446 lines
18 KiB
Python
446 lines
18 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from torch import einsum
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from einops import rearrange
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class VectorQuantizer(nn.Module):
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"""
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see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
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____________________________________________
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Discretization bottleneck part of the VQ-VAE.
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Inputs:
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- n_e : number of embeddings
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- e_dim : dimension of embedding
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- beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
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_____________________________________________
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"""
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# NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for
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# a fix and use legacy=False to apply that fix. VectorQuantizer2 can be
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# used wherever VectorQuantizer has been used before and is additionally
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# more efficient.
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def __init__(self, n_e, e_dim, beta):
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super(VectorQuantizer, self).__init__()
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self.n_e = n_e
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self.e_dim = e_dim
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self.beta = beta
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self.embedding = nn.Embedding(self.n_e, self.e_dim)
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self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
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def forward(self, z):
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"""
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Inputs the output of the encoder network z and maps it to a discrete
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one-hot vector that is the index of the closest embedding vector e_j
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z (continuous) -> z_q (discrete)
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z.shape = (batch, channel, height, width)
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quantization pipeline:
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1. get encoder input (B,C,H,W)
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2. flatten input to (B*H*W,C)
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"""
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# reshape z -> (batch, height, width, channel) and flatten
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z = z.permute(0, 2, 3, 1).contiguous()
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z_flattened = z.view(-1, self.e_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
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torch.sum(self.embedding.weight**2, dim=1) - 2 * \
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torch.matmul(z_flattened, self.embedding.weight.t())
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## could possible replace this here
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# #\start...
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# find closest encodings
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min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
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min_encodings = torch.zeros(
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min_encoding_indices.shape[0], self.n_e).to(z)
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min_encodings.scatter_(1, min_encoding_indices, 1)
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# dtype min encodings: torch.float32
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# min_encodings shape: torch.Size([2048, 512])
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# min_encoding_indices.shape: torch.Size([2048, 1])
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# get quantized latent vectors
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z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
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#.........\end
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# with:
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# .........\start
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#min_encoding_indices = torch.argmin(d, dim=1)
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#z_q = self.embedding(min_encoding_indices)
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# ......\end......... (TODO)
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# compute loss for embedding
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loss = torch.mean((z_q.detach()-z)**2) + self.beta * \
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torch.mean((z_q - z.detach()) ** 2)
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# preserve gradients
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z_q = z + (z_q - z).detach()
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# perplexity
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e_mean = torch.mean(min_encodings, dim=0)
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perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
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def get_codebook_entry(self, indices, shape):
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# shape specifying (batch, height, width, channel)
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# TODO: check for more easy handling with nn.Embedding
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min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)
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min_encodings.scatter_(1, indices[:,None], 1)
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# get quantized latent vectors
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z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
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if shape is not None:
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z_q = z_q.view(shape)
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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return z_q
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class GumbelQuantize(nn.Module):
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"""
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credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!)
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Gumbel Softmax trick quantizer
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Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
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https://arxiv.org/abs/1611.01144
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"""
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def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True,
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kl_weight=5e-4, temp_init=1.0, use_vqinterface=True,
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remap=None, unknown_index="random"):
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super().__init__()
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self.embedding_dim = embedding_dim
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self.n_embed = n_embed
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self.straight_through = straight_through
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self.temperature = temp_init
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self.kl_weight = kl_weight
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self.proj = nn.Conv2d(num_hiddens, n_embed, 1)
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self.embed = nn.Embedding(n_embed, embedding_dim)
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self.use_vqinterface = use_vqinterface
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self.remap = remap
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if self.remap is not None:
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self.register_buffer("used", torch.tensor(np.load(self.remap)))
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self.re_embed = self.used.shape[0]
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self.unknown_index = unknown_index # "random" or "extra" or integer
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if self.unknown_index == "extra":
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self.unknown_index = self.re_embed
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self.re_embed = self.re_embed+1
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print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
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f"Using {self.unknown_index} for unknown indices.")
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else:
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self.re_embed = n_embed
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def remap_to_used(self, inds):
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ishape = inds.shape
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assert len(ishape)>1
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inds = inds.reshape(ishape[0],-1)
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used = self.used.to(inds)
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match = (inds[:,:,None]==used[None,None,...]).long()
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new = match.argmax(-1)
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unknown = match.sum(2)<1
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if self.unknown_index == "random":
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new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
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else:
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new[unknown] = self.unknown_index
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return new.reshape(ishape)
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def unmap_to_all(self, inds):
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ishape = inds.shape
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assert len(ishape)>1
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inds = inds.reshape(ishape[0],-1)
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used = self.used.to(inds)
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if self.re_embed > self.used.shape[0]: # extra token
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inds[inds>=self.used.shape[0]] = 0 # simply set to zero
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back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
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return back.reshape(ishape)
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def forward(self, z, temp=None, return_logits=False):
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# force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work
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hard = self.straight_through if self.training else True
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temp = self.temperature if temp is None else temp
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logits = self.proj(z)
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if self.remap is not None:
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# continue only with used logits
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full_zeros = torch.zeros_like(logits)
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logits = logits[:,self.used,...]
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soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard)
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if self.remap is not None:
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# go back to all entries but unused set to zero
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full_zeros[:,self.used,...] = soft_one_hot
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soft_one_hot = full_zeros
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z_q = einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight)
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# + kl divergence to the prior loss
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qy = F.softmax(logits, dim=1)
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diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean()
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ind = soft_one_hot.argmax(dim=1)
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if self.remap is not None:
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ind = self.remap_to_used(ind)
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if self.use_vqinterface:
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if return_logits:
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return z_q, diff, (None, None, ind), logits
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return z_q, diff, (None, None, ind)
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return z_q, diff, ind
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def get_codebook_entry(self, indices, shape):
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b, h, w, c = shape
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assert b*h*w == indices.shape[0]
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indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w)
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if self.remap is not None:
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indices = self.unmap_to_all(indices)
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one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float()
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z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight)
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return z_q
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class VectorQuantizer2(nn.Module):
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"""
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Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
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avoids costly matrix multiplications and allows for post-hoc remapping of indices.
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"""
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# NOTE: due to a bug the beta term was applied to the wrong term. for
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# backwards compatibility we use the buggy version by default, but you can
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# specify legacy=False to fix it.
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def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
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sane_index_shape=False, legacy=True):
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super().__init__()
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self.n_e = n_e
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self.e_dim = e_dim
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self.beta = beta
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self.legacy = legacy
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self.embedding = nn.Embedding(self.n_e, self.e_dim)
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self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
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self.remap = remap
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if self.remap is not None:
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self.register_buffer("used", torch.tensor(np.load(self.remap)))
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self.re_embed = self.used.shape[0]
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self.unknown_index = unknown_index # "random" or "extra" or integer
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if self.unknown_index == "extra":
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self.unknown_index = self.re_embed
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self.re_embed = self.re_embed+1
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print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
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f"Using {self.unknown_index} for unknown indices.")
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else:
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self.re_embed = n_e
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self.sane_index_shape = sane_index_shape
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def remap_to_used(self, inds):
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ishape = inds.shape
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assert len(ishape)>1
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inds = inds.reshape(ishape[0],-1)
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used = self.used.to(inds)
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match = (inds[:,:,None]==used[None,None,...]).long()
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new = match.argmax(-1)
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unknown = match.sum(2)<1
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if self.unknown_index == "random":
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new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
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else:
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new[unknown] = self.unknown_index
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return new.reshape(ishape)
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def unmap_to_all(self, inds):
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ishape = inds.shape
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assert len(ishape)>1
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inds = inds.reshape(ishape[0],-1)
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used = self.used.to(inds)
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if self.re_embed > self.used.shape[0]: # extra token
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inds[inds>=self.used.shape[0]] = 0 # simply set to zero
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back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
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return back.reshape(ishape)
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def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
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assert temp is None or temp==1.0, "Only for interface compatible with Gumbel"
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assert rescale_logits==False, "Only for interface compatible with Gumbel"
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assert return_logits==False, "Only for interface compatible with Gumbel"
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# reshape z -> (batch, height, width, channel) and flatten
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z = rearrange(z, 'b c h w -> b h w c').contiguous()
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z_flattened = z.view(-1, self.e_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
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torch.sum(self.embedding.weight**2, dim=1) - 2 * \
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torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
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min_encoding_indices = torch.argmin(d, dim=1)
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z_q = self.embedding(min_encoding_indices).view(z.shape)
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perplexity = None
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min_encodings = None
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# compute loss for embedding
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if not self.legacy:
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loss = self.beta * torch.mean((z_q.detach()-z)**2) + \
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torch.mean((z_q - z.detach()) ** 2)
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else:
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loss = torch.mean((z_q.detach()-z)**2) + self.beta * \
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torch.mean((z_q - z.detach()) ** 2)
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# preserve gradients
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z_q = z + (z_q - z).detach()
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# reshape back to match original input shape
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z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
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if self.remap is not None:
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min_encoding_indices = min_encoding_indices.reshape(z.shape[0],-1) # add batch axis
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min_encoding_indices = self.remap_to_used(min_encoding_indices)
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min_encoding_indices = min_encoding_indices.reshape(-1,1) # flatten
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if self.sane_index_shape:
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min_encoding_indices = min_encoding_indices.reshape(
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z_q.shape[0], z_q.shape[2], z_q.shape[3])
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return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
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def get_codebook_entry(self, indices, shape):
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# shape specifying (batch, height, width, channel)
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if self.remap is not None:
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indices = indices.reshape(shape[0],-1) # add batch axis
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indices = self.unmap_to_all(indices)
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indices = indices.reshape(-1) # flatten again
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# get quantized latent vectors
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z_q = self.embedding(indices)
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if shape is not None:
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z_q = z_q.view(shape)
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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return z_q
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class EmbeddingEMA(nn.Module):
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def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5):
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super().__init__()
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self.decay = decay
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self.eps = eps
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weight = torch.randn(num_tokens, codebook_dim)
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self.weight = nn.Parameter(weight, requires_grad = False)
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self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad = False)
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self.embed_avg = nn.Parameter(weight.clone(), requires_grad = False)
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self.update = True
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def forward(self, embed_id):
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return F.embedding(embed_id, self.weight)
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def cluster_size_ema_update(self, new_cluster_size):
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self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay)
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def embed_avg_ema_update(self, new_embed_avg):
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self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay)
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def weight_update(self, num_tokens):
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n = self.cluster_size.sum()
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smoothed_cluster_size = (
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(self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n
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)
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#normalize embedding average with smoothed cluster size
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embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1)
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self.weight.data.copy_(embed_normalized)
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class EMAVectorQuantizer(nn.Module):
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def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5,
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remap=None, unknown_index="random"):
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super().__init__()
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self.codebook_dim = codebook_dim
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self.num_tokens = num_tokens
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self.beta = beta
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self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps)
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self.remap = remap
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if self.remap is not None:
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self.register_buffer("used", torch.tensor(np.load(self.remap)))
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self.re_embed = self.used.shape[0]
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self.unknown_index = unknown_index # "random" or "extra" or integer
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if self.unknown_index == "extra":
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self.unknown_index = self.re_embed
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self.re_embed = self.re_embed+1
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print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
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f"Using {self.unknown_index} for unknown indices.")
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else:
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self.re_embed = n_embed
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def remap_to_used(self, inds):
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ishape = inds.shape
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assert len(ishape)>1
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inds = inds.reshape(ishape[0],-1)
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used = self.used.to(inds)
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match = (inds[:,:,None]==used[None,None,...]).long()
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new = match.argmax(-1)
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unknown = match.sum(2)<1
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if self.unknown_index == "random":
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new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
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else:
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new[unknown] = self.unknown_index
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return new.reshape(ishape)
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def unmap_to_all(self, inds):
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ishape = inds.shape
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assert len(ishape)>1
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inds = inds.reshape(ishape[0],-1)
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used = self.used.to(inds)
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if self.re_embed > self.used.shape[0]: # extra token
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inds[inds>=self.used.shape[0]] = 0 # simply set to zero
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back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
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return back.reshape(ishape)
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def forward(self, z):
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# reshape z -> (batch, height, width, channel) and flatten
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#z, 'b c h w -> b h w c'
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z = rearrange(z, 'b c h w -> b h w c')
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z_flattened = z.reshape(-1, self.codebook_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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d = z_flattened.pow(2).sum(dim=1, keepdim=True) + \
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self.embedding.weight.pow(2).sum(dim=1) - 2 * \
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torch.einsum('bd,nd->bn', z_flattened, self.embedding.weight) # 'n d -> d n'
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encoding_indices = torch.argmin(d, dim=1)
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z_q = self.embedding(encoding_indices).view(z.shape)
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encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype)
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avg_probs = torch.mean(encodings, dim=0)
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perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
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if self.training and self.embedding.update:
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#EMA cluster size
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encodings_sum = encodings.sum(0)
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self.embedding.cluster_size_ema_update(encodings_sum)
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#EMA embedding average
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embed_sum = encodings.transpose(0,1) @ z_flattened
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self.embedding.embed_avg_ema_update(embed_sum)
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#normalize embed_avg and update weight
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self.embedding.weight_update(self.num_tokens)
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# compute loss for embedding
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loss = self.beta * F.mse_loss(z_q.detach(), z)
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# preserve gradients
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z_q = z + (z_q - z).detach()
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# reshape back to match original input shape
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#z_q, 'b h w c -> b c h w'
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z_q = rearrange(z_q, 'b h w c -> b c h w')
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return z_q, loss, (perplexity, encodings, encoding_indices)
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