246 lines
8.0 KiB
Python
246 lines
8.0 KiB
Python
|
import torch
|
||
|
|
from torch import nn, einsum
|
||
|
|
import torch.nn.functional as F
|
||
|
|
from einops import rearrange, repeat
|
||
|
|
|
||
|
|
from models.arch_util import l2norm, sample_vectors, default, ema_inplace
|
||
|
|
|
||
|
|
|
||
|
|
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 = 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[..., None]
|
||
|
|
|
||
|
|
if use_cosine_sim:
|
||
|
|
new_means = l2norm(new_means)
|
||
|
|
|
||
|
|
means = torch.where(zero_mask[..., None], means, new_means)
|
||
|
|
|
||
|
|
return means
|
||
|
|
|
||
|
|
# distance types
|
||
|
|
|
||
|
|
class EuclideanCodebook(nn.Module):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
dim,
|
||
|
|
codebook_size,
|
||
|
|
kmeans_init = False,
|
||
|
|
kmeans_iters = 10,
|
||
|
|
decay = 0.8,
|
||
|
|
eps = 1e-5
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.decay = decay
|
||
|
|
init_fn = torch.randn if not kmeans_init else torch.zeros
|
||
|
|
embed = init_fn(codebook_size, dim)
|
||
|
|
|
||
|
|
self.codebook_size = codebook_size
|
||
|
|
self.kmeans_iters = kmeans_iters
|
||
|
|
self.eps = eps
|
||
|
|
|
||
|
|
self.register_buffer('initted', torch.Tensor([not kmeans_init]))
|
||
|
|
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 = kmeans(data, self.codebook_size, self.kmeans_iters)
|
||
|
|
self.embed.data.copy_(embed)
|
||
|
|
self.embed_avg.data.copy_(embed.clone())
|
||
|
|
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 forward(self, x):
|
||
|
|
shape, dtype = x.shape, x.dtype
|
||
|
|
flatten = rearrange(x, '... d -> (...) d')
|
||
|
|
embed = self.embed.t()
|
||
|
|
|
||
|
|
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(x.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
|
||
|
|
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)
|
||
|
|
|
||
|
|
return quantize, embed_ind
|
||
|
|
|
||
|
|
class CosineSimCodebook(nn.Module):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
dim,
|
||
|
|
codebook_size,
|
||
|
|
kmeans_init = False,
|
||
|
|
kmeans_iters = 10,
|
||
|
|
decay = 0.8,
|
||
|
|
eps = 1e-5
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.decay = decay
|
||
|
|
|
||
|
|
if not kmeans_init:
|
||
|
|
embed = l2norm(torch.randn(codebook_size, dim))
|
||
|
|
else:
|
||
|
|
embed = torch.zeros(codebook_size, dim)
|
||
|
|
|
||
|
|
self.codebook_size = codebook_size
|
||
|
|
self.kmeans_iters = kmeans_iters
|
||
|
|
self.eps = eps
|
||
|
|
|
||
|
|
self.register_buffer('initted', torch.Tensor([not kmeans_init]))
|
||
|
|
self.register_buffer('embed', embed)
|
||
|
|
|
||
|
|
def init_embed_(self, data):
|
||
|
|
embed = kmeans(data, self.codebook_size, self.kmeans_iters, use_cosine_sim = True)
|
||
|
|
self.embed.data.copy_(embed)
|
||
|
|
self.initted.data.copy_(torch.Tensor([True]))
|
||
|
|
|
||
|
|
def replace(self, samples, mask):
|
||
|
|
samples = l2norm(samples)
|
||
|
|
modified_codebook = torch.where(mask[..., None], sample_vectors(samples, self.codebook_size), self.embed)
|
||
|
|
self.embed.data.copy_(modified_codebook)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
shape, dtype = x.shape, x.dtype
|
||
|
|
flatten = rearrange(x, '... d -> (...) d')
|
||
|
|
flatten = l2norm(flatten)
|
||
|
|
|
||
|
|
if not self.initted:
|
||
|
|
self.init_embed_(flatten)
|
||
|
|
|
||
|
|
embed = l2norm(self.embed)
|
||
|
|
dist = flatten @ embed.t()
|
||
|
|
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:
|
||
|
|
bins = embed_onehot.sum(0)
|
||
|
|
zero_mask = (bins == 0)
|
||
|
|
bins = bins.masked_fill(zero_mask, 1.)
|
||
|
|
|
||
|
|
embed_sum = flatten.t() @ embed_onehot
|
||
|
|
embed_normalized = (embed_sum / bins.unsqueeze(0)).t()
|
||
|
|
embed_normalized = l2norm(embed_normalized)
|
||
|
|
embed_normalized = torch.where(zero_mask[..., None], embed, embed_normalized)
|
||
|
|
ema_inplace(self.embed, embed_normalized, self.decay)
|
||
|
|
|
||
|
|
return quantize, embed_ind
|
||
|
|
|
||
|
|
# main class
|
||
|
|
|
||
|
|
class VectorQuantize(nn.Module):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
dim,
|
||
|
|
codebook_size,
|
||
|
|
n_embed = None,
|
||
|
|
codebook_dim = None,
|
||
|
|
decay = 0.8,
|
||
|
|
eps = 1e-5,
|
||
|
|
kmeans_init = False,
|
||
|
|
kmeans_iters = 10,
|
||
|
|
use_cosine_sim = False,
|
||
|
|
max_codebook_misses_before_expiry = 0
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
n_embed = default(n_embed, codebook_size)
|
||
|
|
|
||
|
|
codebook_dim = default(codebook_dim, dim)
|
||
|
|
requires_projection = codebook_dim != dim
|
||
|
|
self.project_in = nn.Linear(dim, codebook_dim) if requires_projection else nn.Identity()
|
||
|
|
self.project_out = nn.Linear(codebook_dim, dim) if requires_projection else nn.Identity()
|
||
|
|
|
||
|
|
self.eps = eps
|
||
|
|
|
||
|
|
klass = EuclideanCodebook if not use_cosine_sim else CosineSimCodebook
|
||
|
|
|
||
|
|
self._codebook = klass(
|
||
|
|
dim = codebook_dim,
|
||
|
|
codebook_size = n_embed,
|
||
|
|
kmeans_init = kmeans_init,
|
||
|
|
kmeans_iters = kmeans_iters,
|
||
|
|
decay = decay,
|
||
|
|
eps = eps
|
||
|
|
)
|
||
|
|
|
||
|
|
self.codebook_size = codebook_size
|
||
|
|
self.max_codebook_misses_before_expiry = max_codebook_misses_before_expiry
|
||
|
|
|
||
|
|
if max_codebook_misses_before_expiry > 0:
|
||
|
|
codebook_misses = torch.zeros(codebook_size)
|
||
|
|
self.register_buffer('codebook_misses', codebook_misses)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def codebook(self):
|
||
|
|
return self._codebook.codebook
|
||
|
|
|
||
|
|
def decode(self, codes):
|
||
|
|
unembed = F.embedding(codes, self._codebook.embed)
|
||
|
|
return self.project_out(unembed)
|
||
|
|
|
||
|
|
def expire_codes_(self, embed_ind, batch_samples):
|
||
|
|
if self.max_codebook_misses_before_expiry == 0:
|
||
|
|
return
|
||
|
|
|
||
|
|
embed_ind = rearrange(embed_ind, '... -> (...)')
|
||
|
|
misses = torch.bincount(embed_ind, minlength = self.codebook_size) == 0
|
||
|
|
self.codebook_misses += misses
|
||
|
|
|
||
|
|
expired_codes = self.codebook_misses >= self.max_codebook_misses_before_expiry
|
||
|
|
if not torch.any(expired_codes):
|
||
|
|
return
|
||
|
|
|
||
|
|
self.codebook_misses.masked_fill_(expired_codes, 0)
|
||
|
|
batch_samples = rearrange(batch_samples, '... d -> (...) d')
|
||
|
|
self._codebook.replace(batch_samples, mask = expired_codes)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.project_in(x)
|
||
|
|
|
||
|
|
quantize, embed_ind = self._codebook(x)
|
||
|
|
commit_loss = F.mse_loss(quantize.detach(), x)
|
||
|
|
|
||
|
|
if self.training:
|
||
|
|
quantize = x + (quantize - x).detach()
|
||
|
|
self.expire_codes_(embed_ind, x)
|
||
|
|
|
||
|
|
quantize = self.project_out(quantize)
|
||
|
|
return quantize, embed_ind, commit_loss
|