import torch import torch.nn as nn import torch.nn.functional as F from models.audio.music.music_quantizer import MusicQuantizer from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear from models.diffusion.unet_diffusion import TimestepBlock from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding 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 TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock): def forward(self, x, emb, rotary_emb): for layer in self: if isinstance(layer, TimestepBlock): x = layer(x, emb, rotary_emb) else: x = layer(x, rotary_emb) return x class DietAttentionBlock(TimestepBlock): def __init__(self, in_dim, dim, heads, dropout): super().__init__() self.rms_scale_norm = RMSScaleShiftNorm(in_dim) self.proj = nn.Linear(in_dim, dim) self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout) self.ff = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True) def forward(self, x, timestep_emb, rotary_emb): h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb) h = self.proj(h) h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb) h = checkpoint(self.ff, h) return h + x 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, prenet_channels=256, prenet_layers=3, model_channels=512, block_channels=256, num_layers=8, in_channels=256, rotary_emb_dim=32, 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.prenet_channels = prenet_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, prenet_channels, 3, 1, 1) self.time_embed = nn.Sequential( linear(prenet_channels, prenet_channels), nn.SiLU(), linear(prenet_channels, prenet_channels), ) prenet_heads = prenet_channels//64 self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, prenet_channels // 2, 3, padding=1, stride=2), nn.Conv1d(prenet_channels//2, prenet_channels,3,padding=1,stride=2)) self.conditioning_encoder = Encoder( dim=prenet_channels, depth=4, heads=prenet_heads, 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.input_converter = nn.Linear(input_vec_dim, prenet_channels) self.code_converter = Encoder( dim=prenet_channels, depth=prenet_layers, heads=prenet_heads, 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,prenet_channels)) self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim) self.cond_intg = nn.Linear(prenet_channels*2, model_channels) self.intg = nn.Linear(prenet_channels*2, model_channels) self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, block_channels, block_channels // 64, dropout) 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 = { 'contextual_embedder': list(self.conditioning_embedder.parameters()), '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, conditioning_input, expected_seq_len): cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1) cond_emb = self.conditioning_encoder(cond_emb)[:, 0] 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, cond_emb def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, precomputed_cond_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) cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1) cond_emb = self.conditioning_encoder(cond_emb)[:, 0] unused_params.extend(list(self.code_converter.parameters())) else: if precomputed_code_embeddings is not None: code_emb = precomputed_code_embeddings cond_emb = precomputed_cond_embeddings else: code_emb, cond_emb = self.timestep_independent(codes, conditioning_input, x.shape[-1]) unused_params.append(self.unconditioned_embedding) blk_emb = torch.cat([self.time_embed(timestep_embedding(timesteps, self.prenet_channels)), cond_emb], dim=-1) blk_emb = self.cond_intg(blk_emb) x = self.inp_block(x).permute(0,2,1) rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device) x = self.intg(torch.cat([x, code_emb], dim=-1)) for layer in self.layers: x = checkpoint(layer, x, blk_emb, rotary_pos_emb) x = x.float().permute(0,2,1) 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.m2v = MusicQuantizer(inp_channels=256, inner_dim=[1024,1024,512], codevector_dim=1024, codebook_size=512, codebook_groups=2) self.m2v.quantizer.temperature = self.m2v.min_gumbel_temperature del self.m2v.up def update_for_step(self, step, *args): self.internal_step = step qstep = max(0, self.internal_step - self.freeze_quantizer_until) self.m2v.quantizer.temperature = max( self.m2v.max_gumbel_temperature * self.m2v.gumbel_temperature_decay**qstep, self.m2v.min_gumbel_temperature, ) def forward(self, x, timesteps, truth_mel, conditioning_input, 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.m2v(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.m2v.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.m2v.total_codes > 0: return {'histogram_codes': self.m2v.codes[:self.m2v.total_codes]} else: return {} @register_model def register_transformer_diffusion7(opt_net, opt): return TransformerDiffusion(**opt_net['kwargs']) @register_model def register_transformer_diffusion7_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, block_channels=1536, prenet_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=2048, block_channels=1024, prenet_channels=1024, input_vec_dim=1024, num_layers=16, prenet_layers=6) quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant\\models\\18000_generator_ema.pth') #diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth') model.m2v.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)