292 lines
14 KiB
Python
292 lines
14 KiB
Python
import torch
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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, 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, checkpoint
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class ConditioningEncoder(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.Conv1d(spec_dim, embedding_dim, kernel_size=3, stride=2, padding=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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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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return h.mean(dim=2)
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class GptMusicLower(nn.Module):
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def __init__(self, dim, layers, dropout=0, num_target_vectors=512, num_target_groups=2, num_upper_vectors=64,
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num_upper_groups=4, fp16=True, freeze_upper_until=0):
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super().__init__()
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self.internal_step = 0
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self.freeze_upper_until = freeze_upper_until
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self.num_groups = num_target_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, use_cache=False)
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self.target_quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024], codevector_dim=1024, codebook_size=256,
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codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
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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, expressive_downsamples=True)
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self.fp16 = fp16
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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.target_quantizer.decoder
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del self.target_quantizer.up
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del self.upper_quantizer.up
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# Freeze the target quantizer.
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for p in self.target_quantizer.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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self.upper_mixer = Encoder(
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dim=dim,
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depth=4,
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heads=dim//64,
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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_emb_dim=True,
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)
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self.conditioning_encoder = ConditioningEncoder(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_target_vectors, dim // num_target_groups) for _ in range(num_target_groups)])
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self.heads = nn.ModuleList([nn.Linear(dim, num_target_vectors) for _ in range(num_target_groups)])
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def forward(self, mel, conditioning, return_latent=False):
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unused_params = []
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with torch.no_grad():
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self.target_quantizer.eval()
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codes = self.target_quantizer.get_codes(mel)
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if self.freeze_upper_until > self.internal_step:
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with torch.no_grad():
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upper_vector, upper_diversity = self.upper_quantizer(mel, return_decoder_latent=True)
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unused_params.extend(list(self.upper_quantizer.parameters()))
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else:
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upper_vector, upper_diversity = self.upper_quantizer(mel, return_decoder_latent=True)
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upper_vector = self.upper_mixer(upper_vector.permute(0,2,1)).permute(0,2,1) # Allow the upper vector to fully attend to itself (the whole thing is a prior.)
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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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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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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_groups, upper_diversity
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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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'upper_mixer': list(self.upper_mixer.parameters()),
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'upper_quant_down': list(self.upper_quantizer.down.parameters()),
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'upper_quant_encoder': list(self.upper_quantizer.encoder.parameters()),
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'upper_quant_codebook': [self.upper_quantizer.quantizer.codevectors],
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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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'gumbel_temperature': self.upper_quantizer.quantizer.temperature}
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else:
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return {}
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def update_for_step(self, step, *args):
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self.internal_step = step
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self.upper_quantizer.quantizer.temperature = max(
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self.upper_quantizer.max_gumbel_temperature * self.upper_quantizer.gumbel_temperature_decay**self.internal_step,
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self.upper_quantizer.min_gumbel_temperature,
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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, fp16=True):
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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.fp16 = fp16
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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, 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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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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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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dropout=.1, unconditioned_percentage=0, freeze_quantizer_until=6000)
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base_diff.load_state_dict(torch.load('x:/dlas/experiments/train_music_diffusion_tfd8/models/47500_generator.pth', map_location=torch.device('cpu')))
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model = GptMusicLower(512, 8, fp16=False, freeze_upper_until=100)
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model.target_quantizer.load_state_dict(base_diff.quantizer.state_dict(), strict=False)
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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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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_lower() |