124 lines
3.7 KiB
Rust
124 lines
3.7 KiB
Rust
use crate::Result;
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use rtx_tensor::{Device, Tensor};
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use serde::{Deserialize, Serialize};
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct DPOConfig {
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pub learning_rate: f64,
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pub beta: f32,
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pub reference_model_weight: f32,
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pub batch_size: usize,
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pub max_grad_norm: f32,
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}
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impl Default for DPOConfig {
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fn default() -> Self {
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Self {
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learning_rate: 1e-4,
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beta: 0.1,
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reference_model_weight: 0.9,
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batch_size: 32,
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max_grad_norm: 1.0,
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}
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}
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}
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#[derive(Debug)]
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pub struct DPOMetrics {
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pub loss: f32,
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pub accuracy: f32,
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pub chosen_reward: f32,
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pub rejected_reward: f32,
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pub reward_margin: f32,
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}
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pub struct DPO {
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config: DPOConfig,
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state_dim: usize,
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action_dim: usize,
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device: Device,
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policy_params: Vec<Tensor>,
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reference_params: Vec<Tensor>,
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}
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impl DPO {
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pub fn new(config: DPOConfig, state_dim: usize, action_dim: usize, device: Device) -> Self {
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// Initialize dummy parameters for testing
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let policy_params = vec![
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Tensor::randn(&[128, state_dim], &device).unwrap(),
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Tensor::randn(&[128], &device).unwrap(),
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Tensor::randn(&[action_dim, 128], &device).unwrap(),
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Tensor::randn(&[action_dim], &device).unwrap(),
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];
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let reference_params = policy_params.clone();
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Self {
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config,
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state_dim,
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action_dim,
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device,
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policy_params,
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reference_params,
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}
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}
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pub async fn compute_preference_loss(
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&self,
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states: &Tensor,
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_chosen_actions: &Tensor,
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_rejected_actions: &Tensor,
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) -> Result<DPOMetrics> {
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let batch_size = states.shape().dims()[0];
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// Compute log probabilities for chosen and rejected actions
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let _chosen_log_probs = Tensor::randn(&[batch_size], &self.device)? * -1.0;
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let _rejected_log_probs = Tensor::randn(&[batch_size], &self.device)? * -2.0;
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// Reference model log probabilities
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let _ref_chosen_log_probs = Tensor::randn(&[batch_size], &self.device)? * -1.5;
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let _ref_rejected_log_probs = Tensor::randn(&[batch_size], &self.device)? * -1.8;
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// DPO loss computation
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let chosen_rewards: Tensor = Tensor::randn(&[batch_size], &self.device)?;
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let rejected_rewards: Tensor = Tensor::randn(&[batch_size], &self.device)?;
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// Simplified for RED phase
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let logits: Tensor = Tensor::randn(&[batch_size], &self.device)?;
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let sigmoid_logits = logits.sigmoid()?;
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let log_sigmoid = sigmoid_logits.log()?;
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let mean_log = log_sigmoid.mean(&[], false)?;
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let loss = mean_log.neg()?;
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// Compute metrics
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// Simplified for RED phase
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let accuracy = Tensor::full(&[1], 0.8, &self.device)?;
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let chosen_reward = chosen_rewards.mean(&[], false)?;
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let rejected_reward = rejected_rewards.mean(&[], false)?;
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let reward_margin = (&chosen_reward - &rejected_reward)?;
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Ok(DPOMetrics {
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loss: loss.item()?,
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accuracy: accuracy.item()?,
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chosen_reward: chosen_reward.item()?,
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rejected_reward: rejected_reward.item()?,
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reward_margin: reward_margin.item()?,
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})
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}
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pub async fn update(
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&mut self,
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states: &Tensor,
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chosen_actions: &Tensor,
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rejected_actions: &Tensor,
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) -> Result<DPOMetrics> {
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let metrics = self
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.compute_preference_loss(states, chosen_actions, rejected_actions)
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.await?;
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// In real implementation, we'd perform gradient updates here
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Ok(metrics)
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}
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}
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