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# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
# reference: https://github.com/lifeiteng/vall-e
import torch
from tqdm import tqdm
from AR.modules.embedding_onnx import SinePositionalEmbedding
from AR.modules.embedding_onnx import TokenEmbedding
from AR.modules.transformer_onnx import LayerNorm
from AR.modules.transformer_onnx import TransformerEncoder
from AR.modules.transformer_onnx import TransformerEncoderLayer
from torch import nn
from torch.nn import functional as F
from torchmetrics.classification import MulticlassAccuracy
default_config = {
"embedding_dim": 512,
"hidden_dim": 512,
"num_head": 8,
"num_layers": 12,
"num_codebook": 8,
"p_dropout": 0.0,
"vocab_size": 1024 + 1,
"phoneme_vocab_size": 512,
"EOS": 1024,
}
inf_tensor_value = torch.FloatTensor([-float("Inf")]).float()
def logits_to_probs(
logits,
previous_tokens = None,
temperature: float = 1.0,
top_k = None,
top_p = None,
repetition_penalty: float = 1.0,
):
previous_tokens = previous_tokens.squeeze()
if previous_tokens is not None and repetition_penalty != 1.0:
previous_tokens = previous_tokens.long()
score = torch.gather(logits, dim=0, index=previous_tokens)
score = torch.where(
score < 0, score * repetition_penalty, score / repetition_penalty
)
logits.scatter_(dim=0, index=previous_tokens, src=score)
if top_p is not None and top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cum_probs = torch.cumsum(
torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
)
sorted_indices_to_remove = cum_probs > top_p
sorted_indices_to_remove[0] = False # keep at least one option
indices_to_remove = sorted_indices_to_remove.scatter(
dim=0, index=sorted_indices, src=sorted_indices_to_remove
)
logits = logits.masked_fill(indices_to_remove, -float("Inf"))
logits = logits / max(temperature, 1e-5)
if top_k is not None:
v, _ = torch.topk(logits, top_k)
pivot = v.select(-1, -1).unsqueeze(-1)
logits = torch.where(logits < pivot, inf_tensor_value, logits)
probs = torch.nn.functional.softmax(logits, dim=-1)
return probs
def multinomial_sample_one_no_sync(
probs_sort
): # Does multinomial sampling without a cuda synchronization
q = torch.randn_like(probs_sort)
return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
def sample(
logits,
previous_tokens,
**sampling_kwargs,
):
probs = logits_to_probs(
logits=logits, previous_tokens=previous_tokens, **sampling_kwargs
)
idx_next = multinomial_sample_one_no_sync(probs)
return idx_next, probs
class OnnxEncoder(nn.Module):
def __init__(self, ar_text_embedding, bert_proj, ar_text_position):
super().__init__()
self.ar_text_embedding = ar_text_embedding
self.bert_proj = bert_proj
self.ar_text_position = ar_text_position
def forward(self, x, bert_feature):
x = self.ar_text_embedding(x)
x = x + self.bert_proj(bert_feature.transpose(1, 2))
return self.ar_text_position(x)
class T2SFirstStageDecoder(nn.Module):
def __init__(self, ar_audio_embedding, ar_audio_position, h, ar_predict_layer, loss_fct, ar_accuracy_metric,
top_k, early_stop_num, num_layers):
super().__init__()
self.ar_audio_embedding = ar_audio_embedding
self.ar_audio_position = ar_audio_position
self.h = h
self.ar_predict_layer = ar_predict_layer
self.loss_fct = loss_fct
self.ar_accuracy_metric = ar_accuracy_metric
self.top_k = top_k
self.early_stop_num = early_stop_num
self.num_layers = num_layers
def forward(self, x, prompt):
y = prompt
x_example = x[:,:,0] * 0.0
#N, 1, 512
cache = {
"all_stage": self.num_layers,
"k": None,
"v": None,
"y_emb": None,
"first_infer": 1,
"stage": 0,
}
y_emb = self.ar_audio_embedding(y)
cache["y_emb"] = y_emb
y_pos = self.ar_audio_position(y_emb)
xy_pos = torch.concat([x, y_pos], dim=1)
y_example = y_pos[:,:,0] * 0.0
x_attn_mask = torch.matmul(x_example.transpose(0, 1) , x_example).bool()
y_attn_mask = torch.ones_like(torch.matmul(y_example.transpose(0, 1), y_example), dtype=torch.int64)
y_attn_mask = torch.cumsum(y_attn_mask, dim=1) - torch.cumsum(
torch.ones_like(y_example.transpose(0, 1), dtype=torch.int64), dim=0
)
y_attn_mask = y_attn_mask > 0
x_y_pad = torch.matmul(x_example.transpose(0, 1), y_example).bool()
y_x_pad = torch.matmul(y_example.transpose(0, 1), x_example).bool()
x_attn_mask_pad = torch.cat([x_attn_mask, torch.ones_like(x_y_pad)], dim=1)
y_attn_mask = torch.cat([y_x_pad, y_attn_mask], dim=1)
xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)
cache["k"] = torch.matmul(x_attn_mask_pad[0].float().unsqueeze(-1), torch.zeros((1, 512)))\
.unsqueeze(1).repeat(self.num_layers, 1, 1, 1)
cache["v"] = torch.matmul(x_attn_mask_pad[0].float().unsqueeze(-1), torch.zeros((1, 512)))\
.unsqueeze(1).repeat(self.num_layers, 1, 1, 1)
xy_dec = self.h(xy_pos, mask=xy_attn_mask, cache=cache)
logits = self.ar_predict_layer(xy_dec[:, -1])
samples = sample(logits[0], y, top_k=self.top_k, top_p=1.0, repetition_penalty=1.35)[0].unsqueeze(0)
y = torch.concat([y, samples], dim=1)
return y, cache["k"], cache["v"], cache["y_emb"], x_example
class T2SStageDecoder(nn.Module):
def __init__(self, ar_audio_embedding, ar_audio_position, h, ar_predict_layer, loss_fct, ar_accuracy_metric,
top_k, early_stop_num, num_layers):
super().__init__()
self.ar_audio_embedding = ar_audio_embedding
self.ar_audio_position = ar_audio_position
self.h = h
self.ar_predict_layer = ar_predict_layer
self.loss_fct = loss_fct
self.ar_accuracy_metric = ar_accuracy_metric
self.top_k = top_k
self.early_stop_num = early_stop_num
self.num_layers = num_layers
def forward(self, y, k, v, y_emb, x_example):
cache = {
"all_stage": self.num_layers,
"k": torch.nn.functional.pad(k, (0, 0, 0, 0, 0, 1)),
"v": torch.nn.functional.pad(v, (0, 0, 0, 0, 0, 1)),
"y_emb": y_emb,
"first_infer": 0,
"stage": 0,
}
y_emb = torch.cat(
[cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1
)
cache["y_emb"] = y_emb
y_pos = self.ar_audio_position(y_emb)
xy_pos = y_pos[:, -1:]
y_example = y_pos[:,:,0] * 0.0
xy_attn_mask = torch.cat([x_example, y_example], dim=1)
xy_attn_mask = torch.zeros_like(xy_attn_mask, dtype=torch.bool)
xy_dec = self.h(xy_pos, mask=xy_attn_mask, cache=cache)
logits = self.ar_predict_layer(xy_dec[:, -1])
samples = sample(logits[0], y, top_k=self.top_k, top_p=1.0, repetition_penalty=1.35)[0].unsqueeze(0)
y = torch.concat([y, samples], dim=1)
return y, cache["k"], cache["v"], cache["y_emb"], logits, samples
class Text2SemanticDecoder(nn.Module):
def __init__(self, config, norm_first=False, top_k=3):
super(Text2SemanticDecoder, self).__init__()
self.model_dim = config["model"]["hidden_dim"]
self.embedding_dim = config["model"]["embedding_dim"]
self.num_head = config["model"]["head"]
self.num_layers = config["model"]["n_layer"]
self.norm_first = norm_first
self.vocab_size = config["model"]["vocab_size"]
self.phoneme_vocab_size = config["model"]["phoneme_vocab_size"]
self.p_dropout = float(config["model"]["dropout"])
self.EOS = config["model"]["EOS"]
self.norm_first = norm_first
assert self.EOS == self.vocab_size - 1
self.bert_proj = nn.Linear(1024, self.embedding_dim)
self.ar_text_embedding = TokenEmbedding(self.embedding_dim, self.phoneme_vocab_size, self.p_dropout)
self.ar_text_position = SinePositionalEmbedding(self.embedding_dim, dropout=0.1, scale=False, alpha=True)
self.ar_audio_embedding = TokenEmbedding(self.embedding_dim, self.vocab_size, self.p_dropout)
self.ar_audio_position = SinePositionalEmbedding(self.embedding_dim, dropout=0.1, scale=False, alpha=True)
self.h = TransformerEncoder(
TransformerEncoderLayer(
d_model=self.model_dim,
nhead=self.num_head,
dim_feedforward=self.model_dim * 4,
dropout=0.1,
batch_first=True,
norm_first=norm_first,
),
num_layers=self.num_layers,
norm=LayerNorm(self.model_dim) if norm_first else None,
)
self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False)
self.loss_fct = nn.CrossEntropyLoss(reduction="sum")
self.ar_accuracy_metric = MulticlassAccuracy(
self.vocab_size,
top_k=top_k,
average="micro",
multidim_average="global",
ignore_index=self.EOS,
)
self.top_k = torch.LongTensor([1])
self.early_stop_num = torch.LongTensor([-1])
def init_onnx(self):
self.onnx_encoder = OnnxEncoder(self.ar_text_embedding, self.bert_proj, self.ar_text_position)
self.first_stage_decoder = T2SFirstStageDecoder(self.ar_audio_embedding, self.ar_audio_position, self.h,
self.ar_predict_layer, self.loss_fct, self.ar_accuracy_metric, self.top_k, self.early_stop_num,
self.num_layers)
self.stage_decoder = T2SStageDecoder(self.ar_audio_embedding, self.ar_audio_position, self.h,
self.ar_predict_layer, self.loss_fct, self.ar_accuracy_metric, self.top_k, self.early_stop_num,
self.num_layers)
def forward(self, x, prompts, bert_feature):
early_stop_num = self.early_stop_num
prefix_len = prompts.shape[1]
x = self.onnx_encoder(x, bert_feature)
y, k, v, y_emb, stage, x_example = self.first_stage_decoder(x, prompts)
stop = False
for idx in range(1, 1500):
enco = self.stage_decoder(y, k, v, y_emb, stage, x_example)
y, k, v, y_emb, stage, logits, samples = enco
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
stop = True
if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
stop = True
if stop:
break
y[0, -1] = 0
return y, idx
def infer(self, x, prompts, bert_feature):
top_k = self.top_k
early_stop_num = self.early_stop_num
x = self.onnx_encoder(x, bert_feature)
y = prompts
prefix_len = y.shape[1]
x_len = x.shape[1]
x_example = x[:,:,0] * 0.0
x_attn_mask = torch.matmul(x_example.transpose(0, 1), x_example)
x_attn_mask = torch.zeros_like(x_attn_mask, dtype=torch.bool)
stop = False
cache = {
"all_stage": self.num_layers,
"k": [None] * self.num_layers,
"v": [None] * self.num_layers,
"y_emb": None,
"first_infer": 1,
"stage": 0,
}
for idx in range(1500):
if cache["first_infer"] == 1:
y_emb = self.ar_audio_embedding(y)
else:
y_emb = torch.cat(
[cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1
)
cache["y_emb"] = y_emb
y_pos = self.ar_audio_position(y_emb)
if cache["first_infer"] == 1:
xy_pos = torch.concat([x, y_pos], dim=1)
else:
xy_pos = y_pos[:, -1:]
y_len = y_pos.shape[1]
if cache["first_infer"] == 1:
x_attn_mask_pad = F.pad(x_attn_mask, (0, y_len), value=True)
y_attn_mask = F.pad(
torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
(x_len, 0), value=False
)
xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)
else:
xy_attn_mask = torch.zeros((1, x_len + y_len), dtype=torch.bool)
xy_dec = self.h(xy_pos, mask=xy_attn_mask, cache=cache)
logits = self.ar_predict_layer(xy_dec[:, -1])
samples = sample(logits[0], y, top_k=top_k, top_p=1.0, repetition_penalty=1.35)[0].unsqueeze(0)
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
stop = True
if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
stop = True
if stop:
if prompts.shape[1] == y.shape[1]:
y = torch.concat([y, torch.zeros_like(samples)], dim=1)
break
y = torch.concat([y, samples], dim=1)
cache["first_infer"] = 0
return y, idx |