diff --git a/codes/models/audio/mel2vec.py b/codes/models/audio/mel2vec.py index 6eea654a..6a5ddb3e 100644 --- a/codes/models/audio/mel2vec.py +++ b/codes/models/audio/mel2vec.py @@ -498,6 +498,17 @@ class Wav2Vec2GumbelVectorQuantizer(nn.Module): perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum() return perplexity + def get_codes(self, hidden_states): + batch_size, sequence_length, hidden_size = hidden_states.shape + + # project to codevector dim + hidden_states = self.weight_proj(hidden_states) + hidden_states = hidden_states.view(batch_size * sequence_length * self.num_groups, -1) + codevector_idx = hidden_states.argmax(dim=-1) + idxs = codevector_idx.view(batch_size, sequence_length, self.num_groups) + return idxs + + def forward(self, hidden_states, mask_time_indices=None): batch_size, sequence_length, hidden_size = hidden_states.shape @@ -595,6 +606,12 @@ class ContrastiveTrainingWrapper(nn.Module): } return groups + def get_codes(self, mel): + proj = self.m2v.input_blocks(mel).permute(0,2,1) + _, proj = self.m2v.projector(proj) + codes = self.quantizer.get_codes(proj) + return codes + def forward(self, mel): mel = mel[:, :, :-1] # The MEL computation always pads with 1, throwing off optimal tensor math. @@ -651,25 +668,8 @@ class ContrastiveTrainingWrapper(nn.Module): num_codevectors = self.quantizer.num_codevectors diversity_loss = (num_codevectors - codevector_perplexity) / num_codevectors - """ - num_losses = mask_time_indices.sum() - if distributed.is_initialized(): - distributed.all_reduce(num_losses) - num_losses = num_losses / distributed.get_world_size() - self.num_losses_record = num_losses.detach() - """ - return contrastive_loss, diversity_loss - """ - def after_backward(self, it): - if self.num_losses_record > 0: - # Unscale the grads by the total number of losses encountered. - for p in self.parameters(): - if p.grad is not None: - p.grad.data.div_(self.num_losses_record) - """ - @register_model def register_mel2vec_pretraining(opt_net, opt): diff --git a/codes/models/audio/music/flat_diffusion.py b/codes/models/audio/music/flat_diffusion.py new file mode 100644 index 00000000..f0bcaf08 --- /dev/null +++ b/codes/models/audio/music/flat_diffusion.py @@ -0,0 +1,315 @@ +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 FlatDiffusion(nn.Module): + def __init__( + self, + model_channels=512, + num_layers=8, + in_channels=256, + in_latent_channels=512, + in_vectors=8, + in_groups=8, + out_channels=512, # mean and variance + dropout=0, + use_fp16=False, + num_heads=8, + # 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.embeddings = nn.ModuleList([nn.Embedding(in_vectors, model_channels//in_groups) for _ in range(in_groups)]) + 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(), + 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_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 = [embedding(aligned_conditioning[:, :, i]) for i, embedding in enumerate(self.embeddings)] + code_emb = torch.cat(code_emb, dim=-1).permute(0,2,1) + 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 + + def get_conditioning_latent(self, conditioning_input): + 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) + return conds.mean(dim=-1) + +@register_model +def register_flat_diffusion(opt_net, opt): + return FlatDiffusion(**opt_net['kwargs']) + + +if __name__ == '__main__': + clip = torch.randn(2, 256, 400) + aligned_latent = torch.randn(2,388,512) + aligned_sequence = torch.randint(0,8,(2,100,8)) + cond = torch.randn(2, 256, 400) + ts = torch.LongTensor([600, 600]) + model = FlatDiffusion(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) +