File size: 8,008 Bytes
14ce5a9 |
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 |
"""Vector quantizer.
Copyright (2024) Bytedance Ltd. and/or its affiliates
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Reference:
https://github.com/CompVis/taming-transformers/blob/master/taming/modules/vqvae/quantize.py
https://github.com/google-research/magvit/blob/main/videogvt/models/vqvae.py
https://github.com/CompVis/latent-diffusion/blob/main/ldm/modules/distributions/distributions.py
https://github.com/lyndonzheng/CVQ-VAE/blob/main/quantise.py
"""
from typing import Mapping, Text, Tuple
import torch
from einops import rearrange
from accelerate.utils.operations import gather
from torch.cuda.amp import autocast
class VectorQuantizer(torch.nn.Module):
def __init__(
self,
codebook_size: int = 1024,
token_size: int = 256,
commitment_cost: float = 0.25,
use_l2_norm: bool = False,
clustering_vq: bool = False,
):
super().__init__()
self.codebook_size = codebook_size
self.token_size = token_size
self.commitment_cost = commitment_cost
self.embedding = torch.nn.Embedding(codebook_size, token_size)
self.embedding.weight.data.uniform_(-1.0 / codebook_size, 1.0 / codebook_size)
self.use_l2_norm = use_l2_norm
self.clustering_vq = clustering_vq
if clustering_vq:
self.decay = 0.99
self.register_buffer("embed_prob", torch.zeros(self.codebook_size))
# Ensure quantization is performed using f32
@autocast(enabled=False)
def forward(
self, z: torch.Tensor
) -> Tuple[torch.Tensor, Mapping[Text, torch.Tensor]]:
z = z.float()
z = rearrange(z, "b c h w -> b h w c").contiguous()
z_flattened = rearrange(z, "b h w c -> (b h w) c")
unnormed_z_flattened = z_flattened
if self.use_l2_norm:
z_flattened = torch.nn.functional.normalize(z_flattened, dim=-1)
embedding = torch.nn.functional.normalize(self.embedding.weight, dim=-1)
else:
embedding = self.embedding.weight
d = (
torch.sum(z_flattened**2, dim=1, keepdim=True)
+ torch.sum(embedding**2, dim=1)
- 2 * torch.einsum("bd,dn->bn", z_flattened, embedding.T)
)
min_encoding_indices = torch.argmin(d, dim=1) # num_ele
z_quantized = self.get_codebook_entry(min_encoding_indices).view(z.shape)
if self.use_l2_norm:
z = torch.nn.functional.normalize(z, dim=-1)
# compute loss for embedding
commitment_loss = self.commitment_cost * torch.mean(
(z_quantized.detach() - z) ** 2
)
codebook_loss = torch.mean((z_quantized - z.detach()) ** 2)
if self.clustering_vq and self.training:
with torch.no_grad():
# Gather distance matrix from all GPUs.
encoding_indices = gather(min_encoding_indices)
if len(min_encoding_indices.shape) != 1:
raise ValueError(
f"min_encoding_indices in a wrong shape, {min_encoding_indices.shape}"
)
# Compute and update the usage of each entry in the codebook.
encodings = torch.zeros(
encoding_indices.shape[0], self.codebook_size, device=z.device
)
encodings.scatter_(1, encoding_indices.unsqueeze(1), 1)
avg_probs = torch.mean(encodings, dim=0)
self.embed_prob.mul_(self.decay).add_(avg_probs, alpha=1 - self.decay)
# Closest sampling to update the codebook.
all_d = gather(d)
all_unnormed_z_flattened = gather(unnormed_z_flattened).detach()
if all_d.shape[0] != all_unnormed_z_flattened.shape[0]:
raise ValueError(
"all_d and all_unnormed_z_flattened have different length"
+ f"{all_d.shape}, {all_unnormed_z_flattened.shape}"
)
indices = torch.argmin(all_d, dim=0)
random_feat = all_unnormed_z_flattened[indices]
# Decay parameter based on the average usage.
decay = (
torch.exp(
-(self.embed_prob * self.codebook_size * 10) / (1 - self.decay)
- 1e-3
)
.unsqueeze(1)
.repeat(1, self.token_size)
)
self.embedding.weight.data = (
self.embedding.weight.data * (1 - decay) + random_feat * decay
)
loss = commitment_loss + codebook_loss
# preserve gradients
z_quantized = z + (z_quantized - z).detach()
# reshape back to match original input shape
z_quantized = rearrange(z_quantized, "b h w c -> b c h w").contiguous()
result_dict = dict(
quantizer_loss=loss,
commitment_loss=commitment_loss,
codebook_loss=codebook_loss,
min_encoding_indices=min_encoding_indices.view(
z_quantized.shape[0], z_quantized.shape[2], z_quantized.shape[3]
),
)
return z_quantized, result_dict
def get_codebook_entry(self, indices):
if len(indices.shape) == 1:
z_quantized = self.embedding(indices)
elif len(indices.shape) == 2:
z_quantized = torch.einsum("bd,dn->bn", indices, self.embedding.weight)
else:
raise NotImplementedError
if self.use_l2_norm:
z_quantized = torch.nn.functional.normalize(z_quantized, dim=-1)
return z_quantized
class DiagonalGaussianDistribution(object):
@autocast(enabled=False)
def __init__(self, parameters, deterministic=False):
"""Initializes a Gaussian distribution instance given the parameters.
Args:
parameters (torch.Tensor): The parameters for the Gaussian distribution. It is expected
to be in shape [B, 2 * C, *], where B is batch size, and C is the embedding dimension.
First C channels are used for mean and last C are used for logvar in the Gaussian distribution.
deterministic (bool): Whether to use deterministic sampling. When it is true, the sampling results
is purely based on mean (i.e., std = 0).
"""
self.parameters = parameters
self.mean, self.logvar = torch.chunk(parameters.float(), 2, dim=1)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean).to(
device=self.parameters.device
)
@autocast(enabled=False)
def sample(self):
x = self.mean.float() + self.std.float() * torch.randn(self.mean.shape).to(
device=self.parameters.device
)
return x
@autocast(enabled=False)
def mode(self):
return self.mean
@autocast(enabled=False)
def kl(self):
if self.deterministic:
return torch.Tensor([0.0])
else:
return 0.5 * torch.sum(
torch.pow(self.mean.float(), 2)
+ self.var.float()
- 1.0
- self.logvar.float(),
dim=[1, 2],
)
|