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tts9 initial commit

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This commit is contained in:
James Betker 2022-03-08 15:50:45 -07:00
parent 38fd9fc985
commit 94222b0216
1 changed files with 84 additions and 148 deletions
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codes/models/gpt_voice/unet_diffusion_tts9.py
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@ -6,7 +6,6 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
from x_transformers.x_transformers import AbsolutePositionalEmbedding, AttentionLayers, CrossAttender
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
@ -14,63 +13,7 @@ from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequent
from models.gpt_voice.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, **xtransformer_kwargs):
super().__init__()
self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
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):
x = x.permute(0,2,1)
h = self.transformer(x, **kwargs)
return h.permute(0,2,1)
from utils.util import checkpoint, opt_get
class ResBlock(TimestepBlock):
@ -172,7 +115,7 @@ class DiffusionTts(nn.Module):
def __init__(
self,
model_channels,
model_channels=1024,
in_channels=1,
in_latent_channels=1024,
out_channels=2, # mean and variance
@ -193,8 +136,6 @@ class DiffusionTts(nn.Module):
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4,
cond_transformer_depth=8,
mid_transformer_depth=8,
# Parameters for regularization.
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
# Parameters for super-sampling.
@ -234,26 +175,16 @@ class DiffusionTts(nn.Module):
conditioning_dim = model_channels * 8
self.latent_converter = nn.Conv1d(in_latent_channels, conditioning_dim, 1)
self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,in_latent_channels,1))
self.contextual_embedder = AudioMiniEncoder(1, conditioning_dim, base_channels=32, depth=6, resnet_blocks=1,
attn_blocks=4, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
self.conditioning_conv = nn.Conv1d(conditioning_dim*2, conditioning_dim, 1)
self.conditioning_encoder = CheckpointedXTransformerEncoder(
max_seq_len=-1, # Should be unused
use_pos_emb=False,
attn_layers=Encoder(
dim=conditioning_dim,
depth=cond_transformer_depth,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
ff_glu=True,
rotary_pos_emb=True
)
)
self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
self.conditioning_timestep_integrator = TimestepEmbedSequential(
ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1),
AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1),
AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1),
)
@ -314,20 +245,6 @@ class DiffusionTts(nn.Module):
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,
@ -336,7 +253,11 @@ class DiffusionTts(nn.Module):
dims=dims,
kernel_size=kernel_size,
),
mid_transformer,
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
),
ResBlock(
ch,
time_embed_dim,
@ -391,10 +312,60 @@ class DiffusionTts(nn.Module):
'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())
}
return groups
def forward(self, x, timesteps, aligned_latent, conditioning_input, conditioning_free):
hs = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
# Note: this block does not need to repeated on inference, since it is not timestep-dependent.
if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
else:
cond_emb = self.contextual_embedder(conditioning_input)
code_emb = self.latent_converter(aligned_latent)
cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
code_emb = self.conditioning_conv(torch.cat([cond_emb, 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.
code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
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:
h = module(h, time_emb)
hs.append(h)
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
class DiffusionTtsWrapper(nn.Module):
"""
Wraps the above module with some set-up logic such that the above module can be traced by the PyTorch JIT.
"""
def __init__(self, jit_enabled=False, **kwargs):
super().__init__()
self.jit_enabled = jit_enabled
self.jit_forward = None
self.underlying = DiffusionTts(**kwargs)
def forward(self, x, timesteps, aligned_latent, conditioning_input, lr_input=None, conditioning_free=False):
"""
Apply the model to an input batch.
@ -408,75 +379,40 @@ class DiffusionTts(nn.Module):
:return: an [N x C x ...] Tensor of outputs.
"""
assert conditioning_input is not None
if self.super_sampling_enabled:
if self.underlying.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)
noising_factor = random.uniform(0,self.underlying.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):
# Shuffle aligned_latent to BxCxS format
aligned_latent = aligned_latent.permute(0,2,1)
# Shuffle aligned_latent to BxCxS format
aligned_latent = aligned_latent.permute(0,2,1)
# Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net.
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]))
# Also fix aligned_latent, which is aligned to x.
aligned_latent = torch.cat([aligned_latent,
self.aligned_latent_padding_embedding.repeat(x.shape[0],1,int(pc*aligned_latent.shape[-1]))], dim=-1)
# Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net.
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]))
# Also fix aligned_latent, which is aligned to x.
aligned_latent = torch.cat([aligned_latent,
self.underlying.aligned_latent_padding_embedding.repeat(x.shape[0],1,int(pc*aligned_latent.shape[-1]))], dim=-1)
hs = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
# Note: this block does not need to repeated on inference, since it is not timestep-dependent.
if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
with autocast(x.device.type, enabled=self.underlying.enable_fp16):
if self.jit_enabled:
if self.jit_forward is None:
self.jit_forward = torch.jit.script(self.underlying, (x, timesteps, aligned_latent, conditioning_input, conditioning_free))
out = self.jit_forward(x, timesteps, aligned_latent, conditioning_input, conditioning_free)
else:
cond_emb = self.contextual_embedder(conditioning_input)
code_emb = self.latent_converter(aligned_latent)
cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb], dim=1))
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)
# Everything after this comment is timestep dependent.
code_emb = self.conditioning_timestep_integrator(code_emb, time_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)
out = self.underlying(x, timesteps, aligned_latent, conditioning_input, conditioning_free)
return out[:, :, :orig_x_shape]
@register_model
def register_diffusion_tts9(opt_net, opt):
return DiffusionTts(**opt_net['kwargs'])
return DiffusionTtsWrapper(**opt_net['kwargs'])
if __name__ == '__main__':
@ -484,7 +420,7 @@ if __name__ == '__main__':
aligned_latent = torch.randn(2,388,1024)
cond = torch.randn(2, 1, 44000)
ts = torch.LongTensor([600, 600])
model = DiffusionTts(128,
model = DiffusionTtsWrapper(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],