import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
from torchaudio.transforms import TimeMasking, FrequencyMasking
from models.audio.tts.unified_voice2 import ConditioningEncoder
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock
from models.lucidrains.x_transformers import Encoder
from trainer.networks import register_model
from utils.util import checkpoint
def is_sequence(t):
return t.dtype == torch.long
class ResBlock(TimestepBlock):
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
dims=2,
kernel_size=3,
efficient_config=True,
use_scale_shift_norm=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_scale_shift_norm = use_scale_shift_norm
padding = {1: 0, 3: 1, 5: 2}[kernel_size]
eff_kernel = 1 if efficient_config else 3
eff_padding = 0 if efficient_config else 1
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
)
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
return checkpoint(
self._forward, x, emb
)
def _forward(self, x, emb):
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class DiffusionLayer(TimestepBlock):
def __init__(self, model_channels, dropout, num_heads):
super().__init__()
self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
def forward(self, x, time_emb):
y = self.resblk(x, time_emb)
return self.attn(y)
class ConditioningEncoder(nn.Module):
def __init__(self,
spec_dim,
embedding_dim,
attn_blocks=6):
super().__init__()
attn = []
self.init = nn.Sequential(nn.Conv1d(spec_dim, embedding_dim//2, kernel_size=3, padding=1, stride=2),
nn.Conv1d(embedding_dim//2, embedding_dim, kernel_size=3, padding=1, stride=2))
self.attn = Encoder(dim=embedding_dim, depth=attn_blocks, use_scalenorm=True, rotary_pos_emb=True,
heads=embedding_dim//64, ff_mult=1)
self.dim = embedding_dim
def forward(self, x):
h = self.init(x)
h = self.attn(h.permute(0,2,1))
return h.mean(dim=1)
class MusicGenerator(nn.Module):
def __init__(
self,
model_channels=512,
num_layers=8,
in_channels=100,
out_channels=200, # mean and variance
dropout=0,
use_fp16=False,
num_heads=16,
# Parameters for regularization.
layer_drop=.1,
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
# Masking parameters.
frequency_mask_percent_max=0.2,
time_mask_percent_max=0.2,
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.dropout = dropout
self.num_heads = num_heads
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.layer_drop = layer_drop
self.time_mask_percent_max = time_mask_percent_max
self.frequency_mask_percent_mask = frequency_mask_percent_max
self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
self.time_embed = nn.Sequential(
linear(model_channels, model_channels),
nn.SiLU(),
linear(model_channels, model_channels),
)
self.conditioner = ConditioningEncoder(in_channels, model_channels)
self.unconditioned_embedding = nn.Parameter(torch.randn(1, model_channels))
self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
[ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)])
self.out = nn.Sequential(
normalization(model_channels),
nn.SiLU(),
zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
)
def get_grad_norm_parameter_groups(self):
groups = {
'layers': list(self.layers.parameters()),
'conditioner': list(self.conditioner.parameters()),
'time_embed': list(self.time_embed.parameters()),
}
return groups
def do_masking(self, truth):
b, c, s = truth.shape
# Frequency mask
mask_freq = torch.ones_like(truth)
cs = random.randint(0, c-10)
ce = min(c-1, cs+random.randint(1, int(self.frequency_mask_percent_mask*c)))
mask_freq[:, cs:ce] = 0
# Time mask
mask_time = torch.ones_like(truth)
cs = random.randint(0, s-5)
ce = min(s-1, cs+random.randint(1, int(self.frequency_mask_percent_mask*s)))
mask_time[:, :, cs:ce] = 0
return truth * mask_time * mask_freq
def timestep_independent(self, truth):
if self.training:
truth = self.do_masking(truth)
truth_emb = self.conditioner(truth)
return truth_emb
def forward(self, x, timesteps, truth=None, precomputed_aligned_embeddings=None, conditioning_free=False):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param truth: Input value is either pre-masked (in inference), or unmasked (during training)
:param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
:return: an [N x C x ...] Tensor of outputs.
"""
assert precomputed_aligned_embeddings is not None or truth is not None
unused_params = []
if conditioning_free:
truth_emb = self.unconditioned_embedding
unused_params.extend(list(self.conditioner.parameters()))
else:
if precomputed_aligned_embeddings is not None:
truth_emb = precomputed_aligned_embeddings
else:
truth_emb = self.timestep_independent(truth)
unused_params.append(self.unconditioned_embedding)
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + truth_emb
x = self.inp_block(x)
for i, lyr in enumerate(self.layers):
# Do layer drop where applicable. Do not drop first and last layers.
if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
unused_params.extend(list(lyr.parameters()))
else:
# First and last blocks will have autocast disabled for improved precision.
with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
x = lyr(x, time_emb)
x = x.float()
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
@register_model
def register_music_gap_gen2(opt_net, opt):
return MusicGenerator(**opt_net['kwargs'])
if __name__ == '__main__':
clip = torch.randn(2, 100, 400)
aligned_latent = torch.randn(2,100,388)
ts = torch.LongTensor([600, 600])
model = MusicGenerator(512, layer_drop=.3, unconditioned_percentage=.5)
o = model(clip, ts, aligned_latent)