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Add python api for lstmp operator

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emailweixu-patch-1
Yibing Liu 8 years ago
parent 7278aa7b1c
commit 4ca19d4e18
2 changed files with 183 additions and 2 deletions
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7
doc/api/v2/fluid/layers.rst
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@ -18,7 +18,12 @@ dynamic_lstm
.. autofunction:: paddle.v2.fluid.layers.dynamic_lstm
:noindex:
dynamic_gru
dynamic_lstmp
------------
.. autofunction:: paddle.v2.fluid.layers.dynamic_lstmp
:noindex:
dynamic_gru
-----------
.. autofunction:: paddle.v2.fluid.layers.dynamic_gru
:noindex:

178
python/paddle/v2/fluid/layers/nn.py
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@ -26,6 +26,7 @@ __all__ = [
'fc',
'embedding',
'dynamic_lstm',
'dynamic_lstmp',
'dynamic_gru',
'gru_unit',
'linear_chain_crf',
@ -282,7 +283,7 @@ def dynamic_lstm(input,
W_{fc}, W_{oc}` are diagonal weight matrices for peephole connections. In
our implementation, we use vectors to reprenset these diagonal weight
matrices. The :math:`b` terms denote bias vectors (:math:`b_i` is the input
gate bias vector), :math:`\sigma` is the non-line activations, such as
gate bias vector), :math:`\sigma` is the non-linear activations, such as
logistic sigmoid function, and :math:`i, f, o` and :math:`c` are the input
gate, forget gate, output gate, and cell activation vectors, respectively,
all of which have the same size as the cell output activation vector :math:`h`.
@ -389,6 +390,181 @@ def dynamic_lstm(input,
return hidden, cell
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'):
"""
**Dynamic LSTMP Layer**
LSTMP (LSTM with recurrent projection) layer has a separate projection
layer after the LSTM layer, projecting the original hidden state to a
lower-dimensional one, which is proposed to reduce the number of total
parameters and furthermore computational complexity for the LSTM,
espeacially for the case that the size of output units is relative
large (https://research.google.com/pubs/archive/43905.pdf).
The formula is as follows:
.. math::
i_t = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i) \\
f_t = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f) \\
\tilde{c_t} = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c) \\
o_t = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o) \\
c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t} \\
h_t = o_t \odot act_h(c_t) \\
r_t = \overline{act_h}(W_{rh}h_t)
where the :math:`W` terms denote weight matrices (e.g. :math:`W_{xi}` is
the matrix of weights from the input gate to the input), :math:`W_{ic}`,
:math:`W_{fc}`, :math:`W_{oc}` are diagonal weight matrices for peephole
connections. In our implementation, we use vectors to reprenset these
diagonal weight matrices. The :math:`b` terms denote bias vectors
(:math:`b_i` is the input gate bias vector), :math:`\sigma` is the
activation, such as logistic sigmoid function, and :math:`i, f, o` and
:math:`c` are the input gate, forget gate, output gate, and cell activation
vectors, respectively, all of which have the same size as the cell output
activation vector :math:`h`. Here :math:`h` is usually called the hidden
state and :math:`r` denotes its recurrent projection. And
:math:`\tilde{c_t}` is also called the candidate hidden state, whose
computation is based on the current input and previous hidden state.
The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
and :math:`act_h` are the cell input and cell output activation functions
and `tanh` is usually used for them. :math:`\overline{act_h}` is the
activation function for the projection output, usually using `identity` or
same as :math:`act_h`.
Set `use_peepholes` to `False` to disable peephole connection. The formula
is omitted here, please refer to the paper
http://www.bioinf.jku.at/publications/older/2604.pdf for details.
Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
operations on the input :math:`x_{t}` are NOT included in this operator.
Users can choose to use fully-connected layer before LSTMP layer.
Args:
input(Variable): The input of dynamic_lstmp layer, which supports
variable-time length input sequence. The underlying
tensor in this Variable is a matrix with shape
(T X 4D), where T is the total time steps in this
mini-batch, D is the hidden size.
size(int): 4 * hidden size.
proj_size(int): The size of projection output.
param_attr(ParamAttr): The parameter attribute for the learnable
hidden-hidden weight and projection weight.
- The shape of hidden-hidden weight is (P x 4D),
where P is the projection size and D the hidden
size.
- The shape of projection weight is (D x P).
- Hidden-hidden weight = {:math:`W_{ch}, W_{ih}, \
W_{fh}, W_{oh}`}.
- Projection weight = {:math:`W_{rh}`}.
bias_attr(ParamAttr): 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`.
1. `use_peepholes = False`
- The shape is (1 x 4D).
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
2. `use_peepholes = True`
- The shape is (1 x 7D).
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
W_{fc}, W_{oc}`}.
use_peepholes(bool): Whether to enable diagonal/peephole connections,
default `True`.
is_reverse(bool): Whether to compute reversed LSTM, default `False`.
gate_activation(str): The activation for input gate, forget gate and
output gate. Choices = ["sigmoid", "tanh", "relu",
"identity"], default "sigmoid".
cell_activation(str): The activation for cell output. Choices = ["sigmoid",
"tanh", "relu", "identity"], default "tanh".
candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"],
default "tanh".
proj_activation(str): The activation for projection output.
Choices = ["sigmoid", "tanh", "relu", "identity"],
default "tanh".
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
Returns:
tuple: The projection of hidden state, and cell state of LSTMP. The
shape of projection is (T x P), for the cell state which is
(T x D), and both LoD is the same with the `input`.
Examples:
.. code-block:: python
hidden_dim = 512
proj_dim = 256
fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
act=None, bias_attr=None)
proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
size=hidden_dim * 4, proj_size=proj_dim, use_peepholes=False)
"""
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_tmp_variable(dtype)
cell = helper.create_tmp_variable(dtype)
ordered_proj0 = helper.create_tmp_variable(dtype)
batch_hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_cell_pre_act = helper.create_tmp_variable(dtype)
helper.append_op(
type='lstmp',
inputs={
'Input': input,
'Weight': weight,
'ProjWeight': proj_weight,
'Bias': bias
},
outputs={
'Projection': projection,
'Cell': cell,
'OrderedP0': ordered_proj0,
'BatchHidden': batch_hidden,
'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,
'proj_activation': proj_activation
})
return projection, cell
def dynamic_gru(input,
size,
param_attr=None,

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