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# Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) 2022 Microsoft
# Modified from
# https://github.com/microsoft/unilm/blob/master/beit2/norm_ema_quantizer.py
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from mmengine.dist import all_reduce
def ema_inplace(moving_avg: torch.Tensor, new: torch.Tensor,
decay: torch.Tensor) -> None:
"""Update moving average."""
moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
def norm_ema_inplace(moving_avg: torch.Tensor, new: torch.Tensor,
decay: torch.Tensor) -> None:
"""Update moving average with norm data."""
moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
moving_avg.data.copy_(F.normalize(moving_avg.data, p=2, dim=-1))
def sample_vectors(samples: torch.Tensor, num: int) -> torch.Tensor:
"""Sample vectors according to the given number."""
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: torch.Tensor,
num_clusters: int,
num_iters: int = 10,
use_cosine_sim: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
"""Run k-means algorithm."""
dim, dtype, _ = 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 = F.normalize(new_means, p=2, dim=-1)
means = torch.where(zero_mask[..., None], means, new_means)
return means, bins
class EmbeddingEMA(nn.Module):
"""The codebook of embedding vectors.
Args:
num_tokens (int): Number of embedding vectors in the codebook.
codebook_dim (int) : The dimension of embedding vectors in the
codebook.
kmeans_init (bool): Whether to use k-means to initialize the
VectorQuantizer. Defaults to True.
codebook_init_path (str): The initialization checkpoint for codebook.
Defaults to None.
"""
def __init__(self,
num_tokens: int,
codebook_dim: int,
kmeans_init: bool = True,
codebook_init_path: Optional[str] = None):
super().__init__()
self.num_tokens = num_tokens
self.codebook_dim = codebook_dim
if codebook_init_path is None:
if not kmeans_init:
weight = torch.randn(num_tokens, codebook_dim)
weight = F.normalize(weight, p=2, dim=-1)
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.update = True
@torch.jit.ignore
def init_embed_(self, data: torch.Tensor) -> None:
"""Initialize embedding vectors of codebook."""
if self.initted:
return
print('Performing K-means init for codebook')
embed, _ = kmeans(data, self.num_tokens, 10, use_cosine_sim=True)
self.weight.data.copy_(embed)
self.initted.data.copy_(torch.Tensor([True]))
def forward(self, embed_id: torch.Tensor) -> torch.Tensor:
"""Get embedding vectors."""
return F.embedding(embed_id, self.weight)
class NormEMAVectorQuantizer(nn.Module):
"""Normed EMA vector quantizer module.
Args:
num_embed (int): Number of embedding vectors in the codebook. Defaults
to 8192.
embed_dims (int) : The dimension of embedding vectors in the codebook.
Defaults to 32.
beta (float): The mutiplier for VectorQuantizer embedding loss.
Defaults to 1.
decay (float): The decay parameter of EMA. Defaults to 0.99.
statistic_code_usage (bool): Whether to use cluster_size to record
statistic. Defaults to True.
kmeans_init (bool): Whether to use k-means to initialize the
VectorQuantizer. Defaults to True.
codebook_init_path (str): The initialization checkpoint for codebook.
Defaults to None.
"""
def __init__(self,
num_embed: int,
embed_dims: int,
beta: float,
decay: float = 0.99,
statistic_code_usage: bool = True,
kmeans_init: bool = True,
codebook_init_path: Optional[str] = None) -> None:
super().__init__()
self.codebook_dim = embed_dims
self.num_tokens = num_embed
self.beta = beta
self.decay = decay
# learnable = True if orthogonal_reg_weight > 0 else False
self.embedding = EmbeddingEMA(
num_tokens=self.num_tokens,
codebook_dim=self.codebook_dim,
kmeans_init=kmeans_init,
codebook_init_path=codebook_init_path)
self.statistic_code_usage = statistic_code_usage
if statistic_code_usage:
self.register_buffer('cluster_size', torch.zeros(num_embed))
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):
"""Forward function."""
# reshape z -> (batch, height, width, channel)
z = rearrange(z, 'b c h w -> b h w c')
z = F.normalize(z, p=2, dim=-1)
z_flattened = z.reshape(-1, self.codebook_dim)
self.embedding.init_embed_(z_flattened)
# 'n d -> d n'
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)
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)
all_reduce(cluster_size)
ema_inplace(self.cluster_size, cluster_size, self.decay)
if self.training and self.embedding.update:
# update cluster size with EMA
bins = encodings.sum(0)
all_reduce(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
all_reduce(embed_sum)
embed_normalized = (embed_sum / bins.unsqueeze(0)).t()
embed_normalized = F.normalize(embed_normalized, p=2, dim=-1)
embed_normalized = torch.where(zero_mask[..., None],
self.embedding.weight,
embed_normalized)
# Update embedding vectors with EMA
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 = rearrange(z_q, 'b h w c -> b c h w')
return z_q, loss, encoding_indices
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