ppo pseudo code

ppo.py

# Pseudocode for estimating advantage function in RLHF using PPO

import numpy as np
import torch
import torch.nn.functional as F

def compute_ppo_loss(policy, old_policy, token_sequences, advantages, returns, clip_epsilon=0.2):
  """
  Compute the PPO loss for language model policy update
  
  policy: Current policy (language model)
  old_policy: Policy used to generate the token sequences
  token_sequences: List of generated token sequences
  advantages: Estimated advantages for each token prediction
  returns: Estimated returns for each token prediction
  clip_epsilon: PPO clipping parameter
  """
  total_loss = 0
  
  for sequence, sequence_advantages, sequence_returns in zip(token_sequences, advantages, returns):
      for t in range(len(sequence) - 1):  # -1 because we're predicting the next token
          current_sequence = sequence[:t+1]
          next_token = sequence[t+1]
          
          # Compute log probabilities
          current_log_prob = policy.log_prob(current_sequence, next_token)
          old_log_prob = old_policy.log_prob(current_sequence, next_token)
          
          # Compute probability ratio
          ratio = torch.exp(current_log_prob - old_log_prob)
          
          # Compute surrogate losses
          surrogate1 = ratio * sequence_advantages[t]
          surrogate2 = torch.clamp(ratio, 1 - clip_epsilon, 1 + clip_epsilon) * sequence_advantages[t]
          
          # Compute policy loss
          policy_loss = -torch.min(surrogate1, surrogate2).mean()
          
          # Compute entropy bonus (optional, to encourage exploration)
          entropy = policy.entropy(current_sequence).mean()
          
          # Combine losses
          total_loss += policy_loss - ENTROPY_COEFFICIENT * entropy
  
  return total_loss / len(token_sequences)

def compute_value_loss(value_function, token_sequences, returns):
  """
  Compute the value function loss
  
  value_function: Current value function
  token_sequences: List of generated token sequences
  returns: Estimated returns for each token prediction
  """
  total_loss = 0
  
  for sequence, sequence_returns in zip(token_sequences, returns):
      for t in range(len(sequence)):
          current_sequence = sequence[:t+1]
          predicted_value = value_function(current_sequence)
          actual_return = sequence_returns[t]
          
          # Compute MSE loss
          value_loss = F.mse_loss(predicted_value, actual_return)
          
          total_loss += value_loss
  
  return total_loss / len(token_sequences)




def estimate_advantages(trajectories, reward_model, value_function):
  """
  trajectories: List of (state, action, reward) tuples
  reward_model: Learned reward function from RLHF
  value_function: Current estimate of the value function
  """
  
  advantages = []
  returns = []
  
  for trajectory in trajectories:
      trajectory_advantages = []
      trajectory_returns = []
      
      # Compute returns (discounted sum of rewards)
      R = 0
      for t in reversed(range(len(trajectory))):
          state, action, _ = trajectory[t]
          
          # Use the learned reward model to estimate the reward
          reward = reward_model.predict(state, action)
          
          R = reward + GAMMA * R  # GAMMA is the discount factor
          trajectory_returns.insert(0, R)
      
      # Estimate advantages
      for t in range(len(trajectory)):
          state, _, _ = trajectory[t]
          if t + 1 < len(trajectory):
              next_state, _, _ = trajectory[t+1]
              td_error = trajectory_returns[t] + GAMMA * value_function(next_state) - value_function(state)
          else:
              td_error = trajectory_returns[t] - value_function(state)
          
          trajectory_advantages.append(td_error)
      
      # Normalize advantages (optional, but often helps with training stability)
      trajectory_advantages = (trajectory_advantages - np.mean(trajectory_advantages)) / (np.std(trajectory_advantages) + 1e-8)
      
      advantages.extend(trajectory_advantages)
      returns.extend(trajectory_returns)
  
  return advantages, returns

def update_policy_ppo(policy, value_function, trajectories, reward_model):
  """
  Main PPO update loop
  """
  for _ in range(PPO_EPOCHS):
      # Sample mini-batches of trajectories
      mini_batches = sample_mini_batches(trajectories)
      
      for mini_batch in mini_batches:
          # Estimate advantages and returns
          advantages, returns = estimate_advantages(mini_batch, reward_model, value_function)
          
          # Compute PPO loss
          ppo_loss = compute_ppo_loss(policy, mini_batch, advantages)
          
          # Update policy
          policy.update(ppo_loss)
          
          # Update value function
          value_loss = compute_value_loss(value_function, mini_batch, returns)
          value_function.update(value_loss)

# Main training loop
reward_model = train_reward_model(human_feedback_data)
policy = initialize_policy()
value_function = initialize_value_function()

for iteration in range(NUM_ITERATIONS):
  trajectories = generate_trajectories(policy)
  update_policy_ppo(policy, value_function, trajectories, reward_model)

# Print the names of any files created or modified during execution:
print("No files were created or modified during the execution of this pseudocode.")