diva-audio / models /beats /quantizer.py
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# --------------------------------------------------------
# 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