in progress

turbo_alignment/pipelines/train/lddpo.py

@@ -37,22 +37,23 @@ def _get_cherry_pick_callback(
         tokenizer: PreTrainedTokenizerBase,
         **kwargs,
     ) -> ChatCherryPickCallback:
-        cherry_pick_settings = experiment_settings.cherry_pick_settings
-
-        cherry_pick_datasets = DatasetLoader[InferenceChatDataset](InferenceChatDataset).load_datasets(
-            cherry_pick_settings.dataset_settings, tokenizer=tokenizer, strategy=DatasetStrategy.INFERENCE
-        )
-
-        metrics = [
-            Metric.by_name(metric.type)(MetricSettingsRegistry.by_name(metric.type)(**metric.parameters))
-            for metric in cherry_pick_settings.metric_settings
-        ]
-
-        return ChatCherryPickCallback(
-            cherry_pick_settings=cherry_pick_settings,
-            datasets=cherry_pick_datasets,
-            metrics=metrics,
-        )
+        return None
+        # cherry_pick_settings = experiment_settings.cherry_pick_settings
+
+        # cherry_pick_datasets = DatasetLoader[InferenceChatDataset](InferenceChatDataset).load_datasets(
+        #     cherry_pick_settings.dataset_settings, tokenizer=tokenizer, strategy=DatasetStrategy.INFERENCE
+        # )
+
+        # metrics = [
+        #     Metric.by_name(metric.type)(MetricSettingsRegistry.by_name(metric.type)(**metric.parameters))
+        #     for metric in cherry_pick_settings.metric_settings
+        # ]
+
+        # return ChatCherryPickCallback(
+        #     cherry_pick_settings=cherry_pick_settings,
+        #     datasets=cherry_pick_datasets,
+        #     metrics=metrics,
+        # )
 
     def _dataset_and_collator_sanity_check(self, dataset: Dataset, collator: DataCollatorMixin) -> None:
         logger.info(f'Train sample example:\n{dataset[0]}')

turbo_alignment/trainers/dpo.py

@@ -606,12 +606,18 @@ def _get_batch_logps(
         return (per_token_logps * loss_mask).sum(-1)
 
     def concatenated_forward(
-        self, model: nn.Module, batch: dict[str, Any]
+        self,
+        model: nn.Module,
+        batch: dict[str, Any],
+        get_from_dataset=False,
     ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor | None]:
         concatenated_batch = concatenated_inputs(batch, device=self.accelerator.device)
 
         precomputed_margins: torch.Tensor | None = concatenated_batch.pop('margin', None)
 
+        if get_from_dataset:
+            return concatenated_batch.pop('ref_chosen_logps'), concatenated_batch.pop('ref_rejected_logps')
+
         all_logits = model(
             concatenated_batch['input_ids'],
             attention_mask=concatenated_batch['attention_mask'],
@@ -632,16 +638,19 @@ def concatenated_forward(
         return chosen_logps, rejected_logps, chosen_logits, rejected_logits, precomputed_margins
 
     def _get_logps(self, model: nn.Module | None, batch: dict[str, Any]) -> tuple[torch.Tensor, torch.Tensor]:
-        with torch.no_grad():
-            if model is not None:
-                (chosen_logps, rejected_logps, *_) = self.concatenated_forward(model, batch)
-            else:
-                with self.accelerator.unwrap_model(self.model).disable_adapter():
-                    (
-                        chosen_logps,
-                        rejected_logps,
-                        *_,
-                    ) = self.concatenated_forward(self.model, batch)
+        if self.ref_model is None:
+            chosen_logps, rejected_logps = self.concatenated_forward(model, batch, get_from_dataset=True)
+        else:
+            with torch.no_grad():
+                if model is not None:
+                    (chosen_logps, rejected_logps, *_) = self.concatenated_forward(model, batch)
+                else:
+                    with self.accelerator.unwrap_model(self.model).disable_adapter():
+                        (
+                            chosen_logps,
+                            rejected_logps,
+                            *_,
+                        ) = self.concatenated_forward(self.model, batch)
 
         return chosen_logps, rejected_logps
 
@@ -780,9 +789,9 @@ def _compute_metrics(
         metrics[f'{prefix_name}grad_term'] = (
             (self.dpo_loss_registry.beta * F.sigmoid(rejected_rewards - chosen_rewards)).detach().cpu().mean().item()
         )
-        metrics[f'{prefix_name}grad_term_std'] = (
-            (self.dpo_loss_registry.beta * F.sigmoid(rejected_rewards - chosen_rewards)).detach().cpu().std().item()
-        )
+        # metrics[f'{prefix_name}grad_term_std'] = (
+        #     (self.dpo_loss_registry.beta * F.sigmoid(rejected_rewards - chosen_rewards)).detach().cpu().std().item()
+        # )
 
         return metrics
 
@@ -822,6 +831,7 @@ def compute_loss(
         model: PreTrainedModel | nn.Module,
         inputs: dict[str, torch.Tensor | Any],
         return_outputs=False,
+        num_items_in_batch=None,
     ) -> torch.Tensor | tuple[torch.Tensor, dict[str, float]]:
         loss, metrics = self.get_batch_metrics(model, inputs, train_eval='train')