File size: 8,907 Bytes
e9acf97 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 |
""" PyTorch Wav2Vec2-Ebranchformer model."""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.activations import ACT2FN
from transformers.models.wav2vec2.modeling_wav2vec2 import (
Wav2Vec2Config,
Wav2Vec2ForCTC,
Wav2Vec2ForPreTraining,
)
from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import (
Wav2Vec2ConformerConfig,
Wav2Vec2ConformerEncoder,
)
from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import (
Wav2Vec2ConformerFeedForward as Wav2Vec2EBranchformerFeedForward,
)
from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import (
Wav2Vec2ConformerModel,
)
from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import (
Wav2Vec2ConformerSelfAttention as Wav2Vec2EBranchformerSelfAttention,
)
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Wav2Vec2EBranchformerConfig(Wav2Vec2ConformerConfig, Wav2Vec2Config):
"""Config for EBranhformer model extending conformer."""
model_type = "wav2vec2-ebranchformer"
def __init__(
self,
ebranchformer_conv_dropout=0.1,
csgu_activation="identity",
csgu_kernel_size=31,
csgu_use_linear_after_conv=False,
merge_conv_kernel=31,
use_macaron_ff=True,
**kwargs,
):
super().__init__(**kwargs)
# EBranchformer related params
self.csgu_kernel_size = csgu_kernel_size
self.csgu_activation = csgu_activation
self.csgu_conv_dropout = ebranchformer_conv_dropout
self.csgu_use_linear_after_conv = csgu_use_linear_after_conv
self.merge_conv_kernel = merge_conv_kernel
self.use_macaron_ff = use_macaron_ff
class ConvolutionalSpatialGatingUnit(torch.nn.Module):
"""Convolutional Spatial Gating Unit (CSGU)."""
def __init__(self, config: Wav2Vec2EBranchformerConfig):
super().__init__()
n_channels = config.intermediate_size // 2 # split input channels
self.norm = torch.nn.LayerNorm(n_channels)
self.conv = torch.nn.Conv1d(
n_channels,
n_channels,
config.csgu_kernel_size,
1,
(config.csgu_kernel_size - 1) // 2,
groups=n_channels,
)
if config.csgu_use_linear_after_conv:
self.linear = torch.nn.Linear(n_channels, n_channels)
else:
self.linear = None
if config.csgu_activation == "identity":
self.act = torch.nn.Identity()
else:
self.act = ACT2FN[config.csgu_activation]
self.dropout = torch.nn.Dropout(config.csgu_conv_dropout)
def forward(self, hidden_states: torch.FloatTensor):
"""Forward method
Args:
hidden_states (torch.Tensor): (N, T, D)
Returns:
out (torch.Tensor): (N, T, D/2)
"""
x_r, x_g = hidden_states.chunk(2, dim=-1)
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)
hidden_states = x_r * x_g # (N, T, D/2)
hidden_states = self.dropout(hidden_states)
return hidden_states
class ConvolutionalGatingMLP(torch.nn.Module):
"""Convolutional Gating MLP (cgMLP)."""
def __init__(self, config: Wav2Vec2EBranchformerConfig):
super().__init__()
self.channel_proj1 = torch.nn.Sequential(
torch.nn.Linear(config.hidden_size, config.intermediate_size), torch.nn.GELU()
)
self.csgu = ConvolutionalSpatialGatingUnit(config)
self.channel_proj2 = torch.nn.Linear(config.intermediate_size // 2, config.hidden_size)
def forward(self, hidden_states: torch.FloatTensor):
hidden_states = self.channel_proj1(hidden_states) # hidden_size -> intermediate_size
hidden_states = self.csgu(hidden_states) # intermediate_size -> intermediate_size/2
hidden_states = self.channel_proj2(hidden_states) # intermediate_size/2 -> hidden_size
return hidden_states
class Wav2Vec2EBranchformerEncoderLayer(nn.Module):
def __init__(self, config: Wav2Vec2EBranchformerConfig):
super().__init__()
embed_dim = config.hidden_size
dropout = config.attention_dropout
# Feed-forward 1
if config.use_macaron_ff:
self.ff1 = nn.Sequential(nn.LayerNorm(embed_dim), Wav2Vec2EBranchformerFeedForward(config))
# Self-Attention
self.self_attn_layer_norm = nn.LayerNorm(embed_dim)
self.self_attn_dropout = torch.nn.Dropout(dropout)
self.self_attn = Wav2Vec2EBranchformerSelfAttention(config)
# cgMLP
self.cgMLP = ConvolutionalGatingMLP(config)
self.cgMLP_layer_norm = nn.LayerNorm(config.hidden_size)
self.cgMLP_dropout = torch.nn.Dropout(dropout)
# Merge
self.final_dropout = torch.nn.Dropout(dropout)
self.merge_proj = torch.nn.Linear(embed_dim + embed_dim, embed_dim)
self.depthwise_conv_fusion = torch.nn.Conv1d(
embed_dim + embed_dim,
embed_dim + embed_dim,
kernel_size=config.merge_conv_kernel,
stride=1,
padding=(config.merge_conv_kernel - 1) // 2,
groups=embed_dim + embed_dim,
bias=True,
)
self.final_layer_norm = nn.LayerNorm(embed_dim)
# Feed-forward 2
if config.use_macaron_ff:
self.ff2 = nn.Sequential(nn.LayerNorm(embed_dim), Wav2Vec2EBranchformerFeedForward(config))
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: Optional[torch.Tensor] = None,
relative_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
):
# 1. Optional ff1
if self.ff1:
residual = hidden_states
hidden_states = residual + 0.5 * self.ff1(hidden_states)
# 2. Split input to three branches
residual = hidden_states
global_branch = hidden_states
local_branch = hidden_states
# 3. Self-Attention branch
global_branch = self.self_attn_layer_norm(global_branch)
global_branch, attn_weigts = self.self_attn(
hidden_states=global_branch,
attention_mask=attention_mask,
relative_position_embeddings=relative_position_embeddings,
output_attentions=output_attentions,
)
global_branch = self.self_attn_dropout(global_branch)
# 4. cgMLP Branch
local_branch = self.cgMLP_layer_norm(local_branch)
local_branch = self.cgMLP(local_branch)
# 5. Merge operator
# a, concat
hidden_states = torch.cat([global_branch, local_branch], dim=-1)
merge_residual = hidden_states
# b, depth-wise conv mixing
hidden_states = merge_residual + self.depthwise_conv_fusion(hidden_states.transpose(1, 2)).transpose(1, 2)
# c, project back to original size and final dropout
hidden_states = self.final_dropout(self.merge_proj(hidden_states))
# 6. Add residual
hidden_states = residual + hidden_states
# 7. Optional ff2
if self.ff2:
residual = hidden_states
hidden_states = residual + 0.5 * self.ff2(hidden_states)
# 8. Final layer norm
hidden_states = self.final_layer_norm(hidden_states)
return hidden_states, attn_weigts
class Wav2Vec2EBranchformerEncoder(Wav2Vec2ConformerEncoder):
def __init__(self, config: Wav2Vec2EBranchformerConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[Wav2Vec2EBranchformerEncoderLayer(config) for _ in range(config.num_hidden_layers)]
)
self.pos_conv_embed = None
class Wav2Vec2EBranchformerModel(Wav2Vec2ConformerModel):
def __init__(self, config: Wav2Vec2EBranchformerConfig):
super().__init__(config)
self.encoder = Wav2Vec2EBranchformerEncoder(config)
# Initialize weights and apply final processing
self.post_init()
class Wav2Vec2EBranchformerForPreTraining(Wav2Vec2ForPreTraining):
config_class = Wav2Vec2EBranchformerConfig
base_model_prefix = "wav2vec2"
def __init__(self, config: Wav2Vec2EBranchformerConfig):
super().__init__(config)
self.wav2vec2 = Wav2Vec2EBranchformerModel(config)
self.post_init()
class Wav2Vec2EBranchformerForCTC(Wav2Vec2ForCTC):
config_class = Wav2Vec2EBranchformerConfig
base_model_prefix = "wav2vec2"
def __init__(self, config: Wav2Vec2EBranchformerConfig):
super().__init__(config)
self.wav2vec2 = Wav2Vec2EBranchformerModel(config)
self.post_init()
|