import itertools import torch import torch.nn as nn import torch.nn.functional as F from models.audio.music.music_quantizer2 import MusicQuantizer2 from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear from models.diffusion.unet_diffusion import TimestepBlock, AttentionBlock, TimestepEmbedSequential from models.lucidrains.x_transformers import Encoder from trainer.networks import register_model from utils.util import checkpoint, print_network def is_latent(t): return t.dtype == torch.float def is_sequence(t): return t.dtype == torch.long class MultiGroupEmbedding(nn.Module): def __init__(self, tokens, groups, dim): super().__init__() self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)]) def forward(self, x): h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)] return torch.cat(h, dim=-1) class ResBlock(TimestepBlock): def __init__( self, channels, emb_channels, dropout, out_channels=None, dims=2, kernel_size=3, efficient_config=False, 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 TransformerDiffusion(nn.Module): """ A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way? """ def __init__( self, model_channels=512, prenet_layers=3, num_layers=8, in_channels=256, input_vec_dim=512, out_channels=512, # mean and variance dropout=0, use_fp16=False, # Parameters for regularization. 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.unconditioned_percentage = unconditioned_percentage self.enable_fp16 = use_fp16 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), ) self.input_converter = nn.Linear(input_vec_dim, model_channels) self.code_converter = Encoder( dim=model_channels, depth=prenet_layers, heads=model_channels//64, ff_dropout=dropout, attn_dropout=dropout, use_rmsnorm=True, ff_glu=True, rotary_pos_emb=True, zero_init_branch_output=True, ff_mult=1, ) self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels)) self.intg = nn.Conv1d(model_channels*2, model_channels, kernel_size=1) self.layers = TimestepEmbedSequential(*[DiffusionLayer(model_channels, dropout, model_channels // 64) for _ in range(num_layers)]) self.out = nn.Sequential( normalization(model_channels), nn.SiLU(), zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)), ) self.debug_codes = {} def get_grad_norm_parameter_groups(self): groups = { 'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()), 'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()), 'time_embed': list(self.time_embed.parameters()), } return groups def timestep_independent(self, codes, expected_seq_len): code_emb = self.input_converter(codes) # 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(codes.shape[0], 1, 1), code_emb) code_emb = self.code_converter(code_emb) expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1) return expanded_code_emb def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, conditioning_free=False): if precomputed_code_embeddings is not None: assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here." unused_params = [] if conditioning_free: code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1) unused_params.extend(list(self.code_converter.parameters())) else: if precomputed_code_embeddings is not None: code_emb = precomputed_code_embeddings else: code_emb = self.timestep_independent(codes, x.shape[-1]) unused_params.append(self.unconditioned_embedding) code_emb = code_emb.permute(0,2,1) blk_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) x = self.inp_block(x) x = self.intg(torch.cat([x, code_emb], dim=1)) for layer in self.layers: x = checkpoint(layer, x, blk_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 class TransformerDiffusionWithQuantizer(nn.Module): def __init__(self, freeze_quantizer_until=20000, **kwargs): super().__init__() self.internal_step = 0 self.freeze_quantizer_until = freeze_quantizer_until self.diff = TransformerDiffusion(**kwargs) self.quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024], codevector_dim=1024, codebook_size=256, codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5) self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature del self.quantizer.up def update_for_step(self, step, *args): self.internal_step = step qstep = max(0, self.internal_step - self.freeze_quantizer_until) self.quantizer.quantizer.temperature = max( self.quantizer.max_gumbel_temperature * self.quantizer.gumbel_temperature_decay ** qstep, self.quantizer.min_gumbel_temperature, ) def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False): quant_grad_enabled = self.internal_step > self.freeze_quantizer_until with torch.set_grad_enabled(quant_grad_enabled): proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True) proj = proj.permute(0,2,1) # Make sure this does not cause issues in DDP by explicitly using the parameters for nothing. if not quant_grad_enabled: unused = 0 for p in self.quantizer.parameters(): unused = unused + p.mean() * 0 proj = proj + unused diversity_loss = diversity_loss * 0 diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free) if disable_diversity: return diff return diff, diversity_loss def get_debug_values(self, step, __): if self.quantizer.total_codes > 0: return {'histogram_codes': self.quantizer.codes[:self.quantizer.total_codes]} else: return {} def get_grad_norm_parameter_groups(self): groups = { 'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])), 'res_layers': list(itertools.chain.from_iterable([lyr.resblk.parameters() for lyr in self.diff.layers])), 'quantizer_encoder': list(self.quantizer.encoder.parameters()), 'quant_codebook': [self.quantizer.quantizer.codevectors], 'out': list(self.diff.out.parameters()), 'x_proj': list(self.diff.inp_block.parameters()), 'layers': list(self.diff.layers.parameters()), 'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()), 'time_embed': list(self.diff.time_embed.parameters()), } return groups @register_model def register_transformer_diffusion9(opt_net, opt): return TransformerDiffusion(**opt_net['kwargs']) @register_model def register_transformer_diffusion9_with_quantizer(opt_net, opt): return TransformerDiffusionWithQuantizer(**opt_net['kwargs']) """ # For TFD5 if __name__ == '__main__': clip = torch.randn(2, 256, 400) aligned_sequence = torch.randn(2,100,512) cond = torch.randn(2, 256, 400) ts = torch.LongTensor([600, 600]) model = TransformerDiffusion(model_channels=3072, model_channels=1536, model_channels=1536) torch.save(model, 'sample.pth') print_network(model) o = model(clip, ts, aligned_sequence, cond) """ if __name__ == '__main__': clip = torch.randn(2, 256, 400) cond = torch.randn(2, 256, 400) ts = torch.LongTensor([600, 600]) model = TransformerDiffusionWithQuantizer(model_channels=1024, input_vec_dim=1024, num_layers=16, prenet_layers=6) model.get_grad_norm_parameter_groups() quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth') #diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth') model.quantizer.load_state_dict(quant_weights, strict=False) #model.diff.load_state_dict(diff_weights) torch.save(model.state_dict(), 'sample.pth') print_network(model) o = model(clip, ts, clip, cond)