diff --git a/codes/models/audio/music/transformer_diffusion8_mup.py b/codes/models/audio/music/transformer_diffusion8_mup.py new file mode 100644 index 00000000..d146c51f --- /dev/null +++ b/codes/models/audio/music/transformer_diffusion8_mup.py @@ -0,0 +1,388 @@ +import itertools + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader + +from models.arch_util import ResBlock +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, 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, + ar_prior=False, + # Parameters for regularization. + unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training. + # mUp base shapes. + mup_base_shapes=None, + ): + 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), + ) + prenet_heads = min(16, prenet_channels//64) + + self.ar_prior = ar_prior + 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, block_channels, + min(16, 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)), + ) + + if mup_base_shapes is not None: + from mup import set_base_shapes + set_base_shapes(self, mup_base_shapes, rescale_params=False) + + 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) + + # 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) + code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else 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) + 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) + + 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, 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, 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])), + 'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])), + '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_diffusion8_mup(opt_net, opt): + return TransformerDiffusion(**opt_net['kwargs']) + + +@register_model +def register_transformer_diffusion8_with_quantizer_mup(opt_net, opt): + return TransformerDiffusionWithQuantizer(**opt_net['kwargs']) + + +@register_model +def register_transformer_diffusion8_with_ar_prior_mup(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(model_channels=2048, block_channels=1024, prenet_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) + + +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, block_channels=1024, prenet_channels=1024, + input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True) + 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) + + +def init_mup(): + base_model = TransformerDiffusion(model_channels=768, block_channels=768, prenet_channels=768, + input_vec_dim=1024, num_layers=16, prenet_layers=4) + delta_model = TransformerDiffusion(model_channels=2048, block_channels=1024, prenet_channels=1024, + input_vec_dim=1024, num_layers=16, prenet_layers=4) + target_model = TransformerDiffusion(model_channels=3072, block_channels=1536, prenet_channels=1536, + input_vec_dim=1024, num_layers=16, prenet_layers=4) + from mup import set_base_shapes, save_base_shapes + set_base_shapes(target_model, base_model, delta=delta_model) + save_base_shapes(target_model, 'mup_base_shapes.bsh') + + """ + # Ah to have a simple loss.. + def lazy_model(width): + return lambda: set_base_shapes(TransformerDiffusion(model_channels=width*2, block_channels=width, + prenet_channels=width, num_layers=16, prenet_layers=4, + input_vec_dim=1024), + 'mup_base_shapes.bsh') + from mup.coord_check import get_coord_data, plot_coord_data + models = {256: lazy_model(256), 512: lazy_model(512), 1024: lazy_model(1024), 1536: lazy_model(1536)} + dataloader = DataLoader(MupSampleDataset()) + df = get_coord_data(models, dataloader, dict_in_out=True) + plot_coord_data(df, 'coord_check') + """ + +if __name__ == '__main__': + init_mup() diff --git a/codes/models/lucidrains/x_transformers.py b/codes/models/lucidrains/x_transformers.py index 53ac1d41..b48eb51e 100644 --- a/codes/models/lucidrains/x_transformers.py +++ b/codes/models/lucidrains/x_transformers.py @@ -509,9 +509,10 @@ class Attention(nn.Module): rel_pos_bias=False, rel_pos_num_buckets=32, rel_pos_max_distance=128, + mup_scale=False ): super().__init__() - self.scale = dim_head ** -0.5 + self.scale = 8/dim_head if mup_scale else dim_head ** -0.5 self.heads = heads self.causal = causal diff --git a/codes/requirements.txt b/codes/requirements.txt index 11b607e7..c4b32d28 100644 --- a/codes/requirements.txt +++ b/codes/requirements.txt @@ -14,6 +14,7 @@ tensorboard orjson einops lambda-networks +mup # For image generation stuff opencv-python diff --git a/codes/trainer/steps.py b/codes/trainer/steps.py index aae9b80d..9ec2f78b 100644 --- a/codes/trainer/steps.py +++ b/codes/trainer/steps.py @@ -127,6 +127,16 @@ class ConfigurableStep(Module): weight_decay=opt_get(opt_config, ['weight_decay'], 1e-2), betas=(opt_get(opt_config, ['beta1'], .9), opt_get(opt_config, ['beta2'], .999))) opt._group_names = [params_names_weights, params_names_notweights] + elif self.step_opt['optimizer'] == 'mu_adamw': + groups = [ + { 'params': params_weights, 'weight_decay': opt_get(opt_config, ['weight_decay'], 0) }, + { 'params': params_notweights, 'weight_decay': 0 } + ] + from mup.optim import MuAdamW + opt = MuAdamW(groups, lr=opt_config['lr'], + weight_decay=opt_get(opt_config, ['weight_decay'], 1e-2), + betas=(opt_get(opt_config, ['beta1'], .9), opt_get(opt_config, ['beta2'], .999))) + opt._group_names = [params_names_weights, params_names_notweights] elif self.step_opt['optimizer'] == 'adamw_zero': # The torch ZeRO implementation does not seem to support parameter groups, so do not shard the non-weighted # parameters and just use a normal AdamW implementation. In a large network, these weights will normally