diff --git a/codes/models/audio/music/transformer_diffusion10.py b/codes/models/audio/music/transformer_diffusion10.py new file mode 100644 index 00000000..3ab15be2 --- /dev/null +++ b/codes/models/audio/music/transformer_diffusion10.py @@ -0,0 +1,385 @@ +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 +from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \ + FeedForward +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 SubBlock(nn.Module): + def __init__(self, inp_dim, contraction_dim, heads, dropout): + super().__init__() + self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout) + self.attnorm = nn.LayerNorm(contraction_dim) + self.ff = FeedForward(inp_dim+contraction_dim, contraction_dim, mult=1, dropout=dropout) + + def forward(self, x, rotary_emb): + ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb) + ah = F.gelu(self.attnorm(ah)) + h = torch.cat([ah, x], dim=-1) + hf = checkpoint(self.ff, h) + h = torch.cat([h, hf], dim=-1) + return h + +class DietAttentionBlock(TimestepBlock): + def __init__(self, trunk_dim, heads, dropout): + super().__init__() + contraction_dim = trunk_dim // 4 + self.prenorm = RMSScaleShiftNorm(trunk_dim, bias=False) + self.block1 = SubBlock(trunk_dim, contraction_dim, heads, dropout) + self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, heads, dropout) + self.out = nn.Linear(trunk_dim+contraction_dim*4, trunk_dim, bias=False) + self.out.weight.data.zero_() + + def forward(self, x, timestep_emb, rotary_emb): + h = self.prenorm(x, norm_scale_shift_inp=timestep_emb) + h = self.block1(h, rotary_emb) + h = self.block2(h, rotary_emb) + h = self.out(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, + num_layers=8, + in_channels=256, + rotary_emb_dim=32, + input_vec_dim=512, + out_channels=512, # mean and variance + num_heads=16, + dropout=0, + use_fp16=False, + ar_prior=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, model_channels), + ) + + self.ar_prior = ar_prior + prenet_heads = prenet_channels//64 + if ar_prior: + self.ar_input = nn.Linear(input_vec_dim, prenet_channels) + self.ar_prior_intg = 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, + ) + else: + 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.intg = nn.Linear(prenet_channels*2, model_channels) + self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, num_heads, 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 = { + '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, prior, expected_seq_len): + code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior) + code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb) + + # 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(prior.shape[0], 1, 1), + 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) + 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) + + with torch.autocast(x.device.type, enabled=self.enable_fp16): + blk_emb = self.time_embed(timestep_embedding(timesteps, self.prenet_channels)) + 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, quantizer_dims=[1024], 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=kwargs['in_channels'], inner_dim=quantizer_dims, + codevector_dim=quantizer_dims[0], 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_quant_codes': self.quantizer.codes[:self.quantizer.total_codes], + 'gumbel_temperature': self.quantizer.quantizer.temperature} + else: + return {} + + def get_grad_norm_parameter_groups(self): + attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.diff.layers])) + attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.diff.layers])) + ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.diff.layers])) + ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.diff.layers])) + groups = { + 'blk1_attention_layers': attn1, + 'blk2_attention_layers': attn2, + 'attention_layers': attn1 + attn2, + 'blk1_ff_layers': ff1, + 'blk2_ff_layers': ff2, + 'ff_layers': ff1 + ff2, + 'quantizer_encoder': list(self.quantizer.encoder.parameters()), + 'quant_codebook': [self.quantizer.quantizer.codevectors], + 'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()), + '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 + + +class TransformerDiffusionWithARPrior(nn.Module): + def __init__(self, freeze_diff=False, **kwargs): + super().__init__() + + self.internal_step = 0 + from models.audio.music.gpt_music import GptMusicLower + self.ar = GptMusicLower(dim=512, layers=12) + for p in self.ar.parameters(): + p.DO_NOT_TRAIN = True + p.requires_grad = False + + self.diff = TransformerDiffusion(ar_prior=True, **kwargs) + if freeze_diff: + for p in self.diff.parameters(): + p.DO_NOT_TRAIN = True + p.requires_grad = False + for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.parameters()): + del p.DO_NOT_TRAIN + p.requires_grad = True + + def get_grad_norm_parameter_groups(self): + groups = { + 'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])), + 'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])), + 'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()), + 'out': list(self.diff.out.parameters()), + 'x_proj': list(self.diff.inp_block.parameters()), + 'layers': list(self.diff.layers.parameters()), + 'ar_prior_intg': list(self.diff.ar_prior_intg.parameters()), + 'time_embed': list(self.diff.time_embed.parameters()), + } + return groups + + def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False): + with torch.no_grad(): + prior = self.ar(truth_mel, conditioning_input, return_latent=True) + + diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free) + return diff + + +@register_model +def register_transformer_diffusion9(opt_net, opt): + return TransformerDiffusion(**opt_net['kwargs']) + + +@register_model +def register_transformer_diffusion10_with_quantizer(opt_net, opt): + return TransformerDiffusionWithQuantizer(**opt_net['kwargs']) + + +@register_model +def register_transformer_diffusion10_with_ar_prior(opt_net, opt): + return TransformerDiffusionWithARPrior(**opt_net['kwargs']) + + +def test_quant_model(): + clip = torch.randn(2, 256, 400) + cond = torch.randn(2, 256, 400) + ts = torch.LongTensor([600, 600]) + model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=1024, + prenet_channels=1024, num_heads=8, + input_vec_dim=1024, num_layers=20, prenet_layers=6, + dropout=.1) + + quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth') + model.quantizer.load_state_dict(quant_weights, strict=False) + + torch.save(model.state_dict(), 'sample.pth') + print_network(model) + o = model(clip, ts, clip, cond) + model.get_grad_norm_parameter_groups() + + +def test_ar_model(): + clip = torch.randn(2, 256, 400) + cond = torch.randn(2, 256, 400) + ts = torch.LongTensor([600, 600]) + model = TransformerDiffusionWithARPrior(model_channels=2048, prenet_channels=1536, + input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True, + unconditioned_percentage=.4) + model.get_grad_norm_parameter_groups() + + ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth') + model.ar.load_state_dict(ar_weights, strict=True) + diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd8\\models\\47500_generator_ema.pth') + pruned_diff_weights = {} + for k,v in diff_weights.items(): + if k.startswith('diff.'): + pruned_diff_weights[k.replace('diff.', '')] = v + model.diff.load_state_dict(pruned_diff_weights, strict=False) + torch.save(model.state_dict(), 'sample.pth') + + model(clip, ts, cond, conditioning_input=cond) + + + +if __name__ == '__main__': + test_quant_model() diff --git a/codes/models/audio/music/transformer_diffusion8.py b/codes/models/audio/music/transformer_diffusion8.py index 5791be02..208f1091 100644 --- a/codes/models/audio/music/transformer_diffusion8.py +++ b/codes/models/audio/music/transformer_diffusion8.py @@ -319,9 +319,9 @@ def test_quant_model(): clip = torch.randn(2, 256, 400) cond = torch.randn(2, 256, 400) ts = torch.LongTensor([600, 600]) - model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=3072, block_channels=1536, - prenet_channels=1024, num_heads=12, - input_vec_dim=1024, num_layers=24, prenet_layers=6) + model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=2048, block_channels=1024, + prenet_channels=1024, num_heads=8, + 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 --git a/codes/models/audio/music/transformer_diffusion9.py b/codes/models/audio/music/transformer_diffusion9.py index a1ec2465..9dc0226e 100644 --- a/codes/models/audio/music/transformer_diffusion9.py +++ b/codes/models/audio/music/transformer_diffusion9.py @@ -287,7 +287,6 @@ class TransformerDiffusionWithARPrior(nn.Module): groups = { 'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])), 'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])), - 'exit_mults': list([lyr.ff.exit_mult for lyr in self.diff.layers]), 'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()), 'out': list(self.diff.out.parameters()), 'x_proj': list(self.diff.inp_block.parameters()), diff --git a/codes/models/lucidrains/x_transformers.py b/codes/models/lucidrains/x_transformers.py index 158d358c..3b482e8b 100644 --- a/codes/models/lucidrains/x_transformers.py +++ b/codes/models/lucidrains/x_transformers.py @@ -489,6 +489,7 @@ class Attention(nn.Module): def __init__( self, dim, + out_dim=None, dim_head=DEFAULT_DIM_HEAD, heads=8, causal=False, @@ -571,7 +572,8 @@ class Attention(nn.Module): # attention on attention self.attn_on_attn = on_attn - self.to_out = nn.Sequential(nn.Linear(v_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(v_dim, dim) + out_dim = default(out_dim, dim) + self.to_out = nn.Sequential(nn.Linear(v_dim, out_dim * 2), nn.GLU()) if on_attn else nn.Linear(v_dim, out_dim) self.rel_pos_bias = rel_pos_bias if rel_pos_bias: