import torch
import torch.nn as nn
import torch.nn.functional as F
from models.audio.music.music_quantizer import MusicQuantizer
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import TimestepBlock
from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
from trainer.networks import register_model
from utils.util import checkpoint, print_network
def is_latent(t):
return t.dtype == torch.float
def is_sequence(t):
return t.dtype == torch.long
class MultiGroupEmbedding(nn.Module):
def __init__(self, tokens, groups, dim):
super().__init__()
self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
def forward(self, x):
h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
return torch.cat(h, dim=-1)
class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
def forward(self, x, emb, rotary_emb):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb, rotary_emb)
else:
x = layer(x, rotary_emb)
return x
class DietAttentionBlock(TimestepBlock):
def __init__(self, in_dim, dim, heads, dropout):
super().__init__()
self.rms_scale_norm = RMSScaleShiftNorm(in_dim)
self.proj = nn.Linear(in_dim, dim)
self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout)
self.ff = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True)
def forward(self, x, timestep_emb, rotary_emb):
h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb)
h = self.proj(h)
h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
h = checkpoint(self.ff, h)
return h + x
class TransformerDiffusion(nn.Module):
"""
A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
"""
def __init__(
self,
prenet_channels=256,
prenet_layers=3,
model_channels=512,
block_channels=256,
num_layers=8,
in_channels=256,
rotary_emb_dim=32,
input_vec_dim=512,
out_channels=512, # mean and variance
dropout=0,
use_fp16=False,
# Parameters for regularization.
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.prenet_channels = prenet_channels
self.out_channels = out_channels
self.dropout = dropout
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
self.time_embed = nn.Sequential(
linear(prenet_channels, prenet_channels),
nn.SiLU(),
linear(prenet_channels, prenet_channels),
)
prenet_heads = prenet_channels//64
self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, prenet_channels // 2, 3, padding=1, stride=2),
nn.Conv1d(prenet_channels//2, prenet_channels,3,padding=1,stride=2))
self.conditioning_encoder = Encoder(
dim=prenet_channels,
depth=4,
heads=prenet_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
zero_init_branch_output=True,
ff_mult=1,
)
self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
self.code_converter = Encoder(
dim=prenet_channels,
depth=prenet_layers,
heads=prenet_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
zero_init_branch_output=True,
ff_mult=1,
)
self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
self.cond_intg = nn.Linear(prenet_channels*2, model_channels)
self.intg = nn.Linear(prenet_channels*2, model_channels)
self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, block_channels, block_channels // 64, dropout) for _ in range(num_layers)])
self.out = nn.Sequential(
normalization(model_channels),
nn.SiLU(),
zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
)
self.debug_codes = {}
def get_grad_norm_parameter_groups(self):
groups = {
'contextual_embedder': list(self.conditioning_embedder.parameters()),
'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()),
'time_embed': list(self.time_embed.parameters()),
}
return groups
def timestep_independent(self, codes, conditioning_input, expected_seq_len):
cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
code_emb = self.input_converter(codes)
# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
if self.training and self.unconditioned_percentage > 0:
unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
device=code_emb.device) < self.unconditioned_percentage
code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(codes.shape[0], 1, 1),
code_emb)
code_emb = self.code_converter(code_emb)
expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
return expanded_code_emb, cond_emb
def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None,
precomputed_cond_embeddings=None, conditioning_free=False):
if precomputed_code_embeddings is not None:
assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
unused_params = []
if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
unused_params.extend(list(self.code_converter.parameters()))
else:
if precomputed_code_embeddings is not None:
code_emb = precomputed_code_embeddings
cond_emb = precomputed_cond_embeddings
else:
code_emb, cond_emb = self.timestep_independent(codes, conditioning_input, x.shape[-1])
unused_params.append(self.unconditioned_embedding)
blk_emb = torch.cat([self.time_embed(timestep_embedding(timesteps, self.prenet_channels)), cond_emb], dim=-1)
blk_emb = self.cond_intg(blk_emb)
x = self.inp_block(x).permute(0,2,1)
rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
x = self.intg(torch.cat([x, code_emb], dim=-1))
for layer in self.layers:
x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
x = x.float().permute(0,2,1)
out = self.out(x)
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
extraneous_addition = 0
for p in unused_params:
extraneous_addition = extraneous_addition + p.mean()
out = out + extraneous_addition * 0
return out
class TransformerDiffusionWithQuantizer(nn.Module):
def __init__(self, freeze_quantizer_until=20000, **kwargs):
super().__init__()
self.internal_step = 0
self.freeze_quantizer_until = freeze_quantizer_until
self.diff = TransformerDiffusion(**kwargs)
self.m2v = MusicQuantizer(inp_channels=256, inner_dim=[1024,1024,512], codevector_dim=1024, codebook_size=512, codebook_groups=2)
self.m2v.quantizer.temperature = self.m2v.min_gumbel_temperature
del self.m2v.up
def update_for_step(self, step, *args):
self.internal_step = step
qstep = max(0, self.internal_step - self.freeze_quantizer_until)
self.m2v.quantizer.temperature = max(
self.m2v.max_gumbel_temperature * self.m2v.gumbel_temperature_decay**qstep,
self.m2v.min_gumbel_temperature,
)
def forward(self, x, timesteps, truth_mel, conditioning_input, disable_diversity=False, conditioning_free=False):
quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
with torch.set_grad_enabled(quant_grad_enabled):
proj, diversity_loss = self.m2v(truth_mel, return_decoder_latent=True)
proj = proj.permute(0,2,1)
# Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
if not quant_grad_enabled:
unused = 0
for p in self.m2v.parameters():
unused = unused + p.mean() * 0
proj = proj + unused
diversity_loss = diversity_loss * 0
diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
if disable_diversity:
return diff
return diff, diversity_loss
def get_debug_values(self, step, __):
if self.m2v.total_codes > 0:
return {'histogram_codes': self.m2v.codes[:self.m2v.total_codes]}
else:
return {}
@register_model
def register_transformer_diffusion7(opt_net, opt):
return TransformerDiffusion(**opt_net['kwargs'])
@register_model
def register_transformer_diffusion7_with_quantizer(opt_net, opt):
return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
"""
# For TFD5
if __name__ == '__main__':
clip = torch.randn(2, 256, 400)
aligned_sequence = torch.randn(2,100,512)
cond = torch.randn(2, 256, 400)
ts = torch.LongTensor([600, 600])
model = TransformerDiffusion(model_channels=3072, block_channels=1536, prenet_channels=1536)
torch.save(model, 'sample.pth')
print_network(model)
o = model(clip, ts, aligned_sequence, cond)
"""
if __name__ == '__main__':
clip = torch.randn(2, 256, 400)
cond = torch.randn(2, 256, 400)
ts = torch.LongTensor([600, 600])
model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024, input_vec_dim=1024, num_layers=16, prenet_layers=6)
quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant\\models\\18000_generator_ema.pth')
#diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth')
model.m2v.load_state_dict(quant_weights, strict=False)
#model.diff.load_state_dict(diff_weights)
torch.save(model.state_dict(), 'sample.pth')
print_network(model)
o = model(clip, ts, clip, cond)