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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 | |
| 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, register_criterion | |
| from fairseq.dataclass import FairseqDataclass | |
| from omegaconf import II | |
| class TextPretrainCriterionConfig(FairseqDataclass): | |
| sentence_avg: bool = II("optimization.sentence_avg") | |
| loss_weights: Optional[List[float]] = field( | |
| default_factory=lambda: [0.1,], | |
| metadata={"help": "weights for additional loss terms (not first one)"}, | |
| ) | |
| bart_weight: float = field( | |
| default=1.0, | |
| metadata={"help": "loss weight for cross entropy"}, | |
| ) | |
| class TextPretrainCriterion(FairseqCriterion): | |
| def __init__(self, task, sentence_avg, bart_weight, loss_weights=None): | |
| super().__init__(task) | |
| self.sentence_avg = sentence_avg | |
| self.loss_weights = loss_weights | |
| self.bart_weight = bart_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 | |
| """ | |
| net_output, codebook_out, encoder_output = model(**sample["net_input"]) | |
| bart_loss, _ = self.compute_loss(model, net_output, sample, reduce=reduce) | |
| sample_size = ( | |
| sample["target"].size(0) if self.sentence_avg else sample["ntokens"] | |
| ) | |
| loss = self.bart_weight * bart_loss | |
| logging_output = { | |
| "loss": loss.item(), | |
| "ntokens": sample["ntokens"], | |
| "nsentences": sample["target"].size(0), | |
| "bart_loss": bart_loss.item(), | |
| "sample_size": sample_size, | |
| } | |
| if "prob_perplexity" in codebook_out: | |
| assert hasattr(model, "get_extra_losses") | |
| extra_losses, names = model.get_extra_losses(codebook_out) | |
| 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.item() | |
| if 'loss_prob_perplexity' in logging_output: | |
| logging_output['code_perplexity'] = codebook_out['code_perplexity'].item() | |
| return loss, sample_size, logging_output | |
| def compute_loss(self, model, net_output, sample, reduce=True): | |
| lprobs = model.get_normalized_probs(net_output, log_probs=True) | |
| lprobs = lprobs.view(-1, lprobs.size(-1)) | |
| target = model.get_targets(sample, net_output).view(-1) | |
| loss = F.nll_loss( | |
| lprobs, | |
| target, | |
| ignore_index=self.padding_idx, | |
| reduction="sum" if reduce else "none", | |
| ) | |
| return loss, loss | |
| def reduce_metrics(logging_outputs) -> None: | |
| """Aggregate logging outputs from data parallel training.""" | |
| 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) | |
| bart_loss_sum = sum(log.get("bart_loss", 0) for log in logging_outputs) | |
| # we divide by log(2) to convert the loss from base e to base 2 | |
| metrics.log_scalar( | |
| "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( | |
| "nll_loss", bart_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["bart_loss"].avg) | |
| ) | |
| if "loss_prob_perplexity" in logging_outputs[0].keys(): | |
| val = sum(log["loss_prob_perplexity"] for log in logging_outputs) | |
| metrics.log_scalar("loss_prob_perplexity", val / sample_size / math.log(2), round=3) | |
| if "code_perplexity" in logging_outputs[0].keys(): | |
| val = sum(log["code_perplexity"] for log in logging_outputs) | |
| metrics.log_scalar("code_perplexity", val / len(logging_outputs), round=3) | |
| 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 True | |