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James Betker 2022-03-24 11:30:40 -06:00
parent 8707a3e0c3
commit b0d2827fad
3 changed files with 287 additions and 5 deletions
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272
codes/models/audio/tts/unet_diffusion_tts_flat0.py Normal file
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@ -0,0 +1,272 @@
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
from x_transformers import Encoder
from x_transformers.x_transformers import RelativePositionBias
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock
from models.audio.tts.mini_encoder import AudioMiniEncoder
from trainer.networks import register_model
from utils.util import checkpoint
def is_latent(t):
return t.dtype == torch.float
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 DiffusionTtsFlat(nn.Module):
def __init__(
self,
model_channels=512,
num_layers=8,
in_channels=100,
in_latent_channels=512,
in_tokens=8193,
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.
):
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.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),
)
# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
# transformer network.
self.code_embedding = nn.Embedding(in_tokens, model_channels)
self.code_converter = nn.Sequential(
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
)
self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True))
self.conditioning_conv = nn.Conv1d(model_channels*2, model_channels, 1)
self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
self.conditioning_timestep_integrator = TimestepEmbedSequential(
DiffusionLayer(model_channels, dropout, num_heads),
DiffusionLayer(model_channels, dropout, num_heads),
DiffusionLayer(model_channels, dropout, num_heads),
)
self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
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 = {
'minicoder': list(self.contextual_embedder.parameters()),
'layers': list(self.layers),
}
return groups
def forward(self, x, timesteps, aligned_conditioning, conditioning_input, lr_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 aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
: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 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.
"""
# Shuffle aligned_latent to BxCxS format
if is_latent(aligned_conditioning):
aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
# Note: this block does not need to repeated on inference, since it is not timestep-dependent or x-dependent.
unused_params = []
if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
else:
unused_params.append(self.unconditioned_embedding)
cond_emb = self.contextual_embedder(conditioning_input)
if len(cond_emb.shape) == 3: # Just take the first element.
cond_emb = cond_emb[:, :, 0]
if is_latent(aligned_conditioning):
code_emb = self.latent_converter(aligned_conditioning)
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
else:
code_emb = self.code_embedding(aligned_conditioning).permute(0,2,1)
code_emb = self.code_converter(code_emb)
unused_params.extend(list(self.latent_converter.parameters()))
cond_emb_spread = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1])
code_emb = self.conditioning_conv(torch.cat([cond_emb_spread, code_emb], dim=1))
# 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)
# Everything after this comment is timestep dependent.
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
x = self.inp_block(x)
x = torch.cat([x, F.interpolate(code_emb, size=x.shape[-1], mode='nearest')], dim=1)
x = self.integrating_conv(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_diffusion_tts_flat0(opt_net, opt):
return DiffusionTtsFlat(**opt_net['kwargs'])
if __name__ == '__main__':
clip = torch.randn(2, 100, 400)
aligned_latent = torch.randn(2,388,512)
aligned_sequence = torch.randint(0,8192,(2,388))
cond = torch.randn(2, 100, 400)
ts = torch.LongTensor([600, 600])
model = DiffusionTtsFlat(512, layer_drop=.3)
# Test with latent aligned conditioning
o = model(clip, ts, aligned_latent, cond)
# Test with sequence aligned conditioning
o = model(clip, ts, aligned_sequence, cond)

16
codes/models/diffusion/unet_diffusion.py
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@ -8,6 +8,7 @@ import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torchvision # For debugging, not actually used.
from x_transformers.x_transformers import RelativePositionBias
from models.diffusion.fp16_util import convert_module_to_f16, convert_module_to_f32
from models.diffusion.nn import (
@ -297,6 +298,7 @@ class AttentionBlock(nn.Module):
num_head_channels=-1,
use_new_attention_order=False,
do_checkpoint=True,
relative_pos_embeddings=False,
):
super().__init__()
self.channels = channels
@ -318,6 +320,10 @@ class AttentionBlock(nn.Module):
self.attention = QKVAttentionLegacy(self.num_heads)
self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
if relative_pos_embeddings:
self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
else:
self.relative_pos_embeddings = None
def forward(self, x, mask=None):
if self.do_checkpoint:
@ -329,7 +335,7 @@ class AttentionBlock(nn.Module):
b, c, *spatial = x.shape
x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x))
h = self.attention(qkv, mask)
h = self.attention(qkv, mask, self.relative_pos_embeddings)
h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial)
@ -363,7 +369,7 @@ class QKVAttentionLegacy(nn.Module):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv, mask=None):
def forward(self, qkv, mask=None, rel_pos=None):
"""
Apply QKV attention.
@ -378,6 +384,8 @@ class QKVAttentionLegacy(nn.Module):
weight = th.einsum(
"bct,bcs->bts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
if rel_pos is not None:
weight = rel_pos(weight.reshape(bs, self.n_heads, weight.shape[-2], weight.shape[-1])).reshape(bs * self.n_heads, weight.shape[-2], weight.shape[-1])
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
if mask is not None:
# The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
@ -401,7 +409,7 @@ class QKVAttention(nn.Module):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv, mask=None):
def forward(self, qkv, mask=None, rel_pos=None):
"""
Apply QKV attention.
@ -418,6 +426,8 @@ class QKVAttention(nn.Module):
(q * scale).view(bs * self.n_heads, ch, length),
(k * scale).view(bs * self.n_heads, ch, length),
) # More stable with f16 than dividing afterwards
if rel_pos is not None:
weight = rel_pos(weight)
if mask is not None:
# The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
mask = mask.repeat(self.n_heads, 1).unsqueeze(1)

4
codes/trainer/eval/audio_diffusion_fid.py
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@ -267,10 +267,10 @@ if __name__ == '__main__':
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_diffusion_tts_mel_flat.yml', 'generator',
also_load_savepoint=False,
load_path='X:\\dlas\\experiments\\train_diffusion_tts_mel_flat\\models\\6500_generator.pth').cuda()
load_path='X:\\dlas\\experiments\\train_diffusion_tts_mel_flat\\models\\19500_generator_ema.pth').cuda()
opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 100,
'conditioning_free': False, 'conditioning_free_k': 1,
'diffusion_schedule': 'linear', 'diffusion_type': 'tts9_mel'}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 557, 'device': 'cuda', 'opt': {}}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 558, 'device': 'cuda', 'opt': {}}
eval = AudioDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval())