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PaddleSpeech/deepspeech/models/ds2/rnn.py

316 lines
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3 years ago
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import paddle
from paddle import nn
from paddle.nn import functional as F
from paddle.nn import initializer as I
from deepspeech.modules.activation import brelu
from deepspeech.modules.mask import make_non_pad_mask
from deepspeech.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ['RNNStack']
class RNNCell(nn.RNNCellBase):
r"""
Elman RNN (SimpleRNN) cell. Given the inputs and previous states, it
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computes the outputs and updates states.
The formula used is as follows:
.. math::
h_{t} & = act(x_{t} + b_{ih} + W_{hh}h_{t-1} + b_{hh})
y_{t} & = h_{t}
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where :math:`act` is for :attr:`activation`.
"""
def __init__(self,
hidden_size: int,
activation="tanh",
weight_ih_attr=None,
weight_hh_attr=None,
bias_ih_attr=None,
bias_hh_attr=None,
name=None):
super().__init__()
std = 1.0 / math.sqrt(hidden_size)
self.weight_hh = self.create_parameter(
(hidden_size, hidden_size),
weight_hh_attr,
default_initializer=I.Uniform(-std, std))
self.bias_ih = None
self.bias_hh = self.create_parameter(
(hidden_size, ),
bias_hh_attr,
is_bias=True,
default_initializer=I.Uniform(-std, std))
self.hidden_size = hidden_size
if activation not in ["tanh", "relu", "brelu"]:
raise ValueError(
"activation for SimpleRNNCell should be tanh or relu, "
"but get {}".format(activation))
self.activation = activation
self._activation_fn = paddle.tanh \
if activation == "tanh" \
else F.relu
if activation == 'brelu':
self._activation_fn = brelu
def forward(self, inputs, states=None):
if states is None:
states = self.get_initial_states(inputs, self.state_shape)
pre_h = states
i2h = inputs
if self.bias_ih is not None:
i2h += self.bias_ih
h2h = paddle.matmul(pre_h, self.weight_hh, transpose_y=True)
if self.bias_hh is not None:
h2h += self.bias_hh
h = self._activation_fn(i2h + h2h)
return h, h
@property
def state_shape(self):
return (self.hidden_size, )
class GRUCell(nn.RNNCellBase):
r"""
Gated Recurrent Unit (GRU) RNN cell. Given the inputs and previous states,
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it computes the outputs and updates states.
The formula for GRU used is as follows:
.. math::
r_{t} & = \sigma(W_{ir}x_{t} + b_{ir} + W_{hr}h_{t-1} + b_{hr})
z_{t} & = \sigma(W_{iz}x_{t} + b_{iz} + W_{hz}h_{t-1} + b_{hz})
\widetilde{h}_{t} & = \tanh(W_{ic}x_{t} + b_{ic} + r_{t} * (W_{hc}h_{t-1} + b_{hc}))
h_{t} & = z_{t} * h_{t-1} + (1 - z_{t}) * \widetilde{h}_{t}
y_{t} & = h_{t}
where :math:`\sigma` is the sigmoid fucntion, and * is the elemetwise
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multiplication operator.
"""
def __init__(self,
input_size: int,
hidden_size: int,
weight_ih_attr=None,
weight_hh_attr=None,
bias_ih_attr=None,
bias_hh_attr=None,
name=None):
super().__init__()
std = 1.0 / math.sqrt(hidden_size)
self.weight_hh = self.create_parameter(
(3 * hidden_size, hidden_size),
weight_hh_attr,
default_initializer=I.Uniform(-std, std))
self.bias_ih = None
self.bias_hh = self.create_parameter(
(3 * hidden_size, ),
bias_hh_attr,
is_bias=True,
default_initializer=I.Uniform(-std, std))
self.hidden_size = hidden_size
self.input_size = input_size
self._gate_activation = F.sigmoid
self._activation = paddle.tanh
def forward(self, inputs, states=None):
if states is None:
states = self.get_initial_states(inputs, self.state_shape)
pre_hidden = states
x_gates = inputs
if self.bias_ih is not None:
x_gates = x_gates + self.bias_ih
h_gates = paddle.matmul(pre_hidden, self.weight_hh, transpose_y=True)
if self.bias_hh is not None:
h_gates = h_gates + self.bias_hh
x_r, x_z, x_c = paddle.split(x_gates, num_or_sections=3, axis=1)
h_r, h_z, h_c = paddle.split(h_gates, num_or_sections=3, axis=1)
r = self._gate_activation(x_r + h_r)
z = self._gate_activation(x_z + h_z)
c = self._activation(x_c + r * h_c) # apply reset gate after mm
h = (pre_hidden - c) * z + c
# https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/fluid/layers/dynamic_gru_cn.html#dynamic-gru
return h, h
@property
def state_shape(self):
r"""
The `state_shape` of GRUCell is a shape `[hidden_size]` (-1 for batch
size would be automatically inserted into shape). The shape corresponds
to the shape of :math:`h_{t-1}`.
"""
return (self.hidden_size, )
class BiRNNWithBN(nn.Layer):
"""Bidirectonal simple rnn layer with sequence-wise batch normalization.
The batch normalization is only performed on input-state weights.
:param size: Dimension of RNN cells.
:type size: int
:param share_weights: Whether to share input-hidden weights between
forward and backward directional RNNs.
:type share_weights: bool
:return: Bidirectional simple rnn layer.
:rtype: Variable
"""
def __init__(self, i_size: int, h_size: int, share_weights: bool):
super().__init__()
self.share_weights = share_weights
if self.share_weights:
#input-hidden weights shared between bi-directional rnn.
self.fw_fc = nn.Linear(i_size, h_size, bias_attr=False)
# batch norm is only performed on input-state projection
self.fw_bn = nn.BatchNorm1D(
h_size, bias_attr=None, data_format='NLC')
self.bw_fc = self.fw_fc
self.bw_bn = self.fw_bn
else:
self.fw_fc = nn.Linear(i_size, h_size, bias_attr=False)
self.fw_bn = nn.BatchNorm1D(
h_size, bias_attr=None, data_format='NLC')
self.bw_fc = nn.Linear(i_size, h_size, bias_attr=False)
self.bw_bn = nn.BatchNorm1D(
h_size, bias_attr=None, data_format='NLC')
self.fw_cell = RNNCell(hidden_size=h_size, activation='brelu')
self.bw_cell = RNNCell(hidden_size=h_size, activation='brelu')
self.fw_rnn = nn.RNN(
self.fw_cell, is_reverse=False, time_major=False) #[B, T, D]
self.bw_rnn = nn.RNN(
self.bw_cell, is_reverse=True, time_major=False) #[B, T, D]
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def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
# x, shape [B, T, D]
fw_x = self.fw_bn(self.fw_fc(x))
bw_x = self.bw_bn(self.bw_fc(x))
fw_x, _ = self.fw_rnn(inputs=fw_x, sequence_length=x_len)
bw_x, _ = self.bw_rnn(inputs=bw_x, sequence_length=x_len)
x = paddle.concat([fw_x, bw_x], axis=-1)
return x, x_len
class BiGRUWithBN(nn.Layer):
"""Bidirectonal gru layer with sequence-wise batch normalization.
The batch normalization is only performed on input-state weights.
:param name: Name of the layer.
:type name: string
:param input: Input layer.
:type input: Variable
:param size: Dimension of GRU cells.
:type size: int
:param act: Activation type.
:type act: string
:return: Bidirectional GRU layer.
:rtype: Variable
"""
def __init__(self, i_size: int, h_size: int):
super().__init__()
hidden_size = h_size * 3
self.fw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
self.fw_bn = nn.BatchNorm1D(
hidden_size, bias_attr=None, data_format='NLC')
self.bw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
self.bw_bn = nn.BatchNorm1D(
hidden_size, bias_attr=None, data_format='NLC')
self.fw_cell = GRUCell(input_size=hidden_size, hidden_size=h_size)
self.bw_cell = GRUCell(input_size=hidden_size, hidden_size=h_size)
self.fw_rnn = nn.RNN(
self.fw_cell, is_reverse=False, time_major=False) #[B, T, D]
self.bw_rnn = nn.RNN(
self.bw_cell, is_reverse=True, time_major=False) #[B, T, D]
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def forward(self, x, x_len):
# x, shape [B, T, D]
fw_x = self.fw_bn(self.fw_fc(x))
bw_x = self.bw_bn(self.bw_fc(x))
fw_x, _ = self.fw_rnn(inputs=fw_x, sequence_length=x_len)
bw_x, _ = self.bw_rnn(inputs=bw_x, sequence_length=x_len)
x = paddle.concat([fw_x, bw_x], axis=-1)
return x, x_len
class RNNStack(nn.Layer):
"""RNN group with stacked bidirectional simple RNN or GRU layers.
:param input: Input layer.
:type input: Variable
:param size: Dimension of RNN cells in each layer.
:type size: int
:param num_stacks: Number of stacked rnn layers.
:type num_stacks: int
:param use_gru: Use gru if set True. Use simple rnn if set False.
:type use_gru: bool
:param share_rnn_weights: Whether to share input-hidden weights between
forward and backward directional RNNs.
It is only available when use_gru=False.
:type share_weights: bool
:return: Output layer of the RNN group.
:rtype: Variable
"""
def __init__(self,
i_size: int,
h_size: int,
num_stacks: int,
use_gru: bool,
share_rnn_weights: bool):
super().__init__()
rnn_stacks = []
for i in range(num_stacks):
if use_gru:
#default:GRU using tanh
rnn_stacks.append(BiGRUWithBN(i_size=i_size, h_size=h_size))
else:
rnn_stacks.append(
BiRNNWithBN(
i_size=i_size,
h_size=h_size,
share_weights=share_rnn_weights))
i_size = h_size * 2
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self.rnn_stacks = nn.LayerList(rnn_stacks)
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def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
"""
x: shape [B, T, D]
x_len: shpae [B]
"""
for i, rnn in enumerate(self.rnn_stacks):
x, x_len = rnn(x, x_len)
masks = make_non_pad_mask(x_len) #[B, T]
masks = masks.unsqueeze(-1) # [B, T, 1]
# TODO(Hui Zhang): not support bool multiply
masks = masks.astype(x.dtype)
x = x.multiply(masks)
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return x, x_len