forked from mrq/DL-Art-School
mods to support cheater ar prior in tfd12
This commit is contained in:
James Betker
parent
286918c581
commit
9c069fc2f2
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@ -98,7 +98,6 @@ class TransformerDiffusion(nn.Module):
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num_heads=4,
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dropout=0,
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use_fp16=False,
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ar_prior=False,
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new_code_expansion=False,
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permute_codes=False,
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# Parameters for regularization.
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@ -127,11 +126,9 @@ class TransformerDiffusion(nn.Module):
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linear(time_embed_dim, time_embed_dim),
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)
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self.ar_prior = ar_prior
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prenet_heads = prenet_channels//64
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if ar_prior:
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self.ar_input = nn.Linear(input_vec_dim, prenet_channels)
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self.ar_prior_intg = Encoder(
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self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
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self.code_converter = Encoder(
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dim=prenet_channels,
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depth=prenet_layers,
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heads=prenet_heads,
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@ -143,20 +140,6 @@ class TransformerDiffusion(nn.Module):
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zero_init_branch_output=True,
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ff_mult=1,
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)
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else:
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self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
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self.code_converter = Encoder(
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dim=prenet_channels,
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depth=prenet_layers,
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heads=prenet_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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ff_mult=1,
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
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self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
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@ -173,11 +156,6 @@ class TransformerDiffusion(nn.Module):
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for p in self.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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if hasattr(self, 'ar_input'):
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for m in [self.ar_input and self.ar_prior_intg]:
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for p in m.parameters():
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del p.DO_NOT_TRAIN
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p.requires_grad = True
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if hasattr(self, 'code_converter'):
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for m in [self.code_converter and self.input_converter]:
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for p in m.parameters():
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@ -213,8 +191,8 @@ class TransformerDiffusion(nn.Module):
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def timestep_independent(self, prior, expected_seq_len):
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if self.new_code_expansion:
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prior = F.interpolate(prior.permute(0,2,1), size=expected_seq_len, mode='linear').permute(0,2,1)
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code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
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code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
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code_emb = self.input_converter(prior)
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code_emb = self.code_converter(code_emb)
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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if self.training and self.unconditioned_percentage > 0:
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@ -350,47 +328,6 @@ class TransformerDiffusionWithQuantizer(nn.Module):
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p.grad *= .2
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class TransformerDiffusionWithARPrior(nn.Module):
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def __init__(self, freeze_diff=False, **kwargs):
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super().__init__()
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self.internal_step = 0
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from models.audio.music.gpt_music import GptMusicLower
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self.ar = GptMusicLower(dim=512, layers=12)
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for p in self.ar.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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self.diff = TransformerDiffusion(ar_prior=True, **kwargs)
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if freeze_diff:
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for p in self.diff.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.parameters()):
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del p.DO_NOT_TRAIN
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p.requires_grad = True
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def get_grad_norm_parameter_groups(self):
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groups = {
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'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
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'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
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'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
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'out': list(self.diff.out.parameters()),
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'x_proj': list(self.diff.inp_block.parameters()),
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'layers': list(self.diff.layers.parameters()),
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'ar_prior_intg': list(self.diff.ar_prior_intg.parameters()),
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'time_embed': list(self.diff.time_embed.parameters()),
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}
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return groups
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def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
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with torch.no_grad():
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prior = self.ar(truth_mel, conditioning_input, return_latent=True)
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diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free)
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return diff
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class TransformerDiffusionWithPretrainedVqvae(nn.Module):
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def __init__(self, vqargs, **kwargs):
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super().__init__()
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@ -592,11 +529,6 @@ def register_transformer_diffusion12_with_quantizer(opt_net, opt):
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return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
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@register_model
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def register_transformer_diffusion12_with_ar_prior(opt_net, opt):
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return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
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@register_model
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def register_transformer_diffusion_12_with_pretrained_vqvae(opt_net, opt):
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return TransformerDiffusionWithPretrainedVqvae(**opt_net['kwargs'])
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@ -659,7 +591,7 @@ def test_vqvae_model():
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model = TransformerDiffusionWithPretrainedVqvae(in_channels=100, out_channels=200,
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model_channels=1024, contraction_dim=512,
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prenet_channels=1024, num_heads=8,
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input_vec_dim=512, num_layers=12, prenet_layers=6, ar_prior=True,
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input_vec_dim=512, num_layers=12, prenet_layers=6,
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dropout=.1, vqargs= {
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'positional_dims': 1, 'channels': 80,
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'hidden_dim': 512, 'num_resnet_blocks': 3, 'codebook_dim': 512, 'num_tokens': 8192,
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@ -720,28 +652,6 @@ def test_multi_vqvae_model():
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model.diff.get_grad_norm_parameter_groups()
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def test_ar_model():
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clip = torch.randn(2, 256, 400)
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cond = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusionWithARPrior(model_channels=2048, prenet_channels=1536,
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input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True,
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unconditioned_percentage=.4)
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model.get_grad_norm_parameter_groups()
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ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
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model.ar.load_state_dict(ar_weights, strict=True)
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diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd8\\models\\47500_generator_ema.pth')
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pruned_diff_weights = {}
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for k,v in diff_weights.items():
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if k.startswith('diff.'):
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pruned_diff_weights[k.replace('diff.', '')] = v
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model.diff.load_state_dict(pruned_diff_weights, strict=False)
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torch.save(model.state_dict(), 'sample.pth')
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model(clip, ts, cond, conditioning_input=cond)
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def test_cheater_model():
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clip = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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@ -776,4 +686,5 @@ def extract_diff(in_f, out_f, remove_head=False):
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if __name__ == '__main__':
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#extract_diff('X:\\dlas\\experiments\\train_music_diffusion_tfd12\\models\\41000_generator_ema.pth', 'extracted_diff.pth', True)
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test_cheater_model()
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#test_cheater_model()
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extract_diff('X:\\dlas\experiments\\train_music_diffusion_tfd_cheater_from_scratch\\models\\56500_generator_ema.pth', 'extracted.pth', remove_head=True)
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@ -13,7 +13,6 @@ from trainer.injectors.audio_injectors import MusicCheaterLatentInjector
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from models.diffusion.respace import SpacedDiffusion
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from models.diffusion.respace import space_timesteps
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from models.diffusion.gaussian_diffusion import get_named_beta_schedule
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from models.audio.music.transformer_diffusion12 import TransformerDiffusionWithCheaterLatent
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def join_music(clip1, clip1_cut, clip2, clip2_cut, mix_time, results_dir):
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@ -339,7 +339,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_ar_cheater_gen.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_music_tfd12_finetune_ar_outputs.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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args = parser.parse_args()
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opt = option.parse(args.opt, is_train=True)
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@ -4,6 +4,7 @@ import torch
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import torch.nn.functional as F
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import torchaudio
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from models.audio.music.cheater_gen_ar import ConditioningAR
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from trainer.inject import Injector
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from utils.music_utils import get_music_codegen
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from utils.util import opt_get, load_model_from_config, pad_or_truncate
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@ -426,3 +427,22 @@ class KmeansQuantizerInjector(Injector):
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distances = distances.reshape(b, s, self.centroids.shape[-1])
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labels = distances.argmin(-1)
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return {self.output: labels}
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class MusicCheaterArInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.cheater_ar = ConditioningAR(1024, layers=24, dropout=0, cond_free_percent=0)
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self.cheater_ar.load_state_dict(torch.load('../experiments/music_cheater_ar.pth', map_location=torch.device('cpu')))
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self.cond_key = opt['cheater_latent_key']
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self.needs_move = True
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def forward(self, state):
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codes = state[self.input]
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cond = state[self.cond_key]
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if self.needs_move:
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self.cheater_ar = self.cheater_ar.to(codes.device)
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self.needs_move = False
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with torch.no_grad():
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latents = self.cheater_ar(codes, cond, return_latent=True)
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return {self.output: latents}
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