Spaces:
Runtime error
Runtime error
File size: 21,938 Bytes
1547a56 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 |
# --------------------------------------------------------
# ArTST: Arabic Text and Speech Transform (https://arxiv.org/abs/2310.16621)
# Github source: https://github.com/mbzuai-nlp/ArTST
# Based on speecht5, fairseq and espnet code bases
# https://github.com/microsoft/SpeechT5/tree/main/SpeechT5; https://github.com/pytorch/fairseq; https://github.com/espnet/espnet
# --------------------------------------------------------
import re
from dataclasses import dataclass
import math
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from artst.criterions.text_to_speech_loss import TexttoSpeechLoss
from artst.criterions.text_pretrain_criterion import TextPretrainCriterion, TextPretrainCriterionConfig
from fairseq.criterions.label_smoothed_cross_entropy import LabelSmoothedCrossEntropyCriterionConfig
from artst.criterions.speech_pretrain_criterion import SpeechPretrainCriterion, SpeechPretrainCriterionConfig
from artst.criterions.speech_to_text_loss import SpeechtoTextLoss, SpeechtoTextLossConfig
from fairseq.logging.meters import safe_round
@dataclass
class ArTSTCriterionConfig(
LabelSmoothedCrossEntropyCriterionConfig,
TextPretrainCriterionConfig,
SpeechPretrainCriterionConfig,
SpeechtoTextLossConfig
):
pass
@register_criterion(
"artst", dataclass=ArTSTCriterionConfig
)
class ArTSTCriterion(FairseqCriterion):
def __init__(
self,
task,
sentence_avg,
label_smoothing,
pred_masked_weight,
pred_nomask_weight,
loss_weights=None,
log_keys=None,
ignore_prefix_size=0,
report_accuracy=False,
use_masking=True,
use_weighted_masking=False,
loss_type="L1",
bce_pos_weight=5.0,
bce_loss_lambda=1.0,
use_guided_attn_loss=False,
num_heads_applied_guided_attn=2,
ce_weight=1.0,
ctc_weight=0.0,
hubert_weight=1.0,
dec_weight=1.0,
bart_weight=1.0,
):
super().__init__(task)
self.speech_criterion = TexttoSpeechLoss(
task,
sentence_avg,
use_masking,
use_weighted_masking,
loss_type,
bce_pos_weight,
bce_loss_lambda,
use_guided_attn_loss,
num_heads_applied_guided_attn=num_heads_applied_guided_attn,
)
self.text_criterion = SpeechtoTextLoss(
SpeechtoTextLossConfig,
task,
sentence_avg,
label_smoothing,
ignore_prefix_size,
report_accuracy,
ce_weight,
ctc_weight
)
self.text_pretrain_criterion = TextPretrainCriterion(
task,
sentence_avg,
bart_weight,
loss_weights,
)
self.speech_pretrain_criterion = SpeechPretrainCriterion(
task,
sentence_avg,
pred_masked_weight,
pred_nomask_weight,
loss_weights,
log_keys,
use_masking,
use_weighted_masking,
loss_type,
bce_pos_weight,
hubert_weight,
dec_weight
)
def forward(self, model, sample, reduce=True):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
"""
task_name = sample['task_name']
if task_name == 's2t' or task_name == 's2c':
return self.text_criterion(model, sample, reduce)
elif task_name == 't2s' or task_name == 's2s':
return self.speech_criterion(model, sample)
elif task_name == 'text_pretrain':
return self.text_pretrain_criterion(model, sample, reduce)
elif task_name == 'speech_pretrain':
return self.speech_pretrain_criterion(model, sample, reduce)
@classmethod
def reduce_metrics(cls, logging_outputs):
"""Aggregate logging outputs from data parallel training."""
logging_outputs_dict = {}
for logging_output in logging_outputs:
for task_name in logging_output:
if task_name not in ['s2t', 't2s', 's2c', 's2s', 'text_pretrain', 'speech_pretrain']:
continue
if task_name not in logging_outputs_dict:
logging_outputs_dict[task_name] = []
logging_outputs_dict[task_name].append(logging_output[task_name])
for task_name in logging_outputs_dict:
if task_name == 's2t':
# LabelSmoothedCrossEntropyCriterion.reduce_metrics([logging_output['s2t'] for logging_output in logging_outputs])
s2t_logging_output = logging_outputs_dict[task_name]
# s2t_sum = sum(log.get("ce_loss", 0) for log in logging_outputs)
loss_sum = sum(log.get("loss", 0) for log in s2t_logging_output)
nll_loss_sum = sum(log.get("nll_loss", 0) for log in s2t_logging_output)
ntokens = sum(log.get("ntokens", 0) for log in s2t_logging_output)
ce_loss_sum = sum(log.get("ce_loss", 0) for log in s2t_logging_output)
ctc_loss_sum = sum(log.get("ctc_loss", 0) for log in s2t_logging_output)
sample_size = max(1, sum(log.get("sample_size", 0) for log in s2t_logging_output))
metrics.log_scalar(
"s2t_loss", loss_sum / sample_size / math.log(2), sample_size, 1, round=3
)
metrics.log_scalar(
"s2t_nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, 2, round=3
)
metrics.log_derived(
"s2t_ppl", lambda meters: utils.get_perplexity(meters["s2t_nll_loss"].avg, 2)
)
metrics.log_scalar(
"ctc_loss", ctc_loss_sum / sample_size / math.log(2), ntokens, 2, round=3
)
metrics.log_scalar(
"ce_loss", ce_loss_sum / ntokens, ntokens, 2, round=3
)
total = utils.item(sum(log.get("total", 0) for log in s2t_logging_output))
if total > 0:
metrics.log_scalar("s2t_total", total)
n_correct = utils.item(
sum(log.get("n_correct", 0) for log in s2t_logging_output)
)
metrics.log_scalar("s2t_n_correct", n_correct)
metrics.log_derived(
"s2t_accuracy",
lambda meters: round(
meters["s2t_n_correct"].sum * 100.0 / meters["s2t_total"].sum, 3
)
if meters["s2t_total"].sum > 0
else float("nan"),
2
)
c_errors = sum(log.get("c_errors", 0) for log in s2t_logging_output)
metrics.log_scalar("_c_errors", c_errors)
c_total = sum(log.get("c_total", 0) for log in s2t_logging_output)
metrics.log_scalar("_c_total", c_total)
w_errors = sum(log.get("w_errors", 0) for log in s2t_logging_output)
metrics.log_scalar("_w_errors", w_errors)
wv_errors = sum(log.get("wv_errors", 0) for log in s2t_logging_output)
metrics.log_scalar("_wv_errors", wv_errors)
w_total = sum(log.get("w_total", 0) for log in s2t_logging_output)
metrics.log_scalar("_w_total", w_total)
if c_total > 0:
metrics.log_derived(
"uer",
lambda meters: safe_round(
meters["_c_errors"].sum * 100.0 / meters["_c_total"].sum, 3
)
if meters["_c_total"].sum > 0
else float("nan"),
)
if w_total > 0:
metrics.log_derived(
"wer",
lambda meters: safe_round(
meters["_w_errors"].sum * 100.0 / meters["_w_total"].sum, 3
)
if meters["_w_total"].sum > 0
else float("nan"),
)
metrics.log_derived(
"raw_wer",
lambda meters: safe_round(
meters["_wv_errors"].sum * 100.0 / meters["_w_total"].sum, 3
)
if meters["_w_total"].sum > 0
else float("nan"),
)
if task_name == 't2s':
# TTSLossCriterion.reduce_metrics([logging_output['t2s'] for logging_output in logging_outputs])
# t2s_sum = sum(log.get("speech_loss", 0) for log in logging_outputs)
t2s_logging_output = logging_outputs_dict[task_name]
loss_sum = sum(log.get("loss", 0) for log in t2s_logging_output)
l1_loss_sum = sum(log.get("l1_loss", 0) for log in t2s_logging_output)
l2_loss_sum = sum(log.get("l2_loss", 0) for log in t2s_logging_output)
bce_loss_sum = sum(log.get("bce_loss", 0) for log in t2s_logging_output)
sample_size = max(1, sum(log.get("sample_size", 0) for log in t2s_logging_output))
metrics.log_scalar(
"t2s_loss", loss_sum / sample_size, sample_size, 1, round=5
)
encoder_alpha_sum = sum(log.get("encoder_alpha", 0) for log in t2s_logging_output)
decoder_alpha_sum = sum(log.get("decoder_alpha", 0) for log in t2s_logging_output)
ngpu = sum(log.get("ngpu", 0) for log in t2s_logging_output)
metrics.log_scalar(
"t2s_l1_loss", l1_loss_sum / sample_size, sample_size, 2, round=5
)
metrics.log_scalar(
"t2s_l2_loss", l2_loss_sum / sample_size, sample_size, 2, round=5
)
metrics.log_scalar(
"t2s_bce_loss", bce_loss_sum / sample_size, sample_size, 2, round=5
)
metrics.log_scalar(
"t2s_encoder_alpha", encoder_alpha_sum / sample_size, sample_size, round=5
)
metrics.log_scalar(
"t2s_decoder_alpha", decoder_alpha_sum / sample_size, sample_size, round=5
)
if "enc_dec_attn_loss" in t2s_logging_output[0]:
enc_dec_attn_loss_sum = sum(log.get("enc_dec_attn_loss", 0) for log in t2s_logging_output)
metrics.log_scalar(
"t2s_enc_dec_attn_loss", enc_dec_attn_loss_sum / sample_size, sample_size, round=8
)
if task_name == 's2c':
s2c_logging_output = logging_outputs_dict[task_name]
loss_sum = sum(log.get("loss", 0) for log in s2c_logging_output)
nll_loss_sum = sum(log.get("nll_loss", 0) for log in s2c_logging_output)
ntokens = sum(log.get("ntokens", 0) for log in s2c_logging_output)
sample_size = max(1, sum(log.get("sample_size", 0) for log in s2c_logging_output))
metrics.log_scalar(
"s2c_loss", loss_sum / sample_size / math.log(2), sample_size, 1, round=3
)
metrics.log_scalar(
"s2c_nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, 2, round=3
)
total = utils.item(sum(log.get("total", 0) for log in s2c_logging_output))
if total > 0:
metrics.log_scalar("s2c_total", total)
n_correct = utils.item(sum(log.get("n_correct", 0) for log in s2c_logging_output))
metrics.log_scalar("s2c_n_correct", n_correct)
metrics.log_derived(
"s2c_accuracy",
lambda meters: round(
meters["s2c_n_correct"].sum * 100.0 / meters["s2c_total"].sum, 3
)
if meters["s2c_total"].sum > 0
else float("nan"),
2
)
if task_name == 's2s':
s2s_logging_output = logging_outputs_dict[task_name]
loss_sum = sum(log.get("loss", 0) for log in s2s_logging_output)
l1_loss_sum = sum(log.get("l1_loss", 0) for log in s2s_logging_output)
l2_loss_sum = sum(log.get("l2_loss", 0) for log in s2s_logging_output)
bce_loss_sum = sum(log.get("bce_loss", 0) for log in s2s_logging_output)
sample_size = max(1, sum(log.get("sample_size", 0) for log in s2s_logging_output))
metrics.log_scalar(
"s2s_loss", loss_sum / sample_size, sample_size, 1, round=5
)
encoder_alpha_sum = sum(log.get("encoder_alpha", 0) for log in s2s_logging_output)
decoder_alpha_sum = sum(log.get("decoder_alpha", 0) for log in s2s_logging_output)
ngpu = sum(log.get("ngpu", 0) for log in s2s_logging_output)
metrics.log_scalar(
"s2s_l1_loss", l1_loss_sum / sample_size, sample_size, 2, round=5
)
metrics.log_scalar(
"s2s_l2_loss", l2_loss_sum / sample_size, sample_size, 2, round=5
)
metrics.log_scalar(
"s2s_bce_loss", bce_loss_sum / sample_size, sample_size, 2, round=5
)
metrics.log_scalar(
"s2s_decoder_alpha", decoder_alpha_sum / sample_size, sample_size, round=5
)
if "enc_dec_attn_loss" in s2s_logging_output[0]:
enc_dec_attn_loss_sum = sum(log.get("enc_dec_attn_loss", 0) for log in s2s_logging_output)
metrics.log_scalar(
"s2s_enc_dec_attn_loss", enc_dec_attn_loss_sum / sample_size, sample_size, round=8
)
if task_name == 'text_pretrain':
bart_logging_output = logging_outputs_dict[task_name]
loss_sum = sum(log.get("loss", 0) for log in bart_logging_output)
ntokens = sum(log.get("ntokens", 0) for log in bart_logging_output)
sample_size = max(1, sum(log.get("sample_size", 0) for log in bart_logging_output))
bart_loss_sum = sum(log.get("bart_loss", 0) for log in bart_logging_output)
# we divide by log(2) to convert the loss from base e to base 2
metrics.log_scalar(
"text_loss", loss_sum / sample_size / math.log(2), sample_size, round=3
)
metrics.log_scalar(
"bart_loss", bart_loss_sum / sample_size / math.log(2), ntokens, 2, round=3
)
if sample_size != ntokens:
metrics.log_scalar(
"bart_nll_loss", bart_loss_sum / ntokens / math.log(2), ntokens, round=3
)
metrics.log_derived(
"bart_ppl", lambda meters: utils.get_perplexity(meters["bart_nll_loss"].avg)
)
else:
metrics.log_derived(
"bart_ppl", lambda meters: utils.get_perplexity(meters["bart_loss"].avg)
)
metrics.log_scalar("bart_wpb", ntokens, priority=180, round=1)
val_prob_perplexity = 0
val_code_perplexity = 0
sample_size_pp = 0
count_log_cp = 0
for log in bart_logging_output:
if "loss_prob_perplexity" in log:
val_prob_perplexity = val_prob_perplexity + log["loss_prob_perplexity"]
sample_size_pp = sample_size_pp + log["sample_size"]
if "code_perplexity" in log:
val_code_perplexity = val_code_perplexity + log["code_perplexity"]
count_log_cp = count_log_cp + 1
if val_prob_perplexity > 0:
metrics.log_scalar("text_loss_prob_perplexity", val_prob_perplexity / sample_size_pp / math.log(2), round=3)
if val_code_perplexity > 0:
metrics.log_scalar("text_code_perplexity", val_code_perplexity / count_log_cp, round=3)
if task_name == 'speech_pretrain':
hubert_logging_output = logging_outputs_dict[task_name]
loss_sum = sum(log.get("loss", 0) for log in hubert_logging_output)
ntokens = sum(log.get("ntokens", 0) for log in hubert_logging_output)
sample_size = max(1, sum(log.get("sample_size", 0) for log in hubert_logging_output))
dec_loss_sum = sum(log.get("dec_loss", 0) for log in hubert_logging_output)
l1_loss_sum = sum(log.get("l1_loss", 0) for log in hubert_logging_output)
l2_loss_sum = sum(log.get("l2_loss", 0) for log in hubert_logging_output)
bce_loss_sum = sum(log.get("bce_loss", 0) for log in hubert_logging_output)
ngpu = sum(log.get("ngpu", 0) for log in hubert_logging_output)
metrics.log_scalar("hubert_loss", loss_sum / sample_size / math.log(2), sample_size, round=3)
if sample_size != ntokens:
metrics.log_scalar("hubert_nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3)
metrics.log_derived("hubert_ppl", lambda meters: utils.get_perplexity(meters["hubert_nll_loss"].avg))
else:
metrics.log_derived("hubert_ppl", lambda meters: utils.get_perplexity(meters["hubert_loss"].avg))
counts = {}
for lk in hubert_logging_output[0].keys():
if lk.startswith("count_"):
val = sum(log[lk] for log in hubert_logging_output)
metrics.log_scalar("hubert_" + lk, val)
counts[lk] = val
for lk in hubert_logging_output[0].keys():
if lk.startswith("loss_") and lk != 'loss_prob_perplexity':
val = sum(log[lk] for log in hubert_logging_output)
metrics.log_scalar("hubert_" + lk, val / sample_size / math.log(2), round=3)
elif lk.startswith("correct_"):
val = sum(log[lk] for log in hubert_logging_output)
metrics.log_scalar("hubert_" + lk, val / counts[re.sub("correct", "count", lk)])
# elif lk == 'code_perplexity':
# val = sum(log[lk] for log in hubert_logging_output)
# metrics.log_scalar("hubert_" + lk, val / len(hubert_logging_output), round=3)
val_prob_perplexity = 0
val_code_perplexity = 0
sample_size_pp = 0
count_log_cp = 0
for log in hubert_logging_output:
if "loss_prob_perplexity" in log:
val_prob_perplexity = val_prob_perplexity + log["loss_prob_perplexity"]
sample_size_pp = sample_size_pp + log["sample_size"]
if "code_perplexity" in log:
val_code_perplexity = val_code_perplexity + log["code_perplexity"]
count_log_cp = count_log_cp + 1
if val_prob_perplexity > 0:
metrics.log_scalar("hubert_loss_prob_perplexity", val_prob_perplexity / sample_size_pp / math.log(2), round=3)
if val_code_perplexity > 0:
metrics.log_scalar("hubert_code_perplexity", val_code_perplexity / count_log_cp, round=3)
metrics.log_scalar(
"hubert_dec_loss", dec_loss_sum / ngpu, sample_size, 2, round=5
)
metrics.log_scalar(
"hubert_l1_loss", l1_loss_sum / ngpu, sample_size, 2, round=5
)
metrics.log_scalar(
"hubert_l2_loss", l2_loss_sum / ngpu, sample_size, 2, round=5
)
metrics.log_scalar(
"hubert_bce_loss", bce_loss_sum / ngpu, sample_size, 2, round=5
)
if "enc_dec_attn_loss" in hubert_logging_output[0]:
enc_dec_attn_loss_sum = sum(log.get("enc_dec_attn_loss", 0) for log in hubert_logging_output)
metrics.log_scalar(
"hubert_enc_dec_attn_loss", enc_dec_attn_loss_sum / ngpu, sample_size, round=8
)
metrics.log_scalar("hubert_wpb", ntokens, priority=180, round=1)
loss = sum(log.get("loss", 0) for log in logging_outputs)
sample_size = max(1, sum(log.get("sample_size", 0) for log in logging_outputs))
metrics.log_scalar(
"loss", loss / sample_size, sample_size, 1, round=5
)
@staticmethod
def logging_outputs_can_be_summed() -> bool:
"""
Whether the logging outputs returned by `forward` can be summed
across workers prior to calling `reduce_metrics`. Setting this
to True will improves distributed training speed.
"""
return False
|