6aad1de658a933d3fa376f7fe9abf419da8a8bd2,python/baseline/pytorch/lm/train.py,LanguageModelTrainerPyTorch,test,#LanguageModelTrainerPyTorch#Any#Any#Any#,38

Before Change

            iters += nctx
        self.valid_epochs += 1

        avg_loss = float(total_loss) / iters / batchsz
        metrics["avg_loss"] = avg_loss
        metrics["perplexity"] = np.exp(avg_loss)

        duration = time.time() - start_time
        print("%s time (%.3f sec)" % (phase, duration))
        self.log.debug({"phase": phase, "time": duration})

        for reporting in reporting_fns:
            reporting(metrics, self.valid_epochs, phase)
        return metrics

    def train(self, ts, reporting_fns):
        start_time = time.time()

After Change

        metrics["perplexity"] = np.exp(metrics["avg_loss"])
        return metrics

    def test(self, vs, reporting_fns, phase="Valid"):
        epoch = 0
        if phase == "Valid":
            self.valid_epochs += 1
            epoch = self.valid_epochs
        start = time.time()
        self.model.eval()
        total_loss = 0
        total_toks = 0
        metrics = {}
        batchsz, nctx = self._get_dims(vs[0])

        hidden = self.model.init_hidden(batchsz)

        for batch_dict in vs:
            inputs = self.model.make_input(batch_dict)
            y = inputs.pop("y")
            output, hidden = self.model(inputs, hidden)
            toks = self._num_toks(batch_dict)
            total_loss += self.crit(output, y).item() * toks
            total_toks += toks
            if hidden is not None:
                hidden = self.repackage_hidden(hidden)
        metrics = self.calc_metrics(total_loss, total_toks)
        self.report(
            epoch, metrics, start,
            phase, "EPOCH", reporting_fns
        )
        return metrics

    def train(self, ts, reporting_fns):
        start = time.time()

Instances