31bf9a3cbbf0739beb745389ee64f6b9200f6d53,pytext/trainers/trainer.py,Trainer,run_step,#Trainer#Any#Any#Any#Any#,415

Before Change

        model = state.model
        self.zero_grads(state)
        for i, (batch_id, (inputs, targets, context)) in enumerate(samples):
            if cuda.DISTRIBUTED_WORLD_SIZE > 1:
                // Whenever *samples* contains more than one mini-batch, we
                // want to accumulate gradients locally and only call
                // all-reduce in the last backwards pass.
                if i < sample_size - 1:
                    // sync gradients in the last sample backward
                    model.accumulate_gradients(True)
                else:
                    model.accumulate_gradients(False)

            // pass context to model to use in forward call if needed
            model.contextualize(context)
            with timing.time("model.forward"):
                logits = model(*inputs)

After Change

        model = state.model
        self.zero_grads(state)
        for idx, (batch_id, (inputs, targets, context)) in enumerate(samples):
            with maybe_no_sync(model, idx, sample_size):
                // pass context to model to use in forward call if needed
                model.contextualize(context)
                with timing.time("model.forward"):
                    logits = model(*inputs)

                with timing.time("compute loss"):
                    loss = precision.maybe_float(
                        model.get_loss(logits, targets, context)
                    )
                    if BatchContext.IGNORE_LOSS in context:
                        loss *= 0
                    elif sample_size > 1:
                        // gradients averaged per batch and accumulated across samples.
                        // divide sample_size to let gradients averaged per example
                        loss = loss / sample_size

                self.backprop(state, loss)

            if report_metric:
                with timing.time("get pred"):
                    preds, scores = model.get_pred(
                        logits, targets, context, state.stage, *inputs

Instances