File size: 11,321 Bytes
9a83644 f32cd36 9a83644 f32cd36 9a83644 |
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 |
import torch
from torch import nn
import math
from modules.gpt_fast.model import ModelArgs, Transformer
from modules.wavenet import WN
from modules.commons import sequence_mask
from torch.nn.utils import weight_norm
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000, scale=1000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=t.device)
args = scale * t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq)
return t_emb
class StyleEmbedder(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(self, input_size, hidden_size, dropout_prob):
super().__init__()
use_cfg_embedding = dropout_prob > 0
self.embedding_table = nn.Embedding(int(use_cfg_embedding), hidden_size)
self.style_in = weight_norm(nn.Linear(input_size, hidden_size, bias=True))
self.input_size = input_size
self.dropout_prob = dropout_prob
def forward(self, labels, train, force_drop_ids=None):
use_dropout = self.dropout_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
labels = self.token_drop(labels, force_drop_ids)
else:
labels = self.style_in(labels)
embeddings = labels
return embeddings
class FinalLayer(nn.Module):
"""
The final layer of DiT.
"""
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = weight_norm(nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True))
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 2 * hidden_size, bias=True)
)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class DiT(torch.nn.Module):
def __init__(
self,
args
):
super(DiT, self).__init__()
self.time_as_token = args.DiT.time_as_token if hasattr(args.DiT, 'time_as_token') else False
self.style_as_token = args.DiT.style_as_token if hasattr(args.DiT, 'style_as_token') else False
self.uvit_skip_connection = args.DiT.uvit_skip_connection if hasattr(args.DiT, 'uvit_skip_connection') else False
model_args = ModelArgs(
block_size=8192,#args.DiT.block_size,
n_layer=args.DiT.depth,
n_head=args.DiT.num_heads,
dim=args.DiT.hidden_dim,
head_dim=args.DiT.hidden_dim // args.DiT.num_heads,
vocab_size=1024,
uvit_skip_connection=self.uvit_skip_connection,
)
self.transformer = Transformer(model_args)
self.in_channels = args.DiT.in_channels
self.out_channels = args.DiT.in_channels
self.num_heads = args.DiT.num_heads
self.x_embedder = weight_norm(nn.Linear(args.DiT.in_channels, args.DiT.hidden_dim, bias=True))
self.content_type = args.DiT.content_type # 'discrete' or 'continuous'
self.content_codebook_size = args.DiT.content_codebook_size # for discrete content
self.content_dim = args.DiT.content_dim # for continuous content
self.cond_embedder = nn.Embedding(args.DiT.content_codebook_size, args.DiT.hidden_dim) # discrete content
self.cond_projection = nn.Linear(args.DiT.content_dim, args.DiT.hidden_dim, bias=True) # continuous content
self.is_causal = args.DiT.is_causal
self.n_f0_bins = args.DiT.n_f0_bins
self.f0_bins = torch.arange(2, 1024, 1024 // args.DiT.n_f0_bins)
self.f0_embedder = nn.Embedding(args.DiT.n_f0_bins, args.DiT.hidden_dim)
self.f0_condition = args.DiT.f0_condition
self.t_embedder = TimestepEmbedder(args.DiT.hidden_dim)
self.t_embedder2 = TimestepEmbedder(args.wavenet.hidden_dim)
# self.style_embedder1 = weight_norm(nn.Linear(1024, args.DiT.hidden_dim, bias=True))
# self.style_embedder2 = weight_norm(nn.Linear(1024, args.style_encoder.dim, bias=True))
input_pos = torch.arange(8192)
self.register_buffer("input_pos", input_pos)
self.conv1 = nn.Linear(args.DiT.hidden_dim, args.wavenet.hidden_dim)
self.conv2 = nn.Conv1d(args.wavenet.hidden_dim, args.DiT.in_channels, 1)
self.final_layer_type = args.DiT.final_layer_type # mlp or wavenet
if self.final_layer_type == 'wavenet':
self.wavenet = WN(hidden_channels=args.wavenet.hidden_dim,
kernel_size=args.wavenet.kernel_size,
dilation_rate=args.wavenet.dilation_rate,
n_layers=args.wavenet.num_layers,
gin_channels=args.wavenet.hidden_dim,
p_dropout=args.wavenet.p_dropout,
causal=False)
self.final_layer = FinalLayer(args.wavenet.hidden_dim, 1, args.wavenet.hidden_dim)
else:
self.final_mlp = nn.Sequential(
nn.Linear(args.DiT.hidden_dim, args.DiT.hidden_dim),
nn.SiLU(),
nn.Linear(args.DiT.hidden_dim, args.DiT.in_channels),
)
self.final_conv = nn.Conv1d(args.DiT.in_channels, args.DiT.in_channels, kernel_size=3, padding=1)
self.transformer_style_condition = args.DiT.style_condition
self.wavenet_style_condition = args.wavenet.style_condition
assert args.DiT.style_condition == args.wavenet.style_condition
self.class_dropout_prob = args.DiT.class_dropout_prob
self.content_mask_embedder = nn.Embedding(1, args.DiT.hidden_dim)
self.res_projection = nn.Linear(args.DiT.hidden_dim, args.wavenet.hidden_dim) # residual connection from tranformer output to final output
self.long_skip_connection = args.DiT.long_skip_connection
self.skip_linear = nn.Linear(args.DiT.hidden_dim + args.DiT.in_channels, args.DiT.hidden_dim)
self.cond_x_merge_linear = nn.Linear(args.DiT.hidden_dim + args.DiT.in_channels * 2 +
args.style_encoder.dim * self.transformer_style_condition * (not self.style_as_token),
args.DiT.hidden_dim)
if self.style_as_token:
self.style_in = nn.Linear(args.style_encoder.dim, args.DiT.hidden_dim)
def setup_caches(self, max_batch_size, max_seq_length):
self.transformer.setup_caches(max_batch_size, max_seq_length, use_kv_cache=False)
def forward(self, x, prompt_x, x_lens, t, style, cond, f0=None, mask_content=False):
class_dropout = False
if self.training and torch.rand(1) < self.class_dropout_prob:
class_dropout = True
if not self.training and mask_content:
class_dropout = True
# cond_in_module = self.cond_embedder if self.content_type == 'discrete' else self.cond_projection
cond_in_module = self.cond_projection
B, _, T = x.size()
t1 = self.t_embedder(t) # (N, D)
cond = cond_in_module(cond)
if self.f0_condition and f0 is not None:
quantized_f0 = torch.bucketize(f0, self.f0_bins.to(f0.device)) # (N, T)
cond = cond + self.f0_embedder(quantized_f0)
x = x.transpose(1, 2)
prompt_x = prompt_x.transpose(1, 2)
x_in = torch.cat([x, prompt_x, cond], dim=-1)
if self.transformer_style_condition and not self.style_as_token:
x_in = torch.cat([x_in, style[:, None, :].repeat(1, T, 1)], dim=-1)
if class_dropout:
x_in[..., self.in_channels:] = x_in[..., self.in_channels:] * 0
x_in = self.cond_x_merge_linear(x_in) # (N, T, D)
if self.style_as_token:
style = self.style_in(style)
style = torch.zeros_like(style) if class_dropout else style
x_in = torch.cat([style.unsqueeze(1), x_in], dim=1)
if self.time_as_token:
x_in = torch.cat([t1.unsqueeze(1), x_in], dim=1)
x_mask = sequence_mask(x_lens + self.style_as_token + self.time_as_token).to(x.device).unsqueeze(1)
input_pos = self.input_pos[:x_in.size(1)] # (T,)
x_mask_expanded = x_mask[:, None, :].repeat(1, 1, x_in.size(1), 1) if not self.is_causal else None
x_res = self.transformer(x_in, None if self.time_as_token else t1.unsqueeze(1), input_pos, x_mask_expanded)
x_res = x_res[:, 1:] if self.time_as_token else x_res
x_res = x_res[:, 1:] if self.style_as_token else x_res
if self.long_skip_connection:
x_res = self.skip_linear(torch.cat([x_res, x], dim=-1))
if self.final_layer_type == 'wavenet':
x = self.conv1(x_res)
x = x.transpose(1, 2)
t2 = self.t_embedder2(t)
x = self.wavenet(x, x_mask, g=t2.unsqueeze(2)).transpose(1, 2) + self.res_projection(
x_res) # long residual connection
x = self.final_layer(x, t1).transpose(1, 2)
x = self.conv2(x)
else:
x = self.final_mlp(x_res)
x = x.transpose(1, 2)
x = self.final_conv(x)
return x
|