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import torch
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from torch import nn
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from torch.nn import functional as F
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from einops import rearrange
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from vector_quantize_pytorch import ResidualVQ, FSQ
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from .nn.quantize import ResidualVectorQuantize as DACResidualVQ
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class Bottleneck(nn.Module):
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def __init__(self, is_discrete: bool = False):
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super().__init__()
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self.is_discrete = is_discrete
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def encode(self, x, return_info=False, **kwargs):
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raise NotImplementedError
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def decode(self, x):
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raise NotImplementedError
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class DiscreteBottleneck(Bottleneck):
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def __init__(self, num_quantizers, codebook_size, tokens_id):
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super().__init__(is_discrete=True)
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self.num_quantizers = num_quantizers
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self.codebook_size = codebook_size
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self.tokens_id = tokens_id
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def decode_tokens(self, codes, **kwargs):
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raise NotImplementedError
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class TanhBottleneck(Bottleneck):
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def __init__(self):
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super().__init__(is_discrete=False)
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self.tanh = nn.Tanh()
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def encode(self, x, return_info=False):
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info = {}
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x = torch.tanh(x)
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if return_info:
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return x, info
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else:
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return x
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def decode(self, x):
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return x
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@torch.jit.script
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def vae_sample_kl(mean, scale):
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stdev = nn.functional.softplus(scale) + 1e-4
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var = stdev * stdev
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logvar = torch.log(var)
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latents = torch.randn_like(mean) * stdev + mean
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kl = (mean * mean + var - logvar - 1).sum(1).mean()
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return latents, kl
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@torch.jit.script
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def vae_sample(mean, scale):
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stdev = nn.functional.softplus(scale) + 1e-4
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latents = torch.randn_like(mean) * stdev + mean
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return latents
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class VAEBottleneck(Bottleneck):
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def __init__(self):
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super().__init__(is_discrete=False)
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def encode(self, x, return_info=False, **kwargs):
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mean, scale = x.chunk(2, dim=1)
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if return_info:
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info = {}
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x, kl = vae_sample_kl(mean, scale)
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info["kl"] = kl
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return x, info
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else:
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x = vae_sample(mean, scale)
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return x
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def decode(self, x):
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return x
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def compute_mean_kernel(x, y):
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kernel_input = (x[:, None] - y[None]).pow(2).mean(2) / x.shape[-1]
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return torch.exp(-kernel_input).mean()
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def compute_mmd(latents):
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latents_reshaped = latents.permute(0, 2, 1).reshape(-1, latents.shape[1])
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noise = torch.randn_like(latents_reshaped)
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latents_kernel = compute_mean_kernel(latents_reshaped, latents_reshaped)
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noise_kernel = compute_mean_kernel(noise, noise)
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latents_noise_kernel = compute_mean_kernel(latents_reshaped, noise)
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mmd = latents_kernel + noise_kernel - 2 * latents_noise_kernel
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return mmd.mean()
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class WassersteinBottleneck(Bottleneck):
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def __init__(self, noise_augment_dim: int = 0):
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super().__init__(is_discrete=False)
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self.noise_augment_dim = noise_augment_dim
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def encode(self, x, return_info=False):
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info = {}
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if self.training and return_info:
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mmd = compute_mmd(x)
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info["mmd"] = mmd
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if return_info:
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return x, info
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return x
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def decode(self, x):
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if self.noise_augment_dim > 0:
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noise = torch.randn(x.shape[0], self.noise_augment_dim,
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x.shape[-1]).type_as(x)
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x = torch.cat([x, noise], dim=1)
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return x
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class L2Bottleneck(Bottleneck):
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def __init__(self):
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super().__init__(is_discrete=False)
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def encode(self, x, return_info=False):
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info = {}
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x = F.normalize(x, dim=1)
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if return_info:
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return x, info
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else:
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return x
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def decode(self, x):
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return F.normalize(x, dim=1)
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class RVQBottleneck(DiscreteBottleneck):
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def __init__(self, **quantizer_kwargs):
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super().__init__(num_quantizers = quantizer_kwargs["num_quantizers"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "quantizer_indices")
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self.quantizer = ResidualVQ(**quantizer_kwargs)
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self.num_quantizers = quantizer_kwargs["num_quantizers"]
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def encode(self, x, return_info=False, **kwargs):
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info = {}
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x = rearrange(x, "b c n -> b n c")
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x, indices, loss = self.quantizer(x)
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x = rearrange(x, "b n c -> b c n")
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info["quantizer_indices"] = indices
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info["quantizer_loss"] = loss.mean()
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if return_info:
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return x, info
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else:
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return x
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def decode(self, x):
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return x
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def decode_tokens(self, codes, **kwargs):
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latents = self.quantizer.get_outputs_from_indices(codes)
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return self.decode(latents, **kwargs)
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class RVQVAEBottleneck(DiscreteBottleneck):
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def __init__(self, **quantizer_kwargs):
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super().__init__(num_quantizers = quantizer_kwargs["num_quantizers"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "quantizer_indices")
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self.quantizer = ResidualVQ(**quantizer_kwargs)
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self.num_quantizers = quantizer_kwargs["num_quantizers"]
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def encode(self, x, return_info=False):
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info = {}
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x, kl = vae_sample(*x.chunk(2, dim=1))
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info["kl"] = kl
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x = rearrange(x, "b c n -> b n c")
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x, indices, loss = self.quantizer(x)
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x = rearrange(x, "b n c -> b c n")
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info["quantizer_indices"] = indices
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info["quantizer_loss"] = loss.mean()
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if return_info:
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return x, info
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else:
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return x
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def decode(self, x):
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return x
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def decode_tokens(self, codes, **kwargs):
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latents = self.quantizer.get_outputs_from_indices(codes)
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return self.decode(latents, **kwargs)
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class DACRVQBottleneck(DiscreteBottleneck):
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def __init__(self, quantize_on_decode=False, **quantizer_kwargs):
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super().__init__(num_quantizers = quantizer_kwargs["n_codebooks"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "codes")
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self.quantizer = DACResidualVQ(**quantizer_kwargs)
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self.num_quantizers = quantizer_kwargs["n_codebooks"]
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self.quantize_on_decode = quantize_on_decode
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def encode(self, x, return_info=False, **kwargs):
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info = {}
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info["pre_quantizer"] = x
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if self.quantize_on_decode:
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return x, info if return_info else x
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z, codes, latents, commitment_loss, codebook_loss = self.quantizer(x, **kwargs)
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output = {
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"z": z,
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"codes": codes,
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"latents": latents,
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"vq/commitment_loss": commitment_loss,
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"vq/codebook_loss": codebook_loss,
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}
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output["vq/commitment_loss"] /= self.num_quantizers
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output["vq/codebook_loss"] /= self.num_quantizers
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info.update(output)
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if return_info:
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return output["z"], info
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return output["z"]
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def decode(self, x):
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if self.quantize_on_decode:
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x = self.quantizer(x)[0]
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return x
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def decode_tokens(self, codes, **kwargs):
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latents, _, _ = self.quantizer.from_codes(codes)
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return self.decode(latents, **kwargs)
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class DACRVQVAEBottleneck(DiscreteBottleneck):
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def __init__(self, quantize_on_decode=False, **quantizer_kwargs):
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super().__init__(num_quantizers = quantizer_kwargs["n_codebooks"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "codes")
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self.quantizer = DACResidualVQ(**quantizer_kwargs)
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self.num_quantizers = quantizer_kwargs["n_codebooks"]
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self.quantize_on_decode = quantize_on_decode
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def encode(self, x, return_info=False, n_quantizers: int = None):
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info = {}
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mean, scale = x.chunk(2, dim=1)
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x, kl = vae_sample(mean, scale)
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info["pre_quantizer"] = x
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info["kl"] = kl
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if self.quantize_on_decode:
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return x, info if return_info else x
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z, codes, latents, commitment_loss, codebook_loss = self.quantizer(x, n_quantizers=n_quantizers)
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output = {
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"z": z,
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"codes": codes,
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"latents": latents,
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"vq/commitment_loss": commitment_loss,
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"vq/codebook_loss": codebook_loss,
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}
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output["vq/commitment_loss"] /= self.num_quantizers
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output["vq/codebook_loss"] /= self.num_quantizers
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info.update(output)
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if return_info:
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return output["z"], info
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return output["z"]
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def decode(self, x):
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if self.quantize_on_decode:
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x = self.quantizer(x)[0]
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return x
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def decode_tokens(self, codes, **kwargs):
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latents, _, _ = self.quantizer.from_codes(codes)
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return self.decode(latents, **kwargs)
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class FSQBottleneck(DiscreteBottleneck):
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def __init__(self, dim, levels):
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super().__init__(num_quantizers = 1, codebook_size = levels ** dim, tokens_id = "quantizer_indices")
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self.quantizer = FSQ(levels=[levels] * dim)
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def encode(self, x, return_info=False):
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info = {}
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x = rearrange(x, "b c n -> b n c")
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x, indices = self.quantizer(x)
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x = rearrange(x, "b n c -> b c n")
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info["quantizer_indices"] = indices
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if return_info:
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return x, info
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else:
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return x
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def decode(self, x):
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return x
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def decode_tokens(self, tokens, **kwargs):
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latents = self.quantizer.indices_to_codes(tokens)
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return self.decode(latents, **kwargs) |
