unsloth/scripts/benchmarks/qwen3_grpo_vllm.py

"""Qwen3-4B GRPO baseline (vLLM colocated) derived from the notebook.

Run:
    CUDA_VISIBLE_DEVICES=2 python scripts/qwen3_grpo_vllm.py \
        --max_steps 61 --output_dir outputs/grpo_vllm \
        --stats_path logs/vllm_stats.json
"""

from __future__ import annotations

import argparse
import os
import sys
import time
from pathlib import Path

# Unsloth must be imported before transformers / trl.
os.environ.setdefault("UNSLOTH_VLLM_STANDBY", "1")

# Allow sibling import of the common module.
HERE = Path(__file__).resolve().parent
sys.path.insert(0, str(HERE))

from unsloth import FastLanguageModel  # noqa: E402
import torch  # noqa: E402

from unsloth_grpo_common import (  # noqa: E402
    StepTimer,
    apply_chat_template_to_tokenizer,
    build_dataset,
    build_grpo_kwargs,
    build_reward_funcs,
    write_stats,
)


def parse_args():
    p = argparse.ArgumentParser()
    p.add_argument("--model_name", default = "unsloth/Qwen3-4B-Base")
    p.add_argument("--max_seq_length", type = int, default = 2048)
    p.add_argument("--lora_rank", type = int, default = 32)
    p.add_argument("--max_steps", type = int, default = 61)
    p.add_argument("--num_generations", type = int, default = 4)
    p.add_argument("--per_device_train_batch_size", type = int, default = 1)
    p.add_argument("--gradient_accumulation_steps", type = int, default = 1)
    p.add_argument("--gpu_memory_utilization", type = float, default = 0.8)
    p.add_argument("--output_dir", default = "outputs/grpo_vllm")
    p.add_argument("--stats_path", default = "logs/vllm_stats.json")
    return p.parse_args()


def main():
    args = parse_args()
    os.makedirs(args.output_dir, exist_ok = True)
    os.makedirs(os.path.dirname(os.path.abspath(args.stats_path)) or ".", exist_ok = True)

    # 1. Load model with vLLM fast inference enabled.
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name = args.model_name,
        max_seq_length = args.max_seq_length,
        load_in_4bit = False,
        fast_inference = True,
        max_lora_rank = args.lora_rank,
        gpu_memory_utilization = args.gpu_memory_utilization,
    )
    model = FastLanguageModel.get_peft_model(
        model,
        r = args.lora_rank,
        target_modules = [
            "q_proj",
            "k_proj",
            "v_proj",
            "o_proj",
            "gate_proj",
            "up_proj",
            "down_proj",
        ],
        lora_alpha = args.lora_rank * 2,
        use_gradient_checkpointing = "unsloth",
        random_state = 3407,
    )
    apply_chat_template_to_tokenizer(tokenizer)

    # 2. Dataset + rewards.
    dataset, maximum_length = build_dataset(
        tokenizer, max_seq_length = args.max_seq_length
    )
    print(f"[vllm] Max prompt length (p90): {maximum_length}")
    reward_funcs = build_reward_funcs(tokenizer)

    # 3. vLLM sampling params match the notebook.
    from vllm import SamplingParams

    vllm_sampling_params = SamplingParams(
        min_p = 0.1,
        top_p = 1.0,
        top_k = -1,
        seed = 3407,
        stop = [tokenizer.eos_token],
        include_stop_str_in_output = True,
    )

    # 4. Build GRPOConfig.
    from trl import GRPOConfig, GRPOTrainer

    shared = build_grpo_kwargs(
        tokenizer,
        maximum_length,
        max_seq_length = args.max_seq_length,
        max_steps = args.max_steps,
        num_generations = args.num_generations,
        per_device_train_batch_size = args.per_device_train_batch_size,
        gradient_accumulation_steps = args.gradient_accumulation_steps,
        output_dir = args.output_dir,
    )
    training_args = GRPOConfig(
        use_vllm = True,
        vllm_mode = "colocate",
        vllm_sampling_params = vllm_sampling_params,
        vllm_gpu_memory_utilization = args.gpu_memory_utilization,
        **shared,
    )

    # 5. Timing callback.
    timer = StepTimer()

    trainer = GRPOTrainer(
        model = model,
        processing_class = tokenizer,
        reward_funcs = reward_funcs,
        args = training_args,
        train_dataset = dataset,
        callbacks = [timer],
    )

    torch.cuda.reset_peak_memory_stats()
    t_start = time.perf_counter()
    trainer.train()
    t_train = time.perf_counter() - t_start

    peak = torch.cuda.max_memory_allocated() / 1024**3

    write_stats(
        args.stats_path,
        backend = "vllm_colocated",
        timer = timer,
        train_wall_s = t_train,
        peak_memory_gb = peak,
        max_prompt_length = shared["max_prompt_length"],
        max_completion_length = shared["max_completion_length"],
        num_generations = args.num_generations,
        max_steps = args.max_steps,
    )
    print(f"[vllm] Wrote stats to {args.stats_path}")
    print(f"[vllm] Total train wall: {t_train:.1f}s   Peak mem: {peak:.2f} GB")


if __name__ == "__main__":
    main()