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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)
"""BranchformerEncoderLayer definition."""
import torch
import torch.nn as nn
from typing import Optional, Tuple
from wenet.transformer.attention import T_CACHE
class BranchformerEncoderLayer(torch.nn.Module):
"""Branchformer encoder layer module.
Args:
size (int): model dimension
attn: standard self-attention or efficient attention, optional
cgmlp: ConvolutionalGatingMLP, optional
dropout_rate (float): dropout probability
merge_method (str): concat, learned_ave, fixed_ave
cgmlp_weight (float): weight of the cgmlp branch, between 0 and 1,
used if merge_method is fixed_ave
attn_branch_drop_rate (float): probability of dropping the attn branch,
used if merge_method is learned_ave
stochastic_depth_rate (float): stochastic depth probability
"""
def __init__(
self,
size: int,
attn: Optional[torch.nn.Module],
cgmlp: Optional[torch.nn.Module],
dropout_rate: float,
merge_method: str,
cgmlp_weight: float = 0.5,
attn_branch_drop_rate: float = 0.0,
stochastic_depth_rate: float = 0.0,
):
super().__init__()
assert (attn is not None) or (
cgmlp is not None), "At least one branch should be valid"
self.size = size
self.attn = attn
self.cgmlp = cgmlp
self.merge_method = merge_method
self.cgmlp_weight = cgmlp_weight
self.attn_branch_drop_rate = attn_branch_drop_rate
self.stochastic_depth_rate = stochastic_depth_rate
self.use_two_branches = (attn is not None) and (cgmlp is not None)
if attn is not None:
self.norm_mha = nn.LayerNorm(size) # for the MHA module
if cgmlp is not None:
self.norm_mlp = nn.LayerNorm(size) # for the MLP module
self.norm_final = nn.LayerNorm(
size) # for the final output of the block
self.dropout = torch.nn.Dropout(dropout_rate)
# # attention-based pooling for two branches
self.pooling_proj1 = torch.nn.Linear(size, 1)
self.pooling_proj2 = torch.nn.Linear(size, 1)
# # linear projections for calculating merging weights
self.weight_proj1 = torch.nn.Linear(size, 1)
self.weight_proj2 = torch.nn.Linear(size, 1)
if self.use_two_branches:
if self.merge_method == "concat":
self.merge_proj = torch.nn.Linear(size + size, size)
elif self.merge_method == "learned_ave":
# linear projection after weighted average
self.merge_proj = torch.nn.Linear(size, size)
elif self.merge_method == "fixed_ave":
assert (0.0 <= cgmlp_weight <=
1.0), "cgmlp weight should be between 0.0 and 1.0"
# remove the other branch if only one branch is used
if cgmlp_weight == 0.0:
self.use_two_branches = False
self.cgmlp = None
self.norm_mlp = None
elif cgmlp_weight == 1.0:
self.use_two_branches = False
self.attn = None
self.norm_mha = None
# linear projection after weighted average
self.merge_proj = torch.nn.Linear(size, size)
else:
raise ValueError(f"unknown merge method: {merge_method}")
else:
self.merge_proj = torch.nn.Identity()
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]:
# Two branches
x1 = x
x2 = x
# Branch 1: multi-headed attention module
if self.attn is not None:
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)
if self.cgmlp is not None:
x2 = self.norm_mlp(x2)
x2, new_cnn_cache = self.cgmlp(x2, mask_pad, cnn_cache)
x2 = self.dropout(x2)
# Merge two branches
if self.use_two_branches:
if self.merge_method == "concat":
x = x + stoch_layer_coeff * self.dropout(
self.merge_proj(torch.cat([x1, x2], dim=-1)))
elif self.merge_method == "learned_ave":
if (self.training and self.attn_branch_drop_rate > 0
and torch.rand(1).item() < self.attn_branch_drop_rate):
# Drop the attn branch
w1, w2 = torch.tensor(0.0), torch.tensor(1.0)
else:
# branch1
score1 = (self.pooling_proj1(x1).transpose(1, 2) /
self.size**0.5)
score1 = score1.masked_fill(mask_pad.eq(0), -float('inf'))
score1 = torch.softmax(score1, dim=-1).masked_fill(
mask_pad.eq(0), 0.0)
pooled1 = torch.matmul(score1,
x1).squeeze(1) # (batch, size)
weight1 = self.weight_proj1(pooled1) # (batch, 1)
# branch2
score2 = (self.pooling_proj2(x2).transpose(1, 2) /
self.size**0.5)
score2 = score2.masked_fill(mask_pad.eq(0), -float('inf'))
score2 = torch.softmax(score2, dim=-1).masked_fill(
mask_pad.eq(0), 0.0)
pooled2 = torch.matmul(score2,
x2).squeeze(1) # (batch, size)
weight2 = self.weight_proj2(pooled2) # (batch, 1)
# normalize weights of two branches
merge_weights = torch.softmax(torch.cat([weight1, weight2],
dim=-1),
dim=-1) # (batch, 2)
merge_weights = merge_weights.unsqueeze(-1).unsqueeze(
-1) # (batch, 2, 1, 1)
w1, w2 = merge_weights[:,
0], merge_weights[:,
1] # (batch, 1, 1)
x = x + stoch_layer_coeff * self.dropout(
self.merge_proj(w1 * x1 + w2 * x2))
elif self.merge_method == "fixed_ave":
x = x + stoch_layer_coeff * self.dropout(
self.merge_proj((1.0 - self.cgmlp_weight) * x1 +
self.cgmlp_weight * x2))
else:
raise RuntimeError(
f"unknown merge method: {self.merge_method}")
else:
if self.attn is None:
x = x + stoch_layer_coeff * self.dropout(self.merge_proj(x2))
elif self.cgmlp is None:
x = x + stoch_layer_coeff * self.dropout(self.merge_proj(x1))
else:
# This should not happen
raise RuntimeError(
"Both branches are not None, which is unexpected.")
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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