import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from torch import einsum from einops import rearrange class VectorQuantizer(nn.Module): """ see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py ____________________________________________ 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 _____________________________________________ """ # NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for # a fix and use legacy=False to apply that fix. VectorQuantizer2 can be # used wherever VectorQuantizer has been used before and is additionally # more efficient. def __init__(self, n_e, e_dim, beta): super(VectorQuantizer, self).__init__() self.n_e = n_e self.e_dim = e_dim self.beta = beta 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) def forward(self, z): """ Inputs the output of the encoder network z and maps it to a discrete one-hot vector that is the index of the closest embedding vector e_j z (continuous) -> z_q (discrete) z.shape = (batch, channel, height, width) quantization pipeline: 1. get encoder input (B,C,H,W) 2. flatten input to (B*H*W,C) """ # reshape z -> (batch, height, width, channel) and flatten z = z.permute(0, 2, 3, 1).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.matmul(z_flattened, self.embedding.weight.t()) ## could possible replace this here # #\start... # find closest encodings min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) min_encodings = torch.zeros( min_encoding_indices.shape[0], self.n_e).to(z) min_encodings.scatter_(1, min_encoding_indices, 1) # dtype min encodings: torch.float32 # min_encodings shape: torch.Size([2048, 512]) # min_encoding_indices.shape: torch.Size([2048, 1]) # get quantized latent vectors z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) #.........\end # with: # .........\start #min_encoding_indices = torch.argmin(d, dim=1) #z_q = self.embedding(min_encoding_indices) # ......\end......... (TODO) # compute loss for embedding loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ torch.mean((z_q - z.detach()) ** 2) # preserve gradients z_q = z + (z_q - z).detach() # perplexity e_mean = torch.mean(min_encodings, dim=0) perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) # reshape back to match original input shape z_q = z_q.permute(0, 3, 1, 2).contiguous() return z_q, loss, (perplexity, min_encodings, min_encoding_indices) def get_codebook_entry(self, indices, shape): # shape specifying (batch, height, width, channel) # TODO: check for more easy handling with nn.Embedding min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) min_encodings.scatter_(1, indices[:,None], 1) # get quantized latent vectors z_q = torch.matmul(min_encodings.float(), self.embedding.weight) 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 GumbelQuantize(nn.Module): """ 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, emb_dim, n_embed, straight_through=True, kl_weight=5e-4, temp_init=1.0, use_vqinterface=True, remap=None, unknown_index="random"): super().__init__() self.emb_dim = emb_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, emb_dim) self.use_vqinterface = use_vqinterface 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] self.unknown_index = unknown_index # "random" or "extra" or integer if self.unknown_index == "extra": self.unknown_index = self.re_embed self.re_embed = self.re_embed+1 print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " f"Using {self.unknown_index} for unknown indices.") else: self.re_embed = n_embed def remap_to_used(self, inds): 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): 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) def forward(self, z, temp=None, return_logits=False): # 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 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 seq 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() ind = soft_one_hot.argmax(dim=1) if self.remap is not None: ind = self.remap_to_used(ind) if self.use_vqinterface: if return_logits: return z_q, diff, (None, None, ind), logits return z_q, diff, (None, None, ind) return z_q, diff, ind def get_codebook_entry(self, indices, shape): 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 VectorQuantizer2(nn.Module): """ Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix multiplications and allows for post-hoc remapping of indices. """ # NOTE: due to a bug the beta term was applied to the wrong term. for # backwards compatibility we use the buggy version by default, but you can # specify legacy=False to fix it. def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", sane_index_shape=False, legacy=True): super().__init__() self.n_e = n_e self.e_dim = e_dim self.beta = beta self.legacy = legacy 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) 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] self.unknown_index = unknown_index # "random" or "extra" or integer if self.unknown_index == "extra": self.unknown_index = self.re_embed self.re_embed = self.re_embed+1 print(f"Remapping {self.n_e} indices to {self.re_embed} indices. " f"Using {self.unknown_index} for unknown indices.") else: self.re_embed = n_e self.sane_index_shape = sane_index_shape def remap_to_used(self, inds): 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): 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) def forward(self, z, temp=None, rescale_logits=False, return_logits=False): assert temp is None or temp==1.0, "Only for interface compatible with Gumbel" assert rescale_logits==False, "Only for interface compatible with Gumbel" assert return_logits==False, "Only for interface compatible with Gumbel" # reshape z -> (batch, height, width, channel) and flatten z = rearrange(z, 'b c h w -> b h w c').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) perplexity = None min_encodings = None # compute loss for embedding if not self.legacy: loss = self.beta * torch.mean((z_q.detach()-z)**2) + \ torch.mean((z_q - z.detach()) ** 2) else: loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ torch.mean((z_q - z.detach()) ** 2) # 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').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: min_encoding_indices = min_encoding_indices.reshape( z_q.shape[0], z_q.shape[2], z_q.shape[3]) return z_q, loss, (perplexity, min_encodings, min_encoding_indices) def get_codebook_entry(self, indices, shape): # shape specifying (batch, height, width, channel) if self.remap is not None: 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(nn.Module): def __init__(self, n_embed, emb_dim, beta, decay=0.99, eps=1e-5, remap=None, unknown_index="random"): super().__init__() self.codebook_dim = codebook_dim self.num_tokens = num_tokens self.beta = beta 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] self.unknown_index = unknown_index # "random" or "extra" or integer if self.unknown_index == "extra": self.unknown_index = self.re_embed self.re_embed = self.re_embed+1 print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " f"Using {self.unknown_index} for unknown indices.") else: self.re_embed = n_embed def remap_to_used(self, inds): 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): 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) 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_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') return z_q, loss, (perplexity, encodings, encoding_indices)