- As titled

add requirements to make some CI tests work

We have implemented the REINFORCE++ algorithm.

To use it, specify the parameter
`algorithm.adv_estimator=reinforce_plus_plus`.

Preliminary performance evaluations were conducted within the
[Unakar/Logic-RL](https://github.com/Unakar/Logic-RL) project, a
reproduction of DeepSeek R1 Zero on the 2K Tiny Logic Puzzle Dataset.
Results indicate that our REINFORCE++ implementation exhibits
performance and training stability comparable to, or potentially
exceeding, that of PPO and GRPO.

Related issue: #68

**Features:**
- Save actor and critic checkpoint:
  - Model
  - Optimizer
  - lr_scheduler
  - rng_state
  - dataloader
- A complete checkpoint represents that dataloader, actor and critic (if
any) state are properly saved
- By default, we will not save the dataset but only store the dataloader
(with sampler) state

**Usage:**
- Support resume mode: auto, disable and resume_from_path
- auto: veRL will automatically check the latest checkpoint from
`trainer.default_local_dir`
   - disable: veRL will always train from scratch
- resume_from_path: When setting `resume_from_path`=True, then user only
need to set the resume_mode to the checkpoint path that you want to
load.

**TODO:**
- Support SFT resume in the next PR
- Support uploader

**Relevant issue:**
- https://github.com/volcengine/verl/issues/76
- https://github.com/volcengine/verl/issues/143

Fix typo tips in bash sft.

Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>

Existing `logprobs_from_logits_v2` doesnt achieve the memory savings it
claims. This is because `logsumexp` still allocates a `bs*seqlen*vocab`
tensor internally to hold the element-wise application of `exp`.
However, by applying a loop over `logsumexp`, we can iteratively compute
logsumexp outputs.

Benchmarks show this uses significantly less memory to compute logprobs.

Fix provided, as well as a separate memory-efficient approach for
bfloat16 case.

[TRACKING] feat: Integrate SwanLab for experiment tracking with online/offline mode and local dashboard support (#218)

---

### Pull Request Description  

This PR introduces **SwanLab**, a lightweight open-source experiment
tracking tool, as a new logging option for the training framework. The
integration provides both online and offline tracking capabilities,
along with a local dashboard for visualizing results. Below is a
detailed overview of the changes and usage instructions:

---

#### **Key Features of SwanLab Integration**

1. **Online and Offline Tracking**:
- **Online Mode**: Track experiments remotely and store data on
SwanLab's cloud platform.
- **Offline Mode**: Use a local dashboard to visualize training logs
without an internet connection.

2. **Hardware Monitoring**:
- Automatically tracks GPU usage, power consumption, temperature, and
other hardware metrics.
   - Supports NVIDIA GPUs and Huawei Ascend NPUs.

3. **Remote Access**:
- View training progress remotely via the SwanLab web interface or
mobile app.

4. **Local Dashboard**:
- Includes an open-source local dashboard for offline visualization of
training logs.

---

#### **Usage Instructions**

##### **Step 1: Set Up Online Tracking (Optional)**

To use SwanLab's online tracking, log in to the [SwanLab
website](https://swanlab.cn) and obtain your API key from the [Settings
page](https://swanlab.cn/space/~/settings). Then, authenticate using the
following command:

```bash
swanlab login
```

If you prefer offline mode, skip this step.

---

##### **Step 2: Configure SwanLab as the Logger**

To enable SwanLab as the experiment tracker, add
`trainer.logger=['swanlab']` to your training command. For example,
using the [Post-train a LLM using PPO with GSM8K
dataset](https://verl.readthedocs.io/en/latest/start/quickstart.html)
workflow:

```bash
PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \
 data.train_files=$HOME/data/gsm8k/train.parquet \
 data.val_files=$HOME/data/gsm8k/test.parquet \
 data.train_batch_size=256 \
 data.val_batch_size=1312 \
 data.max_prompt_length=512 \
 data.max_response_length=256 \
 actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \
 actor_rollout_ref.actor.optim.lr=1e-6 \
 actor_rollout_ref.actor.ppo_mini_batch_size=64 \
 actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \
 actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \
 actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
 actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \
 actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \
 critic.optim.lr=1e-5 \
 critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \
 critic.ppo_micro_batch_size_per_gpu=4 \
 algorithm.kl_ctrl.kl_coef=0.001 \
 trainer.logger=['console','swanlab'] \
 +trainer.val_before_train=False \
 trainer.default_hdfs_dir=null \
 trainer.n_gpus_per_node=1 \
 trainer.nnodes=1 \
 trainer.save_freq=10 \
 trainer.test_freq=10 \
 trainer.total_epochs=15 2>&1 | tee verl_demo.log
```

If you are not logged in, you will be prompted to choose a tracking
mode:

1. **Cloud Mode**: Upload logs to SwanLab's cloud platform.
2. **Cloud-Only Mode**: Upload logs to the cloud but do not save them
locally.
3. **Local Mode**: Save logs locally for offline tracking.

<img width="1325" alt="select"
src="https://github.com/user-attachments/assets/5c55fc45-79a9-4673-ae4e-ea9d0623dd29"
/>

Alternatively, you can configure SwanLab using environment variables:

```bash
export SWANLAB_API_KEY=<your_api_key>          # Set API key for online tracking
export SWANLAB_LOG_DIR=<local_log_path>        # Set local log directory
export SWANLAB_MODE=<mode>                    # Set tracking mode: cloud (default), cloud-only, local, or disabled
```

---

##### **Step 3: View Training Logs**

After logging in, you will see a confirmation message:

<img width="1415" alt="track"
src="https://github.com/user-attachments/assets/87c4ff2f-c8c4-4e7a-a41e-21afa935cb56"
/>

- **Online Tracking**: View logs on the [SwanLab
website](https://swanlab.cn).

<img width="1900" alt="remote"
src="https://github.com/user-attachments/assets/5b44b9f3-948f-4f93-9873-572bce56daf7"
/>

For more details, refer to the [SwanLab Cloud
Documentation](https://docs.swanlab.cn/guide_cloud/experiment_track/view-result.html).

- **Offline Tracking**: Use the local dashboard to visualize logs:

  ```bash
  swanlab watch
  ```

For advanced configurations, such as setting a custom port, refer to the
[Offline Dashboard
Documentation](https://docs.swanlab.cn/guide_cloud/self_host/offline-board.html)
and [CLI
Documentation](https://docs.swanlab.cn/api/cli-swanlab-watch.html#%E8%AE%BE%E7%BD%AEip%E5%92%8C%E7%AB%AF%E5%8F%A3%E5%8F%B7).

---

#### **Impact**

- Provides a lightweight, flexible, and user-friendly experiment
tracking solution.
- Supports both online and offline use cases, making it suitable for
environments with restricted internet access.
- Enhances hardware monitoring capabilities for better resource
utilization.

---

This PR is ready for review. Feedback and suggestions are welcome!

This PR addresses issue https://github.com/volcengine/verl/issues/212.

The changes include:
- read eos_token_id from generation_config to ensure alignment with vLLM
- modified the get_eos_mask function to accept both int and list types
for the eos_token parameter.

- Support FSDPCheckpointManager
- Support hdfs_io import if installed
- Add CI for FSDPCheckpointManager

TODO:
- Will integrate in the next PR

Install the scorecard workflow

use the general purpose LLM for the math task instead of code LLM.

---------

Co-authored-by: Your Name <you@example.com>

- As titled
- Relevant: https://github.com/volcengine/verl/issues/181

- As titled

- Move config to a class method of `RayPPOTrainer`
- Fix config problem when adv_estimator=grpo
- Add GRPO e2e CI

https://github.com/volcengine/verl/pull/182

add a assert statement to make sure flash-attn>=2.4.3 where
cross_entropy_loss returns Tuple[losses, z_losses]🤯

This PR is similar to PR https://github.com/volcengine/verl/pull/174 but
fix the critic save error

I move the old PR to this one due to some redundant commit

sry missed this last one, should be it 

cc @vermouth1992

Co-authored-by: Jayson Francis <jaysonfrancis@users.noreply.github.com>

runs always show "crashed" on my wandb, despite finishing successfully.
"Crashed" indicates that wandb did not finish sending the "success"
signal to the server so the server believes the client was terminated
unexpectedly. Furthermore, wandb log is incomplete (last lines missing).

This PR adds a call to `wandb.finish` when the Tracker was destructed
(oftentimes when `trainer.fit` finished) so that signals are sent to the
server and a data sync is performed.

Without this change:
<img width="526" alt="image"
src="https://github.com/user-attachments/assets/869da24e-c5b8-415c-b15a-bb79c49f96ce"
/>

With this change:
<img width="548" alt="image"
src="https://github.com/user-attachments/assets/16f0a40d-ea3b-48ed-93a4-f40ee01cb7c6"
/>

Co-authored-by: Jayson Francis <jaysonfrancis@users.noreply.github.com>

This PR adds documentation for the LigerKernel option in a new
performance tuning section, addressing the comment from
volcengine/verl#173.

Changes:
- Created new performance tuning section in docs
- Documented LigerKernel option for SFT
- Added performance tuning section to documentation index

Related to volcengine/verl#173

---------

Co-authored-by: openhands <openhands@all-hands.dev>
Co-authored-by: HL <linhaibin.eric@gmail.com>

runnning -> running

since 'lighteval/MATH' is no longer available on huggingface.

- As titled

![image](https://github.com/user-attachments/assets/f0bae990-a4ef-49da-aa1e-58894b41db5f)

---------

Co-authored-by: HL <linhaibin.eric@gmail.com>

This is a follow-up to https://github.com/volcengine/verl/issues/151

## Motivation

Currently, in order to add a custom score function you need to fork verl
and update the `_select_rm_score_fn` to define your logic. This makes it
harder to use verl as part of a larger application while staying up to
date with upstream improvements in verl.

It would be convenient to allow end users to directly pass in a reward
function they wish to use, without requiring them to clone/fork verl to
do so.

## Design

In this PR I slightly modify `main_ppo.py` to allow users to import a
new function `run_ppo`. `run_ppo` behaves very similarly to the existing
`main`, with the important addition of a new `compute_score` argument.
This argument, if passed in, is used to compute the score of every
generation. This is the change that allows

The `compute_score` function is similar in shape to the existing
`compute_score` on gsm8k and math. However, I have added a new
`data_source` parameter so that the user can compute the score
differently if desired depending on the task shape.

## Example Usage

This is a sample script showing how you can use the new functionality. I
have tested that this works.

```python
from verl.trainer.main_ppo import run_ppo
from omegaconf import OmegaConf


def custom_compute_score(data_source, solution_str, ground_truth):
    """Dummy compute_score function that reward the model for generations of exactly 20 characters :)
    """
    return abs(len(solution_str) - 20)


config = OmegaConf.load("vendor/verl/verl/trainer/config/ppo_trainer.yaml")

# Update config as needed
config.data.train_files = "path/to/train.parquet"
config.data.val_files = "path/to/test.parquet"
# ...

run_ppo(config, custom_compute_score)
```

## Breaking changes

There are no breaking changes in this PR. It is still possible to call
`python -m verl.trainer.main_ppo ...` as before (although if you want to
pass in a custom compute_score you will need to use the new method
described above).

## Possible future work

It would be great to move to [structured
configs](https://omegaconf.readthedocs.io/en/2.1_branch/structured_config.html)
as well since they'd allow us to have typesafe, autocompletable
configurations from Python. I thought about adding those changes here as
well but they would be much more extensive and I'm not sure whether
there's interest from the project.

## Summary

This PR enables to use Liger Kernel's `_apply_liger_kernel_to_instance`
to init a fsdp worker model.

## Main Changes

1. Adding an option of using
`liger_kernel.transformers.AutoLigerKernelForCausalLM` to load a model
from pretained, instead of the default
`transformers.AutoModelForCausalLM`
2. Added a test case using configuration file
`tests/e2e/run_qwen_gsm8k_model_rm_liger_kernel.sh`

## Related Issue

#96 

## TODO

#97 optimize the memory usage when computing entropy & log_probs

https://github.com/volcengine/verl/blob/6d96fda3d47f057caaa8f494ca7804181903e911/verl/workers/actor/dp_actor.py#L94-L106

---------

Signed-off-by: Hongpeng Guo <hpguo@anyscale.com>

The logits is of shape `(bsz, response_length, vocab_size)`. This PR
doesn't change any code execution, but explicitly show the logits shape
and easier for readers to understand the code.

Signed-off-by: Hongpeng Guo <hpguo@anyscale.com>