forked from mrq/DL-Art-School
173 lines
7.1 KiB
Python
173 lines
7.1 KiB
Python
import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers import GPT2Config, GPT2Model
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from models.arch_util import AttentionBlock, ResBlock
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from models.audio.tts.lucidrains_dvae import DiscreteVAE
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from trainer.networks import register_model
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from utils.util import opt_get, ceil_multiple, print_network
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class UpperEncoder(nn.Module):
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def __init__(self,
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spec_dim,
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hidden_dim,
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embedding_dim,
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):
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super().__init__()
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attn = []
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def edim(m):
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dd = min(spec_dim + m * 128, hidden_dim)
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return ceil_multiple(dd, 8)
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self.downsampler = nn.Sequential(
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ResBlock(spec_dim, out_channels=edim(1), use_conv=True, dims=1, down=True),
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ResBlock(edim(1), out_channels=edim(2), use_conv=True, dims=1, down=True),
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ResBlock(edim(2), out_channels=edim(3), use_conv=True, dims=1, down=True),
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ResBlock(edim(3), out_channels=edim(4), use_conv=True, dims=1),
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ResBlock(edim(4), out_channels=hidden_dim, use_conv=True, dims=1, down=True))
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self.encoder = nn.Sequential(
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AttentionBlock(hidden_dim, 4, do_activation=True),
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ResBlock(hidden_dim, out_channels=hidden_dim, use_conv=True, dims=1),
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AttentionBlock(hidden_dim, 4, do_activation=True),
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ResBlock(hidden_dim, out_channels=hidden_dim, use_conv=True, dims=1),
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AttentionBlock(hidden_dim, 4, do_activation=True),
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ResBlock(hidden_dim, out_channels=hidden_dim, use_conv=True, dims=1),
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nn.GroupNorm(8, hidden_dim),
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nn.SiLU(),
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nn.Conv1d(hidden_dim, embedding_dim, 1)
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)
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def forward(self, x):
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h = self.downsampler(x)
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h = self.encoder(h)
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return h
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class GptMusicLower(nn.Module):
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def __init__(self, dim, layers, encoder_out_dim, dropout=0, num_target_vectors=8192, fp16=True, num_vaes=4, vqargs={}):
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super().__init__()
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self.num_vaes = num_vaes
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self.start_token = nn.Parameter(torch.randn(1, 1, dim))
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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.target_quantizers = nn.ModuleList([DiscreteVAE(**vqargs).eval() for _ in range(num_vaes)])
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self.upper_encoder = UpperEncoder(256, dim, encoder_out_dim)
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self.encoder_projector = nn.Conv1d(encoder_out_dim, dim, 1)
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self.fp16 = fp16
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# Freeze the target quantizer.
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for p in self.target_quantizers.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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# And delete the decoder, which is unused.
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for tq in self.target_quantizers:
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del tq.decoder
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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_target_vectors, dim // num_vaes) for _ in range(num_vaes)])
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self.heads = nn.ModuleList([nn.Linear(dim, num_target_vectors) for _ in range(num_vaes)])
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def forward(self, mel, return_latent=False):
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unused_params = []
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with torch.no_grad():
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codes = []
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partition_size = mel.shape[1] // len(self.target_quantizers)
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for i, q in enumerate(self.target_quantizers):
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mel_partition = mel[:, i*partition_size:(i+1)*partition_size]
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codes.append(q.get_codebook_indices(mel_partition))
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codes = torch.stack(codes, dim=-1)
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upper_vector = self.upper_encoder(mel)
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upper_vector = self.encoder_projector(upper_vector)
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# WTB slerp
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upper_vector = F.interpolate(upper_vector, size=codes.shape[1], mode='linear')
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upper_vector = upper_vector.permute(0,2,1)
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inputs = codes[:, :-1]
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targets = codes
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upper_vector = upper_vector[:, :-1]
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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) + upper_vector
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with torch.autocast(mel.device.type, enabled=self.fp16):
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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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h = torch.cat([self.start_token.repeat(h.shape[0], 1, 1), 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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unused_adder = 0
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for p in unused_params:
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unused_adder = unused_adder + p.mean() * 0
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losses = losses + unused_adder
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return losses / self.num_vaes
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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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'heads': list(self.heads.parameters()),
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'embeddings': list(self.embeddings.parameters()),
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'upper_latent_encoder': list(self.upper_encoder.encoder.parameters()),
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'upper_latent_downsampler': list(self.upper_encoder.downsampler.parameters()),
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}
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return groups
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@register_model
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def register_music_gpt_lower2(opt_net, opt):
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return GptMusicLower(**opt_get(opt_net, ['kwargs'], {}))
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def test_lower():
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model = GptMusicLower(dim=1024, encoder_out_dim=256, layers=16, fp16=False, num_target_vectors=8192, num_vaes=4,
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vqargs= {'positional_dims': 1, 'channels': 64,
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'hidden_dim': 512, 'num_resnet_blocks': 3, 'codebook_dim': 512, 'num_tokens': 8192,
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'num_layers': 0, 'record_codes': True, 'kernel_size': 3, 'use_transposed_convs': False,
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})
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quants = ['X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_low\\models\\7500_generator.pth',
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'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_mid_low\\models\\11000_generator.pth',
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'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_mid_high\\models\\11500_generator.pth',
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'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_high\\models\\11500_generator.pth']
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for i, qfile in enumerate(quants):
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quant_weights = torch.load(qfile)
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model.target_quantizers[i].load_state_dict(quant_weights, strict=False)
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torch.save(model.state_dict(), 'sample.pth')
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print_network(model)
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mel = torch.randn(2,256,400)
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model(mel)
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pg = model.get_grad_norm_parameter_groups()
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t = 0
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for k, vs in pg.items():
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s = 0
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for v in vs:
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m = 1
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for d in v.shape:
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m *= d
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s += m
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t += s
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print(k, s/1000000)
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print(t/1000000)
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if __name__ == '__main__':
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test_lower() |