File size: 17,879 Bytes
d0831da |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 |
from typing import Tuple
import torch
import torch.nn as nn
import math
from einops import rearrange
from torch.nn.functional import scaled_dot_product_attention
def modulate(x, shift, scale):
return x * (1 + scale) + shift
class Embed(nn.Module):
def __init__(
self,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer = None,
bias: bool = True,
):
super().__init__()
self.in_chans = in_chans
self.embed_dim = embed_dim
self.proj = nn.Linear(in_chans, embed_dim, bias=bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
x = self.proj(x)
x = self.norm(x)
return x
class PatchEmbed(nn.Module):
def __init__(
self,
in_channels=8,
embed_dim=1152,
bias=True,
patch_size=1,
):
super().__init__()
self.patch_h, self.patch_w = patch_size
self.patch_size = patch_size
self.proj = nn.Linear(in_channels * self.patch_h * self.patch_w, embed_dim, bias=bias)
self.in_channels = in_channels
self.embed_dim = embed_dim
def forward(self, latent):
x = rearrange(latent, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)', p1=self.patch_h, p2=self.patch_w)
x = self.proj(x)
return x
class FinalLayer(nn.Module):
"""Final layer with configurable patch_size support"""
def __init__(self, hidden_size, out_channels=8, patch_size=1):
super().__init__()
self.patch_h, self.patch_w = patch_size
self.linear = nn.Linear(hidden_size, out_channels * self.patch_h * self.patch_w, bias=True)
self.out_channels = out_channels
self.patch_size = patch_size
def forward(self, x, target_height, target_width):
x = self.linear(x)
x = rearrange(x, 'b (h w) (c p1 p2) -> b c (h p1) (w p2)',
h=target_height, w=target_width,
p1=self.patch_h, p2=self.patch_w, c=self.out_channels)
return x
class TimestepEmbedder(nn.Module):
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=10):
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half
)
args = 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 RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
class FeedForward(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
):
super().__init__()
hidden_dim = int(2 * hidden_dim / 3)
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
def forward(self, x):
x = self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x))
return x
def precompute_freqs_cis_2d(dim: int, height: int, width: int, theta: float = 10000.0, scale=1.0):
if isinstance(scale, float):
scale = (scale, scale)
x_pos = torch.linspace(0, width * scale[0], width)
y_pos = torch.linspace(0, height * scale[1], height)
y_pos, x_pos = torch.meshgrid(y_pos, x_pos, indexing="ij")
y_pos = y_pos.reshape(-1)
x_pos = x_pos.reshape(-1)
freqs = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
x_freqs = torch.outer(x_pos, freqs).float()
y_freqs = torch.outer(y_pos, freqs).float()
x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs)
y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs)
freqs_cis = torch.cat([x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1)
freqs_cis = freqs_cis.reshape(height * width, -1)
return freqs_cis
@torch.compiler.disable
def apply_rotary_emb_2d(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cis: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
freqs_cis = freqs_cis[None, None, :, :]
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
class RAttention(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_norm: bool = True,
attn_drop: float = 0.,
proj_drop: float = 0.,
norm_layer: nn.Module = RMSNorm,
) -> None:
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x: torch.Tensor, pos, mask) -> torch.Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
q = self.q_norm(q.contiguous())
k = self.k_norm(k.contiguous())
q, k = apply_rotary_emb_2d(q, k, freqs_cis=pos)
q = q.view(B, self.num_heads, -1, C // self.num_heads)
k = k.view(B, self.num_heads, -1, C // self.num_heads).contiguous()
v = v.view(B, self.num_heads, -1, C // self.num_heads).contiguous()
x = scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=self.attn_drop.p if self.training else 0.0)
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class CrossAttention(nn.Module):
def __init__(
self,
dim: int,
context_dim: int,
num_heads: int,
qkv_bias: bool = False,
proj_drop: float = 0.0,
):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim**-0.5
self.q_proj = nn.Linear(dim, dim, bias=qkv_bias)
self.kv_proj = nn.Linear(context_dim, dim * 2, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x: torch.Tensor, context: torch.Tensor, context_mask: torch.Tensor = None) -> torch.Tensor:
B, N, C = x.shape
B_ctx, M, C_ctx = context.shape
q = self.q_proj(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
kv = self.kv_proj(context).reshape(B_ctx, M, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
k, v = kv[0], kv[1]
attn_mask = None
if context_mask is not None:
attn_mask = torch.zeros(B, 1, 1, M, dtype=q.dtype, device=q.device)
attn_mask.masked_fill_(~context_mask.unsqueeze(1).unsqueeze(2), float('-inf'))
attn = scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=self.proj_drop.p if self.training else 0.0)
x = attn.permute(0, 2, 1, 3).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class DDTBlock(nn.Module):
def __init__(self, hidden_size, groups, mlp_ratio=4.0, context_dim=None, is_encoder_block=False):
super().__init__()
self.hidden_size = hidden_size
self.norm1 = RMSNorm(hidden_size, eps=1e-6)
self.attn = RAttention(hidden_size, num_heads=groups, qkv_bias=False)
self.norm_cross = RMSNorm(hidden_size, eps=1e-6) if context_dim else nn.Identity()
self.cross_attn = CrossAttention(hidden_size, context_dim, groups) if context_dim else None
self.norm2 = RMSNorm(hidden_size, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
self.mlp = FeedForward(hidden_size, mlp_hidden_dim)
self.is_encoder_block = is_encoder_block
if not is_encoder_block:
self.adaLN_modulation = nn.Sequential(
nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
def forward(self, x, c, pos, mask=None, context=None, context_mask=None, shared_adaLN=None):
if self.is_encoder_block:
adaLN_output = shared_adaLN(c)
else:
adaLN_output = self.adaLN_modulation(c)
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = adaLN_output.chunk(6, dim=-1)
x = x + gate_msa * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), pos, mask=mask)
if self.cross_attn is not None and context is not None:
x = x + self.cross_attn(self.norm_cross(x), context=context, context_mask=context_mask)
x = x + gate_mlp * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class LocalSongModel(nn.Module):
def __init__(
self,
in_channels=8,
num_groups=16,
hidden_size=1024,
decoder_hidden_size=2048,
num_blocks=36,
patch_size=(16,1),
num_classes=2304,
max_tags=8,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = in_channels
self.hidden_size = hidden_size
self.decoder_hidden_size = decoder_hidden_size
self.num_groups = num_groups
self.num_groups = num_groups
self.num_blocks = num_blocks
self.patch_size = patch_size
self.num_classes = num_classes
self.max_tags = max_tags
self.patch_h, self.patch_w = patch_size
self.x_embedder = PatchEmbed(
in_channels=in_channels,
embed_dim=decoder_hidden_size,
bias=True,
patch_size=patch_size
)
self.s_embedder = PatchEmbed(
in_channels=in_channels,
embed_dim=decoder_hidden_size,
bias=True,
patch_size=patch_size
)
self.encoder_to_decoder = nn.Linear(hidden_size, decoder_hidden_size, bias=False)
self.a_to_b_proj = nn.Linear(decoder_hidden_size, hidden_size, bias=False)
self.t_embedder = TimestepEmbedder(hidden_size)
self.y_embedder = nn.Embedding(num_classes + 1, hidden_size, padding_idx=0)
self.final_layer = FinalLayer(
decoder_hidden_size,
out_channels=in_channels,
patch_size=patch_size
)
self.shared_encoder_adaLN = nn.Sequential(
nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
self.shared_decoder_adaLN = nn.Sequential(
nn.Linear(hidden_size, 6 * decoder_hidden_size, bias=True)
)
self.blocks = nn.ModuleList()
for i in range(self.num_blocks):
is_encoder = i < self.num_blocks
if is_encoder:
if i < 1:
block_hidden_size = decoder_hidden_size
num_heads = self.num_groups
elif i >= self.num_blocks - 3:
block_hidden_size = decoder_hidden_size
num_heads = self.num_groups
else:
block_hidden_size = hidden_size
num_heads = self.num_groups
else:
block_hidden_size = decoder_hidden_size
num_heads = self.num_groups
context_dim = hidden_size if i % 2 == 0 and is_encoder else None
self.blocks.append(
DDTBlock(
block_hidden_size,
num_heads,
context_dim=context_dim,
is_encoder_block=is_encoder
)
)
self.bc_projection = nn.Linear(decoder_hidden_size + hidden_size, decoder_hidden_size, bias=False)
self.initialize_weights()
self.precompute_encoder_pos = dict()
self.precompute_decoder_pos = dict()
from functools import lru_cache
@lru_cache
def fetch_encoder_pos(self, height, width, device):
key = (height, width)
if key in self.precompute_encoder_pos:
return self.precompute_encoder_pos[key].to(device)
else:
pos = precompute_freqs_cis_2d(self.hidden_size // self.num_groups, height, width).to(device)
self.precompute_encoder_pos[key] = pos
return pos
@lru_cache
def fetch_decoder_pos(self, height, width, device):
key = (height, width)
if key in self.precompute_decoder_pos:
return self.precompute_decoder_pos[key].to(device)
else:
pos = precompute_freqs_cis_2d(self.decoder_hidden_size // self.num_groups, height, width).to(device)
self.precompute_decoder_pos[key] = pos
return pos
def initialize_weights(self):
for embedder in [self.x_embedder, self.s_embedder]:
nn.init.xavier_uniform_(embedder.proj.weight)
if embedder.proj.bias is not None:
nn.init.constant_(embedder.proj.bias, 0)
nn.init.xavier_uniform_(self.encoder_to_decoder.weight)
nn.init.xavier_uniform_(self.a_to_b_proj.weight)
nn.init.normal_(self.y_embedder.weight, std=0.02)
with torch.no_grad():
self.y_embedder.weight[0].fill_(0)
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
nn.init.constant_(self.shared_encoder_adaLN[-1].weight, 0)
nn.init.constant_(self.shared_encoder_adaLN[-1].bias, 0)
nn.init.constant_(self.shared_decoder_adaLN[-1].weight, 0)
nn.init.constant_(self.shared_decoder_adaLN[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
nn.init.xavier_uniform_(self.bc_projection.weight)
def embed_condition(self, cond):
device = self.y_embedder.weight.device
max_len = self.max_tags
batch_size = len(cond)
padded_tags = torch.zeros(batch_size, max_len, dtype=torch.long, device=device)
for i, tags in enumerate(cond):
truncated_tags = tags[:max_len]
padded_tags[i, :len(truncated_tags)] = torch.tensor(truncated_tags, dtype=torch.long, device=device)
padding_mask = (padded_tags != 0)
embedded = self.y_embedder(padded_tags)
return embedded, padding_mask
def forward(self, x, t, y):
y_emb, padding_mask = self.embed_condition(y)
return self.forward_emb(x, t, y_emb, padding_mask)
@torch.compile()
def forward_emb(self, x, t, y_emb, padding_mask=None):
B, _, H, W = x.shape
h_patches = H // self.patch_h
w_patches = W // self.patch_w
encoder_pos = self.fetch_encoder_pos(h_patches, w_patches, x.device)
decoder_pos = self.fetch_decoder_pos(h_patches, w_patches, x.device)
t_emb = self.t_embedder(t.view(-1)).view(B, 1, self.hidden_size)
t_cond = nn.functional.silu(t_emb)
s = self.s_embedder(x)
s_section_a = s
for i in range(min(1, self.num_blocks)):
block_context = y_emb if i % 2 == 0 else None
s_section_a = self.blocks[i](s_section_a, t_cond, decoder_pos, None, context=block_context, context_mask=padding_mask, shared_adaLN=self.shared_decoder_adaLN)
s_section_a_projected = self.a_to_b_proj(s_section_a)
s_section_b = s_section_a_projected
for i in range(1, self.num_blocks - 3):
block_context = y_emb if i % 2 == 0 else None
s_section_b = self.blocks[i](s_section_b, t_cond, encoder_pos, None, context=block_context, context_mask=padding_mask, shared_adaLN=self.shared_encoder_adaLN)
s_concat = torch.cat([s_section_a, s_section_b], dim=-1)
s = self.bc_projection(s_concat)
for i in range(max(1, self.num_blocks - 3), self.num_blocks):
block_context = y_emb if i % 2 == 0 else None
s = self.blocks[i](s, t_cond, decoder_pos, None, context=block_context, context_mask=padding_mask, shared_adaLN=self.shared_decoder_adaLN)
s = self.final_layer(s, H // self.patch_h, W // self.patch_w)
return s
|