File size: 13,633 Bytes
c968fc3 |
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 |
# Copyright (c) 2024 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from torch.nn.utils import weight_norm
def WNConv1d(*args, **kwargs):
return weight_norm(nn.Conv1d(*args, **kwargs))
def WNConvTranspose1d(*args, **kwargs):
return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
def l2norm(t):
return F.normalize(t, p=2, dim=-1)
def ema_inplace(moving_avg, new, decay):
moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
def laplace_smoothing(x, n_categories, eps=1e-5):
return (x + eps) / (x.sum() + n_categories * eps)
def sample_vectors(samples, num):
num_samples, device = samples.shape[0], samples.device
if num_samples >= num:
indices = torch.randperm(num_samples, device=device)[:num]
else:
indices = torch.randint(0, num_samples, (num,), device=device)
return samples[indices]
def kmeans(samples, num_clusters, num_iters=10, use_cosine_sim=False):
dim, dtype, device = samples.shape[-1], samples.dtype, samples.device
means = sample_vectors(samples, num_clusters)
for _ in range(num_iters):
if use_cosine_sim:
dists = samples @ means.t()
else:
diffs = rearrange(samples, "n d -> n () d") - rearrange(
means, "c d -> () c d"
)
dists = -(diffs**2).sum(dim=-1)
buckets = dists.max(dim=-1).indices
bins = torch.bincount(buckets, minlength=num_clusters)
zero_mask = bins == 0
bins_min_clamped = bins.masked_fill(zero_mask, 1)
new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
new_means = new_means / bins_min_clamped[..., None]
if use_cosine_sim:
new_means = l2norm(new_means)
means = torch.where(zero_mask[..., None], means, new_means)
return means, bins
class EuclideanCodebook(nn.Module):
def __init__(
self,
dim,
codebook_size,
kmeans_init=False,
kmeans_iters=10,
decay=0.8,
eps=1e-5,
threshold_ema_dead_code=2,
weight_init=False,
):
super().__init__()
self.decay = decay
init_fn = torch.randn if not weight_init else torch.zeros
embed = init_fn(codebook_size, dim)
if weight_init:
nn.init.uniform_(embed, -1 / codebook_size, 1 / codebook_size)
self.codebook_size = codebook_size
self.kmeans_iters = kmeans_iters
self.eps = eps
self.threshold_ema_dead_code = threshold_ema_dead_code
self.register_buffer(
"initted", torch.Tensor([not kmeans_init])
) # if kmeans_init is True, then initted is False; otherwise, initted is True
self.register_buffer("cluster_size", torch.zeros(codebook_size))
self.register_buffer("embed", embed)
self.register_buffer("embed_avg", embed.clone())
def init_embed_(self, data):
embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
self.embed.data.copy_(embed)
self.embed_avg.data.copy_(embed)
self.cluster_size.data.copy_(cluster_size)
self.initted.data.copy_(torch.Tensor([True]))
def replace(self, samples, mask):
modified_codebook = torch.where(
mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
)
self.embed.data.copy_(modified_codebook)
def expire_codes_(self, batch_samples):
if self.threshold_ema_dead_code == 0:
return
expired_codes = self.cluster_size < self.threshold_ema_dead_code
if not torch.any(expired_codes):
return
batch_samples = rearrange(batch_samples, "... d -> (...) d")
self.replace(batch_samples, mask=expired_codes)
def forward(self, x):
shape, dtype = x.shape, x.dtype
flatten = rearrange(x, "... d -> (...) d")
embed = self.embed.t() # (codebook_size, dim) -> (dim, codebook_size)
if not self.initted:
self.init_embed_(flatten)
dist = -(
flatten.pow(2).sum(1, keepdim=True)
- 2 * flatten @ embed
+ embed.pow(2).sum(0, keepdim=True)
)
embed_ind = dist.max(dim=-1).indices
embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
embed_ind = embed_ind.view(*shape[:-1])
quantize = F.embedding(embed_ind, self.embed)
if self.training:
ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
embed_sum = (
flatten.t() @ embed_onehot
) # (dim, ...) @ (..., codebook_size) -> (dim, codebook_size)
ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
cluster_size = (
laplace_smoothing(self.cluster_size, self.codebook_size, self.eps)
* self.cluster_size.sum()
)
embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
self.embed.data.copy_(embed_normalized)
self.expire_codes_(x)
return quantize, embed_ind
def vq2emb(self, vq):
quantize = F.embedding(vq, self.embed)
return quantize
def latent2dist(self, x):
shape, dtype = x.shape, x.dtype
flatten = rearrange(x, "... d -> (...) d")
embed = self.embed.t() # (codebook_size, dim) -> (dim, codebook_size)
if not self.initted:
self.init_embed_(flatten)
dist = -(
flatten.pow(2).sum(1, keepdim=True)
- 2 * flatten @ embed
+ embed.pow(2).sum(0, keepdim=True)
)
embed_ind = dist.max(dim=-1).indices
embed_ind = embed_ind.view(*shape[:-1])
quantize = F.embedding(embed_ind, self.embed)
dist = dist.view(*shape[:-1], -1)
return dist, embed_ind, quantize
class SimpleCodebook(nn.Module):
def __init__(
self,
dim,
codebook_size,
use_l2_normlize=False,
):
super().__init__()
self.dim = dim
self.codebook_size = codebook_size
self.use_l2_normlize = use_l2_normlize
self.embed = nn.Embedding(self.codebook_size, self.dim)
def forward(self, x):
shape, dtype = x.shape, x.dtype
flatten = rearrange(x, "... d -> (...) d")
embed = self.embed.weight.t() # (codebook_size, dim) -> (dim, codebook_size)
if self.use_l2_normlize:
flatten = F.normalize(flatten)
embed = F.normalize(embed)
dist = -(
flatten.pow(2).sum(1, keepdim=True)
- 2 * flatten @ embed
+ embed.pow(2).sum(0, keepdim=True)
)
embed_ind = dist.max(dim=-1).indices
embed_ind = embed_ind.view(*shape[:-1])
quantize = F.embedding(embed_ind, self.embed)
return quantize, embed_ind
def vq2emb(self, vq):
quantize = F.embedding(vq, self.embed.weight)
return quantize
def latent2dist(self, x):
shape, dtype = x.shape, x.dtype
flatten = rearrange(x, "... d -> (...) d")
embed = self.embed.weight.t() # (codebook_size, dim) -> (dim, codebook_size)
if self.use_l2_normlize:
flatten = F.normalize(flatten)
embed = F.normalize(embed)
dist = -(
flatten.pow(2).sum(1, keepdim=True)
- 2 * flatten @ embed
+ embed.pow(2).sum(0, keepdim=True)
)
embed_ind = dist.max(dim=-1).indices
embed_ind = embed_ind.view(*shape[:-1])
quantize = F.embedding(embed_ind, self.embed)
dist = dist.view(*shape[:-1], -1)
return dist, embed_ind, quantize
class VectorQuantize(nn.Module):
"""Vector quantization and factorized vecotor quantization implementation
Args:
input_dim (int): Dimension of input.
codebook_size (int): Codebook size.
codebook_dim (int): Codebook dimension. We suggest use codebook_dim = input_dim
if use codebook_type == "euclidean", otherwise, if you want to use
factorized vector quantization, use codebook_dim as small number (e.g. 8 or 32).
commitment (float): Weight for commitment loss.
use_l2_normlize (bool): Whether to use l2 normlized codes for factorized vecotor quantization,
we suggest use it as True if you want to use factorized vector quantization
kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
kmeans_iters (int): Number of iterations used for kmeans initialization.
decay (float): Decay for exponential moving average over the codebooks.
epsilon (float): Epsilon value for numerical stability.
threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
that have an exponential moving average cluster size less than the specified threshold with
randomly selected vector from the current batch.
"""
def __init__(
self,
input_dim,
codebook_size,
codebook_dim,
commitment=0.005,
codebook_loss_weight=1.0,
use_l2_normlize=False,
codebook_type="euclidean", # "euclidean" or "simple"
kmeans_init=False,
kmeans_iters=10,
decay=0.8,
eps=1e-5,
threshold_ema_dead_code=2,
weight_init=False,
):
super().__init__()
self.input_dim = input_dim
self.codebook_size = codebook_size
self.codebook_dim = codebook_dim
self.commitment = commitment
self.codebook_loss_weight = codebook_loss_weight
self.use_l2_normlize = use_l2_normlize
self.codebook_type = codebook_type
self.kmeans_init = kmeans_init
self.kmeans_iters = kmeans_iters
self.decay = decay
self.eps = eps
self.threshold_ema_dead_code = threshold_ema_dead_code
self.weight_init = weight_init
if self.input_dim != self.codebook_dim:
self.in_project = WNConv1d(self.input_dim, self.codebook_dim, kernel_size=1)
self.out_project = WNConv1d(
self.codebook_dim, self.input_dim, kernel_size=1
)
else:
self.in_project = nn.Identity()
self.out_project = nn.Identity()
if self.codebook_type == "euclidean":
self.codebook = EuclideanCodebook(
self.codebook_dim,
codebook_size=self.codebook_size,
kmeans_init=self.kmeans_init,
kmeans_iters=self.kmeans_iters,
decay=self.decay,
eps=self.eps,
threshold_ema_dead_code=self.threshold_ema_dead_code,
weight_init=self.weight_init,
)
elif self.codebook_type == "simple":
self.codebook = SimpleCodebook(
self.codebook_dim,
codebook_size=self.codebook_size,
use_l2_normlize=self.use_l2_normlize,
)
else:
raise NotImplementedError(
f"codebook_type {self.codebook_type} is not implemented!"
)
def forward(self, z):
"""
Parameters
----------
z: torch.Tensor[B x D x T]
Returns
-------
z_q: torch.Tensor[B x D x T]
Quantized continuous representation of input
commit_loss: Tensor[B]
Commitment loss to train encoder to predict vectors closer to codebook entries
codebook_loss: Tensor[B]
Codebook loss to update the codebook
indices: torch.Tensor[B x T]
Codebook indices (quantized discrete representation of input)
z_e: torch.Tensor[B x D x T]
Projected latents (continuous representation of input before quantization)
"""
# Factorized codes project input into low-dimensional space if self.input_dim != self.codebook_dim
z_e = self.in_project(z)
z_q, indices = self.decode_latents(z_e)
# Compute commitment loss and codebook loss
if self.training:
commit_loss = (
F.mse_loss(z_e, z_q.detach(), reduction="none").mean([1, 2])
* self.commitment
)
codebook_loss = (
F.mse_loss(z_q, z_e.detach(), reduction="none").mean([1, 2])
* self.codebook_loss_weight
)
else:
commit_loss = torch.zeros(z.shape[0], device=z.device)
codebook_loss = torch.zeros(z.shape[0], device=z.device)
z_q = z_e + (z_q - z_e).detach()
z_q = self.out_project(z_q)
return z_q, commit_loss, codebook_loss, indices, z_e
def decode_latents(self, latents):
encodings = rearrange(latents, "b d t -> b t d")
z_q, indices = self.codebook(encodings)
z_q = z_q.transpose(1, 2)
return z_q, indices
def vq2emb(self, vq, out_proj=True):
emb = self.codebook.vq2emb(vq)
emb = emb.transpose(1, 2)
if out_proj:
emb = self.out_project(emb)
return emb
def latent2dist(self, latents):
latents = rearrange(latents, "b d t -> b t d")
dist, embed_ind, quantize = self.codebook.latent2dist(latents)
return dist, embed_ind, quantize.transpose(1, 2)
|