implement-simpo-loss

name	implement_simpo_loss
description	Implements the SimPO (Simple Preference Optimization) loss function with length normalization and reward margin as specified in the research paper.

1. Implement the Logic: In SimPOTrainer.simpo_loss within /root/SimPO/scripts/simpo_trainer.py, implement the following mathematical steps:
   • Calculate the log probabilities for the winning (chosen) and losing (rejected) responses.
   • Length Normalization: Divide the log probabilities of each sequence by its length ($L$): $p_{norm} = \frac{1}{L} \log \pi(x, y)$.
   • Reward Calculation: Calculate rewards as $R = \beta \cdot p_{norm}$.
   • SimPO Loss: Use the formula: $\mathcal{L}{SimPO} = -\mathbb{E}{(x, y_w, y_l)} [\log \sigma(\beta p_{norm}(y_w|x) - \beta p_{norm}(y_l|x) - \gamma)]$ where $\gamma$ is the target reward margin and $\beta$ is the scale.
2. Verification:
   • Import the required libraries (torch, numpy).
   • Ensure the function returns the loss tensor in the format expected by the trainer.