b7920b1c847a5cdbe4728263fda80307fc6b6524,pytorch_lightning/trainer/connectors/logger_connector/epoch_result_store.py,HookResultStore,auto_reduce_results_on_epoch_end,#HookResultStore#,176

Before Change

                // Make sure we didn"t create key
                assert num_opt_idx >= 0

                for opt_idx in range(num_opt_idx + 1):
                    // TODO: Figure out to reduce memory
                    // TODO: How to start training in middle of epoch
                    opt_outputs = epoch_metrics[opt_idx]

                    // reduce across time first
                    time_reduced_outputs = []
                    for batch_idx in opt_outputs.keys():
                        tbptt_outs = opt_outputs[batch_idx]
                        tbptt_outs = tbptt_outs[0].__class__.reduce_across_time(tbptt_outs)
                        if len(tbptt_outs) > 1:
                            time_reduced_outputs.append(tbptt_outs)

                    if len(time_reduced_outputs) == 0:
                        continue

                    // reduce across training steps
                    opt_outputs = time_reduced_outputs[0].__class__.reduce_on_epoch_end(time_reduced_outputs)

                    // with manual opt need 1 + metrics because meta is always there
                    if opt_outputs.minimize is not None:
                        opt_outputs.minimize = opt_outputs.minimize.mean()

                    self._internals_reduced[dl_idx][opt_idx] = opt_outputs

                    // free memory
                    del self._internals[dl_idx][opt_idx]
            else:
                // no need to reduce as called only once
                if len(epoch_metrics) == 1:
                    reduced_epoch_metrics = epoch_metrics[0]

After Change

            epoch_metrics = self._internals[dl_idx]

            if self._internal_type == ResultStoreType.INSIDE_BATCH_TRAIN_LOOP:
                for opt_idx in list(epoch_metrics):
                    // TODO: Figure out to reduce memory
                    // TODO: How to start training in middle of epoch
                    opt_outputs = epoch_metrics[opt_idx]

Instances