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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 | |
| class SentenceRankingCriterion(FairseqCriterion): | |
| def __init__(self, task, ranking_head_name, save_predictions, num_classes): | |
| super().__init__(task) | |
| self.ranking_head_name = ranking_head_name | |
| if save_predictions is not None: | |
| self.prediction_h = open(save_predictions, "w") | |
| else: | |
| self.prediction_h = None | |
| self.num_classes = num_classes | |
| def __del__(self): | |
| if self.prediction_h is not None: | |
| self.prediction_h.close() | |
| def add_args(parser): | |
| # fmt: off | |
| parser.add_argument('--save-predictions', metavar='FILE', | |
| help='file to save predictions to') | |
| parser.add_argument('--ranking-head-name', | |
| default='sentence_classification_head', | |
| help='name of the ranking head to use') | |
| # fmt: on | |
| def forward(self, model, sample, reduce=True): | |
| """Compute ranking 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 | |
| """ | |
| assert ( | |
| hasattr(model, "classification_heads") | |
| and self.ranking_head_name in model.classification_heads | |
| ), "model must provide sentence ranking head for --criterion=sentence_ranking" | |
| scores = [] | |
| for idx in range(self.num_classes): | |
| score, _ = model( | |
| **sample["net_input{idx}".format(idx=idx + 1)], | |
| classification_head_name=self.ranking_head_name, | |
| ) | |
| scores.append(score) | |
| logits = torch.cat(scores, dim=1) | |
| sample_size = logits.size(0) | |
| if "target" in sample: | |
| targets = model.get_targets(sample, [logits]).view(-1) | |
| lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32) | |
| loss = F.nll_loss(lprobs, targets, reduction="sum") | |
| else: | |
| targets = None | |
| loss = torch.tensor(0.0, requires_grad=True) | |
| if self.prediction_h is not None: | |
| preds = logits.argmax(dim=1) | |
| for i, (id, pred) in enumerate(zip(sample["id"].tolist(), preds.tolist())): | |
| if targets is not None: | |
| label = targets[i].item() | |
| print("{}\t{}\t{}".format(id, pred, label), file=self.prediction_h) | |
| else: | |
| print("{}\t{}".format(id, pred), file=self.prediction_h) | |
| logging_output = { | |
| "loss": loss.data, | |
| "ntokens": sample["ntokens"], | |
| "nsentences": sample_size, | |
| "sample_size": sample_size, | |
| } | |
| if targets is not None: | |
| logging_output["ncorrect"] = (logits.argmax(dim=1) == targets).sum() | |
| return loss, sample_size, logging_output | |
| 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) | |
| nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) | |
| sample_size = sum(log.get("sample_size", 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 | |
| ) | |
| if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]: | |
| ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) | |
| metrics.log_scalar( | |
| "accuracy", 100.0 * ncorrect / nsentences, nsentences, round=1 | |
| ) | |
| 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 | |