246 lines
8.0 KiB
Python
246 lines
8.0 KiB
Python
import torch
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from torch import nn, einsum
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from models.arch_util import l2norm, sample_vectors, default, ema_inplace
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def kmeans(samples, num_clusters, num_iters = 10, use_cosine_sim = False):
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dim, dtype, device = samples.shape[-1], samples.dtype, samples.device
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means = sample_vectors(samples, num_clusters)
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for _ in range(num_iters):
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if use_cosine_sim:
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dists = samples @ means.t()
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else:
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diffs = rearrange(samples, 'n d -> n () d') - rearrange(means, 'c d -> () c d')
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dists = -(diffs ** 2).sum(dim = -1)
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buckets = dists.max(dim = -1).indices
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bins = torch.bincount(buckets, minlength = num_clusters)
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zero_mask = bins == 0
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bins = bins.masked_fill(zero_mask, 1)
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new_means = buckets.new_zeros(num_clusters, dim, dtype = dtype)
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new_means.scatter_add_(0, repeat(buckets, 'n -> n d', d = dim), samples)
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new_means = new_means / bins[..., None]
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if use_cosine_sim:
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new_means = l2norm(new_means)
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means = torch.where(zero_mask[..., None], means, new_means)
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return means
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# distance types
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class EuclideanCodebook(nn.Module):
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def __init__(
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self,
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dim,
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codebook_size,
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kmeans_init = False,
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kmeans_iters = 10,
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decay = 0.8,
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eps = 1e-5
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):
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super().__init__()
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self.decay = decay
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init_fn = torch.randn if not kmeans_init else torch.zeros
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embed = init_fn(codebook_size, dim)
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self.codebook_size = codebook_size
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self.kmeans_iters = kmeans_iters
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self.eps = eps
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self.register_buffer('initted', torch.Tensor([not kmeans_init]))
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self.register_buffer('cluster_size', torch.zeros(codebook_size))
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self.register_buffer('embed', embed)
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self.register_buffer('embed_avg', embed.clone())
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def init_embed_(self, data):
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embed = kmeans(data, self.codebook_size, self.kmeans_iters)
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self.embed.data.copy_(embed)
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self.embed_avg.data.copy_(embed.clone())
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self.initted.data.copy_(torch.Tensor([True]))
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def replace(self, samples, mask):
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modified_codebook = torch.where(mask[..., None], sample_vectors(samples, self.codebook_size), self.embed)
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self.embed.data.copy_(modified_codebook)
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def forward(self, x):
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shape, dtype = x.shape, x.dtype
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flatten = rearrange(x, '... d -> (...) d')
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embed = self.embed.t()
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if not self.initted:
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self.init_embed_(flatten)
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dist = -(
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flatten.pow(2).sum(1, keepdim=True)
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- 2 * flatten @ embed
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+ embed.pow(2).sum(0, keepdim=True)
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)
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embed_ind = dist.max(dim = -1).indices
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embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(x.dtype)
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embed_ind = embed_ind.view(*shape[:-1])
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quantize = F.embedding(embed_ind, self.embed)
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if self.training:
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ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
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embed_sum = flatten.t() @ embed_onehot
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ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
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cluster_size = laplace_smoothing(self.cluster_size, self.codebook_size, self.eps) * self.cluster_size.sum()
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embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
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self.embed.data.copy_(embed_normalized)
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return quantize, embed_ind
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class CosineSimCodebook(nn.Module):
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def __init__(
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self,
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dim,
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codebook_size,
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kmeans_init = False,
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kmeans_iters = 10,
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decay = 0.8,
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eps = 1e-5
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):
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super().__init__()
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self.decay = decay
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if not kmeans_init:
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embed = l2norm(torch.randn(codebook_size, dim))
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else:
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embed = torch.zeros(codebook_size, dim)
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self.codebook_size = codebook_size
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self.kmeans_iters = kmeans_iters
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self.eps = eps
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self.register_buffer('initted', torch.Tensor([not kmeans_init]))
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self.register_buffer('embed', embed)
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def init_embed_(self, data):
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embed = kmeans(data, self.codebook_size, self.kmeans_iters, use_cosine_sim = True)
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self.embed.data.copy_(embed)
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self.initted.data.copy_(torch.Tensor([True]))
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def replace(self, samples, mask):
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samples = l2norm(samples)
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modified_codebook = torch.where(mask[..., None], sample_vectors(samples, self.codebook_size), self.embed)
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self.embed.data.copy_(modified_codebook)
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def forward(self, x):
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shape, dtype = x.shape, x.dtype
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flatten = rearrange(x, '... d -> (...) d')
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flatten = l2norm(flatten)
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if not self.initted:
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self.init_embed_(flatten)
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embed = l2norm(self.embed)
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dist = flatten @ embed.t()
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embed_ind = dist.max(dim = -1).indices
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embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
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embed_ind = embed_ind.view(*shape[:-1])
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quantize = F.embedding(embed_ind, self.embed)
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if self.training:
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bins = embed_onehot.sum(0)
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zero_mask = (bins == 0)
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bins = bins.masked_fill(zero_mask, 1.)
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embed_sum = flatten.t() @ embed_onehot
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embed_normalized = (embed_sum / bins.unsqueeze(0)).t()
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embed_normalized = l2norm(embed_normalized)
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embed_normalized = torch.where(zero_mask[..., None], embed, embed_normalized)
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ema_inplace(self.embed, embed_normalized, self.decay)
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return quantize, embed_ind
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# main class
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class VectorQuantize(nn.Module):
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def __init__(
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self,
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dim,
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codebook_size,
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n_embed = None,
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codebook_dim = None,
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decay = 0.8,
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eps = 1e-5,
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kmeans_init = False,
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kmeans_iters = 10,
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use_cosine_sim = False,
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max_codebook_misses_before_expiry = 0
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):
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super().__init__()
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n_embed = default(n_embed, codebook_size)
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codebook_dim = default(codebook_dim, dim)
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requires_projection = codebook_dim != dim
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self.project_in = nn.Linear(dim, codebook_dim) if requires_projection else nn.Identity()
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self.project_out = nn.Linear(codebook_dim, dim) if requires_projection else nn.Identity()
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self.eps = eps
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klass = EuclideanCodebook if not use_cosine_sim else CosineSimCodebook
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self._codebook = klass(
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dim = codebook_dim,
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codebook_size = n_embed,
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kmeans_init = kmeans_init,
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kmeans_iters = kmeans_iters,
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decay = decay,
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eps = eps
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)
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self.codebook_size = codebook_size
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self.max_codebook_misses_before_expiry = max_codebook_misses_before_expiry
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if max_codebook_misses_before_expiry > 0:
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codebook_misses = torch.zeros(codebook_size)
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self.register_buffer('codebook_misses', codebook_misses)
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@property
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def codebook(self):
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return self._codebook.codebook
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def decode(self, codes):
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unembed = F.embedding(codes, self._codebook.embed)
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return self.project_out(unembed)
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def expire_codes_(self, embed_ind, batch_samples):
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if self.max_codebook_misses_before_expiry == 0:
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return
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embed_ind = rearrange(embed_ind, '... -> (...)')
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misses = torch.bincount(embed_ind, minlength = self.codebook_size) == 0
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self.codebook_misses += misses
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expired_codes = self.codebook_misses >= self.max_codebook_misses_before_expiry
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if not torch.any(expired_codes):
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return
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self.codebook_misses.masked_fill_(expired_codes, 0)
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batch_samples = rearrange(batch_samples, '... d -> (...) d')
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self._codebook.replace(batch_samples, mask = expired_codes)
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def forward(self, x):
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x = self.project_in(x)
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quantize, embed_ind = self._codebook(x)
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commit_loss = F.mse_loss(quantize.detach(), x)
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if self.training:
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quantize = x + (quantize - x).detach()
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self.expire_codes_(embed_ind, x)
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quantize = self.project_out(quantize)
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return quantize, embed_ind, commit_loss
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