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# Copyright (c) 2022 Yifan Peng (Carnegie Mellon University)
# 2023 Voicecomm Inc (Kai Li)
#
# 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 ESPnet(https://github.com/espnet/espnet)
"""MLP with convolutional gating (cgMLP) definition.
References:
https://openreview.net/forum?id=RA-zVvZLYIy
https://arxiv.org/abs/2105.08050
"""
from typing import Tuple
import torch
import torch.nn as nn
from wenet.utils.class_utils import WENET_ACTIVATION_CLASSES
class ConvolutionalSpatialGatingUnit(torch.nn.Module):
"""Convolutional Spatial Gating Unit (CSGU)."""
def __init__(
self,
size: int,
kernel_size: int,
dropout_rate: float,
use_linear_after_conv: bool,
gate_activation: str,
causal: bool = True,
):
super().__init__()
# split input channels
n_channels = size // 2
self.norm = nn.LayerNorm(n_channels)
# 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.
# 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.conv = torch.nn.Conv1d(
n_channels,
n_channels,
kernel_size,
1,
padding,
groups=n_channels,
)
if use_linear_after_conv:
self.linear = torch.nn.Linear(n_channels, n_channels)
else:
self.linear = None
if gate_activation == "identity":
self.act = torch.nn.Identity()
else:
self.act = WENET_ACTIVATION_CLASSES[gate_activation]()
self.dropout = torch.nn.Dropout(dropout_rate)
def espnet_initialization_fn(self):
torch.nn.init.normal_(self.conv.weight, std=1e-6)
torch.nn.init.ones_(self.conv.bias)
if self.linear is not None:
torch.nn.init.normal_(self.linear.weight, std=1e-6)
torch.nn.init.ones_(self.linear.bias)
def forward(
self, x: torch.Tensor, cache: torch.Tensor = torch.zeros((0, 0, 0))
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Forward method
Args:
x (torch.Tensor): (batch, time, channels)
cache (torch.Tensor): left context cache, it is only
used in causal convolution (#batch, channels, cache_t),
(0, 0, 0) meas fake cache.
Returns:
out (torch.Tensor): (batch, time, channels/2)
"""
x_r, x_g = x.chunk(2, dim=-1)
# exchange the temporal dimension and the feature dimension
x_g = x_g.transpose(1, 2) # (#batch, channels, time)
if self.lorder > 0:
if cache.size(2) == 0: # cache_t == 0
x_g = nn.functional.pad(x_g, (self.lorder, 0), 'constant', 0.0)
else:
assert cache.size(0) == x_g.size(0) # equal batch
assert cache.size(1) == x_g.size(1) # equal channel
x_g = torch.cat((cache, x_g), dim=2)
assert (x_g.size(2) > self.lorder)
new_cache = x_g[:, :, -self.lorder:]
else:
# It's better we just return None if no cache is required,
# However, for JIT export, here we just fake one tensor instead of
# None.
new_cache = torch.zeros((0, 0, 0),
dtype=x_g.dtype,
device=x_g.device)
x_g = x_g.transpose(1, 2)
x_g = self.norm(x_g) # (N, T, D/2)
x_g = self.conv(x_g.transpose(1, 2)).transpose(1, 2) # (N, T, D/2)
if self.linear is not None:
x_g = self.linear(x_g)
x_g = self.act(x_g)
out = x_r * x_g # (N, T, D/2)
out = self.dropout(out)
return out, new_cache
class ConvolutionalGatingMLP(torch.nn.Module):
"""Convolutional Gating MLP (cgMLP)."""
def __init__(
self,
size: int,
linear_units: int,
kernel_size: int,
dropout_rate: float,
use_linear_after_conv: bool,
gate_activation: str,
causal: bool = True,
):
super().__init__()
self.channel_proj1 = torch.nn.Sequential(
torch.nn.Linear(size, linear_units), torch.nn.GELU())
self.csgu = ConvolutionalSpatialGatingUnit(
size=linear_units,
kernel_size=kernel_size,
dropout_rate=dropout_rate,
use_linear_after_conv=use_linear_after_conv,
gate_activation=gate_activation,
causal=causal,
)
self.channel_proj2 = torch.nn.Linear(linear_units // 2, size)
def forward(
self,
x: torch.Tensor,
mask: torch.Tensor,
cache: torch.Tensor = torch.zeros((0, 0, 0))
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Forward method
Args:
x (torch.Tensor): (batch, time, channels)
mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
(0, 0, 0) means fake mask. Not used yet
cache (torch.Tensor): left context cache, it is only
used in causal convolution (#batch, channels, cache_t),
(0, 0, 0) meas fake cache.
Returns:
out (torch.Tensor): (batch, time, channels/2)
"""
xs_pad = x
# size -> linear_units
xs_pad = self.channel_proj1(xs_pad)
# linear_units -> linear_units/2
xs_pad, new_cnn_cache = self.csgu(xs_pad, cache)
# linear_units/2 -> size
xs_pad = self.channel_proj2(xs_pad)
out = xs_pad
return out, new_cnn_cache
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