""" BioForge GRPO training. Fine-tunes a (SFT-warm-started) Llama-3.1-8B-Instruct with Group Relative Policy Optimisation (GRPO) using TRL + Unsloth, with BioForge reward functions as verifiers. Usage: python train/grpo_train.py \ --model outputs/bioforge-sft/merged \ --env_url http://localhost:7860 \ --out outputs/bioforge-grpo \ --steps 2000 --batch 64 Environment variables: WANDB_PROJECT — W&B project name (optional) HF_TOKEN — HF API key for uploading (optional) """ from __future__ import annotations import argparse import json import os import random import re import sys import time import uuid from pathlib import Path from typing import Any, Dict, List, Optional import httpx import torch from datasets import Dataset try: from unsloth import FastLanguageModel, PatchFastRL PatchFastRL("GRPO", FastLanguageModel) UNSLOTH = True except ImportError: UNSLOTH = False from trl import GRPOConfig, GRPOTrainer # Ensure parent on path ROOT = Path(__file__).parent.parent sys.path.insert(0, str(ROOT)) # ───────────────────────────────────────────────────────────────────────────── # Environment interaction # ───────────────────────────────────────────────────────────────────────────── def env_reset(base_url: str, task_id: str, difficulty: str) -> Dict[str, Any]: resp = httpx.post( f"{base_url}/reset", json={"task_id": task_id, "difficulty": difficulty, "payload": {}}, timeout=30.0, ) resp.raise_for_status() d = resp.json() return d.get("observation", d) def env_step(base_url: str, action: Dict[str, Any]) -> Dict[str, Any]: resp = httpx.post(f"{base_url}/step", json=action, timeout=30.0) resp.raise_for_status() d = resp.json() return d.get("observation", d) # ───────────────────────────────────────────────────────────────────────────── # Reward function (used by GRPOTrainer as reward_fn) # ───────────────────────────────────────────────────────────────────────────── ENV_URL: str = "" # set at training time TASK_LIST = [ ("variant_triage", "easy"), ("pharmacogenomics", "easy"), ("drug_synergy", "medium"), ("genetic_counseling", "medium"), ("neoantigen_vaccine", "hard"), ] def extract_json_safe(text: str) -> Optional[Dict[str, Any]]: """Extract JSON from model generation.""" fence_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL) if fence_match: candidate = fence_match.group(1) else: brace_match = re.search(r"\{.*\}", text, re.DOTALL) if not brace_match: return None candidate = brace_match.group(0) try: return json.loads(candidate) except json.JSONDecodeError: return None def bioforge_reward_fn(completions: List[str], prompts: List[str], **kwargs) -> List[float]: """ TRL GRPO reward function. Each completion is a model generation; we step the env once and return the reward. The task/difficulty are injected via kwargs from the dataset. """ rewards: List[float] = [] task_ids: List[str] = kwargs.get("task_id", ["variant_triage"] * len(completions)) difficulties: List[str] = kwargs.get("difficulty", ["easy"] * len(completions)) episode_obs_list: List[Dict[str, Any]] = kwargs.get("episode_obs", [{}] * len(completions)) for completion, task_id, difficulty, obs in zip(completions, task_ids, difficulties, episode_obs_list): action_dict = extract_json_safe(completion) if action_dict is None: rewards.append(-0.1) # penalty for invalid JSON continue action_payload = { "task_id": task_id, "difficulty": difficulty, "payload": action_dict, } try: step_obs = env_step(ENV_URL, action_payload) reward = float(step_obs.get("reward", 0.0)) except Exception: reward = 0.0 rewards.append(reward) return rewards # ───────────────────────────────────────────────────────────────────────────── # Rollout dataset builder # ───────────────────────────────────────────────────────────────────────────── def build_rollout_dataset(n_prompts: int = 500) -> Dataset: """ Build prompts for GRPO by resetting the environment and extracting initial observations. Each row: {prompt, task_id, difficulty, episode_obs}. """ from bioforge.inference import SYSTEM_PROMPTS, build_user_prompt # lazy import records = [] rng = random.Random(42) for _ in range(n_prompts): task_id, difficulty = rng.choice(TASK_LIST) try: obs = env_reset(ENV_URL, task_id, difficulty) except Exception: obs = {} system = SYSTEM_PROMPTS.get(task_id, "You are a biomedical AI agent. Output JSON.") user = build_user_prompt(task_id, obs) prompt = f"<|system|>{system}<|end|>\n<|user|>{user}<|end|>\n<|assistant|>" records.append({ "prompt": prompt, "task_id": task_id, "difficulty": difficulty, "episode_obs": obs, }) return Dataset.from_list(records) # ───────────────────────────────────────────────────────────────────────────── # Main # ───────────────────────────────────────────────────────────────────────────── def main() -> None: global ENV_URL parser = argparse.ArgumentParser() parser.add_argument("--model", default="unsloth/Qwen2.5-3B-Instruct", help="Base model or fine-tuned checkpoint. Default: Qwen2.5-3B-Instruct (fits T4 16GB)") parser.add_argument("--env_url", default="http://localhost:7860") parser.add_argument("--out", default="outputs/bioforge-grpo") parser.add_argument("--steps", type=int, default=500, help="GRPO training steps. 500=quick (~1hr T4), 2000=full") parser.add_argument("--batch", type=int, default=8) parser.add_argument("--n_prompts", type=int, default=300) parser.add_argument("--max_seq_length", type=int, default=1536) parser.add_argument("--lr", type=float, default=5e-6) parser.add_argument("--push_to_hub", default="", help="HF repo id to push trained model (e.g. user/bioforge-grpo)") args = parser.parse_args() ENV_URL = args.env_url.rstrip("/") # Load model if UNSLOTH: model, tokenizer = FastLanguageModel.from_pretrained( model_name=args.model, max_seq_length=args.max_seq_length, dtype=None, load_in_4bit=True, ) model = FastLanguageModel.get_peft_model( model, r=16, target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], lora_alpha=16, lora_dropout=0.0, bias="none", use_gradient_checkpointing="unsloth", ) else: from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from peft import LoraConfig, get_peft_model tokenizer = AutoTokenizer.from_pretrained(args.model) tokenizer.pad_token = tokenizer.eos_token model = AutoModelForCausalLM.from_pretrained( args.model, quantization_config=BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16), device_map="auto", ) model = get_peft_model(model, LoraConfig(r=16, lora_alpha=16, target_modules=["q_proj", "v_proj"])) # Build dataset print(f"[GRPO] Building rollout dataset ({args.n_prompts} prompts)...") dataset = build_rollout_dataset(args.n_prompts) out_dir = Path(args.out) out_dir.mkdir(parents=True, exist_ok=True) grpo_config = GRPOConfig( output_dir=str(out_dir), max_steps=args.steps, num_generations=8, # G in GRPO: rollouts per prompt per_device_train_batch_size=args.batch // 8, gradient_accumulation_steps=8, learning_rate=args.lr, lr_scheduler_type="cosine", warmup_ratio=0.05, max_completion_length=512, temperature=0.9, top_p=0.95, logging_steps=5, save_steps=200, save_total_limit=2, report_to="wandb" if os.environ.get("WANDB_PROJECT") else "none", run_name="bioforge-grpo", bf16=torch.cuda.is_bf16_supported(), fp16=not torch.cuda.is_bf16_supported(), ) trainer = GRPOTrainer( model=model, processing_class=tokenizer, config=grpo_config, train_dataset=dataset, reward_funcs=bioforge_reward_fn, ) print(f"[GRPO] Starting GRPO training for {args.steps} steps...") trainer.train() if UNSLOTH: model.save_pretrained_merged(str(out_dir / "merged"), tokenizer, save_method="merged_16bit") else: model.save_pretrained(str(out_dir)) tokenizer.save_pretrained(str(out_dir)) print(f"[GRPO] Training complete. Model saved to {out_dir}") if args.push_to_hub: hf_token = os.environ.get("HF_TOKEN", "") if UNSLOTH: model.push_to_hub_merged( args.push_to_hub, tokenizer, save_method="lora", token=hf_token, commit_message="BioForge GRPO fine-tuned", ) else: model.push_to_hub(args.push_to_hub, token=hf_token) tokenizer.push_to_hub(args.push_to_hub, token=hf_token) print(f"[GRPO] Pushed to https://huggingface.co/{args.push_to_hub}") if __name__ == "__main__": main()