import logging from abc import abstractmethod from typing import Dict, Iterator, Literal, Optional, Tuple, Union import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from torch import einsum from .base import AbstractRegularizer, measure_perplexity logpy = logging.getLogger(__name__) class AbstractQuantizer(AbstractRegularizer): def __init__(self): super().__init__() # Define these in your init # shape (N,) self.used: Optional[torch.Tensor] self.re_embed: int self.unknown_index: Union[Literal["random"], int] def remap_to_used(self, inds: torch.Tensor) -> torch.Tensor: assert self.used is not None, "You need to define used indices for remap" ishape = inds.shape assert len(ishape) > 1 inds = inds.reshape(ishape[0], -1) used = self.used.to(inds) match = (inds[:, :, None] == used[None, None, ...]).long() new = match.argmax(-1) unknown = match.sum(2) < 1 if self.unknown_index == "random": new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to( device=new.device ) else: new[unknown] = self.unknown_index return new.reshape(ishape) def unmap_to_all(self, inds: torch.Tensor) -> torch.Tensor: assert self.used is not None, "You need to define used indices for remap" ishape = inds.shape assert len(ishape) > 1 inds = inds.reshape(ishape[0], -1) used = self.used.to(inds) if self.re_embed > self.used.shape[0]: # extra token inds[inds >= self.used.shape[0]] = 0 # simply set to zero back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds) return back.reshape(ishape) @abstractmethod def get_codebook_entry( self, indices: torch.Tensor, shape: Optional[Tuple[int, ...]] = None ) -> torch.Tensor: raise NotImplementedError() def get_trainable_parameters(self) -> Iterator[torch.nn.Parameter]: yield from self.parameters() class GumbelQuantizer(AbstractQuantizer): """ credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!) Gumbel Softmax trick quantizer Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016 https://arxiv.org/abs/1611.01144 """ def __init__( self, num_hiddens: int, embedding_dim: int, n_embed: int, straight_through: bool = True, kl_weight: float = 5e-4, temp_init: float = 1.0, remap: Optional[str] = None, unknown_index: str = "random", loss_key: str = "loss/vq", ) -> None: super().__init__() self.loss_key = loss_key self.embedding_dim = embedding_dim self.n_embed = n_embed self.straight_through = straight_through self.temperature = temp_init self.kl_weight = kl_weight self.proj = nn.Conv2d(num_hiddens, n_embed, 1) self.embed = nn.Embedding(n_embed, embedding_dim) self.remap = remap if self.remap is not None: self.register_buffer("used", torch.tensor(np.load(self.remap))) self.re_embed = self.used.shape[0] else: self.used = None self.re_embed = n_embed if unknown_index == "extra": self.unknown_index = self.re_embed self.re_embed = self.re_embed + 1 else: assert unknown_index == "random" or isinstance( unknown_index, int ), "unknown index needs to be 'random', 'extra' or any integer" self.unknown_index = unknown_index # "random" or "extra" or integer if self.remap is not None: logpy.info( f"Remapping {self.n_embed} indices to {self.re_embed} indices. " f"Using {self.unknown_index} for unknown indices." ) def forward( self, z: torch.Tensor, temp: Optional[float] = None, return_logits: bool = False ) -> Tuple[torch.Tensor, Dict]: # force hard = True when we are in eval mode, as we must quantize. # actually, always true seems to work hard = self.straight_through if self.training else True temp = self.temperature if temp is None else temp out_dict = {} logits = self.proj(z) if self.remap is not None: # continue only with used logits full_zeros = torch.zeros_like(logits) logits = logits[:, self.used, ...] soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard) if self.remap is not None: # go back to all entries but unused set to zero full_zeros[:, self.used, ...] = soft_one_hot soft_one_hot = full_zeros z_q = einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) # + kl divergence to the prior loss qy = F.softmax(logits, dim=1) diff = ( self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean() ) out_dict[self.loss_key] = diff ind = soft_one_hot.argmax(dim=1) out_dict["indices"] = ind if self.remap is not None: ind = self.remap_to_used(ind) if return_logits: out_dict["logits"] = logits return z_q, out_dict def get_codebook_entry(self, indices, shape): # TODO: shape not yet optional b, h, w, c = shape assert b * h * w == indices.shape[0] indices = rearrange(indices, "(b h w) -> b h w", b=b, h=h, w=w) if self.remap is not None: indices = self.unmap_to_all(indices) one_hot = ( F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float() ) z_q = einsum("b n h w, n d -> b d h w", one_hot, self.embed.weight) return z_q class VectorQuantizer(AbstractQuantizer): """ ____________________________________________ Discretization bottleneck part of the VQ-VAE. Inputs: - n_e : number of embeddings - e_dim : dimension of embedding - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 _____________________________________________ """ def __init__( self, n_e: int, e_dim: int, beta: float = 0.25, remap: Optional[str] = None, unknown_index: str = "random", sane_index_shape: bool = False, log_perplexity: bool = False, embedding_weight_norm: bool = False, loss_key: str = "loss/vq", ): super().__init__() self.n_e = n_e self.e_dim = e_dim self.beta = beta self.loss_key = loss_key if not embedding_weight_norm: self.embedding = nn.Embedding(self.n_e, self.e_dim) self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) else: self.embedding = torch.nn.utils.weight_norm( nn.Embedding(self.n_e, self.e_dim), dim=1 ) self.remap = remap if self.remap is not None: self.register_buffer("used", torch.tensor(np.load(self.remap))) self.re_embed = self.used.shape[0] else: self.used = None self.re_embed = n_e if unknown_index == "extra": self.unknown_index = self.re_embed self.re_embed = self.re_embed + 1 else: assert unknown_index == "random" or isinstance( unknown_index, int ), "unknown index needs to be 'random', 'extra' or any integer" self.unknown_index = unknown_index # "random" or "extra" or integer if self.remap is not None: logpy.info( f"Remapping {self.n_e} indices to {self.re_embed} indices. " f"Using {self.unknown_index} for unknown indices." ) self.sane_index_shape = sane_index_shape self.log_perplexity = log_perplexity def forward( self, z: torch.Tensor, ) -> Tuple[torch.Tensor, Dict]: do_reshape = z.ndim == 4 if do_reshape: # # reshape z -> (batch, height, width, channel) and flatten z = rearrange(z, "b c h w -> b h w c").contiguous() else: assert z.ndim < 4, "No reshaping strategy for inputs > 4 dimensions defined" z = z.contiguous() z_flattened = z.view(-1, self.e_dim) # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z d = ( torch.sum(z_flattened**2, dim=1, keepdim=True) + torch.sum(self.embedding.weight**2, dim=1) - 2 * torch.einsum( "bd,dn->bn", z_flattened, rearrange(self.embedding.weight, "n d -> d n") ) ) min_encoding_indices = torch.argmin(d, dim=1) z_q = self.embedding(min_encoding_indices).view(z.shape) loss_dict = {} if self.log_perplexity: perplexity, cluster_usage = measure_perplexity( min_encoding_indices.detach(), self.n_e ) loss_dict.update({"perplexity": perplexity, "cluster_usage": cluster_usage}) # compute loss for embedding loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean( (z_q - z.detach()) ** 2 ) loss_dict[self.loss_key] = loss # preserve gradients z_q = z + (z_q - z).detach() # reshape back to match original input shape if do_reshape: z_q = rearrange(z_q, "b h w c -> b c h w").contiguous() if self.remap is not None: min_encoding_indices = min_encoding_indices.reshape( z.shape[0], -1 ) # add batch axis min_encoding_indices = self.remap_to_used(min_encoding_indices) min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten if self.sane_index_shape: if do_reshape: min_encoding_indices = min_encoding_indices.reshape( z_q.shape[0], z_q.shape[2], z_q.shape[3] ) else: min_encoding_indices = rearrange( min_encoding_indices, "(b s) 1 -> b s", b=z_q.shape[0] ) loss_dict["min_encoding_indices"] = min_encoding_indices return z_q, loss_dict def get_codebook_entry( self, indices: torch.Tensor, shape: Optional[Tuple[int, ...]] = None ) -> torch.Tensor: # shape specifying (batch, height, width, channel) if self.remap is not None: assert shape is not None, "Need to give shape for remap" indices = indices.reshape(shape[0], -1) # add batch axis indices = self.unmap_to_all(indices) indices = indices.reshape(-1) # flatten again # get quantized latent vectors z_q = self.embedding(indices) if shape is not None: z_q = z_q.view(shape) # reshape back to match original input shape z_q = z_q.permute(0, 3, 1, 2).contiguous() return z_q class EmbeddingEMA(nn.Module): def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5): super().__init__() self.decay = decay self.eps = eps weight = torch.randn(num_tokens, codebook_dim) 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.update = 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) self.weight.data.copy_(embed_normalized) class EMAVectorQuantizer(AbstractQuantizer): def __init__( self, n_embed: int, embedding_dim: int, beta: float, decay: float = 0.99, eps: float = 1e-5, remap: Optional[str] = None, unknown_index: str = "random", loss_key: str = "loss/vq", ): super().__init__() self.codebook_dim = embedding_dim self.num_tokens = n_embed self.beta = beta self.loss_key = loss_key self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps) self.remap = remap if self.remap is not None: self.register_buffer("used", torch.tensor(np.load(self.remap))) self.re_embed = self.used.shape[0] else: self.used = None self.re_embed = n_embed if unknown_index == "extra": self.unknown_index = self.re_embed self.re_embed = self.re_embed + 1 else: assert unknown_index == "random" or isinstance( unknown_index, int ), "unknown index needs to be 'random', 'extra' or any integer" self.unknown_index = unknown_index # "random" or "extra" or integer if self.remap is not None: logpy.info( f"Remapping {self.n_embed} indices to {self.re_embed} indices. " f"Using {self.unknown_index} for unknown indices." ) def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Dict]: # 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_flattened = z.reshape(-1, self.codebook_dim) # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z 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) avg_probs = torch.mean(encodings, dim=0) perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10))) if self.training and self.embedding.update: # EMA cluster size encodings_sum = encodings.sum(0) self.embedding.cluster_size_ema_update(encodings_sum) # EMA embedding average embed_sum = encodings.transpose(0, 1) @ z_flattened self.embedding.embed_avg_ema_update(embed_sum) # normalize embed_avg and update weight self.embedding.weight_update(self.num_tokens) # 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") out_dict = { self.loss_key: loss, "encodings": encodings, "encoding_indices": encoding_indices, "perplexity": perplexity, } return z_q, out_dict class VectorQuantizerWithInputProjection(VectorQuantizer): def __init__( self, input_dim: int, n_codes: int, codebook_dim: int, beta: float = 1.0, output_dim: Optional[int] = None, **kwargs, ): super().__init__(n_codes, codebook_dim, beta, **kwargs) self.proj_in = nn.Linear(input_dim, codebook_dim) self.output_dim = output_dim if output_dim is not None: self.proj_out = nn.Linear(codebook_dim, output_dim) else: self.proj_out = nn.Identity() def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Dict]: rearr = False in_shape = z.shape if z.ndim > 3: rearr = self.output_dim is not None z = rearrange(z, "b c ... -> b (...) c") z = self.proj_in(z) z_q, loss_dict = super().forward(z) z_q = self.proj_out(z_q) if rearr: if len(in_shape) == 4: z_q = rearrange(z_q, "b (h w) c -> b c h w ", w=in_shape[-1]) elif len(in_shape) == 5: z_q = rearrange( z_q, "b (t h w) c -> b c t h w ", w=in_shape[-1], h=in_shape[-2] ) else: raise NotImplementedError( f"rearranging not available for {len(in_shape)}-dimensional input." ) return z_q, loss_dict