# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2019 Mobvoi Inc. 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.
# Modified from wenet(https://github.com/wenet-e2e/wenet)
"""ConvolutionModule definition."""
from typing import Tuple
import paddle
from paddle import nn
from paddle.nn import initializer as I
from typeguard import check_argument_types
from paddlespeech.s2t.modules.align import BatchNorm1D
from paddlespeech.s2t.modules.align import Conv1D
from paddlespeech.s2t.modules.align import LayerNorm
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ['ConvolutionModule']
[docs]class ConvolutionModule(nn.Layer):
"""ConvolutionModule in Conformer model."""
def __init__(self,
channels: int,
kernel_size: int=15,
activation: nn.Layer=nn.ReLU(),
norm: str="batch_norm",
causal: bool=False,
bias: bool=True,
adaptive_scale: bool=False,
init_weights: bool=False):
"""Construct an ConvolutionModule object.
Args:
channels (int): The number of channels of conv layers.
kernel_size (int): Kernel size of conv layers.
activation (nn.Layer): Activation Layer.
norm (str): Normalization type, 'batch_norm' or 'layer_norm'
causal (bool): Whether use causal convolution or not
bias (bool): Whether Conv with bias or not
"""
assert check_argument_types()
super().__init__()
self.bias = bias
self.channels = channels
self.kernel_size = kernel_size
self.adaptive_scale = adaptive_scale
if self.adaptive_scale:
ada_scale = self.create_parameter(
[1, 1, channels], default_initializer=I.Constant(1.0))
self.add_parameter('ada_scale', ada_scale)
ada_bias = self.create_parameter(
[1, 1, channels], default_initializer=I.Constant(0.0))
self.add_parameter('ada_bias', ada_bias)
self.pointwise_conv1 = Conv1D(
channels,
2 * channels,
kernel_size=1,
stride=1,
padding=0,
bias_attr=None
if bias else False, # None for True, using bias as default config
)
# self.lorder is used to distinguish if it's a causal convolution,
# if self.lorder > 0:
# it's a causal convolution, the input will be padded with
# `self.lorder` frames on the left in forward (causal conv impl).
# else: it's a symmetrical convolution
if causal:
padding = 0
self.lorder = kernel_size - 1
else:
# kernel_size should be an odd number for none causal convolution
assert (kernel_size - 1) % 2 == 0
padding = (kernel_size - 1) // 2
self.lorder = 0
self.depthwise_conv = Conv1D(
channels,
channels,
kernel_size,
stride=1,
padding=padding,
groups=channels,
bias_attr=None
if bias else False, # None for True, using bias as default config
)
assert norm in ['batch_norm', 'layer_norm']
if norm == "batch_norm":
self.use_layer_norm = False
self.norm = BatchNorm1D(channels)
else:
self.use_layer_norm = True
self.norm = LayerNorm(channels)
self.pointwise_conv2 = Conv1D(
channels,
channels,
kernel_size=1,
stride=1,
padding=0,
bias_attr=None
if bias else False, # None for True, using bias as default config
)
self.activation = activation
if init_weights:
self.init_weights()
[docs] def init_weights(self):
pw_max = self.channels**-0.5
dw_max = self.kernel_size**-0.5
self.pointwise_conv1._param_attr = paddle.nn.initializer.Uniform(
low=-pw_max, high=pw_max)
if self.bias:
self.pointwise_conv1._bias_attr = paddle.nn.initializer.Uniform(
low=-pw_max, high=pw_max)
self.depthwise_conv._param_attr = paddle.nn.initializer.Uniform(
low=-dw_max, high=dw_max)
if self.bias:
self.depthwise_conv._bias_attr = paddle.nn.initializer.Uniform(
low=-dw_max, high=dw_max)
self.pointwise_conv2._param_attr = paddle.nn.initializer.Uniform(
low=-pw_max, high=pw_max)
if self.bias:
self.pointwise_conv2._bias_attr = paddle.nn.initializer.Uniform(
low=-pw_max, high=pw_max)
[docs] def forward(
self,
x: paddle.Tensor,
mask_pad: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0])
) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Compute convolution module.
Args:
x (paddle.Tensor): Input tensor (#batch, time, channels).
mask_pad (paddle.Tensor): used for batch padding (#batch, 1, time),
(0, 0, 0) means fake mask.
cache (paddle.Tensor): left context cache, it is only
used in causal convolution (#batch, channels, cache_t),
(0, 0, 0) meas fake cache.
Returns:
paddle.Tensor: Output tensor (#batch, time, channels).
paddle.Tensor: Output cache tensor (#batch, channels, time')
"""
if self.adaptive_scale:
x = self.ada_scale * x + self.ada_bias
# exchange the temporal dimension and the feature dimension
x = x.transpose([0, 2, 1]) # [B, C, T]
# mask batch padding
if mask_pad.shape[2] > 0: # time > 0
x = x.masked_fill(mask_pad, 0.0)
if self.lorder > 0:
if cache.shape[2] == 0: # cache_t == 0
x = nn.functional.pad(
x, [self.lorder, 0], 'constant', 0.0, data_format='NCL')
else:
assert cache.shape[0] == x.shape[0] # B
assert cache.shape[1] == x.shape[1] # C
x = paddle.concat((cache, x), axis=2)
assert (x.shape[2] > self.lorder)
new_cache = x[:, :, -self.lorder:] #[B, C, T]
else:
# It's better we just return None if no cache is requried,
# However, for JIT export, here we just fake one tensor instead of
# None.
new_cache = paddle.zeros([0, 0, 0], dtype=x.dtype)
# GLU mechanism
x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
x = nn.functional.glu(x, axis=1) # (batch, channel, dim)
# 1D Depthwise Conv
x = self.depthwise_conv(x)
if self.use_layer_norm:
x = x.transpose([0, 2, 1]) # [B, T, C]
x = self.activation(self.norm(x))
if self.use_layer_norm:
x = x.transpose([0, 2, 1]) # [B, C, T]
x = self.pointwise_conv2(x)
# mask batch padding
if mask_pad.shape[2] > 0: # time > 0
x = x.masked_fill(mask_pad, 0.0)
x = x.transpose([0, 2, 1]) # [B, T, C]
return x, new_cache