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import math
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from conformer import ConformerBlock
from diffusers.models.activations import get_activation
from einops import pack, rearrange, repeat
from matcha.models.components.transformer import BasicTransformerBlock
class SinusoidalPosEmb(torch.nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
assert self.dim % 2 == 0, "SinusoidalPosEmb requires dim to be even"
def forward(self, x, scale=1000):
if x.ndim < 1:
x = x.unsqueeze(0)
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class Block1D(torch.nn.Module):
def __init__(self, dim, dim_out, groups=8):
super().__init__()
self.block = torch.nn.Sequential(
torch.nn.Conv1d(dim, dim_out, 3, padding=1),
torch.nn.GroupNorm(groups, dim_out),
nn.Mish(),
)
def forward(self, x, mask):
output = self.block(x * mask)
return output * mask
class ResnetBlock1D(torch.nn.Module):
def __init__(self, dim, dim_out, time_emb_dim, groups=8):
super().__init__()
self.mlp = torch.nn.Sequential(nn.Mish(), torch.nn.Linear(time_emb_dim, dim_out))
self.block1 = Block1D(dim, dim_out, groups=groups)
self.block2 = Block1D(dim_out, dim_out, groups=groups)
self.res_conv = torch.nn.Conv1d(dim, dim_out, 1)
def forward(self, x, mask, time_emb):
h = self.block1(x, mask)
h += self.mlp(time_emb).unsqueeze(-1)
h = self.block2(h, mask)
output = h + self.res_conv(x * mask)
return output
class Downsample1D(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = torch.nn.Conv1d(dim, dim, 3, 2, 1)
def forward(self, x):
return self.conv(x)
class TimestepEmbedding(nn.Module):
def __init__(
self,
in_channels: int,
time_embed_dim: int,
act_fn: str = "silu",
out_dim: int = None,
post_act_fn: Optional[str] = None,
cond_proj_dim=None,
):
super().__init__()
self.linear_1 = nn.Linear(in_channels, time_embed_dim)
if cond_proj_dim is not None:
self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
else:
self.cond_proj = None
self.act = get_activation(act_fn)
if out_dim is not None:
time_embed_dim_out = out_dim
else:
time_embed_dim_out = time_embed_dim
self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out)
if post_act_fn is None:
self.post_act = None
else:
self.post_act = get_activation(post_act_fn)
def forward(self, sample, condition=None):
if condition is not None:
sample = sample + self.cond_proj(condition)
sample = self.linear_1(sample)
if self.act is not None:
sample = self.act(sample)
sample = self.linear_2(sample)
if self.post_act is not None:
sample = self.post_act(sample)
return sample
class Upsample1D(nn.Module):
"""A 1D upsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
use_conv_transpose (`bool`, default `False`):
option to use a convolution transpose.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
"""
def __init__(self, channels, use_conv=False, use_conv_transpose=True, out_channels=None, name="conv"):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_conv_transpose = use_conv_transpose
self.name = name
self.conv = None
if use_conv_transpose:
self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)
elif use_conv:
self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)
def forward(self, inputs):
assert inputs.shape[1] == self.channels
if self.use_conv_transpose:
return self.conv(inputs)
outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest")
if self.use_conv:
outputs = self.conv(outputs)
return outputs
class ConformerWrapper(ConformerBlock):
def __init__( # pylint: disable=useless-super-delegation
self,
*,
dim,
dim_head=64,
heads=8,
ff_mult=4,
conv_expansion_factor=2,
conv_kernel_size=31,
attn_dropout=0,
ff_dropout=0,
conv_dropout=0,
conv_causal=False,
):
super().__init__(
dim=dim,
dim_head=dim_head,
heads=heads,
ff_mult=ff_mult,
conv_expansion_factor=conv_expansion_factor,
conv_kernel_size=conv_kernel_size,
attn_dropout=attn_dropout,
ff_dropout=ff_dropout,
conv_dropout=conv_dropout,
conv_causal=conv_causal,
)
def forward(
self,
hidden_states,
attention_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
timestep=None,
):
return super().forward(x=hidden_states, mask=attention_mask.bool())
class Decoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
channels=(256, 256),
dropout=0.05,
attention_head_dim=64,
n_blocks=1,
num_mid_blocks=2,
num_heads=4,
act_fn="snake",
down_block_type="transformer",
mid_block_type="transformer",
up_block_type="transformer",
):
super().__init__()
channels = tuple(channels)
self.in_channels = in_channels
self.out_channels = out_channels
self.time_embeddings = SinusoidalPosEmb(in_channels)
time_embed_dim = channels[0] * 4
self.time_mlp = TimestepEmbedding(
in_channels=in_channels,
time_embed_dim=time_embed_dim,
act_fn="silu",
)
self.down_blocks = nn.ModuleList([])
self.mid_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
output_channel = in_channels
for i in range(len(channels)): # pylint: disable=consider-using-enumerate
input_channel = output_channel
output_channel = channels[i]
is_last = i == len(channels) - 1
resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
transformer_blocks = nn.ModuleList(
[
self.get_block(
down_block_type,
output_channel,
attention_head_dim,
num_heads,
dropout,
act_fn,
)
for _ in range(n_blocks)
]
)
downsample = (
Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1)
)
self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
for i in range(num_mid_blocks):
input_channel = channels[-1]
out_channels = channels[-1]
resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
transformer_blocks = nn.ModuleList(
[
self.get_block(
mid_block_type,
output_channel,
attention_head_dim,
num_heads,
dropout,
act_fn,
)
for _ in range(n_blocks)
]
)
self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
channels = channels[::-1] + (channels[0],)
for i in range(len(channels) - 1):
input_channel = channels[i]
output_channel = channels[i + 1]
is_last = i == len(channels) - 2
resnet = ResnetBlock1D(
dim=2 * input_channel,
dim_out=output_channel,
time_emb_dim=time_embed_dim,
)
transformer_blocks = nn.ModuleList(
[
self.get_block(
up_block_type,
output_channel,
attention_head_dim,
num_heads,
dropout,
act_fn,
)
for _ in range(n_blocks)
]
)
upsample = (
Upsample1D(output_channel, use_conv_transpose=True)
if not is_last
else nn.Conv1d(output_channel, output_channel, 3, padding=1)
)
self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
self.final_block = Block1D(channels[-1], channels[-1])
self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
self.initialize_weights()
# nn.init.normal_(self.final_proj.weight)
@staticmethod
def get_block(block_type, dim, attention_head_dim, num_heads, dropout, act_fn):
if block_type == "conformer":
block = ConformerWrapper(
dim=dim,
dim_head=attention_head_dim,
heads=num_heads,
ff_mult=1,
conv_expansion_factor=2,
ff_dropout=dropout,
attn_dropout=dropout,
conv_dropout=dropout,
conv_kernel_size=31,
)
elif block_type == "transformer":
block = BasicTransformerBlock(
dim=dim,
num_attention_heads=num_heads,
attention_head_dim=attention_head_dim,
dropout=dropout,
activation_fn=act_fn,
)
else:
raise ValueError(f"Unknown block type {block_type}")
return block
def initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv1d):
nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.GroupNorm):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x, mask, mu, t, spks=None, cond=None):
"""Forward pass of the UNet1DConditional model.
Args:
x (torch.Tensor): shape (batch_size, in_channels, time)
mask (_type_): shape (batch_size, 1, time)
t (_type_): shape (batch_size)
spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
cond (_type_, optional): placeholder for future use. Defaults to None.
Raises:
ValueError: _description_
ValueError: _description_
Returns:
_type_: _description_
"""
t = self.time_embeddings(t)
t = self.time_mlp(t)
x = pack([x, mu], "b * t")[0]
if spks is not None:
spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
x = pack([x, spks], "b * t")[0]
hiddens = []
masks = [mask]
for resnet, transformer_blocks, downsample in self.down_blocks:
mask_down = masks[-1]
x = resnet(x, mask_down, t)
x = rearrange(x, "b c t -> b t c")
mask_down = rearrange(mask_down, "b 1 t -> b t")
for transformer_block in transformer_blocks:
x = transformer_block(
hidden_states=x,
attention_mask=mask_down,
timestep=t,
)
x = rearrange(x, "b t c -> b c t")
mask_down = rearrange(mask_down, "b t -> b 1 t")
hiddens.append(x) # Save hidden states for skip connections
x = downsample(x * mask_down)
masks.append(mask_down[:, :, ::2])
masks = masks[:-1]
mask_mid = masks[-1]
for resnet, transformer_blocks in self.mid_blocks:
x = resnet(x, mask_mid, t)
x = rearrange(x, "b c t -> b t c")
mask_mid = rearrange(mask_mid, "b 1 t -> b t")
for transformer_block in transformer_blocks:
x = transformer_block(
hidden_states=x,
attention_mask=mask_mid,
timestep=t,
)
x = rearrange(x, "b t c -> b c t")
mask_mid = rearrange(mask_mid, "b t -> b 1 t")
for resnet, transformer_blocks, upsample in self.up_blocks:
mask_up = masks.pop()
x = resnet(pack([x, hiddens.pop()], "b * t")[0], mask_up, t)
x = rearrange(x, "b c t -> b t c")
mask_up = rearrange(mask_up, "b 1 t -> b t")
for transformer_block in transformer_blocks:
x = transformer_block(
hidden_states=x,
attention_mask=mask_up,
timestep=t,
)
x = rearrange(x, "b t c -> b c t")
mask_up = rearrange(mask_up, "b t -> b 1 t")
x = upsample(x * mask_up)
x = self.final_block(x, mask_up)
output = self.final_proj(x * mask_up)
return output * mask
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