GRPO Llama-1B

grpo_demo.py

# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

# Load and prep dataset

SYSTEM_PROMPT = """
Respond in the following format:

<reasoning>
...
</reasoning>
<answer>
...
</answer>
"""

XML_COT_FORMAT = """\
<reasoning>
{reasoning}
</reasoning>
<answer>
{answer}
</answer>
"""

def extract_xml_answer(text: str) -> str:
    answer = text.split("<answer>")[-1]
    answer = answer.split("</answer>")[0]
    return answer.strip()

def extract_hash_answer(text: str) -> str | None:
    if "####" not in text:
        return None
    return text.split("####")[1].strip().replace(",", "").replace("$", "")

# uncomment middle messages for 1-shot prompting
def get_gsm8k_questions(split = "train") -> Dataset:
    data = load_dataset('openai/gsm8k', 'main')[split] # type: ignore
    data = data.map(lambda x: { # type: ignore
        'prompt': [
            {'role': 'system', 'content': SYSTEM_PROMPT},
            #{'role': 'user', 'content': 'What is the largest single-digit prime number?'},
            #{'role': 'assistant', 'content': XML_COT_FORMAT.format(
            #    reasoning="9 is divisble by 3 and 8 is divisible by 2, but 7 is prime.",
            #    answer="7"
            #)},
            {'role': 'user', 'content': x['question']}
        ],
        'answer': extract_hash_answer(x['answer'])
    }) # type: ignore
    return data # type: ignore

dataset = get_gsm8k_questions()

# Reward functions
def correctness_reward_func(prompts, completions, answer, **kwargs) -> list[float]:
    responses = [completion[0]['content'] for completion in completions]
    q = prompts[0][-1]['content']
    extracted_responses = [extract_xml_answer(r) for r in responses]
    print('-'*20, f"Question:\n{q}", f"\nAnswer:\n{answer[0]}", f"\nResponse:\n{responses[0]}", f"\nExtracted:\n{extracted_responses[0]}")
    return [2.0 if r == a else 0.0 for r, a in zip(extracted_responses, answer)]

def int_reward_func(completions, **kwargs) -> list[float]:
    responses = [completion[0]['content'] for completion in completions]
    extracted_responses = [extract_xml_answer(r) for r in responses]
    return [0.5 if r.isdigit() else 0.0 for r in extracted_responses]

def strict_format_reward_func(completions, **kwargs) -> list[float]:
    """Reward function that checks if the completion has a specific format."""
    pattern = r"^<reasoning>\n.*?\n</reasoning>\n<answer>\n.*?\n</answer>\n$"
    responses = [completion[0]["content"] for completion in completions]
    matches = [re.match(pattern, r, flags=re.DOTALL) for r in responses] 
    return [0.5 if match else 0.0 for match in matches]

def soft_format_reward_func(completions, **kwargs) -> list[float]:
    """Reward function that checks if the completion has a specific format."""
    pattern = r"<reasoning>.*?</reasoning>\s*<answer>.*?</answer>"
    responses = [completion[0]["content"] for completion in completions]
    matches = [re.match(pattern, r, flags=re.DOTALL) for r in responses] 
    return [0.5 if match else 0.0 for match in matches]

def count_xml(text) -> float:
    count = 0.0
    if text.count("<reasoning>\n") == 1:
        count += 0.125
    if text.count("\n</reasoning>\n") == 1:
        count += 0.125
    if text.count("\n<answer>\n") == 1:
        count += 0.125
        count -= len(text.split("\n</answer>\n")[-1])*0.001
    if text.count("\n</answer>") == 1:
        count += 0.125
        count -= (len(text.split("\n</answer>")[-1]) - 1)*0.001
    return count

def xmlcount_reward_func(completions, **kwargs) -> list[float]:
    contents = [completion[0]["content"] for completion in completions]
    return [count_xml(c) for c in contents]

#model_name = "meta-llama/Llama-3.2-1B-Instruct"
model_name = "Qwen/Qwen2.5-1.5B-Instruct"

if "Llama" in model_name:
    output_dir = "outputs/Llama-1B-GRPO"
    run_name = "Llama-1B-GRPO-gsm8k"
else:
    output_dir="outputs/Qwen-1.5B-GRPO"
    run_name="Qwen-1.5B-GRPO-gsm8k"
    
training_args = GRPOConfig(
    output_dir=output_dir,
    run_name=run_name,
    learning_rate=5e-6,
    adam_beta1 = 0.9,
    adam_beta2 = 0.99,
    weight_decay = 0.1,
    warmup_ratio = 0.1,
    lr_scheduler_type='cosine',
    logging_steps=1,
    bf16=True,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=4,
    num_generations=16,
    max_prompt_length=256,
    max_completion_length=786,
    num_train_epochs=1,
    save_steps=100,
    max_grad_norm=0.1,
    report_to="wandb",
    log_on_each_node=False,
)
peft_config = LoraConfig(
    r=16,
    lora_alpha=64,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "down_proj", "gate_proj"],
    task_type="CAUSAL_LM",
    lora_dropout=0.05,
)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map=None
).to("cuda")
        
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

# use peft at your own risk; not working for me with multi-GPU training
trainer = GRPOTrainer(
    model=model,
    processing_class=tokenizer,
    reward_funcs=[
        xmlcount_reward_func,
        soft_format_reward_func,
        strict_format_reward_func,
        int_reward_func,
        correctness_reward_func],
    args=training_args,
    train_dataset=dataset,
    #peft_config=peft_config
)
trainer.train()

Has any one been able to train anything >3B I still cant finetune large models without OOM even with a H100x8有人训练过 30 亿以上参数的模型吗？我甚至无法在 H100x8 上微调大型模型，因为会内存不足

I got the same problem. I trained 7B with batch_size == 1, but it just keep reporting oom.我遇到了同样的问题。我用 batch_size == 1 训练了 7B，但它一直报告 oom。

Did you solve the problem?你解决这个问题了吗？

me， too！！I also issue my problem in the trl's github. If you solve the problem, please help me.

I used an A100（40 GB） on the colab. But it can barely support the training of a 1.5B model.
And I found that GRPO does not support efficient fine-tuning, and can only fine-tune all parameters，issue, Can anyone solve this problem?or.. Are there other reinforcement learning frameworks that support efficient parameter fine-tuning（peft）?
After about 3 hours of fine-tuning, the effect of the 1.5B model is indeed better than that of the 0.5B model.

a 1.5B model can be trained for nearly 460 steps in 3 hours with an a100

Can someone share the code of evaluating the model on Test set of gsm8k? Thanks a lot!!!!!!有人能提供一下在gsm'8k数据的测试集上评估模型训练结果的代码吗？非常感谢！

how to adjust the params with just two A10?

@willccbb, why would we prefer separate reward functions instead of having a single unified one in GRPO?

being able to log individual rewards is pretty useful for debugging imo

consolidating them into one shouldn't affect actual training dynamics though

IMHO separate, additive rewards introduce a lot of repetition (e.g., parsing responses) and limit creativity in reward design, e.g., I may want the formatting reward to be a gate for the others, as I may not even want to evaluate a response if the formatting is wrong.

definitely get creative with it! nothing wrong with using if statements + multiplication in your reward functions

Does it also work on smaller models like >3B params model?

When training on the GPU with qwen model, I encountered the error: " probability tensor contains either inf, nan or element < 0"

Hi @fsxbhyy, did you load the model in torch.bfloat16? I used to encounter such issue when I loaded models in torch.float16 instead of bfloat. I guess float16 in this context leads to numerical instability, leading to NaN probs. Hope this helps!

I got the same problem. I trained 7B with batch_size == 1, but it just keep reporting oom.

@harrywoo @Tuziking I had the same problem. I then noticed that these values are actually huge for most cases:

max_prompt_length=256,
max_completion_length=786,

786 generated tokens to process per generation requires a lot of memory, especially if your group size is large. Try to set this to 150 or 250 and see if it reduces memory usage. Hope this helps!