base DVAE & vector_quantizer

codes/models/gpt_voice/unet_diffusion_vocoder_with_ref.py

@@ -285,7 +285,7 @@ class DiffusionVocoderWithRef(nn.Module):
         self.middle_block.apply(convert_module_to_f32)
         self.output_blocks.apply(convert_module_to_f32)
 
-    def forward(self, x, timesteps, discrete_spectrogram, conditioning_inputs=None, num_conditioning_signals=None):
+    def forward(self, x, timesteps, spectrogram, conditioning_inputs=None, num_conditioning_signals=None):
         """
         Apply the model to an input batch.
 
@@ -311,7 +311,7 @@ class DiffusionVocoderWithRef(nn.Module):
         h = x.type(self.dtype)
         for k, module in enumerate(self.input_blocks):
             if isinstance(module, DiscreteSpectrogramConditioningBlock):
-                h = module(h, discrete_spectrogram)
+                h = module(h, spectrogram)
             else:
                 h = module(h, emb)
                 hs.append(h)

codes/models/vqvae/dvae.py

@@ -0,0 +1,241 @@
+import functools
+import math
+from math import sqrt
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch import einsum
+
+from models.gpt_voice.dvae_arch_playground.discretization_loss import DiscretizationLoss
+from models.vqvae.vector_quantizer import VectorQuantize
+from models.vqvae.vqvae import Quantize
+from trainer.networks import register_model
+from utils.util import opt_get
+
+
+def eval_decorator(fn):
+    def inner(model, *args, **kwargs):
+        was_training = model.training
+        model.eval()
+        out = fn(model, *args, **kwargs)
+        model.train(was_training)
+        return out
+    return inner
+
+
+class ResBlock(nn.Module):
+    def __init__(self, chan, conv, activation):
+        super().__init__()
+        self.net = nn.Sequential(
+            conv(chan, chan, 3, padding = 1),
+            activation(),
+            conv(chan, chan, 3, padding = 1),
+            activation(),
+            conv(chan, chan, 1)
+        )
+
+    def forward(self, x):
+        return self.net(x) + x
+
+
+class UpsampledConv(nn.Module):
+    def __init__(self, conv, *args, **kwargs):
+        super().__init__()
+        assert 'stride' in kwargs.keys()
+        self.stride = kwargs['stride']
+        del kwargs['stride']
+        self.conv = conv(*args, **kwargs)
+
+    def forward(self, x):
+        up = nn.functional.interpolate(x, scale_factor=self.stride, mode='nearest')
+        return self.conv(up)
+
+
+class DiscreteVAE(nn.Module):
+    def __init__(
+        self,
+        positional_dims=2,
+        num_tokens = 512,
+        codebook_dim = 512,
+        num_layers = 3,
+        num_resnet_blocks = 0,
+        hidden_dim = 64,
+        channels = 3,
+        stride = 2,
+        kernel_size = 3,
+        activation = 'relu',
+        straight_through = False,
+        record_codes = False,
+        discretization_loss_averaging_steps = 100,
+        quantizer_use_cosine_sim=True,
+        quantizer_codebook_misses_to_expiration=40,
+        quantizer_codebook_embedding_compression=None,
+    ):
+        super().__init__()
+        assert num_layers >= 1, 'number of layers must be greater than or equal to 1'
+        has_resblocks = num_resnet_blocks > 0
+
+        self.num_tokens = num_tokens
+        self.num_layers = num_layers
+        self.straight_through = straight_through
+        self.positional_dims = positional_dims
+        self.discrete_loss = DiscretizationLoss(num_tokens, 2, 1 / (num_tokens*2), discretization_loss_averaging_steps)
+
+        assert positional_dims > 0 and positional_dims < 3  # This VAE only supports 1d and 2d inputs for now.
+        if positional_dims == 2:
+            conv = nn.Conv2d
+            conv_transpose = functools.partial(UpsampledConv, conv)
+        else:
+            conv = nn.Conv1d
+            conv_transpose = functools.partial(UpsampledConv, conv)
+
+        if activation == 'relu':
+            act = nn.ReLU
+        elif activation == 'silu':
+            act = nn.SiLU
+        else:
+            assert NotImplementedError()
+
+
+        enc_chans = [hidden_dim * 2 ** i for i in range(num_layers)]
+        dec_chans = list(reversed(enc_chans))
+
+        enc_chans = [channels, *enc_chans]
+
+        dec_init_chan = codebook_dim if not has_resblocks else dec_chans[0]
+        dec_chans = [dec_init_chan, *dec_chans]
+
+        enc_chans_io, dec_chans_io = map(lambda t: list(zip(t[:-1], t[1:])), (enc_chans, dec_chans))
+
+        enc_layers = []
+        dec_layers = []
+
+        pad = (kernel_size - 1) // 2
+        for (enc_in, enc_out), (dec_in, dec_out) in zip(enc_chans_io, dec_chans_io):
+            enc_layers.append(nn.Sequential(conv(enc_in, enc_out, kernel_size, stride = stride, padding = pad), act()))
+            dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, kernel_size, stride = stride, padding = pad), act()))
+
+        for _ in range(num_resnet_blocks):
+            dec_layers.insert(0, ResBlock(dec_chans[1], conv, act))
+            enc_layers.append(ResBlock(enc_chans[-1], conv, act))
+
+        if num_resnet_blocks > 0:
+            dec_layers.insert(0, conv(codebook_dim, dec_chans[1], 1))
+
+        enc_layers.append(conv(enc_chans[-1], codebook_dim, 1))
+        dec_layers.append(conv(dec_chans[-1], channels, 1))
+
+        self.encoder = nn.Sequential(*enc_layers)
+        self.quantizer = VectorQuantize(codebook_dim, num_tokens, codebook_dim=quantizer_codebook_embedding_compression,
+                                        use_cosine_sim=quantizer_use_cosine_sim,
+                                        max_codebook_misses_before_expiry=quantizer_codebook_misses_to_expiration)
+        self.decoder = nn.Sequential(*dec_layers)
+
+        self.loss_fn = F.mse_loss
+
+        self.record_codes = record_codes
+        if record_codes:
+            self.codes = torch.zeros((1228800,), dtype=torch.long)
+            self.code_ind = 0
+        self.internal_step = 0
+
+    def get_debug_values(self, step, __):
+        if self.record_codes:
+            # Report annealing schedule
+            return {'histogram_codes': self.codes}
+        else:
+            return {}
+
+    @torch.no_grad()
+    @eval_decorator
+    def get_codebook_indices(self, images):
+        logits = self.encoder(images).permute((0,2,3,1) if len(images.shape) == 4 else (0,2,1))
+        sampled, codes, commitment_loss = self.quantizer(logits)
+        return codes
+
+    def decode(
+        self,
+        img_seq
+    ):
+        self.log_codes(img_seq)
+        image_embeds = self.quantizer.decode(img_seq)
+        b, n, d = image_embeds.shape
+
+        kwargs = {}
+        if self.positional_dims == 1:
+            arrange = 'b n d -> b d n'
+        else:
+            h = w = int(sqrt(n))
+            arrange = 'b (h w) d -> b d h w'
+            kwargs = {'h': h, 'w': w}
+        image_embeds = rearrange(image_embeds, arrange, **kwargs)
+        images = [image_embeds]
+        for layer in self.decoder:
+            images.append(layer(images[-1]))
+        return images[-1], images[-2]
+
+    def infer(self, img):
+        logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
+        sampled, codes, commitment_loss = self.quantizer(logits)
+        return self.decode(codes)
+
+    # Note: This module is not meant to be run in forward() except while training. It has special logic which performs
+    # evaluation using quantized values when it detects that it is being run in eval() mode, which will be substantially
+    # more lossy (but useful for determining network performance).
+    def forward(
+        self,
+        img
+    ):
+        logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
+        sampled, codes, commitment_loss = self.quantizer(logits)
+        sampled = sampled.permute((0,3,1,2) if len(img.shape) == 4 else (0,2,1))
+
+        if self.training:
+            out = sampled
+            for d in self.decoder:
+                out = d(out)
+        else:
+            # This is non-differentiable, but gives a better idea of how the network is actually performing.
+            out, _ = self.decode(codes)
+
+        # reconstruction loss
+        recon_loss = self.loss_fn(img, out, reduction='none')
+
+        # This is so we can debug the distribution of codes being learned.
+        self.log_codes(codes)
+
+        return recon_loss, commitment_loss, out
+
+    def log_codes(self, codes):
+        # This is so we can debug the distribution of codes being learned.
+        if self.record_codes and self.internal_step % 50 == 0:
+            codes = codes.flatten()
+            l = codes.shape[0]
+            i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l
+            self.codes[i:i+l] = codes.cpu()
+            self.code_ind = self.code_ind + l
+            if self.code_ind >= self.codes.shape[0]:
+                self.code_ind = 0
+        self.internal_step += 1
+
+
+@register_model
+def register_dvae(opt_net, opt):
+    return DiscreteVAE(**opt_get(opt_net, ['kwargs'], {}))
+
+
+if __name__ == '__main__':
+    #v = DiscreteVAE()
+    #o=v(torch.randn(1,3,256,256))
+    #print(o.shape)
+    v = DiscreteVAE(channels=80, positional_dims=1, num_tokens=4096, codebook_dim=1024,
+                    hidden_dim=512, stride=2, num_resnet_blocks=2, kernel_size=3, num_layers=2,
+                    quantizer_codebook_embedding_compression=64)
+    #v.eval()
+    loss, commitment, out = v(torch.randn(1,80,256))
+    print(out.shape)
+    codes = v.get_codebook_indices(torch.randn(1,80,256))
+    back, back_emb = v.decode(codes)
+    print(back.shape)

codes/models/vqvae/vector_quantizer.py

@@ -0,0 +1,245 @@
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+from einops import rearrange, repeat
+
+from models.arch_util import l2norm, sample_vectors, default, ema_inplace
+
+
+def kmeans(samples, num_clusters, num_iters = 10, use_cosine_sim = False):
+    dim, dtype, device = samples.shape[-1], samples.dtype, samples.device
+
+    means = sample_vectors(samples, num_clusters)
+
+    for _ in range(num_iters):
+        if use_cosine_sim:
+            dists = samples @ means.t()
+        else:
+            diffs = rearrange(samples, 'n d -> n () d') - rearrange(means, 'c d -> () c d')
+            dists = -(diffs ** 2).sum(dim = -1)
+
+        buckets = dists.max(dim = -1).indices
+        bins = torch.bincount(buckets, minlength = num_clusters)
+        zero_mask = bins == 0
+        bins = bins.masked_fill(zero_mask, 1)
+
+        new_means = buckets.new_zeros(num_clusters, dim, dtype = dtype)
+        new_means.scatter_add_(0, repeat(buckets, 'n -> n d', d = dim), samples)
+        new_means = new_means / bins[..., None]
+
+        if use_cosine_sim:
+            new_means = l2norm(new_means)
+
+        means = torch.where(zero_mask[..., None], means, new_means)
+
+    return means
+
+# distance types
+
+class EuclideanCodebook(nn.Module):
+    def __init__(
+        self,
+        dim,
+        codebook_size,
+        kmeans_init = False,
+        kmeans_iters = 10,
+        decay = 0.8,
+        eps = 1e-5
+    ):
+        super().__init__()
+        self.decay = decay
+        init_fn = torch.randn if not kmeans_init else torch.zeros
+        embed = init_fn(codebook_size, dim)
+
+        self.codebook_size = codebook_size
+        self.kmeans_iters = kmeans_iters
+        self.eps = eps
+
+        self.register_buffer('initted', torch.Tensor([not kmeans_init]))
+        self.register_buffer('cluster_size', torch.zeros(codebook_size))
+        self.register_buffer('embed', embed)
+        self.register_buffer('embed_avg', embed.clone())
+
+    def init_embed_(self, data):
+        embed = kmeans(data, self.codebook_size, self.kmeans_iters)
+        self.embed.data.copy_(embed)
+        self.embed_avg.data.copy_(embed.clone())
+        self.initted.data.copy_(torch.Tensor([True]))
+
+    def replace(self, samples, mask):
+        modified_codebook = torch.where(mask[..., None], sample_vectors(samples, self.codebook_size), self.embed)
+        self.embed.data.copy_(modified_codebook)
+
+    def forward(self, x):
+        shape, dtype = x.shape, x.dtype
+        flatten = rearrange(x, '... d -> (...) d')
+        embed = self.embed.t()
+
+        if not self.initted:
+            self.init_embed_(flatten)
+
+        dist = -(
+            flatten.pow(2).sum(1, keepdim=True)
+            - 2 * flatten @ embed
+            + embed.pow(2).sum(0, keepdim=True)
+        )
+
+        embed_ind = dist.max(dim = -1).indices
+        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(x.dtype)
+        embed_ind = embed_ind.view(*shape[:-1])
+        quantize = F.embedding(embed_ind, self.embed)
+
+        if self.training:
+            ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
+            embed_sum = flatten.t() @ embed_onehot
+            ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
+            cluster_size = laplace_smoothing(self.cluster_size, self.codebook_size, self.eps) * self.cluster_size.sum()
+            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
+            self.embed.data.copy_(embed_normalized)
+
+        return quantize, embed_ind
+
+class CosineSimCodebook(nn.Module):
+    def __init__(
+        self,
+        dim,
+        codebook_size,
+        kmeans_init = False,
+        kmeans_iters = 10,
+        decay = 0.8,
+        eps = 1e-5
+    ):
+        super().__init__()
+        self.decay = decay
+
+        if not kmeans_init:
+            embed = l2norm(torch.randn(codebook_size, dim))
+        else:
+            embed = torch.zeros(codebook_size, dim)
+
+        self.codebook_size = codebook_size
+        self.kmeans_iters = kmeans_iters
+        self.eps = eps
+
+        self.register_buffer('initted', torch.Tensor([not kmeans_init]))
+        self.register_buffer('embed', embed)
+
+    def init_embed_(self, data):
+        embed = kmeans(data, self.codebook_size, self.kmeans_iters, use_cosine_sim = True)
+        self.embed.data.copy_(embed)
+        self.initted.data.copy_(torch.Tensor([True]))
+
+    def replace(self, samples, mask):
+        samples = l2norm(samples)
+        modified_codebook = torch.where(mask[..., None], sample_vectors(samples, self.codebook_size), self.embed)
+        self.embed.data.copy_(modified_codebook)
+
+    def forward(self, x):
+        shape, dtype = x.shape, x.dtype
+        flatten = rearrange(x, '... d -> (...) d')
+        flatten = l2norm(flatten)
+
+        if not self.initted:
+            self.init_embed_(flatten)
+
+        embed = l2norm(self.embed)
+        dist = flatten @ embed.t()
+        embed_ind = dist.max(dim = -1).indices
+        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
+        embed_ind = embed_ind.view(*shape[:-1])
+
+        quantize = F.embedding(embed_ind, self.embed)
+
+        if self.training:
+            bins = embed_onehot.sum(0)
+            zero_mask = (bins == 0)
+            bins = bins.masked_fill(zero_mask, 1.)
+
+            embed_sum = flatten.t() @ embed_onehot
+            embed_normalized = (embed_sum / bins.unsqueeze(0)).t()
+            embed_normalized = l2norm(embed_normalized)
+            embed_normalized = torch.where(zero_mask[..., None], embed, embed_normalized)
+            ema_inplace(self.embed, embed_normalized, self.decay)
+
+        return quantize, embed_ind
+
+# main class
+
+class VectorQuantize(nn.Module):
+    def __init__(
+        self,
+        dim,
+        codebook_size,
+        n_embed = None,
+        codebook_dim = None,
+        decay = 0.8,
+        eps = 1e-5,
+        kmeans_init = False,
+        kmeans_iters = 10,
+        use_cosine_sim = False,
+        max_codebook_misses_before_expiry = 0
+    ):
+        super().__init__()
+        n_embed = default(n_embed, codebook_size)
+
+        codebook_dim = default(codebook_dim, dim)
+        requires_projection = codebook_dim != dim
+        self.project_in = nn.Linear(dim, codebook_dim) if requires_projection else nn.Identity()
+        self.project_out = nn.Linear(codebook_dim, dim) if requires_projection else nn.Identity()
+
+        self.eps = eps
+
+        klass = EuclideanCodebook if not use_cosine_sim else CosineSimCodebook
+
+        self._codebook = klass(
+            dim = codebook_dim,
+            codebook_size = n_embed,
+            kmeans_init = kmeans_init,
+            kmeans_iters = kmeans_iters,
+            decay = decay,
+            eps = eps
+        )
+
+        self.codebook_size = codebook_size
+        self.max_codebook_misses_before_expiry = max_codebook_misses_before_expiry
+
+        if max_codebook_misses_before_expiry > 0:
+            codebook_misses = torch.zeros(codebook_size)
+            self.register_buffer('codebook_misses', codebook_misses)
+
+    @property
+    def codebook(self):
+        return self._codebook.codebook
+
+    def decode(self, codes):
+        unembed = F.embedding(codes, self._codebook.embed)
+        return self.project_out(unembed)
+
+    def expire_codes_(self, embed_ind, batch_samples):
+        if self.max_codebook_misses_before_expiry == 0:
+            return
+
+        embed_ind = rearrange(embed_ind, '... -> (...)')
+        misses = torch.bincount(embed_ind, minlength = self.codebook_size) == 0
+        self.codebook_misses += misses
+
+        expired_codes = self.codebook_misses >= self.max_codebook_misses_before_expiry
+        if not torch.any(expired_codes):
+            return
+
+        self.codebook_misses.masked_fill_(expired_codes, 0)
+        batch_samples = rearrange(batch_samples, '... d -> (...) d')
+        self._codebook.replace(batch_samples, mask = expired_codes)
+
+    def forward(self, x):
+        x = self.project_in(x)
+
+        quantize, embed_ind = self._codebook(x)
+        commit_loss = F.mse_loss(quantize.detach(), x)
+
+        if self.training:
+            quantize = x + (quantize - x).detach()
+            self.expire_codes_(embed_ind, x)
+
+        quantize = self.project_out(quantize)
+        return quantize, embed_ind, commit_loss