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from torch import Tensor
from torch import nn
from local_attention.transformer import DynamicPositionBias, LocalMHA, FeedForward
from .distill_layers import ChannelNorm, Conv1d, Linear, GRN, Snake1d
from .tconv.t_first import FirstBlock
class LocalTrans(nn.Module):
def __init__(
self,
dim=512,
depth=6,
causal=True,
local_attn_window_size=512,
dim_head=64,
heads=8,
ff_mult=4,
attn_dropout=0.0,
ff_dropout=0.0,
use_dynamic_pos_bias=False,
qk_rmsnorm=False,
):
super().__init__()
self.layers = nn.ModuleList([])
self.window_size = local_attn_window_size
self.use_rotary_pos_emb = not use_dynamic_pos_bias
self.dynamic_pos_bias = (
None
if self.use_rotary_pos_emb
else DynamicPositionBias(dim=dim // 2, heads=heads)
)
for _ in range(depth):
self.layers.append(
nn.ModuleList(
[
LocalMHA(
dim=dim,
dim_head=dim_head,
heads=heads,
dropout=attn_dropout,
causal=causal,
window_size=self.window_size,
use_xpos=False,
xpos_scale_base=None,
use_rotary_pos_emb=self.use_rotary_pos_emb,
prenorm=True,
qk_rmsnorm=qk_rmsnorm,
exact_windowsize=False,
),
FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout),
]
)
)
def forward(self, x, mask=None):
attn_bias = (
None
if self.use_rotary_pos_emb
else self.dynamic_pos_bias(self.window_size, self.window_size * 2)
)
for attn, ff in self.layers:
x = attn(x, mask=mask, attn_bias=attn_bias) + x
x = ff(x) + x
return x
@classmethod
def builder(
cls, feature_dim=128, depth=2, local_window_size=200, use_dynamic_pos_bias=False
):
return cls(
dim=feature_dim,
depth=depth,
dim_head=feature_dim // 4,
heads=6,
ff_mult=4,
causal=True,
local_attn_window_size=local_window_size,
use_dynamic_pos_bias=use_dynamic_pos_bias,
)
class LocalEncoder(nn.Module):
def __init__(
self,
feature_dim=128,
depth=2,
local_window_size=200,
use_dynamic_pos_bias=False,
):
super().__init__()
self.local_trans = LocalTrans.builder(
feature_dim=feature_dim,
depth=depth,
local_window_size=local_window_size,
use_dynamic_pos_bias=use_dynamic_pos_bias,
)
def forward(self, feature):
"""
Args:
feature: (B, C, T)
Returns:
local_feature: (B, T, C)
"""
feature = feature.permute(0, 2, 1)
feature = self.local_trans(feature)
return feature
class DownTrans(nn.Module):
def __init__(
self, feature_dim=128, window_size=200, compress_rate=2, depth=2, **kwargs
):
super().__init__()
assert window_size % compress_rate == 0
self.feature_dim = feature_dim
self.compress_rate = compress_rate
self.trans = LocalTrans.builder(
feature_dim, local_window_size=window_size, depth=depth, **kwargs
)
self.down_layer = Conv1d(
feature_dim, feature_dim, kernel_size=compress_rate, stride=compress_rate
)
def forward(self, x):
x = self.trans(x)
# x = x[:, ::self.compress_rate, :] # v1
x = self.down_layer(x.permute(0, 2, 1)).permute(0, 2, 1) # v2
return x
class CompressedLocalEncoderWithCache(nn.Module):
def __init__(
self,
feature_dim=128,
local_window_size=200,
compress_rate=2,
cache_size=3,
depth=4,
**kwargs,
):
super().__init__()
self.local_window_size = local_window_size
self.cache_size = cache_size
self.compress_rate = compress_rate
self.trans_window_size = local_window_size + cache_size
self.cache_token = nn.Parameter(
torch.randn(1, self.cache_size * self.compress_rate, feature_dim)
)
self.down_trans = DownTrans(
feature_dim,
window_size=self.trans_window_size * compress_rate,
compress_rate=compress_rate,
depth=2,
**kwargs,
)
self.local_trans = LocalTrans.builder(
feature_dim,
local_window_size=self.trans_window_size,
depth=depth - 2,
**kwargs,
)
def forward(self, feature):
feature = feature.permute(0, 2, 1)
split_feature = torch.split(
feature, self.local_window_size * self.compress_rate, dim=1
)
cache_token = self.cache_token.expand(feature.shape[0], -1, -1)
feature = torch.cat(
[
f
for fs in split_feature
for f in (
cache_token,
fs,
)
],
dim=1,
)
# assert feature[:, self.down_trans_window_size: 2*self.down_trans_window_size, :].equal(
# feature.reshape(B, -1, self.down_trans_window_size, C)[:, 1, :, :])
feature = self.down_trans(feature)
feature = self.local_trans(feature)
return feature
class ConvUnit(nn.Module):
"""
Args:
dim (int): Number of input channels.
"""
def __init__(self, dim, snake_act=True, norm=False, dilation=1, kernel_size=7):
super().__init__()
total_pad = (kernel_size - 1) * dilation
self.dw_conv = Conv1d(
dim,
dim,
kernel_size=kernel_size,
dilation=dilation,
padding=total_pad // 2,
groups=dim,
) # depth-wise conv
self.norm = (
ChannelNorm(dim, data_format="channels_last") if norm else nn.Identity()
)
self.pw_conv1 = Linear(
dim, 4 * dim
) # point-wise/1x1 conv, implemented with linear layer
if snake_act:
self.act = Snake1d(4 * dim, data_format="channels_last")
else:
self.act = nn.GELU()
self.grn = GRN(4 * dim)
self.pw_conv2 = Linear(4 * dim, dim)
def forward(self, x):
x = self.dw_conv(x)
x = x.permute(0, 2, 1) # (N, C, T) -> (N, T, C)
x = self.norm(x)
x = self.pw_conv1(x)
x = self.act(x)
x = self.grn(x)
x = self.pw_conv2(x)
x = x.permute(0, 2, 1) # (N, T, C) -> (N, C, T)
return x
class Residual(nn.Module):
def __init__(
self, module: nn.Module, drop_prob: float = 0.0, scale_by_keep: bool = True
):
super().__init__()
assert 0 <= drop_prob < 1
self.module = module
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def drop_path(self, x_side: Tensor):
if self.drop_prob == 0.0 or not self.training:
return x_side
keep_prob = 1 - self.drop_prob
shape = (x_side.shape[0],) + (1,) * (x_side.ndim - 1)
keep_mask = x_side.new_empty(shape).bernoulli_(keep_prob)
if self.scale_by_keep:
keep_mask.div_(keep_prob)
return x_side * keep_mask
def forward(self, x: Tensor):
x_side = self.module(x)
x_side = self.drop_path(x_side)
return x + x_side
ResidualUnit = lambda *args, drop_rate=0.0, **kwargs: Residual(
ConvUnit(*args, **kwargs), drop_prob=drop_rate
)
class LegacyUnit(nn.Module):
def __init__(self, dim, snake_act=True, norm=False, dilation=1, kernel_size=7):
super().__init__()
assert snake_act, "LegacyUnit only supports snake_act=True"
assert norm == False, "LegacyUnit only supports norm=False"
total_pad = (kernel_size - 1) * dilation
self.block = nn.Sequential(
Snake1d(dim),
Conv1d(
dim,
dim,
kernel_size=kernel_size,
dilation=dilation,
padding=total_pad // 2,
),
Snake1d(dim),
Conv1d(dim, dim, kernel_size=1),
)
def forward(self, x):
return self.block(x)
ResidualLegacyUnit = lambda *args, **kwargs: Residual(
LegacyUnit(*args, **kwargs), drop_prob=0.0
)
BaseUnit = ResidualUnit
class Encoder(nn.Module):
def __init__(
self,
feature_dim: int = 512,
strides: tuple = (2, 2, 2, 2),
depths: tuple = (1, 1, 1, 1, 1),
dims: tuple = (32, 64, 128, 256, 512),
drop_path_rate: float = 0.0,
use_norm=False,
use_snake_act=True,
):
super().__init__()
# Create first convolution
blocks = [
# Conv1d(1, dims[0], kernel_size=7, padding=3),
FirstBlock(dims[0]),
]
drop_path_rates = [
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
]
cur = 0
for i_d, o_d, stride, depth in zip(dims[:-1], dims[1:], strides, depths):
stage = nn.Sequential(
*[
BaseUnit(
dim=i_d,
drop_rate=drop_path_rates[cur + j],
snake_act=use_snake_act,
norm=use_norm,
)
for j in range(depth)
]
)
down_layer = nn.Sequential(
Conv1d(i_d, o_d, kernel_size=stride, stride=stride),
ChannelNorm(o_d, data_format="channels_first")
if use_norm
else nn.Identity(),
)
blocks += [stage, down_layer]
cur += depth
# Create last convolution
blocks += [
nn.Sequential(
*[
BaseUnit(
dim=dims[-1],
drop_rate=drop_path_rates[cur + j],
snake_act=use_snake_act,
norm=use_norm,
)
for j in range(depths[-1])
]
),
# Snake1d(dims[-1]),
Conv1d(dims[-1], feature_dim, kernel_size=3, padding=1),
]
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
return self.blocks(x)
class DistillCodecEncoder(nn.Module):
def __init__(self):
super().__init__()
self.encoder = Encoder(
feature_dim=512,
strides=(4, 4, 4, 4),
depths=(1, 1, 1, 2),
dims=(32, 64, 128, 256),
)
self.en_encoder = CompressedLocalEncoderWithCache(
feature_dim=512,
local_window_size=300,
compress_rate=5,
cache_size=0,
depth=5,
use_dynamic_pos_bias=True,
)
def forward(self, x):
x = self.encoder(x)
x = self.en_encoder(x)
return x
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