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# Copyright (c) 2024 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
import torch.nn as nn
import math
from einops import rearrange
from models.tts.maskgct.llama_nar import DiffLlama
def top_k(logits, thres=0.9):
k = math.ceil((1 - thres) * logits.shape[-1])
val, ind = logits.topk(k, dim=-1)
probs = torch.full_like(logits, float("-inf"))
probs.scatter_(2, ind, val)
return probs
def log(t, eps=1e-10):
return torch.log(t + eps)
def gumbel_noise(t):
noise = torch.zeros_like(t).uniform_(0, 1)
return -log(-log(noise))
def gumbel_sample(t, temperature=1.0, dim=-1):
return ((t / max(temperature, 1e-10)) + gumbel_noise(t)).argmax(dim=dim)
def top_k(logits, thres=0.9):
k = math.ceil((1 - thres) * logits.shape[-1])
val, ind = logits.topk(k, dim=-1)
probs = torch.full_like(logits, float("-inf"))
probs.scatter_(2, ind, val)
return probs
def log(t, eps=1e-10):
return torch.log(t + eps)
def gumbel_noise(t):
noise = torch.zeros_like(t).uniform_(0, 1)
return -log(-log(noise))
def gumbel_sample(t, temperature=1.0, dim=-1):
return ((t / max(temperature, 1e-10)) + gumbel_noise(t)).argmax(dim=dim)
class MaskGCT_S2A(nn.Module):
def __init__(
self,
num_quantizer=12,
hidden_size=1024,
num_layers=16,
num_heads=16,
codebook_size=1024,
cfg_scale=0.15,
mask_layer_schedule="linear",
cond_codebook_size=1024,
cond_dim=1024,
predict_layer_1=True,
cfg=None,
):
super().__init__()
num_quantizer = (
cfg.num_quantizer
if cfg is not None and hasattr(cfg, "num_quantizer")
else num_quantizer
)
hidden_size = (
cfg.hidden_size
if cfg is not None and hasattr(cfg, "hidden_size")
else hidden_size
)
num_layers = (
cfg.num_layers
if cfg is not None and hasattr(cfg, "num_layers")
else num_layers
)
num_heads = (
cfg.num_heads
if cfg is not None and hasattr(cfg, "num_heads")
else num_heads
)
codebook_size = (
cfg.codebook_size
if cfg is not None and hasattr(cfg, "codebook_size")
else codebook_size
)
cfg_scale = (
cfg.cfg_scale
if cfg is not None and hasattr(cfg, "cfg_scale")
else cfg_scale
)
mask_layer_schedule = (
cfg.mask_layer_schedule
if cfg is not None and hasattr(cfg, "mask_layer_schedule")
else mask_layer_schedule
)
cond_codebook_size = (
cfg.cond_codebook_size
if cfg is not None and hasattr(cfg, "cond_codebook_size")
else cond_codebook_size
)
cond_dim = (
cfg.cond_dim if cfg is not None and hasattr(cfg, "cond_dim") else cond_dim
)
predict_layer_1 = (
cfg.predict_layer_1
if cfg is not None and hasattr(cfg, "predict_layer_1")
else predict_layer_1
)
self.num_quantizer = num_quantizer
self.hidden_size = hidden_size
self.codebook_size = codebook_size
self.num_layers = num_layers
self.num_heads = num_heads
self.cfg_scale = cfg_scale
self.mask_layer_schedule = mask_layer_schedule
self.cond_codebook_size = cond_codebook_size
self.cond_dim = cond_dim
self.predict_layer_1 = predict_layer_1
self.layer_emb = nn.Embedding(self.num_quantizer, self.hidden_size)
self.mask_emb = nn.Embedding(1, self.hidden_size)
self.token_emb = torch.nn.ModuleList(
[
nn.Embedding(self.codebook_size, self.hidden_size)
for _ in range(self.num_quantizer)
]
)
self.to_logits = torch.nn.ModuleList(
[
nn.Linear(self.hidden_size, self.codebook_size)
for _ in range(self.num_quantizer)
]
)
self.cond_emb = nn.Embedding(cond_codebook_size, self.hidden_size)
self.reset_parameters()
self.diff_estimator = DiffLlama(
hidden_size=hidden_size,
num_heads=self.num_heads,
num_layers=num_layers,
)
def mask_prob(self, t):
return torch.sin(t * np.pi / 2).to(t.device)
def mask_layer(self, t):
# print(self.predict_layer_1)
if self.mask_layer_schedule == "uniform":
if self.predict_layer_1:
mask_layer = torch.randint(0, self.num_quantizer, (1,)).to(t.device)
else:
mask_layer = torch.randint(1, self.num_quantizer, (1,)).to(t.device)
elif self.mask_layer_schedule == "cosine":
if self.predict_layer_1:
weights = torch.tensor(
[
np.cos(i / self.num_quantizer * np.pi / 2)
for i in range(self.num_quantizer)
]
)
else:
weights = torch.tensor(
[0]
+ [
np.cos((i - 1) / self.num_quantizer * np.pi / 2)
for i in range(1, self.num_quantizer)
]
)
mask_layer = torch.multinomial(weights, 1).to(t.device)
elif self.mask_layer_schedule == "linear":
if self.predict_layer_1:
weights = torch.tensor(
[self.num_quantizer - i for i in range(self.num_quantizer)]
)
else:
weights = torch.tensor(
[0]
+ [
self.num_quantizer - (i - 1)
for i in range(1, self.num_quantizer)
]
)
weights = weights / weights.sum()
mask_layer = torch.multinomial(weights, 1).to(t.device)
# print(mask_layer)
new_t = t
return mask_layer, new_t
def forward_diffusion(self, x0, t):
# x0: (B, T, num_quantizer)
mask_layer, new_t = self.mask_layer(t) # (1,)
mask_prob = self.mask_prob(new_t) # (B,)
mask_token = self.mask_emb(torch.zeros_like(mask_layer)) # (1, hidden_size)
xt = torch.zeros(x0.shape[0], x0.shape[1], self.hidden_size).to(x0.device)
cfg_scale = self.cfg_scale
# get prompt len
if torch.rand(1) > cfg_scale:
prompt_len = torch.randint(
min(x0.shape[1] // 4, 5), x0.shape[1] // 2, (x0.shape[0],)
).to(
x0.device
) # (B,)
else:
prompt_len = torch.zeros(x0.shape[0]).to(x0) # (B,)
# get is prompt
is_prompt = torch.zeros_like(x0[:, :, 0]) # (B, T)
col_indices = (
torch.arange(is_prompt.shape[1])
.repeat(is_prompt.shape[0], 1)
.to(prompt_len)
) # (B, T)
is_prompt[col_indices < prompt_len.unsqueeze(1)] = 1 # (B, T) 1 if prompt
for idx, token_emb_idx in enumerate(self.token_emb):
if idx < mask_layer:
xt = xt + token_emb_idx(x0[:, :, idx]) # (B, T, hidden_size)
elif idx == mask_layer:
mask = torch.bernoulli(
torch.ones_like(x0[:, :, idx]) * mask_prob[..., None]
) # mask if 1, not mask if 0
# prompt part don't need to be masked
mask[is_prompt.bool()] = 0
# Ensure at least one token is masked
mask_num = mask[:,].sum(dim=1, keepdim=False)
all_zero_mask = (mask_num == 0).bool()
row_indices_to_modify = torch.nonzero(all_zero_mask)
# mask the first token if all tokens are not masked (may mask pad if random indices)
mask[row_indices_to_modify, prompt_len[row_indices_to_modify]] = 1
mask = mask[..., None] # (B, T, 1)
xt = (
xt
+ mask * mask_token[:, None, :]
+ (1 - mask) * token_emb_idx(x0[:, :, idx])
) # (B, T, hidden_size)
else:
# prompt part don't need to be masked
xt = (
xt
+ token_emb_idx(x0[:, :, idx]) * is_prompt[..., None]
+ mask_token * (1 - is_prompt[..., None])
)
return xt, new_t, mask_layer, mask, prompt_len, mask_prob
def loss_t(self, x0, x_mask, t, cond=None):
xt, new_t, mask_layer, mask, prompt_len, mask_prob = self.forward_diffusion(
x0, t
)
# xt: (B, T, hidden_size)
# new_t: (B,)
# mask_layer: (1,)
# mask: (B, T, 1) mask if 1, not mask if 0
# prompt_len: (B,)
# mask_prob: (B,)
mask_layer_cond = self.layer_emb(mask_layer).unsqueeze(1) # (1, 1, hidden_size)
cond = cond + mask_layer_cond # (B, T, hidden_size)
embeds = self.diff_estimator(xt, new_t, cond, x_mask) # (B, T, hidden_size)
logits = self.to_logits[mask_layer.item()](embeds) # (B, T, codebook_size)
# final mask used for loss calculation
final_mask = mask * x_mask[..., None] # (B, T, 1)
return logits, mask_layer, final_mask, x0, prompt_len, mask_prob
def compute_loss(self, x0, x_mask, cond=None):
# x0: (B, T, num_quantizer)
# x_mask: (B, T) mask is 0 for padding
t = torch.rand(x0.shape[0], device=x0.device, requires_grad=False)
t = torch.clamp(t, 1e-5, 1.0)
return self.loss_t(x0, x_mask, t, cond)
def reset_parameters(self):
def _reset_parameters(m):
if isinstance(m, nn.MultiheadAttention):
if m._qkv_same_embed_dim:
nn.init.normal_(m.in_proj_weight, std=0.02)
else:
nn.init.normal_(m.q_proj_weight, std=0.02)
nn.init.normal_(m.k_proj_weight, std=0.02)
nn.init.normal_(m.v_proj_weight, std=0.02)
if m.in_proj_bias is not None:
nn.init.constant_(m.in_proj_bias, 0.0)
nn.init.constant_(m.out_proj.bias, 0.0)
if m.bias_k is not None:
nn.init.xavier_normal_(m.bias_k)
if m.bias_v is not None:
nn.init.xavier_normal_(m.bias_v)
elif (
isinstance(m, nn.Conv1d)
or isinstance(m, nn.ConvTranspose1d)
or isinstance(m, nn.Conv2d)
or isinstance(m, nn.ConvTranspose2d)
):
m.weight.data.normal_(0.0, 0.02)
elif isinstance(m, nn.Linear):
m.weight.data.normal_(mean=0.0, std=0.02)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Embedding):
m.weight.data.normal_(mean=0.0, std=0.02)
if m.padding_idx is not None:
m.weight.data[m.padding_idx].zero_()
self.apply(_reset_parameters)
@torch.no_grad()
def reverse_diffusion(
self,
cond,
prompt,
x_mask=None,
prompt_mask=None,
temp=1.5,
filter_thres=0.98,
max_layer=None,
gt_code=None,
n_timesteps=[10, 4, 4, 4, 4, 4, 4, 4],
cfg=1.0,
rescale_cfg=1.0,
):
assert (
len(n_timesteps) == self.num_quantizer
) # each layer has a number of steps
prompt_code = prompt # (B, prompt_len, num_quantizer)
prompt_len = prompt_code.shape[1]
target_len = cond.shape[1] - prompt_len
if x_mask == None:
x_mask = torch.ones(cond.shape[0], target_len).to(cond.device) # (B, T)
if prompt_mask == None:
prompt_mask = torch.ones(cond.shape[0], prompt_len).to(
cond.device
) # (B, prompt_len)
cum = torch.zeros(x_mask.shape[0], x_mask.shape[1], self.hidden_size).to(
x_mask.device
) # (B, T, hidden_size)
bsz, seq_len, _ = cum.shape
choice_temp = 1.0
start_temp = temp # temperature for sampling
start_choice_temp = choice_temp # temperature for choicing mask tokens
if max_layer is None:
max_layer = self.num_quantizer
xt = torch.LongTensor(bsz, seq_len, max_layer).to(x_mask.device)
if gt_code is not None:
gt_layer = gt_code.shape[-1]
xt[:, :, :gt_layer] = gt_code
for i in range(gt_layer):
cum += self.token_emb[i](xt[:, :, i])
else:
gt_layer = 0
for mask_layer in range(gt_layer, max_layer):
steps = n_timesteps[mask_layer]
to_logits = self.to_logits[mask_layer]
token_emb = self.token_emb[mask_layer]
mask_layer = torch.tensor(mask_layer).to(x_mask.device).long().unsqueeze(0)
mask_layer_cond = self.layer_emb(mask_layer).unsqueeze(
1
) # (1,) -> (1, 1, hidden_size)
temp_cond = cond + mask_layer_cond # (B, T, hidden_size)
mask_token = self.mask_emb(torch.zeros_like(mask_layer)) # (1, hidden_size)
mask = torch.full((bsz, seq_len, 1), True).to(x_mask.device) # (B, T, 1)
seq = torch.full((bsz, seq_len), 0).to(x_mask.device)
h = 1.0 / steps
# prompt_code: (B, prompt_len, num_quantizer)
cur_prompt = 0
for idx, emb in enumerate(self.token_emb):
cur_prompt = cur_prompt + emb(
prompt_code[:, :, idx]
) # (B, prompt_len, hidden_size)
t_list = [1.0 - i * h for i in range(steps)]
t_list.append(0.0)
for i in range(steps):
t = t_list[i] * torch.ones(bsz).to(x_mask.device)
token = token_emb(seq) # (B, T, hidden_size)
cur = cum + mask * mask_token[:, None, :] + (~mask) * token
cur = cur + mask_token[:, None, :] * (max_layer - 1 - mask_layer)
xt_input = torch.cat([cur_prompt, cur], dim=1) # (B, T, hidden_size)
xt_mask = torch.cat(
[prompt_mask, x_mask], dim=1
) # (B, T), mask is 0 for padding
embeds = self.diff_estimator(xt_input, t, temp_cond, xt_mask)
embeds = embeds[:, prompt_len:, :]
# cfg
if cfg > 0:
mask_embeds = self.diff_estimator(
cur, t, temp_cond[:, prompt_len:, :], x_mask
)
pos_emb_std = embeds.std() # std(g_cond)
embeds = embeds + cfg * (embeds - mask_embeds) # g_cfg
rescale_embeds = embeds * pos_emb_std / embeds.std() # g_final
embeds = rescale_cfg * rescale_embeds + (1 - rescale_cfg) * embeds
logits = to_logits(embeds) # (B, T, codebook_size)
annealing_scale = t_list[i]
choice_temp = start_choice_temp * annealing_scale
temp = start_temp * annealing_scale
logits = top_k(logits, filter_thres)
if i == steps - 1:
# greedy
if steps == 1:
temp = 0.2
sampled_ids = gumbel_sample(logits, temperature=max(temp, 1e-3))
else:
sampled_ids = logits.argmax(dim=-1)
else:
# sampling
sampled_ids = gumbel_sample(logits, temperature=max(temp, 1e-3))
seq = torch.where(mask.squeeze(-1), sampled_ids, seq)
scores = logits.softmax(dim=-1)
scores = scores.gather(2, rearrange(sampled_ids, "b n -> b n 1"))
scores = rearrange(scores, "b n 1 -> b n")
scores = choice_temp * gumbel_noise(scores) + scores
scores = 1 - scores
next_t = t_list[i + 1] * torch.ones(bsz).to(x_mask.device)
next_mask_num = (self.mask_prob(next_t) * seq_len).long()[0].item()
if next_mask_num == 0:
break
scores = scores.masked_fill(
~mask.squeeze(-1), -torch.finfo(scores.dtype).max
)
mask_indices = scores.topk(next_mask_num, dim=-1).indices
mask = torch.zeros_like(scores, dtype=torch.bool).scatter(
1, mask_indices, True
)
seq = seq.masked_fill(mask, 0)
mask = mask.unsqueeze(-1)
cum = cum + token_emb(seq)
xt[..., mask_layer.squeeze(0).item()] = seq
return xt
def forward(self, x0, x_mask, cond_code=None):
# x0: (B, T, num_quantizer)
# x_mask: (B, T) mask is 0 for padding
# cond_code: semantic token (B, T)
cond = self.cond_emb(cond_code)
logits, mask_layer, final_mask, x0, prompt_len, mask_prob = self.compute_loss(
x0,
x_mask,
cond,
)
return logits, mask_layer, final_mask, x0, prompt_len, mask_prob
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