File size: 13,402 Bytes
3c7a160 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 |
# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_model.py
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 |