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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 utils | |
| from fairseq.criterions import FairseqCriterion, register_criterion | |
| class SentencePredictionR3F(FairseqCriterion): | |
| def __init__( | |
| self, | |
| task, | |
| eps, | |
| r3f_lambda, | |
| noise_type, | |
| classification_head_name, | |
| regression_target, | |
| ): | |
| super().__init__(task) | |
| self.eps = eps | |
| self.r3f_lambda = r3f_lambda | |
| self.noise_type = noise_type | |
| self.classification_head_name = classification_head_name | |
| self.regression_target = regression_target | |
| 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): | |
| # fmt: off | |
| 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='uniform', | |
| choices=['normal', 'uniform'], | |
| help='type of noises for RXF methods') | |
| parser.add_argument('--classification-head-name', | |
| default='sentence_classification_head', | |
| help='name of the classification head to use') | |
| parser.add_argument('--regression-target', action='store_true') | |
| # 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 | |
| """ | |
| assert ( | |
| hasattr(model, "classification_heads") | |
| and self.classification_head_name in model.classification_heads | |
| ), "model must provide sentence classification head for --criterion=sentence_prediction" | |
| token_embeddings = model.encoder.sentence_encoder.embed_tokens( | |
| sample["net_input"]["src_tokens"] | |
| ) | |
| input_logits, _ = model( | |
| **sample["net_input"], | |
| features_only=True, | |
| classification_head_name=self.classification_head_name, | |
| token_embeddings=token_embeddings, | |
| ) | |
| if model.training and self.noise_sampler: | |
| noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to( | |
| token_embeddings | |
| ) | |
| noised_embeddings = token_embeddings.detach().clone() + noise | |
| noised_logits, _ = model( | |
| **sample["net_input"], | |
| features_only=True, | |
| classification_head_name=self.classification_head_name, | |
| token_embeddings=noised_embeddings, | |
| ) | |
| symm_kl = self._get_symm_kl(noised_logits, input_logits) | |
| else: | |
| symm_kl = 0 | |
| targets = model.get_targets(sample, [input_logits]).view(-1) | |
| sample_size = targets.numel() | |
| if not self.regression_target: | |
| loss = F.nll_loss( | |
| F.log_softmax(input_logits, dim=-1, dtype=torch.float32), | |
| targets, | |
| reduction="sum", | |
| ) | |
| if model.training: | |
| symm_kl = symm_kl * sample_size | |
| loss = loss + self.r3f_lambda * symm_kl | |
| else: | |
| logits = input_logits.squeeze().float() | |
| targets = targets.float() | |
| loss = F.mse_loss(logits, targets, reduction="sum") | |
| logging_output = { | |
| "loss": utils.item(loss.data) if reduce else loss.data, | |
| "ntokens": sample["ntokens"], | |
| "nsentences": sample_size, | |
| "sample_size": sample_size, | |
| } | |
| if not self.regression_target: | |
| preds = input_logits.max(dim=1)[1] | |
| logging_output.update(ncorrect=(preds == targets).sum().item()) | |
| if model.training and self.noise_sampler: | |
| logging_output.update( | |
| symm_kl=utils.item(symm_kl.data) if reduce else symm_kl.data | |
| ) | |
| return loss, sample_size, logging_output | |
| def aggregate_logging_outputs(logging_outputs): | |
| """Aggregate logging outputs from data parallel training.""" | |
| loss_sum = sum(log.get("loss", 0) for log in logging_outputs) | |
| symm_kl_sum = sum(log.get("symm_kl", 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) | |
| agg_output = { | |
| "loss": loss_sum / sample_size / math.log(2), | |
| "symm_kl": symm_kl_sum / sample_size, | |
| "ntokens": ntokens, | |
| "nsentences": nsentences, | |
| "sample_size": sample_size, | |
| } | |
| if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]: | |
| ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) | |
| agg_output.update(accuracy=ncorrect / nsentences) | |
| if sample_size != ntokens: | |
| agg_output["nll_loss"] = loss_sum / ntokens / math.log(2) | |
| return agg_output | |