train_grpo.py

import torch
from trl import GRPOTrainer, GRPOConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
from context_pruning_env.env import ContextPruningEnv
from context_pruning_env.models import PruningAction

# 1. Setup Environment
env = ContextPruningEnv(squad_split="train")

def reward_func(prompts, completions, **kwargs):
    """
    Reward function wrapper for GRPOTrainer.
    """
    rewards = []
    for prompt, completion in zip(prompts, completions):
        # In a real GRPOTrainer setup, we process multiple completions for the same prompt.
        # Here we simulate the interface mapping back to our environment logic.
        
        # 1. Extract action mask from completion (LLM output)
        # Assuming the model outputs something like "Action: [1, 0, 1, 1, 0]"
        try:
            # Simple parse logic
            if "[" in completion and "]" in completion:
                mask_str = completion.split("[")[1].split("]")[0]
                mask = [int(x.strip()) for x in mask_str.split(",")]
            else:
                mask = [1, 1, 1, 1, 1] # Fallback to keeping everything
        except:
            mask = [1, 1, 1, 1, 1]

        # 2. Step the environment (Simulated for the snippet)
        # In actual GRPO, we might reset env to the state corresponding to the prompt.
        # env.reset(seed=...)
        action = PruningAction(mask=mask)
        obs = env.step(action)
        rewards.append(obs.reward)
        
    return rewards

def main():
    model_id = "meta-llama/Llama-3-8B" # Reference model
    
    # 2. Config for GRPO
    training_args = GRPOConfig(
        output_dir="./llama-3-rag-pruning",
        learning_rate=5e-6,
        per_batch_size=1,
        gradient_accumulation_steps=16,
        num_train_epochs=3,
        logging_steps=10,
        group_size=8, # GRPO specific: group size for relative reward calculation
    )

    # 3. Initialize Trainer
    # Note: In a real implementation, you'd need the dataset formatted for the trainer
    trainer = GRPOTrainer(
        model=model_id,
        reward_funcs=[reward_func],
        args=training_args,
        # train_dataset=rag_pruning_dataset, # Pre-formatted dataset
    )

    print("Starting Training with GRPOTrainer...")
    # trainer.train()

if __name__ == "__main__":
    main()