmoid".
cell_activation (str, optional): The activation for cell output. Default "tanh".
candidate_activation (str, optional): The activation for candidate hidden state. Default "tanh".
proj_activation(str, optional): The activation for projection output. Default "tanh".
dtype (str, optional): Data type, can be "float32" or "float64". Default "float32".
name (str, optional): A name for this layer. Please refer to :ref:`api_guide_Name` . Default: None.
h_0( :ref:`api_guide_Variable` , optional): The initial hidden state is an optional input, default is zero.
This is a tensor with shape :math:`[batch\_size, P]` , where P is the projection size. Default: None.
c_0( :ref:`api_guide_Variable` , optional): The initial cell state is an optional input, default is zero.
This is a tensor with shape :math:`[batch\_size, P]` , where P is the projection size.
`h_0` and `c_0` can be None but only at the same time. Default: None.
cell_clip(float, optional): If not None, the cell state is clipped
by this value prior to the cell output activation. Default: None.
proj_clip(float, optional): If `num_proj > 0` and `proj_clip` is
provided, then the projected values are clipped elementwise to within
`[-proj_clip, proj_clip]`. Default: None.
Returns:
tuple ( :ref:`api_guide_Variable` , :ref:`api_guide_Variable` ) :
The hidden state and cell state of LSTMP
- hidden: LoDTensor with shape of :math:`[T, P]` , and its lod and dtype is the same as the input.
- cell: LoDTensor with shape of :math:`[T, hidden\_size]` , and its lod and dtype is the same as the input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
dict_dim, emb_dim = 128, 64
data = fluid.data(name='sequence', shape=[None], dtype='int64', lod_level=1)
emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
hidden_dim, proj_dim = 512, 256
fc_out = fluid.layers.fc(input=emb, size=hidden_dim * 4,
act=None, bias_attr=None)
proj_out, last_c = fluid.layers.dynamic_lstmp(input=fc_out,
size=hidden_dim * 4,
proj_size=proj_dim,
use_peepholes=False,
is_reverse=True,
cell_activation="tanh",
proj_activation="tanh")
proj_out.shape # (-1, 256)
last_c.shape # (-1, 512)
"""
assert in_dygraph_mode(
) is not True, "please use lstm instead of dynamic_lstmp in dygraph mode!"
assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
helper = LayerHelper('lstmp', **locals())
size = size // 4
weight = helper.create_parameter(
attr=helper.param_attr, shape=[proj_size, 4 * size], dtype=dtype)
proj_weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, proj_size], dtype=dtype)
bias_size = [1, 7 * size]
if not use_peepholes:
bias_size[1] = 4 * size
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
projection = helper.create_variable_for_type_inference(dtype)
cell = helper.create_variable_for_type_inference(dtype)
ordered_proj0 = helper.create_variable_for_type_inference(dtype)
batch_hidden = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
inputs = {
'Input': input,
'Weight': weight,
'ProjWeight': proj_weight,
'Bias': bias
}
batch_size = input.shape[0]
if h_0:
assert h_0.shape == (batch_size, proj_size), \
'The shape of h0 should be (batch_size, %d)' % proj_size
inputs['H0'] = h_0
if c_0:
assert c_0.shape == (batch_size, size), \
'The shape of c0 should be (batch_size, %d)' % size
inputs['C0'] = c_0
if cell_clip:
assert cell_clip >= 0, "cell_clip should not be negative."
if proj_clip:
assert proj_clip >= 0, "proj_clip should not be negative."
helper.append_op(
type='lstmp',
inputs=inputs,
outputs={
'Projection': projection,
'Cell': cell,
'BatchHidden': batch_hidden,
'BatchGate': batch_gate,
'BatchCellPreAct': batch_cell_pre_act
},
attrs={
'use_peepholes': use_peepholes,
'cell_clip': cell_clip,
'proj_clip': proj_clip,
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'cell_activation': cell_activation,
'candidate_activation': candidate_activation,
'proj_activation': proj_activation
})
return projection, cell
def dynamic_gru(input,
size,
param_attr=None,
bias_attr=None,
is_reverse=False,
gate_activation='sigmoid',
candidate_activation='tanh',
h_0=None,
origin_mode=False):
"""
**Note: The input type of this must be LoDTensor. If the input type to be
processed is Tensor, use** :ref:`api_fluid_layers_StaticRNN` .
This operator is used to perform the calculations for a single layer of
Gated Recurrent Unit (GRU) on full sequences step by step. The calculations
in one time step support these two modes:
If ``origin_mode`` is True, then the formula used is from paper
`Learning Phrase Representations using RNN Encoder Decoder for Statistical
Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_ .
.. math::
u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
h_t & = u_t \odot h_{t-1} + (1-u_t) \odot \\tilde{h_t}
if ``origin_mode`` is False, then the formula used is from paper
`Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_
.. math::
u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
:math:`x_t` is the input of current time step, but it is not from ``input`` .
This operator does not include the calculations :math:`W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}` ,
**Note** thus a fully-connect layer whose size is 3 times of ``size`` should
be used before this operator, and the output should be used as ``input`` here.
:math:`h_{t-1}` is the hidden state from previous time step.
:math:`u_t` , :math:`r_t` , :math:`\\tilde{h_t}` and :math:`h_t` stand for
update gate, reset gate, candidate hidden and hidden output separately.
:math:`W_{uh}, b_u` , :math:`W_{rh}, b_r` and :math:`W_{ch}, b_c` stand for
the weight matrix and bias used in update gate, reset gate, candidate hidden
calculations. For implementation, the three weight matrix are merged into a
tensor shaped :math:`[D, D \\times 3]` , the three bias are concatenated as
a tensor shaped :math:`[1, D \\times 3]` , where :math:`D` stands for the
hidden size; The data layout of weight tensor is: :math:`W_{uh}` and :math:`W_{rh}`
are concatenated with shape :math:`[D, D \\times 2]` lying on the first part,
and :math:`W_{ch}` lying on the latter part with shape :math:`[D, D]` .
Args:
input(Variable): A LoDTensor whose lod level is 1, representing the input
after linear projection. Its shape should be :math:`[T, D \\times 3]` ,
where :math:`T` stands for the total sequence lengths in this mini-batch,
:math:`D` for the hidden size. The data type should be float32 or float64.
size(int): Indicate the hidden size.
param_attr(ParamAttr, optional): To specify the weight parameter property.
Default: None, which means the default weight parameter property is used.
See usage for details in :ref:`api_fluid_ParamAttr` .
bias_attr (ParamAttr, optional): To specify the bias parameter property.
Default: None, which means the default bias parameter property is used.
See usage for details in :ref:`api_fluid_ParamAttr` .
is_reverse(bool, optional): Whether to compute in the reversed order of
input sequences. Default False.
gate_activation(str, optional): The activation function corresponding to
:math:`act_g` in the formula. "sigmoid", "tanh", "relu" and "identity"
are supported. Default "sigmoid".
candidate_activation(str, optional): The activation function corresponding to
:math:`act_c` in the formula. "sigmoid", "tanh", "relu" and "identity"
are supported. Default "tanh".
h_0 (Variable, optional): A Tensor representing the initial hidden state.
It not provided, the default initial hidden state is 0. The shape is
:math:`[N, D]` , where :math:`N` is the number of sequences in the
mini-batch, :math:`D` for the hidden size. The data type should be
same as ``input`` . Default None.
Returns:
Variable: A LoDTensor whose lod level is 1 and shape is :math:`[T, D]` , \
where :math:`T` stands for the total sequence lengths in this mini-batch \
:math:`D` for the hidden size. It represents GRU transformed sequence output, \
and has the same lod and data type with ``input`` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
dict_dim, emb_dim = 128, 64
data = fluid.data(name='sequence',
shape=[None],
dtype='int64',
lod_level=1)
emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
hidden_dim = 512
x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
hidden = fluid.layers.dynamic_gru(input=x, size=hidden_dim)
"""
assert in_dygraph_mode(
) is not True, "please use gru instead of dynamic_gru in dygraph mode!"
helper = LayerHelper('gru', **locals())
dtype = helper.input_dtype()
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
bias = helper.create_parameter(
attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
batch_size = input.shape[0]
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
if h_0:
assert h_0.shape == (
batch_size, size
), 'The shape of h0 should be(batch_size, %d)' % size
inputs['H0'] = h_0
hidden = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_reset_hidden_prev = helper.create_variable_for_type_inference(dtype)
batch_hidden = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='gru',
inputs=inputs,
outputs={
'Hidden': hidden,
'BatchGate': batch_gate,
'BatchResetHiddenPrev': batch_reset_hidden_prev,
'BatchHidden': batch_hidden
},
attrs={
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'activation': candidate_activation,
'origin_mode': origin_mode
})
return hidden
def gru_unit(input,
hidden,
size,
param_attr=None,
bias_attr=None,
activation='tanh',
gate_activation='sigmoid',
origin_mode=False):
"""
Gated Recurrent Unit (GRU) RNN cell. This operator performs GRU calculations for
one time step and it supports these two modes:
If ``origin_mode`` is True, then the formula used is from paper
`Learning Phrase Representations using RNN Encoder Decoder for Statistical
Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_ .
.. math::
u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
h_t & = u_t \odot h_{t-1} + (1-u_t) \odot \\tilde{h_t}
if ``origin_mode`` is False, then the formula used is from paper
`Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_
.. math::
u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
:math:`x_t` is the input of current time step, but it is not ``input`` .
This operator does not include the calculations :math:`W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}` ,
**Note** thus a fully-connect layer whose size is 3 times of GRU hidden size should
be used before this operator, and the output should be used as ``input`` here.
:math:`h_{t-1}` is the hidden state from previous time step.
:math:`u_t` , :math:`r_t` , :math:`\\tilde{h_t}` and :math:`h_t` stand for
update gate, reset gate, candidate hidden and hidden output separately.
:math:`W_{uh}, b_u` , :math:`W_{rh}, b_r` and :math:`W_{ch}, b_c` stand for
the weight matrix and bias used in update gate, reset gate, candidate hidden
calculations. For implementation, the three weight matrix are merged into a
tensor shaped :math:`[D, D \\times 3]` , the three bias are concatenated as
a tensor shaped :math:`[1, D \\times 3]` , where :math:`D` stands for the
hidden size; The data layout of weight tensor is: :math:`W_{uh}` and :math:`W_{rh}`
are concatenated with shape :math:`[D, D \\times 2]` lying on the first part,
and :math:`W_{ch}` lying on the latter part with shape :math:`[D, D]` .
Args:
input(Variable): A 2D Tensor representing the input after linear projection
after linear projection. Its shape should be :math:`[N, D \\times 3]` ,
where :math:`N` stands for batch size, :math:`D` for the hidden size.
The data type should be float32 or float64.
hidden(Variable): A 2D Tensor representing the hidden state from previous step.
Its shape should be :math:`[N, D]` , where :math:`N` stands for batch size,
:math:`D` for the hidden size. The data type should be same as ``input`` .
size(int): Indicate the hidden size.
param_attr(ParamAttr, optional): To specify the weight parameter property.
Default: None, which means the default weight parameter property is used.
See usage for details in :ref:`api_fluid_ParamAttr` .
bias_attr (ParamAttr, optional): To specify the bias parameter property.
Default: None, which means the default bias parameter property is used.
See usage for details in :ref:`api_fluid_ParamAttr` .
activation(str, optional): The activation function corresponding to
:math:`act_c` in the formula. "sigmoid", "tanh", "relu" and "identity"
are supported. Default "tanh".
gate_activation(str, optional): The activation function corresponding to
:math:`act_g` in the formula. "sigmoid", "tanh", "relu" and "identity"
are supported. Default "sigmoid".
Returns:
tuple: The tuple contains three Tensor variables with the same data type \
as ``input`` . They represent the hidden state for next time step ( :math:`h_t` ), \
reset previous hidden state ( :math:`r_t \odot h_{t-1}` ), and the \
concatenation of :math:`h_t, r_t, \\tilde{h_t}` . And they have shape \
:math:`[N, D]` , :math:`[N, D]` , :math:`[N, D \times 3]` separately. \
Usually only the hidden state for next time step ( :math:`h_t` ) is used \
as output and state, the other two are intermediate results of calculations.
Examples:
.. code-block:: python
import paddle.fluid as fluid
dict_dim, emb_dim = 128, 64
data = fluid.data(name='step_data', shape=[None], dtype='int64')
emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
hidden_dim = 512
x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
pre_hidden = fluid.data(
name='pre_hidden', shape=[None, hidden_dim], dtype='float32')
hidden = fluid.layers.gru_unit(
input=x, hidden=pre_hidden, size=hidden_dim * 3)
"""
activation_dict = dict(
identity=0,
sigmoid=1,
tanh=2,
relu=3, )
activation = activation_dict[activation]
gate_activation = activation_dict[gate_activation]
helper = LayerHelper('gru_unit', **locals())
dtype = helper.input_dtype()
size = size // 3
# create weight
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
gate = helper.create_variable_for_type_inference(dtype)
reset_hidden_pre = helper.create_variable_for_type_inference(dtype)
updated_hidden = helper.create_variable_for_type_inference(dtype)
inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': weight}
# create bias
if helper.bias_attr:
bias_size = [1, 3 * size]
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
inputs['Bias'] = bias
helper.append_op(
type='gru_unit',
inputs=inputs,
outputs={
'Gate': gate,
'ResetHiddenPrev': reset_hidden_pre,
'Hidden': updated_hidden,
},
attrs={
'activation': 2, # tanh
'gate_activation': 1, # sigmoid
'origin_mode': origin_mode
})
return updated_hidden, reset_hidden_pre, gate
def beam_search(pre_ids,
pre_scores,
ids,
scores,
beam_size,
end_id,
level=0,
is_accumulated=True,
name=None,
return_parent_idx=False):
"""
Beam search is a classical algorithm for selecting candidate words in a
machine translation task.
Refer to `Beam search <https://en.wikipedia.org/wiki/Beam_search>`_
for more details.
**This operator only supports LoDTensor.** It is used after finishing
scores calculation to perform beam search for one time step. Specifically,
after ``ids`` and ``scores`` have been produced, it selects the top-K
( `k` is ``beam_size`` ) candidate word ids of current step from ``ids``
according to the corresponding ``scores``. Additionally, ``pre_id`` and
``pre_scores`` are the output of `beam_search` at previous step, they
are needed for special use to handle ended candidate translations.
Note that if ``is_accumulated`` is True, the ``scores`` passed in should
be accumulated scores. Otherwise, the ``scores`` are
considered as the probabilities of single step and would be transformed to
the log field and added up with ``pre_scores`` for final scores in this
operator. Length penalty should be done with extra operators before calculating
the accumulated scores if needed.
Please see the following demo for a fully beam search usage example:
fluid/tests/book/test_machine_translation.py
Args:
pre_ids(Variable): A LodTensor variable (lod level is 2), representing
the selected ids of previous step. It is the output of beam_search
at previous step. Its shape is `[batch_size, 1]` and its lod is
`[[0, 1, ... , batch_size], [0, 1, ..., batch_size]]` at the
first step. The data type should be int64.
pre_scores(Variable): A LodTensor variable has the same shape and lod
with ``pre_ids`` , representing the accumulated scores corresponding
to the selected ids of previous step. It is the output of
beam_search at previous step. The data type should be float32.
ids(Variable|None): A LodTensor variable containing the candidates ids.
It has the same lod with ``pre_ids`` and its shape should be
`[batch_size * beam_size, K]`, where `K` supposed to be greater than
``beam_size`` and the first dimension size (decrease as samples reach
to the end) should be same as that of ``pre_ids`` . The data type
should be int64. It can be None, which use index in ``scores`` as
ids.
scores(Variable): A LodTensor variable containing the accumulated
scores corresponding to ``ids`` . Both its shape and lod are same as
those of ``ids`` . The data type should be float32.
beam_size(int): The beam width used in beam search.
end_id(int): The id of end token.
level(int): **It can be ignored and mustn't change currently.**
The 2 level lod used in this operator has the following
meaning: The first level describes how many beams each sample has,
which would change to 0 when beams of the sample all end (batch reduce);
The second level describes how many times each beam is selected.
Default 0, which shouldn't be changed currently.
is_accumulated(bool): Whether the input ``score`` is accumulated scores.
Default True.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
return_parent_idx(bool, optional): Whether to return an extra Tensor variable
in output, which stores the selected ids' parent index in
``pre_ids`` and can be used to update RNN's states by gather operator.
Default False.
Returns:
tuple: The tuple contains two or three LodTensor variables. The two LodTensor, \
representing the selected ids and the corresponding accumulated scores of \
current step, have the same shape `[batch_size, beam_size]` and lod with 2 levels, \
and have data types int64 and float32. If ``return_parent_idx`` is True, \
an extra Tensor variable preserving the selected ids' parent index \
is included, whose shape is `[batch_size * beam_size]` and data type \
is int64.
Examples:
.. code-block:: python
import paddle.fluid as fluid
# Suppose `probs` contains predicted results from the computation
# cell and `pre_ids` and `pre_scores` is the output of beam_search
# at previous step.
beam_size = 4
end_id = 1
pre_ids = fluid.data(
name='pre_id', shape=[None, 1], lod_level=2, dtype='int64')
pre_scores = fluid.data(
name='pre_scores', shape=[None, 1], lod_level=2, dtype='float32')
probs = fluid.data(
name='probs', shape=[None, 10000], dtype='float32')
topk_scores, topk_indices = fluid.layers.topk(probs, k=beam_size)
accu_scores = fluid.layers.elementwise_add(
x=fluid.layers.log(x=topk_scores),
y=fluid.layers.reshape(pre_scores, shape=[-1]),
axis=0)
selected_ids, selected_scores = fluid.layers.beam_search(
pre_ids=pre_ids,
pre_scores=pre_scores,
ids=topk_indices,
scores=accu_scores,
beam_size=beam_size,
end_id=end_id)
"""
helper = LayerHelper('beam_search', **locals())
score_type = pre_scores.dtype
id_type = pre_ids.dtype
inputs = {"pre_ids": pre_ids, "pre_scores": pre_scores, "scores": scores}
if ids is not None:
inputs["ids"] = ids
selected_scores = helper.create_variable_for_type_inference(
dtype=score_type)
selected_ids = helper.create_variable_for_type_inference(dtype=id_type)
# parent_idx is a tensor used to gather cell states at the next time
# step. Though lod in selected_ids can also be used to gather by
# sequence_expand, it is not efficient.
# gather_op's index input only supports int32 dtype currently
parent_idx = helper.create_variable_for_type_inference(dtype="int32")
helper.append_op(
type='beam_search',
inputs=inputs,
outputs={
'selected_ids': selected_ids,
'selected_scores': selected_scores,
'parent_idx': parent_idx
},
attrs={
# TODO(ChunweiYan) to assure other value support
'level': level,
'beam_size': beam_size,
'end_id': end_id,
'is_accumulated': is_accumulated,
})
if return_parent_idx:
return selected_ids, selected_scores, parent_idx
else:
return selected_ids, selected_scores
def beam_search_decode(ids, scores, beam_size, end_id, name=None):
"""
This operator is used after beam search has completed. It constructs the
full predicted sequences for each sample by walking back along the search
paths stored in lod of ``ids`` . The result sequences are stored in a
LoDTensor, which uses the following way to parse:
.. code-block:: text
If lod = [[0, 3, 6], [0, 12, 24, 40, 54, 67, 82]]
The first level of lod stands for: There are 2 samples each having 3
(beam width) predicted sequence.
The second level of lod stands for: The lengths of the first sample's
3 predicted sequences are 12, 12, 16; The lengths of the second sample's
3 predicted sequences are 14, 13, 15.
Please see the following demo for a fully beam search usage example:
fluid/tests/book/test_machine_translation.py
Args:
ids(Variable): The LoDTensorArray variable containing the selected ids
of all steps. Each LoDTensor in it has int64 data type and 2 level
lod which can be used to get the search paths.
scores(Variable): The LodTensorArray variable containing the accumulated
scores corresponding to selected ids of all steps. It has the same size
as ``ids`` . Each LoDTensor in it has the same shape and lod as the
counterpart in ``ids`` , and has a float32 data type.
beam_size(int): The beam width used in beam search.
end_id(int): The id of end token.
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: The tuple contains two LodTensor variables. The two LodTensor, \
containing the full sequences of ids and the corresponding accumulated \
scores, have the same shape flattened to 1D and have the same 2 level \
lod. The lod can be used to get how many predicted sequences each sample \
has and how many ids each predicted sequence has.
Examples:
.. code-block:: python
import paddle.fluid as fluid
# Suppose `ids` and `scores` are LodTensorArray variables reserving
# the selected ids and scores of all steps
ids = fluid.layers.create_array(dtype='int64')
scores = fluid.layers.create_array(dtype='float32')
finished_ids, finished_scores = fluid.layers.beam_search_decode(
ids, scores, beam_size=5, end_id=0)
"""
helper = LayerHelper('beam_search_decode', **locals())
sentence_ids = helper.create_variable_for_type_inference(dtype=ids.dtype)
sentence_scores = helper.create_variable_for_type_inference(dtype=ids.dtype)
helper.append_op(
type="beam_search_decode",
inputs={"Ids": ids,
"Scores": scores},
outputs={
"SentenceIds": sentence_ids,
"SentenceScores": sentence_scores
},
attrs={"beam_size": beam_size,
"end_id": end_id})
return sentence_ids, sentence_scores
def lstm_unit(x_t,
hidden_t_prev,
cell_t_prev,
forget_bias=0.0,
param_attr=None,
bias_attr=None,
name=None):
"""
Long-Short Term Memory (LSTM) RNN cell. This operator performs LSTM calculations for
one time step, whose implementation is based on calculations described in `RECURRENT
NEURAL NETWORK REGULARIZATION <http://arxiv.org/abs/1409.2329>`_ .
We add forget_bias to the biases of the forget gate in order to
reduce the scale of forgetting. The formula is as follows:
.. math::
i_{t} & = \sigma(W_{x_{i}}x_{t} + W_{h_{i}}h_{t-1} + b_{i})
f_{t} & = \sigma(W_{x_{f}}x_{t} + W_{h_{f}}h_{t-1} + b_{f} + forget\\_bias)
c_{t} & = f_{t}c_{t-1} + i_{t} tanh (W_{x_{c}}x_{t} + W_{h_{c}}h_{t-1} + b_{c})
o_{t} & = \sigma(W_{x_{o}}x_{t} + W_{h_{o}}h_{t-1} + b_{o})
h_{t} & = o_{t} tanh (c_{t})
:math:`x_{t}` stands for ``x_t`` , corresponding to the input of current time step;
:math:`h_{t-1}` and :math:`c_{t-1}` correspond to ``hidden_t_prev`` and ``cell_t_prev`` ,
representing the output of from previous time step.
:math:`i_{t}, f_{t}, c_{t}, o_{t}, h_{t}` are input gate, forget gate, cell, output gate
and hidden calculation.
Args:
x_t(Variable): A 2D Tensor representing the input of current time step.
Its shape should be :math:`[N, M]` , where :math:`N` stands for batch
size, :math:`M` for the feature size of input. The data type should
be float32 or float64.
hidden_t_prev(Variable): A 2D Tensor representing the hidden value from
previous step. Its shape should be :math:`[N, D]` , where :math:`N`
stands for batch size, :math:`D` for the hidden size. The data type
should be same as ``x_t`` .
cell_t_prev(Variable): A 2D Tensor representing the cell value from
previous step. It has the same shape and data type with ``hidden_t_prev`` .
forget_bias (float, optional): :math:`forget\\_bias` added to the biases
of the forget gate. Default 0.
param_attr(ParamAttr, optional): To specify the weight parameter property.
Default: None, which means the default weight parameter property is used.
See usage for details in :ref:`api_fluid_ParamAttr` .
bias_attr (ParamAttr, optional): To specify the bias parameter property.
Default: None, which means the default bias parameter property is used.
See usage for details in :ref:`api_fluid_ParamAttr` .
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: The tuple contains two Tensor variables with the same shape and \
data type with ``hidden_t_prev`` , representing the hidden value and \
cell value which correspond to :math:`h_{t}` and :math:`c_{t}` in \
the formula.
Raises:
ValueError: Rank of x_t must be 2.
ValueError: Rank of hidden_t_prev must be 2.
ValueError: Rank of cell_t_prev must be 2.
ValueError: The 1st dimensions of x_t, hidden_t_prev and cell_t_prev must be the same.
ValueError: The 2nd dimensions of hidden_t_prev and cell_t_prev must be the same.
Examples:
.. code-block:: python
import paddle.fluid as fluid
dict_dim, emb_dim, hidden_dim = 128, 64, 512
data = fluid.data(name='step_data', shape=[None], dtype='int64')
x = fluid.embedding(input=data, size=[dict_dim, emb_dim])
pre_hidden = fluid.data(
name='pre_hidden', shape=[None, hidden_dim], dtype='float32')
pre_cell = fluid.data(
name='pre_cell', shape=[None, hidden_dim], dtype='float32')
hidden = fluid.layers.lstm_unit(
x_t=x,
hidden_t_prev=pre_hidden,
cell_t_prev=pre_cell)
"""
helper = LayerHelper('lstm_unit', **locals())
if len(x_t.shape) != 2:
raise ValueError("Rank of x_t must be 2.")
if len(hidden_t_prev.shape) != 2:
raise ValueError("Rank of hidden_t_prev must be 2.")
if len(cell_t_prev.shape) != 2:
raise ValueError("Rank of cell_t_prev must be 2.")
if x_t.shape[0] != hidden_t_prev.shape[0] or x_t.shape[
0] != cell_t_prev.shape[0]:
raise ValueError("The 1st dimensions of x_t, hidden_t_prev and "
"cell_t_prev must be the same.")
if hidden_t_prev.shape[1] != cell_t_prev.shape[1]:
raise ValueError("The 2nd dimensions of hidden_t_prev and "
"cell_t_prev must be the same.")
if bias_attr is None:
bias_attr = ParamAttr()
size = cell_t_prev.shape[1]
concat_out = nn.concat(input=[x_t, hidden_t_prev], axis=1)
fc_out = nn.fc(input=concat_out,
size=4 * size,
param_attr=param_attr,
bias_attr=bias_attr)
dtype = x_t.dtype
c = helper.create_variable_for_type_inference(dtype)
h = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='lstm_unit',
inputs={"X": fc_out,
"C_prev": cell_t_prev},
outputs={"C": c,
"H": h},
attrs={"forget_bias": forget_bias})
return h, c
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