def make_vqvae(hps, device='cuda'): from jukebox.vqvae.vqvae import VQVAE block_kwargs = dict( width=hps.width, depth=hps.depth, m_conv=hps.m_conv, dilation_growth_rate=hps.dilation_growth_rate, dilation_cycle=hps.dilation_cycle, reverse_decoder_dilation=hps.vqvae_reverse_decoder_dilation) if not hps.sample_length: assert hps.sample_length_in_seconds != 0 downsamples = calculate_strides(hps.strides_t, hps.downs_t) top_raw_to_tokens = np.prod(downsamples) hps.sample_length = (hps.sample_length_in_seconds * hps.sr // top_raw_to_tokens) * top_raw_to_tokens print( f"Setting sample length to {hps.sample_length} (i.e. {hps.sample_length/hps.sr} seconds) to be multiple of {top_raw_to_tokens}" ) vqvae = VQVAE(input_shape=(hps.sample_length, 1), levels=hps.levels, downs_t=hps.downs_t, strides_t=hps.strides_t, emb_width=hps.emb_width, l_bins=hps.l_bins, mu=hps.l_mu, commit=hps.commit, spectral=hps.spectral, multispectral=hps.multispectral, multipliers=hps.hvqvae_multipliers, use_bottleneck=hps.use_bottleneck, **block_kwargs) vqvae = vqvae.to(device) restore(hps, vqvae, hps.restore_vqvae) if hps.train and not hps.prior: print_all(f"Loading vqvae in train mode") if hps.restore_vqvae != '': print_all("Reseting bottleneck emas") for level, bottleneck in enumerate(vqvae.bottleneck.level_blocks): num_samples = hps.sample_length downsamples = calculate_strides(hps.strides_t, hps.downs_t) raw_to_tokens = np.prod(downsamples[:level + 1]) num_tokens = (num_samples // raw_to_tokens) * dist.get_world_size() bottleneck.restore_k(num_tokens=num_tokens, threshold=hps.revival_threshold) else: print_all(f"Loading vqvae in eval mode") vqvae.eval() freeze_model(vqvae) return vqvae
def __init__(self, z_shapes, l_bins, encoder, decoder, level, downs_t, strides_t, labels, prior_kwargs, x_cond_kwargs, y_cond_kwargs, prime_kwargs, copy_input, labels_v3=False, merged_decoder=False, single_enc_dec=False): super().__init__() self.use_tokens = prime_kwargs.pop('use_tokens') self.n_tokens = prime_kwargs.pop('n_tokens') self.prime_loss_fraction = prime_kwargs.pop('prime_loss_fraction') self.copy_input = copy_input if self.copy_input: prime_kwargs['bins'] = l_bins self.z_shapes = z_shapes self.levels = len(self.z_shapes) self.z_shape = self.z_shapes[level] self.level = level assert level < self.levels, f"Total levels {self.levels}, got level {level}" self.l_bins = l_bins # Passing functions instead of the vqvae module to avoid getting params self.encoder = encoder self.decoder = decoder # X conditioning self.x_cond = (level != (self.levels - 1)) self.cond_level = level + 1 # Y conditioning self.y_cond = labels self.single_enc_dec = single_enc_dec # X conditioning if self.x_cond: self.conditioner_blocks = nn.ModuleList() conditioner_block = lambda _level: Conditioner( input_shape=z_shapes[_level], bins=l_bins, down_t=downs_t[_level], stride_t=strides_t[_level], **x_cond_kwargs) if dist.get_rank() == 0: print(f"Conditioning on 1 above level(s)") self.conditioner_blocks.append(conditioner_block(self.cond_level)) # Y conditioning if self.y_cond: self.n_time = self.z_shape[ 0] # Assuming STFT=TF order and raw=T1 order, so T is first dim self.y_emb = LabelConditioner(n_time=self.n_time, include_time_signal=not self.x_cond, **y_cond_kwargs) # Lyric conditioning if single_enc_dec: # Single encoder-decoder transformer self.prior_shapes = [(self.n_tokens, ), prior_kwargs.pop('input_shape')] self.prior_bins = [prime_kwargs['bins'], prior_kwargs.pop('bins')] self.prior_dims = [np.prod(shape) for shape in self.prior_shapes] self.prior_bins_shift = np.cumsum([0, *self.prior_bins])[:-1] self.prior_width = prior_kwargs['width'] print_once( f'Creating cond. autoregress with prior bins {self.prior_bins}, ' ) print_once(f'dims {self.prior_dims}, ') print_once(f'shift {self.prior_bins_shift}') print_once(f'input shape {sum(self.prior_dims)}') print_once(f'input bins {sum(self.prior_bins)}') print_once(f'Self copy is {self.copy_input}') self.prime_loss_dims, self.gen_loss_dims = self.prior_dims[ 0], self.prior_dims[1] self.total_loss_dims = self.prime_loss_dims + self.gen_loss_dims self.prior = ConditionalAutoregressive2D( input_shape=(sum(self.prior_dims), ), bins=sum(self.prior_bins), x_cond=(self.x_cond or self.y_cond), y_cond=True, prime_len=self.prime_loss_dims, **prior_kwargs) else: # Separate encoder-decoder transformer if self.n_tokens != 0 and self.use_tokens: from jukebox.transformer.ops import Conv1D prime_input_shape = (self.n_tokens, ) self.prime_loss_dims = np.prod(prime_input_shape) self.prime_acts_width, self.prime_state_width = prime_kwargs[ 'width'], prior_kwargs['width'] self.prime_prior = ConditionalAutoregressive2D( input_shape=prime_input_shape, x_cond=False, y_cond=False, only_encode=True, **prime_kwargs) self.prime_state_proj = Conv1D( self.prime_acts_width, self.prime_state_width, init_scale=prime_kwargs['init_scale']) self.prime_state_ln = LayerNorm(self.prime_state_width) self.prime_bins = prime_kwargs['bins'] self.prime_x_out = nn.Linear(self.prime_state_width, self.prime_bins, bias=False) nn.init.normal_(self.prime_x_out.weight, std=0.02 * prior_kwargs['init_scale']) else: self.prime_loss_dims = 0 self.gen_loss_dims = np.prod(self.z_shape) self.total_loss_dims = self.prime_loss_dims + self.gen_loss_dims self.prior = ConditionalAutoregressive2D( x_cond=(self.x_cond or self.y_cond), y_cond=self.y_cond, encoder_dims=self.prime_loss_dims, merged_decoder=merged_decoder, **prior_kwargs) self.n_ctx = self.gen_loss_dims self.downsamples = calculate_strides(strides_t, downs_t) self.cond_downsample = self.downsamples[ level + 1] if level != self.levels - 1 else None self.raw_to_tokens = np.prod(self.downsamples[:level + 1]) self.sample_length = self.n_ctx * self.raw_to_tokens if labels: self.labels_v3 = labels_v3 self.labeller = Labeller(self.y_emb.max_bow_genre_size, self.n_tokens, self.sample_length, v3=self.labels_v3) print( f"Level:{level}, Cond downsample:{self.cond_downsample}, Raw to tokens:{self.raw_to_tokens}, Sample length:{self.sample_length}" )