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Author SHA1 Message Date
Chi Zhang
2e1a1a6603 [BREAKING] [rollout] chore: remove default rollout selection (#2757) 
### What does this PR do?

As title

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# Add code snippet or script demonstrating how to use this
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 2025-07-26 10:11:24 -07:00
Kai-Hsun Chen
a31a8f251f [doc] fix: quickstart example can't work on zsh (#2509) 
### What does this PR do?

I followed the instructions at
https://verl.readthedocs.io/en/latest/start/quickstart.html to run the
PPO example on my devbox, which uses zsh. However, I got the error zsh:
no matches found: `trainer.logger=[console]` because `[]` is interpreted
as a glob pattern in zsh.

```
(verl) ➜  verl git:(20250713-devbox-2-tmux0-verl-2) ✗ 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.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'] \
 trainer.val_before_train=False \
 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
zsh: no matches found: trainer.logger=[console]
```

This PR has 3 changes:
* `trainer.logger=['console']` -> `trainer.logger=console`
* `trainer.logger=['console','wandb']` ->
`trainer.logger='["console","wandb"]'`
* `trainer.logger=['console','tensorboard']` ->
`trainer.logger='["console","tensorboard"]'`

### Checklist Before Starting

- [x] Search for similar PRs. Paste at least one query link here: ...
- [x] Format the PR title as `[{modules}] {type}: {description}` (This
will be checked by the CI)
- `{modules}` include `fsdp`, `megatron`, `sglang`, `vllm`, `rollout`,
`trainer`, `ci`, `training_utils`, `recipe`, `hardware`, `deployment`,
`ray`, `worker`, `single_controller`, `misc`, `perf`, `model`, `algo`,
`env`, `tool`, `ckpt`, `doc`, `data`
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`[megatron, fsdp, doc]`
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- If this PR breaks any API (CLI arguments, config, function signature,
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  - Example: `[BREAKING][fsdp, megatron] feat: dynamic batching`

### Test

* `trainer.logger=console` (zsh)
<img width="898" height="564" alt="image"
src="https://github.com/user-attachments/assets/a957a493-75e6-462b-9974-6b1c4cdf5a80"
/>

* ``trainer.logger='["console","wandb"]'`` (zsh)
<img width="870" height="565" alt="image"
src="https://github.com/user-attachments/assets/e20613bf-2ccc-4653-b23f-90edc3d568d1"
/>

* `trainer.logger=console` (bash)
  ```bash
ubuntu@ip-xxx-xx-x-xxx:~/verl$ 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.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 \
  >  trainer.val_before_train=False \
  >  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
2025-07-14 02:52:27,669 INFO worker.py:1908 -- Started a local Ray
instance. View the dashboard at 127.0.0.1:8265
(TaskRunner pid=1799248) TaskRunner hostname: ip-172-31-9-244, PID:
1799248
(TaskRunner pid=1799248) {'actor_rollout_ref': {'actor': {'checkpoint':
{'load_contents': ['model',
(TaskRunner pid=1799248) 'optimizer',
(TaskRunner pid=1799248) 'extra'],
(TaskRunner pid=1799248) 'save_contents': ['model',
(TaskRunner pid=1799248) 'optimizer',
(TaskRunner pid=1799248) 'extra']},
  ```

* `trainer.logger='["console","wandb"]'` (bash)
  ```bash
ubuntu@ip-xxx-xx-x-xxx:~/verl$ 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.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","wandb"]' \
  >  trainer.val_before_train=False \
  >  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
2025-07-14 02:54:13,989 INFO worker.py:1908 -- Started a local Ray
instance. View the dashboard at 127.0.0.1:8265
(TaskRunner pid=1805000) TaskRunner hostname: ip-172-31-9-244, PID:
1805000
(TaskRunner pid=1805000) {'actor_rollout_ref': {'actor': {'checkpoint':
{'load_contents': ['model',
(TaskRunner pid=1805000) 'optimizer',
(TaskRunner pid=1805000) 'extra'],
(TaskRunner pid=1805000) 'save_contents': ['model',
(TaskRunner pid=1805000) 'optimizer',
(TaskRunner pid=1805000) 'extra']},
  ```

### API and Usage Example

No

### Design & Code Changes

No

### Checklist Before Submitting

> [!IMPORTANT]
> Please check all the following items before requesting a review,
otherwise the reviewer might deprioritize this PR for review.

- [x] Read the [Contribute
Guide](https://github.com/volcengine/verl/blob/main/CONTRIBUTING.md).
- [x] Apply [pre-commit
checks](https://github.com/volcengine/verl/blob/main/CONTRIBUTING.md#code-linting-and-formatting):
`pre-commit install && pre-commit run --all-files --show-diff-on-failure
--color=always`
- [x] Add / Update [the
documentation](https://github.com/volcengine/verl/tree/main/docs).
- [ ] Add unit or end-to-end test(s) to [the CI
workflow](https://github.com/volcengine/verl/tree/main/.github/workflows)
to cover all the code. If not feasible, explain why: ...
- [x] Once your PR is ready for CI, send a message in [the `ci-request`
channel](https://verl-project.slack.com/archives/C091TCESWB1) in [the
`verl` Slack
workspace](https://join.slack.com/t/verl-project/shared_invite/zt-3855yhg8g-CTkqXu~hKojPCmo7k_yXTQ).

---------

Signed-off-by: Kai-Hsun Chen <kaihsun@anyscale.com>
 2025-07-14 13:26:32 +08:00
H
249c26fdc8 [tests] BREAKING: move recipe.dapo.src to recipe.dapo; move test files to their own namespaces (tests/verl/xxx -> tests/xxx) (#1392) 2025-05-10 11:21:53 +08:00
Shawn/Yuxuan Tong
ed498f9fa5 [recipe] feat: latest reproduction of DAPO (#1336) 
# What does this PR do?

This PR updates the latest reproduction results of DAPO.

## Before submitting

- [x] Did you read the [Contribute
Guide](https://github.com/volcengine/verl?tab=readme-ov-file#contribution-guide)
and finish the [code format
check](https://github.com/volcengine/verl?tab=readme-ov-file#code-linting-and-formatting)?
- [x] Did you make sure to update the documentations with your changes
in the [docs](https://github.com/volcengine/verl/tree/main/docs)
especially for breaking config etc?
- [x] Did you write any test cases if neccessary? Please add CI tests to
your new feature.

# Additional Info: 

- **Issue Number**: none
- **Training**: none
- **Inference**: none
 2025-05-01 12:03:46 +08:00