import random import torch import torch.nn as nn import torch.nn.functional as F from torch import autocast from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock from trainer.networks import register_model from utils.util import checkpoint def is_latent(t): return t.dtype == torch.float def is_sequence(t): return t.dtype == torch.long 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(), conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding), ) self.emb_layers = nn.Sequential( nn.SiLU(), 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), zero_module( conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding) ), ) if self.out_channels == channels: self.skip_connection = nn.Identity() else: self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding) def forward(self, x, emb): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ return checkpoint( self._forward, x, emb ) 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 DiffusionTtsFlat(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 = conv_nd(1, in_channels, model_channels, 3, 1, 1) self.time_embed = nn.Sequential( linear(model_channels, model_channels), nn.SiLU(), 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_converter = nn.Conv1d(in_latent_channels, model_channels, 1) 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.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(), zero_module(conv_nd(1, 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_converter.parameters()) + list(self.latent_converter.parameters()), 'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()), 'time_embed': list(self.time_embed.parameters()), } return groups def forward(self, x, timesteps, aligned_conditioning, conditioning_input, conditioning_free=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 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. """ # 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. unused_params = [] if conditioning_free: code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1) else: unused_params.append(self.unconditioned_embedding) 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_converter(aligned_conditioning) unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters())) else: code_emb = self.code_embedding(aligned_conditioning).permute(0,2,1) code_emb = self.code_converter(code_emb) unused_params.extend(list(self.latent_converter.parameters())) code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1) # 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(x.shape[0], 1, 1), code_emb) # Everything after this comment is timestep dependent. 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, F.interpolate(code_emb, size=x.shape[-1], mode='nearest')], 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 return out @register_model def register_diffusion_tts_flat0(opt_net, opt): return DiffusionTtsFlat(**opt_net['kwargs']) if __name__ == '__main__': clip = torch.randn(2, 100, 400) aligned_latent = torch.randn(2,388,512) aligned_sequence = torch.randint(0,8192,(2,388)) cond = torch.randn(2, 100, 400) ts = torch.LongTensor([600, 600]) model = DiffusionTtsFlat(512, layer_drop=.3) # Test with latent aligned conditioning o = model(clip, ts, aligned_latent, cond) # Test with sequence aligned conditioning o = model(clip, ts, aligned_sequence, cond)