Metadata-Version: 2.4
Name: verl
Version: 0.2
Summary: verl: Volcano Engine Reinforcement Learning for LLM
Home-page: https://github.com/volcengine/verl
Author: Bytedance - Seed - MLSys
Author-email: Bytedance - Seed - MLSys <zhangchi.usc1992@bytedance.com>, Bytedance - Seed - MLSys <gmsheng@connect.hku.hk>
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# verl 使用文档

## 使用流程

verl 整体 rl 流程可以看 `verl/trainer/ppo/ray_trainer.py`，也可以看他们论文（https://arxiv.org/abs/2409.19256）
1. 用 actor 做 rollout，会把 batch_size 个 query 按卡分配（现在修改为：把 query 重复 rollout.n 次，然后把 batch_size * rollout.n 个 query 按卡分配），然后用 vllm 把相应卡上的 query 推一遍，推完会把 vllm offload。rollout 参数是起 job 时传入的
2. 计算 old_log_prob 和 ref_log_prob。ref_log_prob 用的是 init 时的 ref model，old_log_prob 是梯度更新前的 actor
3. 用 critic model 算 value，grpo 里不用这个
4. 计算 reward 和 advantage，具体 reward 的计算部分写在了 `verl/trainer/main_ppo.py`里
5. actor 梯度更新。这里会用 mini_batch_size 个 query 及其相应的 response 来分步更新（所以非首步的其实都是 semi on-policy 样本）
6. 把 actor 参数更新给 vllm
7. 测 val 分数

## 参数说明

rl 部分参数都在 `verl/trainer/config/ppo_trainer.yaml` 和 `verl/trainer/config/ppo_megatron_trainer.yaml` 这里面。性能调优可以看下 verl 文档里的这章：https://verl.readthedocs.io/en/latest/perf/perf_tuning.html。运行脚本可以参考这个：`scripts/custom_math.sh`

有几个值得说明的点：
- 需要提前预处理好数据，参考 `examples/data_preprocess/custom_math_easy.py`
   - 目前 val 时的解码参数是 hardcode，所以采样多次是通过在预处理数据里重复测试样本实现的。例如，测 aime24 的 16 次采样平均，目前是把提前复制了 16 份
- **batch_size 是 rollout 的 query 数，需要满足 batch_size * rollout.n 被 gpu_num 整除**
- **mini_batch_size 是每次梯度更新所用的 query 数，需要满足 batch_size 被 mini_batch_size 整除，且 mini_batch_size 被 gpu_num // sp_size（即 dp_size）整除**
- vllm 的 gpu_memory_utilization 需要结合显存和 actor、ref、critic 的 offload 情况设置。显存很大且其他东西不存在显存里时，gpu_memory_utilization 可以开大。同样，rollout.n 也可以开得很大，因为就是 vllm 本身的实现
- （hf 模型）use_remove_padding，其实就是sample packing，默认打开就完事。注意如果这个不开，后面的 ulysses_sequence_parallel_size 就会无效（可见 `verl/workers/actor/dp_actor.py`）
- （hf 模型）use_dynamic_bsz 和 ppo_max_token_len_per_gpu，可以动态调整算梯度时的 batch size，无需考虑梯度累积时的 batch size。hf 模型也可以通过 ulysses_sequence_parallel_size 开序列并行。具体怎么调就得看模型和数据了，不 oom 就行

## dlc 与版本说明

- 参考 dlc（注意修改成自己的工作目录和 wandb 项目名）：http://pai-console.cf8069398312a46ff8da458965cefe2cb.cn-shanghai.alicontainer.com/index?workspaceId=ws1hvwsg7mc6esqb#/dlc2/job/dlc8wyjgnhsrhkdp/detail?Token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJleHAiOjE3MzkwOTgxODEsImlhdCI6MTczODQ5MzM4MSwidXNlcl9pZCI6InpoZW5nY2h1amllLnpjaiIsInRhcmdldF9pZCI6ImRsYzh3eWpnbmhzcmhrZHAiLCJ0YXJnZXRfdHlwZSI6ImpvYiJ9.pZBxgjuTBIUjelhKaGVhZYrzK0iOqu27G1p__FULNTA
- dlc 镜像：**m6-docker-registry-vpc.cn-shanghai.cr.aliyuncs.com/eflops/zhengchujie-zcj:ngc-cuda124-torch240-vllm063-ray210**
   - 镜像内已经修复了 vllm063 的生成乱码的问题（https://github.com/vllm-project/vllm/pull/9549）
   - **镜像里的 ray 版本是 2.10.0。之前尝试 2.40.0 会报错（内存泄露？）然后挂掉。2.10.0 也会报错但不会挂掉**
      - ray 2.40.0 报错信息（报完后就挂掉了）：![](figs/ray2.40.0_err_msg_0.png) ![](figs/ray2.40.0_err_msg_1.png)
      - ray 2.10.0 报错信息（报完后继续跑）：![](figs/ray2.10.0_err_msg.png)
- verl：目前是 clone 了 verl 截至 250127 的版本（同时修了[这个 pr](https://github.com/volcengine/verl/commit/ab525bce267e4b3805ceabac8441afc4385bba96) 里的 bug）
   - **改动 1**：基于 symeval 写了一个数学答案抽取和判等的 rule-based rm（`verl/utils/reward_score/custom_math.py`, symeval 自带超时控制防止 hang 住），并以 batch 形式计算reward (`verl/trainer/main_ppo.py`) 提高速度
   - **改动 2**：在 val 时 hardcode 写死了采样参数（`verl/trainer/ppo/ray_trainer.py`, 413 行；`verl/workers/rollout/vllm_rollout/vllm_rollout.py`, 179 行），以便实时监控评测集分数（如 aime24 的采样平均值）
   - **改动 3**：数据预处理时，提前套好 chat template (`verl/utils/dataset/rl_dataset.py`)，然后训练时直接把 prompt 字段喂给模型。这样 instruct 和 base 可以走同样的流程
   - **改动 4**：rollout.n 的采样，实现为重复 query n 次，有利于长生成的稳定性

## 一些优化点

- [ ] 【高优支持】Megatron+MOE
- [ ] 【高优支持】支持任务加载续跑，目前挂掉后需要手动起新job、指定 ckpt 路径重新训
- [ ] 【高优支持】支持 validation 阶段的解码参数传入，rollout 生成结果和validation结果保存
- [ ] 【性能优化】ray 报错信息，各种报错信息有更好的统一管理与观察的方式
- [ ] 【性能优化】rollout 时各节点的推理长度和耗时不一，生成完的节点会提前结束并闲置。碰到一两个生成特别长的节点可能会效率过低
- [ ] 【性能优化】vllm深度优化；新版 vllm 也开始加入“休眠”模式，以支持 rl 的需求。verl 那边说后续会接黑盒 vllm 进来，可以保持关注

---

**以下是原文档**

---

<h1 style="text-align: center;">veRL: Volcano Engine Reinforcement Learning for LLM</h1>

verl is a flexible, efficient and production-ready RL training library for large language models (LLMs).

verl is the open-source version of **[HybridFlow: A Flexible and Efficient RLHF Framework](https://arxiv.org/abs/2409.19256v2)** paper.

verl is flexible and easy to use with:

- **Easy extension of diverse RL algorithms**: The Hybrid programming model combines the strengths of single-controller and multi-controller paradigms to enable flexible representation and efficient execution of complex Post-Training dataflows. Allowing users to build RL dataflows in a few lines of code.

- **Seamless integration of existing LLM infra with modular APIs**: Decouples computation and data dependencies, enabling seamless integration with existing LLM frameworks, such as PyTorch FSDP, Megatron-LM and vLLM. Moreover, users can easily extend to other LLM training and inference frameworks.

- **Flexible device mapping**: Supports various placement of models onto different sets of GPUs for efficient resource utilization and scalability across different cluster sizes.

- Readily integration with popular HuggingFace models


verl is fast with:

- **State-of-the-art throughput**: By seamlessly integrating existing SOTA LLM training and inference frameworks, verl achieves high generation and training throughput.

- **Efficient actor model resharding with 3D-HybridEngine**: Eliminates memory redundancy and significantly reduces communication overhead during transitions between training and generation phases.

<p align="center">
| <a href="https://verl.readthedocs.io/en/latest/index.html"><b>Documentation</b></a> | <a href="https://arxiv.org/abs/2409.19256v2"><b>Paper</b></a> | <a href="https://join.slack.com/t/verlgroup/shared_invite/zt-2w5p9o4c3-yy0x2Q56s_VlGLsJ93A6vA"><b>Slack</b></a> | <a href="https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/WeChat.JPG"><b>Wechat</b></a> | <a href="https://x.com/verl_project"><b>Twitter</b></a>

<!-- <a href=""><b>Slides</b></a> | -->
</p>

## News

- [2025/2] We will present verl in the [Bytedance/NVIDIA/Anyscale Ray Meetup](https://lu.ma/ji7atxux) in bay area on Feb 13th. Come join us in person!
- [2025/1] [Doubao-1.5-pro](https://team.doubao.com/zh/special/doubao_1_5_pro) is released with SOTA-level performance on LLM & VLM. The RL scaling preview model is trained using verl, reaching OpenAI O1-level performance on math benchmarks (70.0 pass@1 on AIME).
- [2024/12] The team presented <a href="https://neurips.cc/Expo/Conferences/2024/workshop/100677">Post-training LLMs: From Algorithms to Infrastructure</a> at NeurIPS 2024. [Slides](https://github.com/eric-haibin-lin/verl-data/tree/neurips) and [video](https://neurips.cc/Expo/Conferences/2024/workshop/100677) available.
- [2024/10] verl is presented at Ray Summit. [Youtube video](https://www.youtube.com/watch?v=MrhMcXkXvJU&list=PLzTswPQNepXntmT8jr9WaNfqQ60QwW7-U&index=37) available.
- [2024/08] HybridFlow (verl) is accepted to EuroSys 2025.

## Key Features

- **FSDP** and **Megatron-LM** for training.
- **vLLM** and **TGI** for rollout generation, **SGLang** support coming soon.
- huggingface models support
- Supervised fine-tuning
- Reinforcement learning from human feedback with [PPO](https://github.com/volcengine/verl/tree/main/examples/ppo_trainer), [GRPO](https://github.com/volcengine/verl/tree/main/examples/grpo_trainer), and [ReMax](https://github.com/volcengine/verl/tree/main/examples/remax_trainer)
  - Support model-based reward and function-based reward (verifiable reward)
- flash-attention, [sequence packing](examples/ppo_trainer/run_qwen2-7b_seq_balance.sh), [long context](examples/ppo_trainer/run_deepseek7b_llm_sp2.sh) support via DeepSpeed Ulysses, [LoRA](examples/sft/gsm8k/run_qwen_05_peft.sh), [Liger-kernel](examples/sft/gsm8k/run_qwen_05_sp2_liger.sh)
- scales up to 70B models and hundreds of GPUs
- experiment tracking with wandb, swanlab and mlflow

## Upcoming Features
- Reward model training
- DPO training
- DeepSeek integration with Megatron backend
- SGLang integration

## Getting Started

Checkout this [Jupyter Notebook](https://github.com/volcengine/verl/tree/main/examples/ppo_trainer/verl_getting_started.ipynb) to get started with PPO training with a single 24GB L4 GPU (**FREE** GPU quota provided by [Lighting Studio](https://lightning.ai/hlin-verl/studios/verl-getting-started))!

**Quickstart:**
- [Installation](https://verl.readthedocs.io/en/latest/start/install.html)
- [Quickstart](https://verl.readthedocs.io/en/latest/start/quickstart.html)
- [Programming Guide](https://verl.readthedocs.io/en/latest/hybrid_flow.html)

**Running a PPO example step-by-step:**
- Data and Reward Preparation
  - [Prepare Data for Post-Training](https://verl.readthedocs.io/en/latest/preparation/prepare_data.html)
  - [Implement Reward Function for Dataset](https://verl.readthedocs.io/en/latest/preparation/reward_function.html)
- Understanding the PPO Example
  - [PPO Example Architecture](https://verl.readthedocs.io/en/latest/examples/ppo_code_architecture.html)
  - [Config Explanation](https://verl.readthedocs.io/en/latest/examples/config.html)
  - [Run GSM8K Example](https://verl.readthedocs.io/en/latest/examples/gsm8k_example.html)

**Reproducible algorithm baselines:**
- [PPO and GRPO](https://verl.readthedocs.io/en/latest/experiment/ppo.html)

**For code explanation and advance usage (extension):**
- PPO Trainer and Workers
  - [PPO Ray Trainer](https://verl.readthedocs.io/en/latest/workers/ray_trainer.html)
  - [PyTorch FSDP Backend](https://verl.readthedocs.io/en/latest/workers/fsdp_workers.html)
  - [Megatron-LM Backend](https://verl.readthedocs.io/en/latest/index.html)
- Advance Usage and Extension
  - [Ray API design tutorial](https://verl.readthedocs.io/en/latest/advance/placement.html)
  - [Extend to Other RL(HF) algorithms](https://verl.readthedocs.io/en/latest/advance/dpo_extension.html)
  - [Add Models with the FSDP Backend](https://verl.readthedocs.io/en/latest/advance/fsdp_extension.html)
  - [Add Models with the Megatron-LM Backend](https://verl.readthedocs.io/en/latest/advance/megatron_extension.html)
  - [Deployment using Separate GPU Resources](https://github.com/volcengine/verl/tree/main/examples/split_placement)

## Performance Tuning Guide
The performance is essential for on-policy RL algorithm. We write a detailed performance tuning guide to allow people tune the performance. See [here](https://verl.readthedocs.io/en/latest/perf/perf_tuning.html) for more details.

## vLLM v0.7 testing version
We have released a testing version of veRL that supports vLLM>=0.7.0. Please refer to [this document](https://github.com/volcengine/verl/docs/README_vllm0.7.md) for installation guide and more information.

## Contribution Guide
Contributions from the community are welcome!

### Code formatting
We use yapf (Google style) to enforce strict code formatting when reviewing PRs. To reformat you code locally, make sure you installed **latest** `yapf`
```bash
pip3 install yapf --upgrade
```
Then, make sure you are at top level of verl repo and run
```bash
bash scripts/format.sh
```

## Citation and acknowledgement

If you find the project helpful, please cite:
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://arxiv.org/abs/2409.19256v2)
- [A Framework for Training Large Language Models for Code Generation via Proximal Policy Optimization](https://i.cs.hku.hk/~cwu/papers/gmsheng-NL2Code24.pdf)

```tex
@article{sheng2024hybridflow,
  title   = {HybridFlow: A Flexible and Efficient RLHF Framework},
  author  = {Guangming Sheng and Chi Zhang and Zilingfeng Ye and Xibin Wu and Wang Zhang and Ru Zhang and Yanghua Peng and Haibin Lin and Chuan Wu},
  year    = {2024},
  journal = {arXiv preprint arXiv: 2409.19256}
}
```

verl is inspired by the design of Nemo-Aligner, Deepspeed-chat and OpenRLHF. The project is adopted and supported by Anyscale, Bytedance, LMSys.org, Shanghai AI Lab, Tsinghua University, UC Berkeley, UCLA, UIUC, and University of Hong Kong.

## Awesome work using verl
- [Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization](https://arxiv.org/abs/2410.09302)
- [Flaming-hot Initiation with Regular Execution Sampling for Large Language Models](https://arxiv.org/abs/2410.21236)
- [Process Reinforcement Through Implicit Rewards](https://github.com/PRIME-RL/PRIME/)
- [TinyZero](https://github.com/Jiayi-Pan/TinyZero): a reproduction of DeepSeek R1 Zero recipe for reasoning tasks
- [RAGEN](https://github.com/ZihanWang314/ragen): a general-purpose reasoning agent training framework
- [Logic R1](https://github.com/Unakar/Logic-RL): a reproduced DeepSeek R1 Zero on 2K Tiny Logic Puzzle Dataset.
- [deepscaler](https://github.com/agentica-project/deepscaler): iterative context scaling with GRPO
- [critic-rl](https://github.com/HKUNLP/critic-rl): Teaching Language Models to Critique via Reinforcement Learning

We are HIRING! Send us an [email](mailto:haibin.lin@bytedance.com) if you are interested in internship/FTE opportunities in MLSys/LLM reasoning/multimodal alignment.
