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# --------------------------------------------------------
# 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 math
import re
from dataclasses import dataclass, field
from typing import List, Optional
import torch
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion
from artst.criterions.text_to_speech_loss import TexttoSpeechLoss, TexttoSpeechLossConfig
@dataclass
class SpeechPretrainCriterionConfig(TexttoSpeechLossConfig):
pred_masked_weight: float = field(
default=1.0,
metadata={"help": "weight for predictive loss for masked frames"},
)
pred_nomask_weight: float = field(
default=0.0,
metadata={"help": "weight for predictive loss for unmasked frames"},
)
loss_weights: Optional[List[float]] = field(
default_factory=lambda: [10,],
metadata={"help": "weights for additional loss terms (not first one)"},
)
log_keys: List[str] = field(
default_factory=lambda: [],
metadata={"help": "output keys to log"},
)
hubert_weight: float = field(
default=1.0,
metadata={"help": "weight of hubert loss"},
)
dec_weight: float = field(
default=1.0,
metadata={"help": "weight of decoder loss"},
)
class SpeechPretrainCriterion(FairseqCriterion):
def __init__(
self,
task,
sentence_avg,
pred_masked_weight,
pred_nomask_weight,
loss_weights=None,
log_keys=None,
use_masking=True,
use_weighted_masking=False,
loss_type="L1",
bce_pos_weight=5.0,
hubert_weight=1.0,
dec_weight=1.0,
):
super().__init__(task)
self.pred_masked_weight = pred_masked_weight
self.pred_nomask_weight = pred_nomask_weight
self.loss_weights = loss_weights
self.log_keys = [] if log_keys is None else log_keys
self.hubert_weight = hubert_weight
self.dec_weight = dec_weight
self.speech_criterion = TexttoSpeechLoss(
task,
sentence_avg,
use_masking,
use_weighted_masking,
loss_type,
bce_pos_weight,
)
def forward(self, model, sample, reduce=True, log_pred=False):
"""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
"""
if self.dec_weight == 0:
sample["net_input"]["only_hubert"] = True
net_output, net_output_dec = model(target_list=sample["target_list"], **sample["net_input"])
loss = 0.
sample_size = 0
logging_output = {}
reduction = "sum" if reduce else "none"
loss_m_list = []
logp_m_list = model.get_logits(net_output, True)
targ_m_list = model.get_targets(None, net_output, True)
assert self.pred_masked_weight == 0 or len(logp_m_list) > 0
for i, (logp_m, targ_m) in enumerate(zip(logp_m_list, targ_m_list)):
loss_m = F.cross_entropy(logp_m, targ_m, reduction=reduction)
loss_m_list.append(loss_m)
logging_output[f"loss_m_{i}"] = loss_m.detach().item()
if self.pred_masked_weight > 0:
loss += self.pred_masked_weight * sum(loss_m_list)
sample_size += targ_m_list[0].numel()
loss_u_list = []
logp_u_list = model.get_logits(net_output, False)
targ_u_list = model.get_targets(None, net_output, False)
assert self.pred_nomask_weight == 0 or len(logp_u_list) > 0
for i, (logp_u, targ_u) in enumerate(zip(logp_u_list, targ_u_list)):
loss_u = F.cross_entropy(logp_u, targ_u, reduction=reduction)
loss_u_list.append(loss_u)
logging_output[f"loss_u_{i}"] = loss_u.detach().item()
if self.pred_nomask_weight > 0:
loss += self.pred_nomask_weight * sum(loss_u_list)
sample_size += targ_u_list[0].numel()
if self.loss_weights is not None:
assert hasattr(model, "get_extra_losses")
extra_losses, names = model.get_extra_losses(net_output)
if torch.is_tensor(extra_losses):
extra_losses = [extra_losses]
names = [names]
if len(self.loss_weights) == 1 and len(extra_losses) != 1:
self.loss_weights = [self.loss_weights[0]] * len(extra_losses)
if len(self.loss_weights) > len(extra_losses):
modified_loss_weight = self.loss_weights[:len(extra_losses)]
else:
modified_loss_weight = self.loss_weights
# assert len(extra_losses) == len(self.loss_weights), f"{len(extra_losses)}, {len(self.loss_weights)}"
for p, n, coef in zip(extra_losses, names, modified_loss_weight):
# print(n + str(coef))
if coef != 0 and p is not None:
p = coef * p.float() * sample_size
loss += p
logging_output[f"loss_{n}"] = p.detach().item()
logging_output = {
"ntokens": sample_size,
"nsentences": sample["id"].numel(),
"sample_size": sample_size,
"ngpu": 1,
**logging_output,
}
if 'loss_prob_perplexity' in logging_output:
logging_output['code_perplexity'] = net_output['code_perplexity'].detach().item()
for lk in self.log_keys:
if lk in net_output:
logging_output[lk] = float((net_output[lk].item()))
def compute_correct(logits):
if logits.numel() == 0:
return 0, 0
else:
assert logits.dim() > 1, logits.shape
max = logits.argmax(-1) == 0
min = logits.argmin(-1) == 0
both = max & min
corr = max.long().sum().item() - both.long().sum().item()
count = max.numel()
return corr, count
with torch.no_grad():
for i, logp_m in enumerate(logp_m_list):
corr_m, count_m = compute_correct(logp_m)
logging_output[f"correct_m_{i}"] = corr_m
logging_output[f"count_m_{i}"] = count_m
for i, logp_u in enumerate(logp_u_list):
corr_u, count_u = compute_correct(logp_u)
logging_output[f"correct_u_{i}"] = corr_u
logging_output[f"count_u_{i}"] = count_u
if self.dec_weight == 0.0:
logging_output["loss"] = loss.item() if reduce else loss
return loss, sample_size, logging_output
# ## dec loss
dec_loss, l1_loss, l2_loss, bce_loss, enc_dec_attn_loss = self.speech_criterion.compute_loss(model, net_output_dec, sample)
# Log tts loss
logging_output['dec_loss'] = dec_loss.item()
logging_output['l1_loss'] = l1_loss.item()
logging_output['l2_loss'] = l2_loss.item()
logging_output['bce_loss'] = bce_loss.item()
if enc_dec_attn_loss is not None:
logging_output['enc_dec_attn_loss'] = enc_dec_attn_loss.item()
loss = self.hubert_weight * loss + self.dec_weight * sample_size * dec_loss
logging_output["loss"] = loss.item() if reduce else loss
return loss, sample_size, logging_output
@staticmethod
def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training (copied from normal cross entropy)."""
loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
dec_loss_sum = sum(log.get("dec_loss", 0) for log in logging_outputs)
l1_loss_sum = sum(log.get("l1_loss", 0) for log in logging_outputs)
l2_loss_sum = sum(log.get("l2_loss", 0) for log in logging_outputs)
bce_loss_sum = sum(log.get("bce_loss", 0) for log in logging_outputs)
ngpu = sum(log.get("ngpu", 0) for log in logging_outputs)
metrics.log_scalar("loss", loss_sum / sample_size / math.log(2), sample_size, round=3)
if sample_size != ntokens:
metrics.log_scalar("nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3)
metrics.log_derived("ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg))
else:
metrics.log_derived("ppl", lambda meters: utils.get_perplexity(meters["loss"].avg))
counts = {}
for lk in logging_outputs[0].keys():
if lk.startswith("count_"):
val = sum(log[lk] for log in logging_outputs)
metrics.log_scalar(lk, val)
counts[lk] = val
for lk in logging_outputs[0].keys():
if lk.startswith("loss_"):
val = sum(log[lk] for log in logging_outputs)
metrics.log_scalar(lk, val / sample_size / math.log(2), round=3)
elif lk.startswith("correct_"):
val = sum(log[lk] for log in logging_outputs)
metrics.log_scalar(lk, val / counts[re.sub("correct", "count", lk)])
elif lk == 'code_perplexity':
val = sum(log[lk] for log in logging_outputs)
metrics.log_scalar(lk, val / len(logging_outputs), round=3)
metrics.log_scalar(
"dec_loss", dec_loss_sum / ngpu, sample_size, 2, round=5
)
metrics.log_scalar(
"l1_loss", l1_loss_sum / ngpu, sample_size, 2, round=5
)
metrics.log_scalar(
"l2_loss", l2_loss_sum / ngpu, sample_size, 2, round=5
)
metrics.log_scalar(
"bce_loss", bce_loss_sum / ngpu, sample_size, 2, round=5
)
if "enc_dec_attn_loss" in logging_outputs[0]:
enc_dec_attn_loss_sum = sum(log.get("enc_dec_attn_loss", 0) for log in logging_outputs)
metrics.log_scalar(
"enc_dec_attn_loss", enc_dec_attn_loss_sum / ngpu, sample_size, round=8
)
@staticmethod
def aggregate_logging_outputs(logging_outputs):
"""Aggregate logging outputs from data parallel training."""
raise NotImplementedError()
@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
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