forked from mrq/DL-Art-School
network updates
This commit is contained in:
James Betker
parent
5a54d7db11
commit
c61cd64bc9
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@ -369,7 +369,7 @@ class ResBlock(nn.Module):
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def __init__(
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self,
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channels,
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dropout,
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dropout=0,
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out_channels=None,
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use_conv=False,
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dims=2,
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@ -3,12 +3,12 @@ from torch import nn
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import torch.nn.functional as F
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from transformers import GPT2Config, GPT2Model
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from models.arch_util import AttentionBlock
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from models.arch_util import AttentionBlock, ResBlock
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from models.audio.music.music_quantizer import MusicQuantizer
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from models.audio.music.music_quantizer2 import MusicQuantizer2
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from models.lucidrains.x_transformers import Encoder
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from trainer.networks import register_model
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from utils.util import opt_get
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from utils.util import opt_get, checkpoint
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class ConditioningEncoder(nn.Module):
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@ -25,6 +25,32 @@ class ConditioningEncoder(nn.Module):
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self.attn = nn.Sequential(*attn)
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self.dim = embedding_dim
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def forward(self, x):
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h = checkpoint(self.init, x)
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h = checkpoint(self.attn, h)
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return h.mean(dim=2)
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class UpperConditioningEncoder(nn.Module):
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def __init__(self,
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spec_dim,
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embedding_dim,
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attn_blocks=6,
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num_attn_heads=4):
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super().__init__()
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attn = []
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self.init = nn.Sequential(nn.Conv1d(spec_dim, min(spec_dim+128, embedding_dim), kernel_size=3, stride=2, padding=1),
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nn.Conv1d(min(spec_dim+128, embedding_dim), min(spec_dim+256, embedding_dim), kernel_size=3, stride=2, padding=1),
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nn.Conv1d(min(spec_dim+256, embedding_dim), min(spec_dim+384, embedding_dim), kernel_size=3, stride=2, padding=1),
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nn.Conv1d(min(spec_dim+384, embedding_dim), min(spec_dim+512, embedding_dim), kernel_size=3, stride=2, padding=1),
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ResBlock(min(spec_dim+512, embedding_dim), dims=1),
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nn.Conv1d(min(spec_dim+512, embedding_dim), min(spec_dim+512, embedding_dim), kernel_size=3, stride=2, padding=1),
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ResBlock(min(spec_dim+512, embedding_dim), dims=1))
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for a in range(attn_blocks):
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attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_activation=True))
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self.attn = nn.Sequential(*attn)
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self.dim = embedding_dim
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def forward(self, x):
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h = self.init(x)
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h = self.attn(h)
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@ -135,12 +161,92 @@ class GptMusicLower(nn.Module):
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)
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class GptMusicUpper(nn.Module):
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def __init__(self, dim, layers, dropout=0, num_upper_vectors=64, num_upper_groups=4):
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super().__init__()
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self.internal_step = 0
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self.num_groups = num_upper_groups
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self.config = GPT2Config(vocab_size=1, n_positions=8192, n_embd=dim, n_layer=layers, n_head=dim//64,
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n_inner=dim*2, attn_pdrop=dropout, resid_pdrop=dropout, gradient_checkpointing=True,
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use_cache=False)
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self.upper_quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[dim,
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max(512,dim-128),
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max(512,dim-256),
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max(512,dim-384),
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max(512,dim-512),
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max(512,dim-512)], codevector_dim=dim,
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codebook_size=num_upper_vectors, codebook_groups=num_upper_groups,
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expressive_downsamples=True)
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# Following are unused quantizer constructs we delete to avoid DDP errors (and to be efficient.. of course..)
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del self.upper_quantizer.up
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# Freeze the quantizer.
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for p in self.upper_quantizer.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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self.conditioning_encoder = UpperConditioningEncoder(256, dim, attn_blocks=4, num_attn_heads=dim//64)
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self.gpt = GPT2Model(self.config)
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del self.gpt.wte # Unused, we'll do our own embeddings.
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self.embeddings = nn.ModuleList([nn.Embedding(num_upper_vectors, dim // num_upper_groups) for _ in range(num_upper_groups)])
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self.heads = nn.ModuleList([nn.Linear(dim, num_upper_vectors) for _ in range(num_upper_groups)])
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def forward(self, mel, conditioning, return_latent=False):
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with torch.no_grad():
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self.upper_quantizer.eval()
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codes = self.upper_quantizer.get_codes(mel)
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inputs = codes[:, :-1]
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targets = codes
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h = [embedding(inputs[:, :, i]) for i, embedding in enumerate(self.embeddings)]
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h = torch.cat(h, dim=-1)
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with torch.autocast(mel.device.type):
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# Stick the conditioning embedding on the front of the input sequence.
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# The transformer will learn how to integrate it.
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# This statement also serves to pre-pad the inputs by one token, which is the basis of the next-token-prediction task. IOW: this is the "START" token.
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cond_emb = self.conditioning_encoder(conditioning).unsqueeze(1)
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h = torch.cat([cond_emb, h], dim=1)
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h = self.gpt(inputs_embeds=h, return_dict=True).last_hidden_state
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if return_latent:
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return h.float()
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losses = 0
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for i, head in enumerate(self.heads):
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logits = head(h).permute(0,2,1)
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loss = F.cross_entropy(logits, targets[:,:,i])
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losses = losses + loss
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return losses / self.num_groups
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def get_grad_norm_parameter_groups(self):
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groups = {
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'gpt': list(self.gpt.parameters()),
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'conditioning': list(self.conditioning_encoder.parameters()),
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}
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return groups
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def get_debug_values(self, step, __):
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if self.upper_quantizer.total_codes > 0:
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return {'histogram_upper_codes': self.upper_quantizer.codes[:self.upper_quantizer.total_codes]}
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else:
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return {}
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@register_model
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def register_music_gpt_lower(opt_net, opt):
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return GptMusicLower(**opt_get(opt_net, ['kwargs'], {}))
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@register_model
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def register_music_gpt_upper(opt_net, opt):
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return GptMusicUpper(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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def test_lower():
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from models.audio.music.transformer_diffusion8 import TransformerDiffusionWithQuantizer
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base_diff = TransformerDiffusionWithQuantizer(in_channels=256, out_channels=512, model_channels=2048, block_channels=1024,
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prenet_channels=1024, prenet_layers=6, num_layers=16, input_vec_dim=1024,
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@ -152,4 +258,19 @@ if __name__ == '__main__':
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torch.save(model.state_dict(), "sample.pth")
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mel = torch.randn(2,256,400)
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model(mel, mel)
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model.get_grad_norm_parameter_groups()
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model.get_grad_norm_parameter_groups()
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def test_upper():
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lower = GptMusicLower(512, 12)
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lower.load_state_dict(torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth'))
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model = GptMusicUpper(512, 12)
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model.upper_quantizer.load_state_dict(lower.upper_quantizer.state_dict())
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torch.save(model.state_dict(), 'sample.pth')
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mel = torch.randn(2,256,2500)
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model(mel, mel)
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model.get_grad_norm_parameter_groups()
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if __name__ == '__main__':
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test_upper()
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@ -73,6 +73,7 @@ class TransformerDiffusion(nn.Module):
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out_channels=512, # mean and variance
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dropout=0,
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use_fp16=False,
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ar_prior=False,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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):
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@ -95,8 +96,10 @@ class TransformerDiffusion(nn.Module):
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)
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prenet_heads = prenet_channels//64
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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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self.ar_prior = ar_prior
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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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dim=prenet_channels,
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depth=prenet_layers,
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heads=prenet_heads,
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@ -108,6 +111,20 @@ 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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@ -130,16 +147,16 @@ class TransformerDiffusion(nn.Module):
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}
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return groups
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def timestep_independent(self, codes, expected_seq_len):
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code_emb = self.input_converter(codes)
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def timestep_independent(self, prior, expected_seq_len):
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code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
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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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unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
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device=code_emb.device) < self.unconditioned_percentage
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(codes.shape[0], 1, 1),
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(prior.shape[0], 1, 1),
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code_emb)
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code_emb = self.code_converter(code_emb)
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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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expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
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return expanded_code_emb
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@ -151,7 +168,6 @@ class TransformerDiffusion(nn.Module):
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unused_params = []
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if conditioning_free:
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code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
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unused_params.extend(list(self.code_converter.parameters()))
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else:
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if precomputed_code_embeddings is not None:
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code_emb = precomputed_code_embeddings
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@ -240,6 +256,47 @@ class TransformerDiffusionWithQuantizer(nn.Module):
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return groups
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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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@register_model
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def register_transformer_diffusion8(opt_net, opt):
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return TransformerDiffusion(**opt_net['kwargs'])
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@ -250,24 +307,17 @@ def register_transformer_diffusion8_with_quantizer(opt_net, opt):
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return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
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"""
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# For TFD5
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if __name__ == '__main__':
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clip = torch.randn(2, 256, 400)
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aligned_sequence = torch.randn(2,100,512)
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cond = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusion(model_channels=3072, block_channels=1536, prenet_channels=1536)
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torch.save(model, 'sample.pth')
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print_network(model)
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o = model(clip, ts, aligned_sequence, cond)
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"""
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@register_model
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def register_transformer_diffusion8_with_ar_prior(opt_net, opt):
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return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
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if __name__ == '__main__':
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def test_quant_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 = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024, input_vec_dim=1024, num_layers=16, prenet_layers=6)
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model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024,
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input_vec_dim=1024, num_layers=16, prenet_layers=6)
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model.get_grad_norm_parameter_groups()
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quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
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@ -279,3 +329,28 @@ if __name__ == '__main__':
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print_network(model)
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o = model(clip, ts, clip, cond)
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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, block_channels=1024, prenet_channels=1024,
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input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True)
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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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if __name__ == '__main__':
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test_ar_model()
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@ -780,6 +780,15 @@ class UNetMusicModelARPrior(nn.Module):
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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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'input_blocks': list(self.diff.input_blocks.parameters()),
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'output_blocks': list(self.diff.output_blocks.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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@ -805,6 +814,7 @@ if __name__ == '__main__':
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attention_resolutions=(2,4), channel_mult=(1,2,3), dims=1,
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use_scale_shift_norm=True, dropout=.1, num_heads=8, unconditioned_percentage=.4, freeze_unet=True)
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print_network(model)
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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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@ -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_music_gpt.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_music_gpt_upper.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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Reference in New Issue
Block a user