All the stuff needed for cheater latent generation
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@ -95,6 +95,8 @@ def create_dataset(dataset_opt, return_collate=False):
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from data.audio.unsupervised_audio_dataset import UnsupervisedAudioDataset as D
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from data.audio.unsupervised_audio_dataset import UnsupervisedAudioDataset as D
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elif mode == 'unsupervised_audio_with_noise':
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elif mode == 'unsupervised_audio_with_noise':
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from data.audio.audio_with_noise_dataset import AudioWithNoiseDataset as D
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from data.audio.audio_with_noise_dataset import AudioWithNoiseDataset as D
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elif mode == 'preprocessed_mel':
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from data.audio.preprocessed_mel_dataset import PreprocessedMelDataset as D
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elif mode == 'grand_conjoined_voice':
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elif mode == 'grand_conjoined_voice':
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from data.audio.grand_conjoined_dataset import GrandConjoinedDataset as D
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from data.audio.grand_conjoined_dataset import GrandConjoinedDataset as D
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from data.zero_pad_dict_collate import ZeroPadDictCollate as C
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from data.zero_pad_dict_collate import ZeroPadDictCollate as C
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71
codes/data/audio/preprocessed_mel_dataset.py
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71
codes/data/audio/preprocessed_mel_dataset.py
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@ -0,0 +1,71 @@
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import os
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from pathlib import Path
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.data
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import torchaudio
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import torchvision
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from tqdm import tqdm
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from utils.util import opt_get
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class PreprocessedMelDataset(torch.utils.data.Dataset):
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def __init__(self, opt):
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path = opt['path']
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cache_path = opt['cache_path'] # Will fail when multiple paths specified, must be specified in this case.
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if os.path.exists(cache_path):
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self.paths = torch.load(cache_path)
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else:
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path = Path(path)
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self.paths = [str(p) for p in path.rglob("*.npz")]
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torch.save(self.paths, cache_path)
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self.pad_to = opt_get(opt, ['pad_to_samples'], 10336)
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def __getitem__(self, index):
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with np.load(self.paths[index]) as npz_file:
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mel = torch.tensor(npz_file['arr_0'])
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assert mel.shape[-1] <= self.pad_to
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padding_needed = self.pad_to - mel.shape[-1]
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mask = torch.zeros_like(mel)
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if padding_needed > 0:
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mel = F.pad(mel, (0,padding_needed))
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mask = F.pad(mask, (0,padding_needed), value=1)
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output = {
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'mel': mel,
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'mel_lengths': torch.tensor(mel.shape[-1]),
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'mask': mask,
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'mask_lengths': torch.tensor(mask.shape[-1]),
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'path': self.paths[index],
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}
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return output
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def __len__(self):
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return len(self.paths)
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if __name__ == '__main__':
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params = {
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'mode': 'preprocessed_mel',
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'path': 'Y:\\separated\\large_mels',
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'cache_path': 'Y:\\separated\\large_mels.pth',
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'pad_to_samples': 10336,
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'phase': 'train',
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'n_workers': 0,
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'batch_size': 16,
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}
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from data import create_dataset, create_dataloader
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ds = create_dataset(params)
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dl = create_dataloader(ds, params)
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i = 0
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for b in tqdm(dl):
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#pass
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torchvision.utils.save_image((b['mel'].unsqueeze(1)+1)/2, f'{i}.png')
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i += 1
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if i > 20:
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break
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@ -0,0 +1,272 @@
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import itertools
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from time import time
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from models.arch_util import ResBlock, AttentionBlock
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from models.audio.music.gpt_music2 import UpperEncoder, GptMusicLower
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from models.audio.music.music_quantizer2 import MusicQuantizer2
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from models.audio.tts.lucidrains_dvae import DiscreteVAE
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import TimestepBlock
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from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
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FeedForward
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from trainer.networks import register_model
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from utils.util import checkpoint, print_network
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def is_latent(t):
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return t.dtype == torch.float
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def is_sequence(t):
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return t.dtype == torch.long
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class MultiGroupEmbedding(nn.Module):
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def __init__(self, tokens, groups, dim):
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super().__init__()
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self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
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def forward(self, x):
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h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
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return torch.cat(h, dim=-1)
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class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
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def forward(self, x, emb, rotary_emb):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb, rotary_emb)
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else:
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x = layer(x, rotary_emb)
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return x
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class SubBlock(nn.Module):
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def __init__(self, inp_dim, contraction_dim, heads, dropout):
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super().__init__()
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self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout)
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self.attnorm = nn.LayerNorm(contraction_dim)
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self.ff = FeedForward(inp_dim+contraction_dim, dim_out=contraction_dim, mult=2, dropout=dropout)
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self.ffnorm = nn.LayerNorm(contraction_dim)
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def forward(self, x, rotary_emb):
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ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb)
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ah = F.gelu(self.attnorm(ah))
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h = torch.cat([ah, x], dim=-1)
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hf = checkpoint(self.ff, h)
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hf = F.gelu(self.ffnorm(hf))
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h = torch.cat([h, hf], dim=-1)
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return h
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class ConcatAttentionBlock(TimestepBlock):
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def __init__(self, trunk_dim, contraction_dim, time_embed_dim, heads, dropout):
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super().__init__()
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self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False)
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self.block1 = SubBlock(trunk_dim, contraction_dim, heads, dropout)
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self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, heads, dropout)
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self.out = nn.Linear(contraction_dim*4, trunk_dim, bias=False)
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self.out.weight.data.zero_()
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def forward(self, x, conditioning, timestep_emb, rotary_emb):
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h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
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h = torch.cat([conditioning, h], dim=1)
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h = self.block1(h, rotary_emb)
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h = self.block2(h, rotary_emb)
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h = self.out(h[:,:,x.shape[-1]:])
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return h[:, 1:] + x
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class TransformerDiffusion(nn.Module):
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"""
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A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
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"""
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def __init__(
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self,
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in_channels=256,
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out_channels=512, # mean and variance
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model_channels=1024,
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contraction_dim=256,
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time_embed_dim=256,
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num_layers=8,
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rotary_emb_dim=32,
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input_cond_dim=1024,
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num_heads=8,
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dropout=0,
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use_fp16=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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super().__init__()
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.time_embed_dim = time_embed_dim
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self.out_channels = out_channels
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self.dropout = dropout
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self.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
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self.time_embed = nn.Sequential(
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linear(time_embed_dim, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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self.conditioner = nn.Linear(input_cond_dim, model_channels) if input_cond_dim != model_channels else nn.Identity()
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
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self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
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self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, time_embed_dim, num_heads, dropout) for _ in range(num_layers)])
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self.out = nn.Sequential(
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normalization(model_channels),
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nn.SiLU(),
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zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
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)
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self.debug_codes = {}
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def get_grad_norm_parameter_groups(self):
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attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.layers]))
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attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.layers]))
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ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.layers]))
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ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.layers]))
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blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers]))
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groups = {
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'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers])),
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'blk1_attention_layers': attn1,
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'blk2_attention_layers': attn2,
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'attention_layers': attn1 + attn2,
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'blk1_ff_layers': ff1,
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'blk2_ff_layers': ff2,
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'ff_layers': ff1 + ff2,
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'block_out_layers': blkout_layers,
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'rotary_embeddings': list(self.rotary_embeddings.parameters()),
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'out': list(self.out.parameters()),
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'x_proj': list(self.inp_block.parameters()),
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'layers': list(self.layers.parameters()),
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'time_embed': list(self.time_embed.parameters()),
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}
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return groups
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def forward(self, x, timesteps, conditioning_input, conditioning_free=False):
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unused_params = []
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if conditioning_free:
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cond = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
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else:
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cond = self.conditioner(conditioning_input)
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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((cond.shape[0], 1, 1),
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device=cond.device) < self.unconditioned_percentage
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cond = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(cond.shape[0], 1, 1),
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cond)
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unused_params.append(self.unconditioned_embedding)
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with torch.autocast(x.device.type, enabled=self.enable_fp16):
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blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
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x = self.inp_block(x).permute(0,2,1)
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rotary_pos_emb = self.rotary_embeddings(x.shape[1]+1, x.device)
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for layer in self.layers:
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x = checkpoint(layer, x, cond, blk_emb, rotary_pos_emb)
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x = x.float().permute(0,2,1)
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out = self.out(x)
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# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
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extraneous_addition = 0
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for p in unused_params:
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extraneous_addition = extraneous_addition + p.mean()
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out = out + extraneous_addition * 0
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return out
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class ConditioningEncoder(nn.Module):
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def __init__(self,
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cond_dim,
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embedding_dim,
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attn_blocks=6,
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num_attn_heads=8,
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do_checkpointing=False):
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super().__init__()
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attn = []
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self.init = nn.Conv1d(cond_dim, embedding_dim, kernel_size=1)
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for a in range(attn_blocks):
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attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing))
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self.attn = nn.Sequential(*attn)
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self.dim = embedding_dim
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self.do_checkpointing = do_checkpointing
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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).unsqueeze(1)
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class TransformerDiffusionWithConditioningEncoder(nn.Module):
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def __init__(self, **kwargs):
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super().__init__()
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self.internal_step = 0
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self.diff = TransformerDiffusion(**kwargs)
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self.conditioning_encoder = ConditioningEncoder(256, kwargs['model_channels'])
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self.encoder = UpperEncoder(256, 1024, 256).eval()
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for p in self.encoder.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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def forward(self, x, timesteps, true_cheater, conditioning_input=None, disable_diversity=False, conditioning_free=False):
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cond = self.conditioning_encoder(true_cheater)
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diff = self.diff(x, timesteps, conditioning_input=cond, conditioning_free=conditioning_free)
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return diff
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def get_debug_values(self, step, __):
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self.internal_step = step
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return {}
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def get_grad_norm_parameter_groups(self):
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groups = self.diff.get_grad_norm_parameter_groups()
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groups['conditioning_encoder'] = list(self.conditioning_encoder.parameters())
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return
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def before_step(self, step):
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scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
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list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers]))
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# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
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# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
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# directly fiddling with the gradients.
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for p in scaled_grad_parameters:
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if hasattr(p, 'grad') and p.grad is not None:
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p.grad *= .2
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@register_model
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def register_transformer_diffusion_with_point_conditioning(opt_net, opt):
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return TransformerDiffusion(**opt_net['kwargs'])
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@register_model
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def register_tfdpc_with_conditioning_encoder(opt_net, opt):
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return TransformerDiffusionWithConditioningEncoder(**opt_net['kwargs'])
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def test_cheater_model():
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clip = torch.randn(2, 256, 400)
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cl = torch.randn(2, 1, 400)
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||||||
|
ts = torch.LongTensor([600, 600])
|
||||||
|
|
||||||
|
# For music:
|
||||||
|
model = TransformerDiffusionWithConditioningEncoder(model_channels=1024)
|
||||||
|
print_network(model)
|
||||||
|
o = model(clip, ts, cl)
|
||||||
|
pg = model.get_grad_norm_parameter_groups()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
test_cheater_model()
|
|
@ -339,7 +339,7 @@ class Trainer:
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
parser = argparse.ArgumentParser()
|
parser = argparse.ArgumentParser()
|
||||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_music_gpt.yml')
|
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_music_cheater_gen.yml')
|
||||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||||
args = parser.parse_args()
|
args = parser.parse_args()
|
||||||
opt = option.parse(args.opt, is_train=True)
|
opt = option.parse(args.opt, is_train=True)
|
||||||
|
|
|
@ -378,5 +378,20 @@ class ChannelClipInjector(Injector):
|
||||||
return {self.output: inp[:,self.lo:self.hi]}
|
return {self.output: inp[:,self.lo:self.hi]}
|
||||||
|
|
||||||
|
|
||||||
|
class MusicCheaterLatentInjector(Injector):
|
||||||
|
def __init__(self, opt, env):
|
||||||
|
super().__init__(opt, env)
|
||||||
|
from models.audio.music.gpt_music2 import UpperEncoder
|
||||||
|
self.encoder = UpperEncoder(256, 1024, 256).eval()
|
||||||
|
self.encoder.load_state_dict(torch.load('../experiments/music_cheater_encoder_256.pth', map_location=torch.device('cpu')))
|
||||||
|
|
||||||
|
def forward(self, state):
|
||||||
|
with torch.no_grad():
|
||||||
|
mel = state[self.input]
|
||||||
|
self.encoder = self.encoder.to(mel.device)
|
||||||
|
proj = self.encoder(mel)
|
||||||
|
return {self.output: proj}
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
print('hi')
|
print('hi')
|
Loading…
Reference in New Issue
Block a user