import math import random from abc import abstractmethod import torch import torch.nn as nn import torch.nn.functional as F from torch import autocast from models.arch_util import normalization, AttentionBlock def is_latent(t): return t.dtype == torch.float def is_sequence(t): return t.dtype == torch.long def timestep_embedding(timesteps, dim, max_period=10000): """ Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings. """ half = dim // 2 freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half ).to(device=timesteps.device) args = timesteps[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) return embedding class TimestepBlock(nn.Module): @abstractmethod def forward(self, x, emb): """ Apply the module to `x` given `emb` timestep embeddings. """ class TimestepEmbedSequential(nn.Sequential, TimestepBlock): def forward(self, x, emb): for layer in self: if isinstance(layer, TimestepBlock): x = layer(x, emb) else: x = layer(x) return x class ResBlock(TimestepBlock): def __init__( self, channels, emb_channels, dropout, out_channels=None, dims=2, kernel_size=3, efficient_config=True, use_scale_shift_norm=False, ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_scale_shift_norm = use_scale_shift_norm padding = {1: 0, 3: 1, 5: 2}[kernel_size] eff_kernel = 1 if efficient_config else 3 eff_padding = 0 if efficient_config else 1 self.in_layers = nn.Sequential( normalization(channels), nn.SiLU(), nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding), ) self.emb_layers = nn.Sequential( nn.SiLU(), nn.Linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, ), ) self.out_layers = nn.Sequential( normalization(self.out_channels), nn.SiLU(), nn.Dropout(p=dropout), nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding), ) if self.out_channels == channels: self.skip_connection = nn.Identity() else: self.skip_connection = nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding) def forward(self, x, emb): h = self.in_layers(x) emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] scale, shift = torch.chunk(emb_out, 2, dim=1) h = out_norm(h) * (1 + scale) + shift h = out_rest(h) else: h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class DiffusionLayer(TimestepBlock): def __init__(self, model_channels, dropout, num_heads): super().__init__() self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True) self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True) def forward(self, x, time_emb): y = self.resblk(x, time_emb) return self.attn(y) class DiffusionTts(nn.Module): def __init__( self, model_channels=512, num_layers=8, in_channels=100, in_latent_channels=512, in_tokens=8193, out_channels=200, # mean and variance dropout=0, use_fp16=False, num_heads=16, # Parameters for regularization. layer_drop=.1, unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training. ): super().__init__() self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.dropout = dropout self.num_heads = num_heads self.unconditioned_percentage = unconditioned_percentage self.enable_fp16 = use_fp16 self.layer_drop = layer_drop self.inp_block = nn.Conv1d(in_channels, model_channels, 3, 1, 1) self.time_embed = nn.Sequential( nn.Linear(model_channels, model_channels), nn.SiLU(), nn.Linear(model_channels, model_channels), ) # Either code_converter or latent_converter is used, depending on what type of conditioning data is fed. # This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally # complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive # transformer network. self.code_embedding = nn.Embedding(in_tokens, model_channels) self.code_converter = nn.Sequential( AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), ) self.code_norm = normalization(model_channels) self.latent_conditioner = nn.Sequential( nn.Conv1d(in_latent_channels, model_channels, 3, padding=1), AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), ) self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2), nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2), AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False)) self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1)) self.conditioning_timestep_integrator = TimestepEmbedSequential( DiffusionLayer(model_channels, dropout, num_heads), DiffusionLayer(model_channels, dropout, num_heads), DiffusionLayer(model_channels, dropout, num_heads), ) self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1) self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1) self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] + [ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)]) self.out = nn.Sequential( normalization(model_channels), nn.SiLU(), nn.Conv1d(model_channels, out_channels, 3, padding=1), ) def get_grad_norm_parameter_groups(self): groups = { 'minicoder': list(self.contextual_embedder.parameters()), 'layers': list(self.layers.parameters()), 'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()) + list(self.latent_conditioner.parameters()), 'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()), 'time_embed': list(self.time_embed.parameters()), } return groups def timestep_independent(self, aligned_conditioning, conditioning_input, expected_seq_len, return_code_pred): # Shuffle aligned_latent to BxCxS format if is_latent(aligned_conditioning): aligned_conditioning = aligned_conditioning.permute(0, 2, 1) # Note: this block does not need to repeated on inference, since it is not timestep-dependent or x-dependent. speech_conditioning_input = conditioning_input.unsqueeze(1) if len( conditioning_input.shape) == 3 else conditioning_input conds = [] for j in range(speech_conditioning_input.shape[1]): conds.append(self.contextual_embedder(speech_conditioning_input[:, j])) conds = torch.cat(conds, dim=-1) cond_emb = conds.mean(dim=-1) cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1) if is_latent(aligned_conditioning): code_emb = self.latent_conditioner(aligned_conditioning) else: code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1) code_emb = self.code_converter(code_emb) code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1) unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device) # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance. if self.training and self.unconditioned_percentage > 0: unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1), device=code_emb.device) < self.unconditioned_percentage code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(aligned_conditioning.shape[0], 1, 1), code_emb) expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest') if not return_code_pred: return expanded_code_emb else: mel_pred = self.mel_head(expanded_code_emb) # Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches. This is because we don't want that gradient being used to train parameters through the codes_embedder as it unbalances contributions to that network from the MSE loss. mel_pred = mel_pred * unconditioned_batches.logical_not() return expanded_code_emb, mel_pred def forward(self, x, timesteps, aligned_conditioning=None, conditioning_input=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=False): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced. :param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded. :param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent() :param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered. :return: an [N x C x ...] Tensor of outputs. """ assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_input is not None) assert not (return_code_pred and precomputed_aligned_embeddings is not None) # These two are mutually exclusive. unused_params = [] if conditioning_free: code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1]) unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters())) unused_params.extend(list(self.latent_conditioner.parameters())) else: if precomputed_aligned_embeddings is not None: code_emb = precomputed_aligned_embeddings else: code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_input, x.shape[-1], True) if is_latent(aligned_conditioning): unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters())) else: unused_params.extend(list(self.latent_conditioner.parameters())) unused_params.append(self.unconditioned_embedding) time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) code_emb = self.conditioning_timestep_integrator(code_emb, time_emb) x = self.inp_block(x) x = torch.cat([x, code_emb], dim=1) x = self.integrating_conv(x) for i, lyr in enumerate(self.layers): # Do layer drop where applicable. Do not drop first and last layers. if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop: unused_params.extend(list(lyr.parameters())) else: # First and last blocks will have autocast disabled for improved precision. with autocast(x.device.type, enabled=self.enable_fp16 and i != 0): x = lyr(x, time_emb) x = x.float() out = self.out(x) # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors. extraneous_addition = 0 for p in unused_params: extraneous_addition = extraneous_addition + p.mean() out = out + extraneous_addition * 0 if return_code_pred: return out, mel_pred return out if __name__ == '__main__': clip = torch.randn(2, 100, 400) aligned_latent = torch.randn(2,388,512) aligned_sequence = torch.randint(0,8192,(2,100)) cond = torch.randn(2, 100, 400) ts = torch.LongTensor([600, 600]) model = DiffusionTts(512, layer_drop=.3, unconditioned_percentage=.5) # Test with latent aligned conditioning #o = model(clip, ts, aligned_latent, cond) # Test with sequence aligned conditioning o = model(clip, ts, aligned_sequence, cond)