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# Copyright (c) 2022 Yifan Peng (Carnegie Mellon University)
# 2023 Voicecomm Inc (Kai Li)
# 2023 Lucky Wong
#
# 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)
"""EBranchformerEncoderLayer definition."""
import torch
import torch.nn as nn
from typing import Optional, Tuple
from wenet.transformer.attention import T_CACHE
class EBranchformerEncoderLayer(torch.nn.Module):
"""E-Branchformer encoder layer module.
Args:
size (int): model dimension
attn: standard self-attention or efficient attention
cgmlp: ConvolutionalGatingMLP
feed_forward: feed-forward module, optional
feed_forward: macaron-style feed-forward module, optional
dropout_rate (float): dropout probability
merge_conv_kernel (int): kernel size of the depth-wise conv in merge module
"""
def __init__(
self,
size: int,
attn: torch.nn.Module,
cgmlp: torch.nn.Module,
feed_forward: Optional[torch.nn.Module],
feed_forward_macaron: Optional[torch.nn.Module],
dropout_rate: float,
merge_conv_kernel: int = 3,
causal: bool = True,
stochastic_depth_rate=0.0,
):
super().__init__()
self.size = size
self.attn = attn
self.cgmlp = cgmlp
self.feed_forward = feed_forward
self.feed_forward_macaron = feed_forward_macaron
self.ff_scale = 1.0
if self.feed_forward is not None:
self.norm_ff = nn.LayerNorm(size)
if self.feed_forward_macaron is not None:
self.ff_scale = 0.5
self.norm_ff_macaron = nn.LayerNorm(size)
self.norm_mha = nn.LayerNorm(size) # for the MHA module
self.norm_mlp = nn.LayerNorm(size) # for the MLP module
# for the final output of the block
self.norm_final = nn.LayerNorm(size)
self.dropout = torch.nn.Dropout(dropout_rate)
if causal:
padding = 0
self.lorder = merge_conv_kernel - 1
else:
# kernel_size should be an odd number for none causal convolution
assert (merge_conv_kernel - 1) % 2 == 0
padding = (merge_conv_kernel - 1) // 2
self.lorder = 0
self.depthwise_conv_fusion = torch.nn.Conv1d(
size + size,
size + size,
kernel_size=merge_conv_kernel,
stride=1,
padding=padding,
groups=size + size,
bias=True,
)
self.merge_proj = torch.nn.Linear(size + size, size)
self.stochastic_depth_rate = stochastic_depth_rate
def _forward(
self,
x: torch.Tensor,
mask: torch.Tensor,
pos_emb: torch.Tensor,
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
att_cache: T_CACHE = (torch.zeros(
(0, 0, 0, 0)), torch.zeros(0, 0, 0, 0)),
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
stoch_layer_coeff: float = 1.0
) -> Tuple[torch.Tensor, torch.Tensor, T_CACHE, torch.Tensor]:
if self.feed_forward_macaron is not None:
residual = x
x = self.norm_ff_macaron(x)
x = residual + stoch_layer_coeff * self.ff_scale * self.dropout(
self.feed_forward_macaron(x))
# Two branches
x1 = x
x2 = x
# Branch 1: multi-headed attention module
x1 = self.norm_mha(x1)
x_att, new_att_cache = self.attn(x1, x1, x1, mask, pos_emb, att_cache)
x1 = self.dropout(x_att)
# Branch 2: convolutional gating mlp
# Fake new cnn cache here, and then change it in conv_module
new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
x2 = self.norm_mlp(x2)
x2, new_cnn_cache = self.cgmlp(x2, mask_pad, cnn_cache)
x2 = self.dropout(x2)
# Merge two branches
x_concat = torch.cat([x1, x2], dim=-1)
x_tmp = x_concat.transpose(1, 2)
if self.lorder > 0:
x_tmp = nn.functional.pad(x_tmp, (self.lorder, 0), "constant", 0.0)
assert x_tmp.size(2) > self.lorder
x_tmp = self.depthwise_conv_fusion(x_tmp)
x_tmp = x_tmp.transpose(1, 2)
x = x + stoch_layer_coeff * self.dropout(
self.merge_proj(x_concat + x_tmp))
if self.feed_forward is not None:
# feed forward module
residual = x
x = self.norm_ff(x)
x = residual + stoch_layer_coeff * self.ff_scale * self.dropout(
self.feed_forward(x))
x = self.norm_final(x)
return x, mask, new_att_cache, new_cnn_cache
def forward(
self,
x: torch.Tensor,
mask: torch.Tensor,
pos_emb: torch.Tensor,
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
att_cache: T_CACHE = (torch.zeros(
(0, 0, 0, 0)), torch.zeros(0, 0, 0, 0)),
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
) -> Tuple[torch.Tensor, torch.Tensor, T_CACHE, torch.Tensor]:
"""Compute encoded features.
Args:
x (Union[Tuple, torch.Tensor]): Input tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, time, time).
pos_emb (torch.Tensor): positional encoding, must not be None
for BranchformerEncoderLayer.
mask_pad (torch.Tensor): batch padding mask used for conv module.
(#batch, 1，time), (0, 0, 0) means fake mask.
att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
cnn_cache (torch.Tensor): Convolution cache in cgmlp layer
(#batch=1, size, cache_t2)
Returns:
torch.Tensor: Output tensor (#batch, time, size).
torch.Tensor: Mask tensor (#batch, time, time.
torch.Tensor: att_cache tensor,
(#batch=1, head, cache_t1 + time, d_k * 2).
torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
"""
stoch_layer_coeff = 1.0
# with stochastic depth, residual connection `x + f(x)` becomes
# `x <- x + 1 / (1 - p) * f(x)` at training time.
if self.training:
stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
return self._forward(x, mask, pos_emb, mask_pad, att_cache, cnn_cache,
stoch_layer_coeff)
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