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ludwig/trainers/trainer_rlhf.py

@@ -1,14 +1,15 @@
 import logging
-import torch
 from typing import Dict, List, Optional, Tuple
 
+import torch
+
+from ludwig.distributed.base import DistributedStrategy
 from ludwig.models.llm import LLM
 from ludwig.modules.loss_modules import RewardLoss
 from ludwig.schema.trainer import RewardModelTrainerConfig
 from ludwig.trainers.registry import register_llm_trainer
 from ludwig.trainers.trainer import Trainer
 from ludwig.utils.defaults import default_random_seed
-from ludwig.distributed.base import DistributedStrategy
 
 logger = logging.getLogger(__name__)
 
@@ -85,8 +86,8 @@ def train_step(
  # Run forward-propagation of the chosen and rejected inputs
  with self.distributed.prepare_model_update(self.dist_model, should_step=should_step):
  # Obtain model predictions and loss
- model_output_chosen = self.dist_model((inputs[chosen_value]))
- model_output_rejected = self.dist_model((inputs[rejected_value]))
+ model_output_chosen = self.dist_model(inputs[chosen_value])
+ model_output_rejected = self.dist_model(inputs[rejected_value])
  loss = self.reward_loss_function(model_output_chosen, model_output_rejected)
  loss = loss / self.gradient_accumulation_steps
  all_losses = loss