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#!/usr/bin/env python3
"""
GRPO training script for arithmetic countdown problems.
This script trains a language model using GRPO (Group Relative Policy Optimization)
to solve arithmetic problems with proper reasoning and formatting.
"""
import argparse
import logging
import os
from collections.abc import Callable
from pathlib import Path
from datasets import Dataset
from peft import LoraConfig, get_peft_model
from src.utils.dataset import load_csv_dataset
from transformers import AutoModelForCausalLM, PreTrainedModel
from trl import GRPOConfig, GRPOTrainer
from src.utils.rewards import (
mathematical_correctness_reward_function,
)
# Set up logging
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger("grpo_training")
def load_train_dataset(
dataset_csv: str, max_rows: int = 2000, seed: int = 42
) -> Dataset:
"""
Load, shuffle, and subsample the training dataset.
Args:
dataset_csv: Absolute path to the dataset CSV file
max_rows: Maximum number of rows to select for training
seed: Seed for dataset shuffling
Returns:
Dataset: A datasets.Dataset ready for GRPO training
"""
raw_dataset: Dataset = load_csv_dataset(dataset_csv)
raw_dataset = raw_dataset.shuffle(seed=seed)
train_dataset = raw_dataset.select(range(min(max_rows, len(raw_dataset))))
logger.info("Train rows: %d", len(train_dataset))
return train_dataset
def create_lora_model(model_id: str, device_map: str = "cuda") -> PreTrainedModel:
"""
Create a base causal LM and wrap it with LoRA adapters.
Args:
model_id: Hugging Face model identifier to load as the base model
device_map: Device mapping strategy for model loading
Returns:
PreTrainedModel: A transformers.PreTrainedModel with LoRA adapters applied
"""
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map=device_map,
)
lora_cfg = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_cfg)
logger.info("Model with LoRA ready")
return model
def create_grpo_config(
output_dir: str,
learning_rate: float = 5e-6,
num_train_epochs: int = 1,
per_device_train_batch_size: int = 1,
gradient_accumulation_steps: int = 16,
max_completion_length: int = 512,
num_generations: int = 16,
temperature: float = 1.0,
save_steps: int = 50,
logging_steps: int = 1,
max_prompt_length: int = 4096,
) -> GRPOConfig:
"""
Create GRPO training configuration.
Args:
output_dir: Directory where checkpoints and logs will be written
learning_rate: Learning rate for training
num_train_epochs: Number of training epochs
per_device_train_batch_size: Batch size per device
gradient_accumulation_steps: Steps to accumulate gradients
max_completion_length: Maximum length for completions
num_generations: Number of generations per prompt
temperature: Sampling temperature
save_steps: Steps between model saves
logging_steps: Steps between log outputs
max_prompt_length: Maximum length for input prompts
Returns:
GRPOConfig: A configured trl.GRPOConfig instance
"""
return GRPOConfig(
output_dir=output_dir,
learning_rate=learning_rate,
weight_decay=0.01,
warmup_ratio=0.1,
lr_scheduler_type="linear",
optim="adamw_8bit",
remove_unused_columns=False,
gradient_accumulation_steps=gradient_accumulation_steps,
num_train_epochs=num_train_epochs,
bf16=True,
per_device_train_batch_size=per_device_train_batch_size,
temperature=temperature,
# Preprocessing controls
max_completion_length=max_completion_length,
num_generations=num_generations,
max_prompt_length=max_prompt_length,
# Logging and saving
report_to=["tensorboard"],
logging_steps=logging_steps,
save_strategy="steps",
save_steps=save_steps,
)
def create_trainer(
model: PreTrainedModel,
train_dataset: Dataset,
args: GRPOConfig,
) -> GRPOTrainer:
"""
Construct a GRPOTrainer with arithmetic-specific reward functions.
Args:
model: The LoRA-wrapped pretrained model to train
train_dataset: The dataset to use for training
args: The GRPO configuration
Returns:
GRPOTrainer: An initialized trl.GRPOTrainer instance
"""
reward_funcs: list[Callable[..., list[float]]] = [
mathematical_correctness_reward_function,
]
trainer = GRPOTrainer(
model=model,
reward_funcs=reward_funcs,
args=args,
train_dataset=train_dataset,
)
return trainer
def train_and_save(trainer: GRPOTrainer, output_dir: str) -> None:
"""
Run training and save the final model to disk.
Args:
trainer: The configured GRPO trainer instance
output_dir: Output directory to save the trained model
Returns:
None
"""
train_result = trainer.train()
logger.info("Training complete: %s", str(train_result))
trainer.save_model(output_dir)
logger.info("Saved to %s", output_dir)
def main() -> None:
"""
Run the full GRPO training workflow with command-line arguments.
Returns:
None
"""
parser = argparse.ArgumentParser(
description="Train a language model using GRPO for arithmetic countdown problems"
)
# Dataset arguments
parser.add_argument(
"--dataset_csv",
type=str,
required=True,
help="Path to the training dataset CSV file",
)
parser.add_argument(
"--max_rows", type=int, default=2000, help="Maximum number of training samples"
)
parser.add_argument(
"--seed", type=int, default=42, help="Random seed for dataset shuffling"
)
# Model arguments
parser.add_argument(
"--model_id",
type=str,
default="Qwen/Qwen2.5-3B-Instruct",
help="Hugging Face model identifier",
)
parser.add_argument(
"--device_map", type=str, default="x", help="Device mapping strategy"
)
# Training arguments
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Directory to save model checkpoints and logs",
)
parser.add_argument(
"--learning_rate", type=float, default=5e-6, help="Learning rate"
)
parser.add_argument(
"--num_train_epochs", type=int, default=1, help="Number of training epochs"
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=1,
help="Batch size per device",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=16,
help="Gradient accumulation steps",
)
parser.add_argument(
"--max_completion_length",
type=int,
default=512,
help="Maximum completion length",
)
parser.add_argument(
"--num_generations",
type=int,
default=16,
help="Number of generations per prompt",
)
parser.add_argument(
"--temperature", type=float, default=1.0, help="Sampling temperature"
)
parser.add_argument(
"--save_steps", type=int, default=50, help="Steps between model saves"
)
parser.add_argument(
"--logging_steps", type=int, default=1, help="Steps between log outputs"
)
parser.add_argument(
"--max_prompt_length",
type=int,
default=4096,
help="Maximum length for input prompts",
)
args = parser.parse_args()
# Validate arguments
if not Path(args.dataset_csv).exists():
logger.error("Dataset CSV file does not exist: %s", args.dataset_csv)
return
if args.max_rows <= 0:
logger.error("max_rows must be positive")
return
# Create output directory
os.makedirs(args.output_dir, exist_ok=True)
logger.info("Output dir: %s", args.output_dir)
# Load dataset
train_dataset = load_train_dataset(args.dataset_csv, args.max_rows, args.seed)
# Create model
model = create_lora_model(args.model_id, args.device_map)
# Create training configuration
training_args = create_grpo_config(
output_dir=args.output_dir,
learning_rate=args.learning_rate,
num_train_epochs=args.num_train_epochs,
per_device_train_batch_size=args.per_device_train_batch_size,
gradient_accumulation_steps=args.gradient_accumulation_steps,
max_completion_length=args.max_completion_length,
num_generations=args.num_generations,
temperature=args.temperature,
save_steps=args.save_steps,
logging_steps=args.logging_steps,
max_prompt_length=args.max_prompt_length,
)
# Create trainer
trainer = create_trainer(
model=model, train_dataset=train_dataset, args=training_args
)
# Train and save
train_and_save(trainer=trainer, output_dir=args.output_dir)
if __name__ == "__main__":
main()
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