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tortoise / tortoise /models /diffusion_decoder.py

jbetker

Support totally random voices (and make fixes to previous changes)

39ec1b0 about 2 years ago

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	import math
	import random
	from abc import abstractmethod

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torch import autocast

	from tortoise.models.arch_util import normalization, AttentionBlock


	def is_latent(t):
	return t.dtype == torch.float


	def is_sequence(t):
	return t.dtype == torch.long


	def timestep_embedding(timesteps, dim, max_period=10000):
	"""
	Create sinusoidal timestep embeddings.

	:param timesteps: a 1-D Tensor of N indices, one per batch element.
	These may be fractional.
	:param dim: the dimension of the output.
	:param max_period: controls the minimum frequency of the embeddings.
	:return: an [N x dim] Tensor of positional embeddings.
	"""
	half = dim // 2
	freqs = torch.exp(
	-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
	).to(device=timesteps.device)
	args = timesteps[:, None].float() * freqs[None]
	embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
	if dim % 2:
	embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
	return embedding


	class TimestepBlock(nn.Module):
	@abstractmethod
	def forward(self, x, emb):
	"""
	Apply the module to `x` given `emb` timestep embeddings.
	"""


	class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
	def forward(self, x, emb):
	for layer in self:
	if isinstance(layer, TimestepBlock):
	x = layer(x, emb)
	else:
	x = layer(x)
	return x


	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(),
	nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding),
	)

	self.emb_layers = nn.Sequential(
	nn.SiLU(),
	nn.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),
	nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding),
	)

	if self.out_channels == channels:
	self.skip_connection = nn.Identity()
	else:
	self.skip_connection = nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding)

	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 DiffusionTts(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 = nn.Conv1d(in_channels, model_channels, 3, 1, 1)
	self.time_embed = nn.Sequential(
	nn.Linear(model_channels, model_channels),
	nn.SiLU(),
	nn.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.code_norm = normalization(model_channels)
	self.latent_conditioner = nn.Sequential(
	nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
	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.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
	nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2),
	AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
	AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
	AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
	AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
	AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False))
	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.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=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(),
	nn.Conv1d(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.parameters()),
	'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()) + list(self.latent_conditioner.parameters()),
	'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
	'time_embed': list(self.time_embed.parameters()),
	}
	return groups

	def get_conditioning(self, conditioning_input):
	speech_conditioning_input = conditioning_input.unsqueeze(1) if len(
	conditioning_input.shape) == 3 else conditioning_input
	conds = []
	for j in range(speech_conditioning_input.shape[1]):
	conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
	conds = torch.cat(conds, dim=-1)
	conds = conds.mean(dim=-1)
	return conds

	def timestep_independent(self, aligned_conditioning, conditioning_latent, expected_seq_len, return_code_pred):
	# Shuffle aligned_latent to BxCxS format
	if is_latent(aligned_conditioning):
	aligned_conditioning = aligned_conditioning.permute(0, 2, 1)

	cond_scale, cond_shift = torch.chunk(conditioning_latent, 2, dim=1)
	if is_latent(aligned_conditioning):
	code_emb = self.latent_conditioner(aligned_conditioning)
	else:
	code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1)
	code_emb = self.code_converter(code_emb)
	code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1)

	unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
	# 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(aligned_conditioning.shape[0], 1, 1),
	code_emb)
	expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest')

	if not return_code_pred:
	return expanded_code_emb
	else:
	mel_pred = self.mel_head(expanded_code_emb)
	# Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches. This is because we don't want that gradient being used to train parameters through the codes_embedder as it unbalances contributions to that network from the MSE loss.
	mel_pred = mel_pred * unconditioned_batches.logical_not()
	return expanded_code_emb, mel_pred

	def forward(self, x, timesteps, aligned_conditioning=None, conditioning_latent=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=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_latent: a pre-computed conditioning latent; see get_conditioning().
	:param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
	:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
	:return: an [N x C x ...] Tensor of outputs.
	"""
	assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_latent is not None)
	assert not (return_code_pred and precomputed_aligned_embeddings is not None) # These two are mutually exclusive.

	unused_params = []
	if conditioning_free:
	code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
	unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
	unused_params.extend(list(self.latent_conditioner.parameters()))
	else:
	if precomputed_aligned_embeddings is not None:
	code_emb = precomputed_aligned_embeddings
	else:
	code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_latent, x.shape[-1], True)
	if is_latent(aligned_conditioning):
	unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
	else:
	unused_params.extend(list(self.latent_conditioner.parameters()))

	unused_params.append(self.unconditioned_embedding)

	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, code_emb], 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

	if return_code_pred:
	return out, mel_pred
	return out


	if __name__ == '__main__':
	clip = torch.randn(2, 100, 400)
	aligned_latent = torch.randn(2,388,512)
	aligned_sequence = torch.randint(0,8192,(2,100))
	cond = torch.randn(2, 100, 400)
	ts = torch.LongTensor([600, 600])
	model = DiffusionTts(512, layer_drop=.3, unconditioned_percentage=.5)
	# Test with latent aligned conditioning
	#o = model(clip, ts, aligned_latent, cond)
	# Test with sequence aligned conditioning
	o = model(clip, ts, aligned_sequence, cond)