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James Betker 2022-02-04 11:00:42 -07:00
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87
codes/models/gpt_voice/ctc_code_generator.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from x_transformers import Encoder, XTransformer
from models.gpt_voice.unet_diffusion_tts6 import CheckpointedLayer
from trainer.networks import register_model
from utils.util import opt_get
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 = XTransformer(**xtransformer_kwargs)
for xform in [self.transformer.encoder, self.transformer.decoder.net]:
for i in range(len(xform.attn_layers.layers)):
n, b, r = xform.attn_layers.layers[i]
xform.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
def forward(self, *args, **kwargs):
return self.transformer(*args, **kwargs)
class CtcCodeGenerator(nn.Module):
def __init__(self, model_dim=512, layers=10, num_heads=8, dropout=.1, ctc_codes=36, max_pad=120, max_repeat=30):
super().__init__()
self.max_pad = max_pad
self.max_repeat = max_repeat
self.transformer = XTransformer(
dim=model_dim,
enc_depth=layers,
dec_depth=layers,
enc_heads=num_heads,
dec_heads=num_heads,
enc_num_tokens=ctc_codes,
dec_num_tokens=(max_pad+1)*(max_repeat+1),
enc_max_seq_len=-1,
dec_max_seq_len=-1,
enc_ff_dropout=dropout,
enc_attn_dropout=dropout,
enc_use_rmsnorm=True,
enc_ff_glu=True,
enc_rotary_pos_emb=True,
dec_ff_dropout=dropout,
dec_attn_dropout=dropout,
dec_use_rmsnorm=True,
dec_ff_glu=True,
dec_rotary_pos_emb=True)
def forward(self, codes, pads, repeats, unpadded_lengths=None):
if unpadded_lengths is not None:
max_len = unpadded_lengths.max()
codes = codes[:, :max_len]
pads = pads[:, :max_len]
repeats = repeats[:, :max_len]
if pads.max() > self.max_pad:
print(f"Got unexpectedly long pads. Max: {pads.max()}, {pads}")
pads = torch.clip(pads, 0, self.max_pad)
if repeats.max() > self.max_repeat:
print(f"Got unexpectedly long repeats. Max: {repeats.max()}, {repeats}")
repeats = torch.clip(repeats, 0, self.max_repeat)
assert codes.max() < 36, codes.max()
labels = pads + repeats * self.max_pad
loss = self.transformer(codes, labels)
return loss
@register_model
def register_ctc_code_generator(opt_net, opt):
return CtcCodeGenerator(**opt_get(opt_net, ['kwargs'], {}))
if __name__ == '__main__':
model = CtcCodeGenerator()
inps = torch.randint(0,36, (4, 300))
pads = torch.randint(0,100, (4,300))
repeats = torch.randint(0,20, (4,300))
loss = model(inps, pads, repeats)
print(loss.shape)

347
codes/models/gpt_voice/unet_diffusion_tts.py
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@ -1,347 +0,0 @@
import operator
from collections import OrderedDict
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \
Downsample, Upsample
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
from trainer.networks import register_model
from utils.util import get_mask_from_lengths
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=30,
out_channels=2, # mean and variancexs
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,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
kernel_size=3,
scale_factor=2,
conditioning_inputs_provided=True,
time_embed_dim_multiplier=4,
only_train_dvae_connection_layers=False,
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
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.dtype = torch.float16 if use_fp16 else torch.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.dims = dims
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),
)
self.conditioning_enabled = conditioning_inputs_provided
if conditioning_inputs_provided:
self.contextual_embedder = AudioMiniEncoder(in_channels, time_embed_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.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.Embedding(num_tokens, ch)
token_conditioning_block.weight.data.normal_(mean=0.0, std=.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,
use_scale_shift_norm=use_scale_shift_norm,
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,
use_new_attention_order=use_new_attention_order,
)
)
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(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
kernel_size=kernel_size,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
),
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
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,
use_scale_shift_norm=use_scale_shift_norm,
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,
use_new_attention_order=use_new_attention_order,
)
)
if level and i == num_blocks:
out_ch = ch
layers.append(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
kernel_size=kernel_size,
)
if resblock_updown
else 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)),
)
if only_train_dvae_connection_layers:
for p in self.parameters():
p.DO_NOT_TRAIN = True
p.requires_grad = False
for sb in token_conditioning_blocks:
for p in sb.parameters():
del p.DO_NOT_TRAIN
p.requires_grad = True
def forward(self, x, timesteps, tokens, conditioning_input=None):
"""
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.
:return: an [N x C x ...] Tensor of outputs.
"""
assert x.shape[-1] % 4096 == 0 # This model operates at base//4096 at it's bottom levels, thus this requirement.
if self.conditioning_enabled:
assert conditioning_input is not None
hs = []
emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
if self.conditioning_enabled:
emb2 = self.contextual_embedder(conditioning_input)
emb = emb1 + emb2
else:
emb = emb1
h = x.type(self.dtype)
for k, module in enumerate(self.input_blocks):
if isinstance(module, nn.Embedding):
h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
h = h + h_tok
else:
h = module(h, emb)
hs.append(h)
h = self.middle_block(h, emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, emb)
h = h.type(x.dtype)
return self.out(h)
def benchmark(self, x, timesteps, tokens, conditioning_input):
profile = OrderedDict()
hs = []
emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
from torchprofile import profile_macs
profile['contextual_embedder'] = profile_macs(self.contextual_embedder, args=(conditioning_input,))
emb2 = self.contextual_embedder(conditioning_input)
emb = emb1 + emb2
h = x.type(self.dtype)
for k, module in enumerate(self.input_blocks):
if isinstance(module, nn.Embedding):
h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
h = h + h_tok
else:
profile[f'in_{k}'] = profile_macs(module, args=(h,emb))
h = module(h, emb)
hs.append(h)
profile['middle'] = profile_macs(self.middle_block, args=(h,emb))
h = self.middle_block(h, emb)
for k, module in enumerate(self.output_blocks):
h = torch.cat([h, hs.pop()], dim=1)
profile[f'out_{k}'] = profile_macs(module, args=(h,emb))
h = module(h, emb)
h = h.type(x.dtype)
profile['out'] = profile_macs(self.out, args=(h,))
return profile
@register_model
def register_diffusion_tts(opt_net, opt):
return DiffusionTts(**opt_net['kwargs'])
# Test for ~4 second audio clip at 22050Hz
if __name__ == '__main__':
clip = torch.randn(2, 1, 86016)
tok = torch.randint(0,30, (2,388))
cond = torch.randn(2, 1, 44000)
ts = torch.LongTensor([555, 556])
model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8 ], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 2, 1, 1 ],
token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
p = model.benchmark(clip, ts, tok, cond)
p = {k: v / 1000000000 for k, v in p.items()}
p = sorted(p.items(), key=operator.itemgetter(1))
print(p)
print(sum([j[1] for j in p]))

395
codes/models/gpt_voice/unet_diffusion_tts2.py
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@ -1,395 +0,0 @@
import operator
from collections import OrderedDict
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, TimestepEmbedSequential, \
Downsample, Upsample, TimestepBlock
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
from trainer.networks import register_model
from utils.util import checkpoint
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,
conditioning_inputs_provided=True,
time_embed_dim_multiplier=4,
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
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.dtype = torch.float16 if use_fp16 else torch.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.dims = dims
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),
)
self.conditioning_enabled = conditioning_inputs_provided
if conditioning_inputs_provided:
self.contextual_embedder = AudioMiniEncoder(in_channels, time_embed_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.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.Embedding(num_tokens, ch)
token_conditioning_block.weight.data.normal_(mean=0.0, std=.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
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
kernel_size=kernel_size,
),
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
),
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 forward(self, x, timesteps, tokens, conditioning_input=None):
"""
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.
:return: an [N x C x ...] Tensor of outputs.
"""
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]))
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
if self.conditioning_enabled:
assert conditioning_input is not None
hs = []
emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
if self.conditioning_enabled:
actual_cond = self.contextual_embedder(conditioning_input)
emb = emb1 + actual_cond
else:
emb = emb1
h = x.type(self.dtype)
for k, module in enumerate(self.input_blocks):
if isinstance(module, nn.Embedding):
h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
h = h + h_tok
else:
h = module(h, emb)
hs.append(h)
h = self.middle_block(h, emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, emb)
h = h.type(x.dtype)
out = self.out(h)
return out[:, :, :orig_x_shape]
def benchmark(self, x, timesteps, tokens, conditioning_input):
profile = OrderedDict()
params = OrderedDict()
hs = []
emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
from torchprofile import profile_macs
profile['contextual_embedder'] = profile_macs(self.contextual_embedder, args=(conditioning_input,))
params['contextual_embedder'] = sum(p.numel() for p in self.contextual_embedder.parameters())
emb2 = self.contextual_embedder(conditioning_input)
emb = emb1 + emb2
h = x.type(self.dtype)
for k, module in enumerate(self.input_blocks):
if isinstance(module, nn.Embedding):
h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
h = h + h_tok
else:
profile[f'in_{k}'] = profile_macs(module, args=(h,emb))
params[f'in_{k}'] = sum(p.numel() for p in module.parameters())
h = module(h, emb)
hs.append(h)
profile['middle'] = profile_macs(self.middle_block, args=(h,emb))
params['middle'] = sum(p.numel() for p in self.middle_block.parameters())
h = self.middle_block(h, emb)
for k, module in enumerate(self.output_blocks):
h = torch.cat([h, hs.pop()], dim=1)
profile[f'out_{k}'] = profile_macs(module, args=(h,emb))
params[f'out_{k}'] = sum(p.numel() for p in module.parameters())
h = module(h, emb)
h = h.type(x.dtype)
profile['out'] = profile_macs(self.out, args=(h,))
params['out'] = sum(p.numel() for p in self.out.parameters())
return profile, params
@register_model
def register_diffusion_tts2(opt_net, opt):
return DiffusionTts(**opt_net['kwargs'])
# Test for ~4 second audio clip at 22050Hz
if __name__ == '__main__':
clip = torch.randn(4, 1, 86016)
tok = torch.randint(0,30, (4,388))
cond = torch.randn(4, 1, 44000)
ts = torch.LongTensor([555, 556, 600, 600])
model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4],
token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
model(clip, ts, tok, cond)
p, r = model.benchmark(clip, ts, tok, cond)
p = {k: v / 1000000000 for k, v in p.items()}
p = sorted(p.items(), key=operator.itemgetter(1))
print("Computational complexity:")
print(p)
print(sum([j[1] for j in p]))
print()
print("Memory complexity:")
r = {k: v / 1000000 for k, v in r.items()}
r = sorted(r.items(), key=operator.itemgetter(1))
print(r)
print(sum([j[1] for j in r]))

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codes/models/gpt_voice/unet_diffusion_tts3.py
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@ -1,429 +0,0 @@
import functools
from collections import OrderedDict
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
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.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
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, **kwargs):
kw_requires_grad = {}
kw_no_grad = {}
for k, v in kwargs.items():
if v is not None and isinstance(v, torch.Tensor) and v.requires_grad:
kw_requires_grad[k] = v
else:
kw_no_grad[k] = v
partial = functools.partial(self.wrap, **kw_no_grad)
return torch.utils.checkpoint.checkpoint(partial, x, **kw_requires_grad)
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,
conditioning_inputs_provided=True,
time_embed_dim_multiplier=4,
nil_guidance_fwd_proportion=.3,
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
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.dtype = torch.float16 if use_fp16 else torch.float32
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
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 * 4
self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
self.conditioning_enabled = conditioning_inputs_provided
if conditioning_inputs_provided:
self.contextual_embedder = AudioMiniEncoder(in_channels, 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.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
class Permute(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x.permute(0,2,1)
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 = ContinuousTransformerWrapper(
max_seq_len=-1, # Should be unused
use_pos_emb=False,
attn_layers=Encoder(
dim=ch,
depth=8,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
)
)
for i in range(len(mid_transformer.attn_layers.layers)):
n, b, r = mid_transformer.attn_layers.layers[i]
mid_transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
kernel_size=kernel_size,
),
Permute(),
mid_transformer,
Permute(),
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 load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]',
strict: bool = True):
# Temporary hack to allow the addition of nil-guidance token embeddings to the existing guidance embeddings.
lsd = self.state_dict()
revised = 0
for i, blk in enumerate(self.input_blocks):
if isinstance(blk, nn.Embedding):
key = f'input_blocks.{i}.weight'
if state_dict[key].shape[0] != lsd[key].shape[0]:
t = torch.randn_like(lsd[key]) * .02
t[:state_dict[key].shape[0]] = state_dict[key]
state_dict[key] = t
revised += 1
print(f"Loaded experimental unet_diffusion_net with {revised} modifications.")
return super().load_state_dict(state_dict, strict)
def forward(self, x, timesteps, tokens, conditioning_input=None):
"""
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.
:return: an [N x C x ...] Tensor of outputs.
"""
with autocast(x.device.type):
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]))
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
if self.conditioning_enabled:
assert conditioning_input is not None
hs = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
# Mask out guidance tokens for un-guided diffusion.
if self.training and self.nil_guidance_fwd_proportion > 0:
token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion
tokens = torch.where(token_mask, self.mask_token_id, tokens)
code_emb = self.code_embedding(tokens).permute(0,2,1)
if self.conditioning_enabled:
cond_emb = self.contextual_embedder(conditioning_input)
code_emb = cond_emb.unsqueeze(-1) * code_emb
first = False # First block has autocast disabled.
time_emb = time_emb.float()
h = x
for k, module in enumerate(self.input_blocks):
with autocast(x.device.type, enabled=not first):
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)
first = True
with autocast(x.device.type):
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)
h = h.type(x.dtype)
h = h.float()
out = self.out(h) # Last block also has autocast disabled.
return out[:, :, :orig_x_shape]
@register_model
def register_diffusion_tts3(opt_net, opt):
return DiffusionTts(**opt_net['kwargs'])
# Test for ~4 second audio clip at 22050Hz
if __name__ == '__main__':
clip = torch.randn(4, 1, 86016)
tok = torch.randint(0,30, (4,388))
cond = torch.randn(4, 1, 44000)
ts = torch.LongTensor([555, 556, 600, 600])
model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4],
token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
model(clip, ts, tok, cond)

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codes/models/gpt_voice/unet_diffusion_tts4.py
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@ -1,406 +0,0 @@
import functools
from collections import OrderedDict
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
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.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
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, **kwargs):
kw_requires_grad = {}
kw_no_grad = {}
for k, v in kwargs.items():
if v is not None and isinstance(v, torch.Tensor) and v.requires_grad:
kw_requires_grad[k] = v
else:
kw_no_grad[k] = v
partial = functools.partial(self.wrap, **kw_no_grad)
return torch.utils.checkpoint.checkpoint(partial, x, **kw_requires_grad)
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,
conditioning_inputs_provided=True,
time_embed_dim_multiplier=4,
nil_guidance_fwd_proportion=.3,
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
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.dtype = torch.float16 if use_fp16 else torch.float32
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
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 * 4
self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
self.conditioning_enabled = conditioning_inputs_provided
if conditioning_inputs_provided:
self.contextual_embedder = AudioMiniEncoder(in_channels, 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.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
class Permute(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x.permute(0,2,1)
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
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
kernel_size=kernel_size,
),
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 load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]',
strict: bool = True):
# Temporary hack to allow the addition of nil-guidance token embeddings to the existing guidance embeddings.
lsd = self.state_dict()
revised = 0
for i, blk in enumerate(self.input_blocks):
if isinstance(blk, nn.Embedding):
key = f'input_blocks.{i}.weight'
if state_dict[key].shape[0] != lsd[key].shape[0]:
t = torch.randn_like(lsd[key]) * .02
t[:state_dict[key].shape[0]] = state_dict[key]
state_dict[key] = t
revised += 1
print(f"Loaded experimental unet_diffusion_net with {revised} modifications.")
return super().load_state_dict(state_dict, strict)
def forward(self, x, timesteps, tokens, conditioning_input=None):
"""
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.
:return: an [N x C x ...] Tensor of outputs.
"""
with autocast(x.device.type):
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]))
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
if self.conditioning_enabled:
assert conditioning_input is not None
hs = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
# Mask out guidance tokens for un-guided diffusion.
if self.training and self.nil_guidance_fwd_proportion > 0:
token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion
tokens = torch.where(token_mask, self.mask_token_id, tokens)
code_emb = self.code_embedding(tokens).permute(0,2,1)
if self.conditioning_enabled:
cond_emb = self.contextual_embedder(conditioning_input)
code_emb = cond_emb.unsqueeze(-1) * code_emb
first = False # First block has autocast disabled.
time_emb = time_emb.float()
h = x
for k, module in enumerate(self.input_blocks):
with autocast(x.device.type, enabled=not first):
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)
first = True
with autocast(x.device.type):
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)
h = h.type(x.dtype)
h = h.float()
out = self.out(h) # Last block also has autocast disabled.
return out[:, :, :orig_x_shape]
@register_model
def register_diffusion_tts4(opt_net, opt):
return DiffusionTts(**opt_net['kwargs'])
# Test for ~4 second audio clip at 22050Hz
if __name__ == '__main__':
clip = torch.randn(4, 1, 86016)
tok = torch.randint(0,30, (4,388))
cond = torch.randn(4, 1, 44000)
ts = torch.LongTensor([555, 556, 600, 600])
model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4],
token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
model(clip, ts, tok, cond)