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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import math | |
| import torch | |
| import torch.nn.functional as F | |
| from fairseq import metrics, utils | |
| from fairseq.criterions import FairseqCriterion, register_criterion | |
| from fairseq.criterions.label_smoothed_cross_entropy import label_smoothed_nll_loss | |
| class LabelSmoothedCrossEntropyR3FCriterion(FairseqCriterion): | |
| def __init__( | |
| self, task, sentence_avg, label_smoothing, eps, r3f_lambda, noise_type | |
| ): | |
| super().__init__(task) | |
| self.sentence_avg = sentence_avg | |
| self.label_smoothing = label_smoothing | |
| self.eps = eps | |
| self.r3f_lambda = r3f_lambda | |
| self.noise_type = noise_type | |
| if self.noise_type in {"normal"}: | |
| self.noise_sampler = torch.distributions.normal.Normal( | |
| loc=0.0, scale=self.eps | |
| ) | |
| elif self.noise_type == "uniform": | |
| self.noise_sampler = torch.distributions.uniform.Uniform( | |
| low=-self.eps, high=self.eps | |
| ) | |
| else: | |
| raise Exception(f"unrecognized noise type {self.noise_type}") | |
| def add_args(parser): | |
| """Add criterion-specific arguments to the parser.""" | |
| # fmt: off | |
| parser.add_argument('--label-smoothing', default=0., type=float, metavar='D', | |
| help='epsilon for label smoothing, 0 means no label smoothing') | |
| parser.add_argument('--eps', type=float, default=1e-5, | |
| help='noise eps') | |
| parser.add_argument('--r3f-lambda', type=float, default=1.0, | |
| help='lambda for combining logistic loss and noisy KL loss') | |
| parser.add_argument('--noise-type', type=str, default='normal', | |
| choices=['normal', 'uniform'], | |
| help='type of noises') | |
| # fmt: on | |
| def _get_symm_kl(self, noised_logits, input_logits): | |
| return ( | |
| F.kl_div( | |
| F.log_softmax(noised_logits, dim=-1, dtype=torch.float32), | |
| F.softmax(input_logits, dim=-1, dtype=torch.float32), | |
| None, | |
| None, | |
| "sum", | |
| ) | |
| + F.kl_div( | |
| F.log_softmax(input_logits, dim=-1, dtype=torch.float32), | |
| F.softmax(noised_logits, dim=-1, dtype=torch.float32), | |
| None, | |
| None, | |
| "sum", | |
| ) | |
| ) / noised_logits.size(0) | |
| 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 | |
| """ | |
| token_embeddings = model.encoder.embed_tokens(sample["net_input"]["src_tokens"]) | |
| input_logits, extra = model(**sample["net_input"]) | |
| loss, nll_loss = self.compute_loss( | |
| model, (input_logits, extra), sample, reduce=reduce | |
| ) | |
| sample_size = ( | |
| sample["target"].size(0) if self.sentence_avg else sample["ntokens"] | |
| ) | |
| if model.training: | |
| noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to( | |
| token_embeddings | |
| ) | |
| noised_embeddings = token_embeddings.clone() + noise | |
| noised_logits, _ = model( | |
| **sample["net_input"], token_embeddings=noised_embeddings | |
| ) | |
| symm_kl = self._get_symm_kl(noised_logits, input_logits) | |
| if model.training: | |
| symm_kl = symm_kl * sample_size | |
| loss = loss + self.r3f_lambda * symm_kl | |
| logging_output = { | |
| "loss": loss.data, | |
| "nll_loss": nll_loss.data, | |
| "ntokens": sample["ntokens"], | |
| "nsentences": sample["target"].size(0), | |
| "sample_size": sample_size, | |
| } | |
| if model.training: | |
| logging_output.update( | |
| symm_kl=utils.item(symm_kl.data) if reduce else symm_kl.data | |
| ) | |
| 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, 1) | |
| loss, nll_loss = label_smoothed_nll_loss( | |
| lprobs, | |
| target, | |
| self.label_smoothing, | |
| ignore_index=self.padding_idx, | |
| reduce=reduce, | |
| ) | |
| return loss, nll_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) | |
| nll_loss_sum = sum(log.get("nll_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) | |
| symm_kl_sum = sum(log.get("symm_kl", 0) for log in logging_outputs) | |
| metrics.log_scalar("symm_kl", symm_kl_sum / sample_size, sample_size, round=3) | |
| metrics.log_scalar( | |
| "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 | |
| ) | |
| metrics.log_scalar( | |
| "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3 | |
| ) | |
| metrics.log_derived( | |
| "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) | |
| ) | |
| 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 | |