Spaces:
Running
Running
File size: 8,171 Bytes
b20af9f |
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 |
# --------------------------------------------------------
# BEATs: Audio Pre-Training with Acoustic Tokenizers (https://arxiv.org/abs/2212.09058)
# Github source: https://github.com/microsoft/unilm/tree/master/beats
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Based on VQGAN code bases
# https://github.com/CompVis/taming-transformers
# --------------------------------------------------------'
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as distributed
try:
from einops import rearrange, repeat
except ImportError:
pass
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 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 EmbeddingEMA(nn.Module):
def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5, kmeans_init=True, codebook_init_path=''):
super().__init__()
self.num_tokens = num_tokens
self.codebook_dim = codebook_dim
self.decay = decay
self.eps = eps
if codebook_init_path == '':
if not kmeans_init:
weight = torch.randn(num_tokens, codebook_dim)
weight = l2norm(weight)
else:
weight = torch.zeros(num_tokens, codebook_dim)
self.register_buffer('initted', torch.Tensor([not kmeans_init]))
else:
print(f"load init codebook weight from {codebook_init_path}")
codebook_ckpt_weight = torch.load(codebook_init_path, map_location='cpu')
weight = codebook_ckpt_weight.clone()
self.register_buffer('initted', torch.Tensor([True]))
self.weight = nn.Parameter(weight, requires_grad=False)
self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad=False)
self.embed_avg = nn.Parameter(weight.clone(), requires_grad=False)
# self.register_buffer('initted', torch.Tensor([not kmeans_init]))
self.update = True
@torch.jit.ignore
def init_embed_(self, data):
if self.initted:
return
print("Performing Kemans init for codebook")
embed, cluster_size = kmeans(data, self.num_tokens, 10, use_cosine_sim=True)
self.weight.data.copy_(embed)
self.cluster_size.data.copy_(cluster_size)
self.initted.data.copy_(torch.Tensor([True]))
def forward(self, embed_id):
return F.embedding(embed_id, self.weight)
def cluster_size_ema_update(self, new_cluster_size):
self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay)
def embed_avg_ema_update(self, new_embed_avg):
self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay)
def weight_update(self, num_tokens):
n = self.cluster_size.sum()
smoothed_cluster_size = (
(self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n
)
# normalize embedding average with smoothed cluster size
embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1)
# embed_normalized = l2norm(self.embed_avg / smoothed_cluster_size.unsqueeze(1))
self.weight.data.copy_(embed_normalized)
def norm_ema_inplace(moving_avg, new, decay):
moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
moving_avg.data.copy_(l2norm(moving_avg.data))
class NormEMAVectorQuantizer(nn.Module):
def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5,
statistic_code_usage=True, kmeans_init=False, codebook_init_path=''):
super().__init__()
self.codebook_dim = embedding_dim
self.num_tokens = n_embed
self.beta = beta
self.decay = decay
# learnable = True if orthogonal_reg_weight > 0 else False
self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps, kmeans_init, codebook_init_path)
self.statistic_code_usage = statistic_code_usage
if statistic_code_usage:
self.register_buffer('cluster_size', torch.zeros(n_embed))
if distributed.is_available() and distributed.is_initialized():
print("ddp is enable, so use ddp_reduce to sync the statistic_code_usage for each gpu!")
self.all_reduce_fn = distributed.all_reduce
else:
self.all_reduce_fn = nn.Identity()
def reset_cluster_size(self, device):
if self.statistic_code_usage:
self.register_buffer('cluster_size', torch.zeros(self.num_tokens))
self.cluster_size = self.cluster_size.to(device)
def forward(self, z):
# reshape z -> (batch, height, width, channel) and flatten
# z, 'b c h w -> b h w c'
# z = rearrange(z, 'b c h w -> b h w c')
# z = z.transpose(1, 2)
z = l2norm(z)
z_flattened = z.reshape(-1, self.codebook_dim)
self.embedding.init_embed_(z_flattened)
d = z_flattened.pow(2).sum(dim=1, keepdim=True) + \
self.embedding.weight.pow(2).sum(dim=1) - 2 * \
torch.einsum('bd,nd->bn', z_flattened, self.embedding.weight) # 'n d -> d n'
encoding_indices = torch.argmin(d, dim=1)
z_q = self.embedding(encoding_indices).view(z.shape)
encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype)
if not self.training:
with torch.no_grad():
cluster_size = encodings.sum(0)
self.all_reduce_fn(cluster_size)
ema_inplace(self.cluster_size, cluster_size, self.decay)
if self.training and self.embedding.update:
# EMA cluster size
bins = encodings.sum(0)
self.all_reduce_fn(bins)
# self.embedding.cluster_size_ema_update(bins)
ema_inplace(self.cluster_size, bins, self.decay)
zero_mask = (bins == 0)
bins = bins.masked_fill(zero_mask, 1.)
embed_sum = z_flattened.t() @ encodings
self.all_reduce_fn(embed_sum)
embed_normalized = (embed_sum / bins.unsqueeze(0)).t()
embed_normalized = l2norm(embed_normalized)
embed_normalized = torch.where(zero_mask[..., None], self.embedding.weight,
embed_normalized)
norm_ema_inplace(self.embedding.weight, embed_normalized, self.decay)
# compute loss for embedding
loss = self.beta * F.mse_loss(z_q.detach(), z)
# preserve gradients
z_q = z + (z_q - z).detach()
# reshape back to match original input shape
# z_q, 'b h w c -> b c h w'
# z_q = rearrange(z_q, 'b h w c -> b c h w')
# z_q = z_q.transpose(1, 2)
return z_q, loss, encoding_indices |