import functools
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
Downsample, Upsample, TimestepBlock
from models.audio.tts.mini_encoder import AudioMiniEncoder
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
from trainer.networks import register_model
from utils.util import checkpoint
from x_transformers import Encoder, ContinuousTransformerWrapper
def clustered_mask(probability, shape, dev, lateral_expansion_radius_max=3, inverted=False):
"""
Produces a masking vector of the specified shape where each element has probability to be zero.
lateral_expansion_radius_max neighbors of any element that is zero also have a 50% chance to be zero.
Effectively, this produces clusters of masks tending to be lateral_expansion_radius_max wide.
"""
# Each masked token spreads out to 1+lateral_expansion_radius_max on average, therefore reduce the probability in
# kind
probability = probability / (1+lateral_expansion_radius_max)
mask = torch.rand(shape, device=dev)
mask = (mask < probability).float()
kernel = torch.tensor([.5 for _ in range(lateral_expansion_radius_max)] + [1] + [.5 for _ in range(lateral_expansion_radius_max)], device=dev)
mask = F.conv1d(mask.unsqueeze(1), kernel.view(1,1,2*lateral_expansion_radius_max+1), padding=lateral_expansion_radius_max).squeeze(1)
if inverted:
return torch.bernoulli(torch.clamp(mask, 0, 1)) != 0
else:
return torch.bernoulli(torch.clamp(mask, 0, 1)) == 0
class CheckpointedLayer(nn.Module):
"""
Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
checkpoint for all other args.
"""
def __init__(self, wrap):
super().__init__()
self.wrap = wrap
def forward(self, x, *args, **kwargs):
for k, v in kwargs.items():
assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing.
partial = functools.partial(self.wrap, **kwargs)
return torch.utils.checkpoint.checkpoint(partial, x, *args)
class CheckpointedXTransformerEncoder(nn.Module):
"""
Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
to channels-last that XTransformer expects.
"""
def __init__(self, needs_permute=True, exit_permute=True, checkpoint=True, **xtransformer_kwargs):
super().__init__()
self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
self.needs_permute = needs_permute
self.exit_permute = exit_permute
if not checkpoint:
return
for i in range(len(self.transformer.attn_layers.layers)):
n, b, r = self.transformer.attn_layers.layers[i]
self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
def forward(self, x, **kwargs):
if self.needs_permute:
x = x.permute(0,2,1)
h = self.transformer(x, **kwargs)
if self.exit_permute:
h = h.permute(0,2,1)
return h
class ResBlock(TimestepBlock):
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
dims=2,
kernel_size=3,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
padding = 1 if kernel_size == 3 else 2
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 1, padding=0),
)
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
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, 1)
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]
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class DiffusionTts(nn.Module):
"""
The full UNet model with attention and timestep embedding.
Customized to be conditioned on an aligned token prior.
:param in_channels: channels in the input Tensor.
:param num_tokens: number of tokens (e.g. characters) which can be provided.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_heads: the number of attention heads in each attention layer.
:param num_heads_channels: if specified, ignore num_heads and instead use
a fixed channel width per attention head.
:param num_heads_upsample: works with num_heads to set a different number
of heads for upsampling. Deprecated.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
:param use_new_attention_order: use a different attention pattern for potentially
increased efficiency.
"""
def __init__(
self,
model_channels,
in_channels=1,
num_tokens=32,
out_channels=2, # mean and variance
dropout=0,
# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
# spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
# attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
token_conditioning_resolutions=(1,16,),
attention_resolutions=(512,1024,2048),
conv_resample=True,
dims=1,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4,
cond_transformer_depth=8,
mid_transformer_depth=8,
# Parameters for regularization.
nil_guidance_fwd_proportion=.3,
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
# Parameters for super-sampling.
super_sampling=False,
super_sampling_max_noising_factor=.1,
# Parameters for unaligned inputs.
enabled_unaligned_inputs=False,
num_unaligned_tokens=164,
unaligned_encoder_depth=8,
# Experimental parameters
component_gradient_boosting=False,
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
if super_sampling:
in_channels *= 2 # In super-sampling mode, the LR input is concatenated directly onto the input.
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.dims = dims
self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
self.mask_token_id = num_tokens
self.super_sampling_enabled = super_sampling
self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.component_gradient_boosting = component_gradient_boosting
padding = 1 if kernel_size == 3 else 2
time_embed_dim = model_channels * time_embed_dim_multiplier
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
embedding_dim = model_channels * 8
self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
self.contextual_embedder = AudioMiniEncoder(1, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
self.conditioning_conv = nn.Conv1d(embedding_dim*3, embedding_dim, 1)
self.enable_unaligned_inputs = enabled_unaligned_inputs
if enabled_unaligned_inputs:
self.unaligned_embedder = nn.Embedding(num_unaligned_tokens, embedding_dim)
self.unaligned_encoder = CheckpointedXTransformerEncoder(
max_seq_len=-1,
use_pos_emb=False,
attn_layers=Encoder(
dim=embedding_dim,
depth=unaligned_encoder_depth,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_emb_dim=True,
)
)
self.conditioning_encoder = CheckpointedXTransformerEncoder(
max_seq_len=-1, # Should be unused
use_pos_emb=False,
attn_layers=Encoder(
dim=embedding_dim,
depth=cond_transformer_depth,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
cross_attend=self.enable_unaligned_inputs,
)
)
self.unconditioned_embedding = nn.Parameter(torch.randn(1,embedding_dim,1))
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
)
]
)
token_conditioning_blocks = []
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
if ds in token_conditioning_resolutions:
token_conditioning_block = nn.Conv1d(embedding_dim, ch, 1)
token_conditioning_block.weight.data *= .02
self.input_blocks.append(token_conditioning_block)
token_conditioning_blocks.append(token_conditioning_block)
for _ in range(num_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=int(mult * model_channels),
dims=dims,
kernel_size=kernel_size,
)
]
ch = int(mult * model_channels)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
mid_transformer = CheckpointedXTransformerEncoder(
max_seq_len=-1, # Should be unused
use_pos_emb=False,
attn_layers=Encoder(
dim=ch,
depth=mid_transformer_depth,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
)
)
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
kernel_size=kernel_size,
),
mid_transformer,
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
kernel_size=kernel_size,
),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
for i in range(num_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(
ch + ich,
time_embed_dim,
dropout,
out_channels=int(model_channels * mult),
dims=dims,
kernel_size=kernel_size,
)
]
ch = int(model_channels * mult)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads_upsample,
num_head_channels=num_head_channels,
)
)
if level and i == num_blocks:
out_ch = ch
layers.append(
Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
)
def get_grad_norm_parameter_groups(self):
groups = {
'minicoder': list(self.contextual_embedder.parameters()),
'input_blocks': list(self.input_blocks.parameters()),
'output_blocks': list(self.output_blocks.parameters()),
'middle_transformer': list(self.middle_block.parameters()),
'conditioning_encoder': list(self.conditioning_encoder.parameters())
}
if self.enable_unaligned_inputs:
groups['unaligned_encoder'] = list(self.unaligned_encoder.parameters())
return groups
def before_step(self, it):
if not self.component_gradient_boosting:
return
MIN_PROPORTIONAL_BOOST_LEVEL = .5
MAX_MULTIPLIER = 100
components = [list(self.contextual_embedder.parameters()), list(self.middle_block.parameters()), list(self.conditioning_encoder.parameters()),
list(self.unaligned_encoder.parameters())]
input_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in self.input_blocks.parameters()]), 2)
output_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in self.output_blocks.parameters()]), 2)
diffusion_norm = (input_norm + output_norm) / 2
min_norm = diffusion_norm * MIN_PROPORTIONAL_BOOST_LEVEL
for component in components:
norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in component]), 2)
if norm < min_norm:
mult = min_norm / (norm + 1e-8)
mult = min(mult, MAX_MULTIPLIER)
for p in component:
p.grad.data.mul_(mult)
def forward(self, x, timesteps, tokens=None, conditioning_input=None, lr_input=None, unaligned_input=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 tokens: an aligned text input.
:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
:param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate.
:param unaligned_input: A structural input that is not properly aligned with the output of the diffusion model.
Can be combined with a conditioning input to produce more robust conditioning.
:param conditioning_free: When set, all conditioning inputs (including tokens, conditioning_input and unaligned_input) will not be considered.
:return: an [N x C x ...] Tensor of outputs.
"""
assert conditioning_input is not None
if self.super_sampling_enabled:
assert lr_input is not None
if self.training and self.super_sampling_max_noising_factor > 0:
noising_factor = random.uniform(0,self.super_sampling_max_noising_factor)
lr_input = torch.randn_like(lr_input) * noising_factor + lr_input
lr_input = F.interpolate(lr_input, size=(x.shape[-1],), mode='nearest')
x = torch.cat([x, lr_input], dim=1)
with autocast(x.device.type, enabled=self.enable_fp16):
orig_x_shape = x.shape[-1]
cm = ceil_multiple(x.shape[-1], 2048)
if cm != 0:
pc = (cm-x.shape[-1])/x.shape[-1]
x = F.pad(x, (0,cm-x.shape[-1]))
if tokens is not None:
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
hs = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
else:
if self.enable_unaligned_inputs:
assert unaligned_input is not None
unaligned_h = self.unaligned_embedder(unaligned_input).permute(0,2,1)
unaligned_h = self.unaligned_encoder(unaligned_h).permute(0,2,1)
cond_emb = self.contextual_embedder(conditioning_input)
if tokens is not None:
# Mask out guidance tokens for un-guided diffusion.
if self.training and self.nil_guidance_fwd_proportion > 0:
token_mask = clustered_mask(self.nil_guidance_fwd_proportion, tokens.shape, tokens.device, inverted=True)
tokens = torch.where(token_mask, self.mask_token_id, tokens)
code_emb = self.code_embedding(tokens).permute(0,2,1)
cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
cond_time_emb = timestep_embedding(torch.zeros_like(timesteps), code_emb.shape[1]) # This was something I was doing (adding timesteps into this computation), but removed on second thought. TODO: completely remove.
cond_time_emb = cond_time_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb, cond_time_emb], dim=1))
else:
code_emb = cond_emb.unsqueeze(-1)
if self.enable_unaligned_inputs:
code_emb = self.conditioning_encoder(code_emb, context=unaligned_h)
else:
code_emb = self.conditioning_encoder(code_emb)
# 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(x.shape[0], 1, 1), code_emb)
first = True
time_emb = time_emb.float()
h = x
for k, module in enumerate(self.input_blocks):
if isinstance(module, nn.Conv1d):
h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
h = h + h_tok
else:
with autocast(x.device.type, enabled=self.enable_fp16 and not first):
# First block has autocast disabled to allow a high precision signal to be properly vectorized.
h = module(h, time_emb)
hs.append(h)
first = False
h = self.middle_block(h, time_emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, time_emb)
# Last block also has autocast disabled for high-precision outputs.
h = h.float()
out = self.out(h)
return out[:, :, :orig_x_shape]
@register_model
def register_diffusion_tts7(opt_net, opt):
return DiffusionTts(**opt_net['kwargs'])
# Test for ~4 second audio clip at 22050Hz
if __name__ == '__main__':
clip = torch.randn(2, 1, 32768)
tok = torch.randint(0,30, (2,388))
cond = torch.randn(2, 1, 44000)
ts = torch.LongTensor([600, 600])
lr = torch.randn(2,1,10000)
un = torch.randint(0,120, (2,100))
model = DiffusionTts(128,
channel_mult=[1,1.5,2, 3, 4, 6, 8],
num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
token_conditioning_resolutions=[1,4,16,64],
attention_resolutions=[],
num_heads=8,
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4, super_sampling=False,
enabled_unaligned_inputs=True,
component_gradient_boosting=True)
o = model(clip, ts, tok, cond, lr, un)
o.sum().backward()
model.before_step(0)
torch.save(model.state_dict(), 'test_out.pth')