Nca pair loss

turbo_alignment/settings/pipelines/train/dpo.py

@@ -26,6 +26,7 @@ class DPOLossesType(str, Enum):
     APO_DOWN = 'apo_down'
     ASFT = 'asft'
     DPOP = 'dpop'
+    NCA_PAIR = 'nca_pair'
 
 
 class DPOLossSettings(ExtraFieldsNotAllowedBaseModel):
@@ -93,6 +94,10 @@ class DPOPLossSettings(DPOLossSettings):
     lam: float = 0.1
 
 
+class NCAPairLossSettings(DPOLossSettings):
+    loss_type: Literal[DPOLossesType.NCA_PAIR]
+
+
 class SyncRefModelSettings(ExtraFieldsNotAllowedBaseModel):
     sync_ref_model: bool = False
     alpha: float = 1.0
@@ -114,6 +119,7 @@ class DPOTrainerSettings(TrainerSettings):
         | APOZeroLossSettings
         | APODownLossSettings
         | DPOPLossSettings
+        | NCAPairLossSettings
     )
     sync_ref_settings: SyncRefModelSettings
     use_ref_model: bool = True

turbo_alignment/trainers/dpo.py

@@ -33,6 +33,7 @@
     HingeLossSettings,
     IPOLossSettings,
     KTOLossSettings,
+    NCAPairLossSettings,
     ORPOLossSettings,
     SigmoidLossSettings,
     SigmoidLossWithMarginSettings,
@@ -456,6 +457,35 @@ def compute_loss(
         return loss, chosen_rewards, rejected_rewards
 
 
+@DPOLossRegistry.register(DPOLossesType.NCA_PAIR)
+class NCAPairLoss(DPOLossRegistry):
+    def __init__(self, *args, beta: float = 0.1, **kwargs) -> None:
+        self.beta = beta
+        super().__init__(*args, **kwargs)
+
+    def compute_loss(
+        self,
+        policy_chosen_logps: torch.FloatTensor,
+        policy_rejected_logps: torch.FloatTensor,
+        reference_chosen_logps: torch.FloatTensor,
+        reference_rejected_logps: torch.FloatTensor,
+        precomputed_margins: torch.FloatTensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        chosen_logratios = policy_chosen_logps - reference_chosen_logps
+        rejected_logratios = policy_rejected_logps - reference_rejected_logps
+
+        chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach()
+        rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach()
+
+        loss = (
+            -F.logsigmoid(chosen_logratios * self.beta)
+            - 0.5 * F.logsigmoid(-chosen_logratios * self.beta)
+            - 0.5 * F.logsigmoid(-rejected_logratios * self.beta)
+        )
+
+        return loss, chosen_rewards, rejected_rewards
+
+
 @dataclass
 class DPOTrainingArguments(TrainingArguments):
     loss_settings: (
@@ -473,6 +503,7 @@ class DPOTrainingArguments(TrainingArguments):
         | APOZeroLossSettings
         | APODownLossSettings
         | DPOPLossSettings
+        | NCAPairLossSettings
     ) = field(
         default_factory=SigmoidLossSettings(loss_type=DPOLossesType.SIGMOID)
     )  # type: ignore[call-overload]
@@ -822,6 +853,7 @@ 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')