Files
verl/scripts/print_cfg.py
H d0c7bbbc05 [cfg] refactor: support +extra.any_key usage for the base dataclass config in verl (#2502)
### What does this PR do?

This PR makes update to the base config in verl:
- support +extra.any_key usage for the base config in verl.
- allow selective subfields to be frozen
- add a auto-generated config yaml file
`verl/trainer/config/_generated_ppo_trainer.yaml` for reference purpose,
in case the nested inheritance structure makes the config information
too scattered

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### Test

- added frozen field tests

### API and Usage Example

> Demonstrate how the API changes if any, and provide usage example(s)
if possible.

Now you can pass `--xx.profiler.extra.any_new_key=any_plain_value` in
command line to a dataclass inheriting `verl.BaseConfig`. This way we
can still pass dataclass configs inside verl but allow some flexiblity
in accepting new keys from users' adhoc usage.


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---------

Co-authored-by: Lin <haibin@Lins-Laptop.hsd1.wa.comcast.net>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
2025-07-15 09:06:56 +08:00

36 lines
1.2 KiB
Python

# Copyright 2025 Bytedance Ltd. and/or its affiliates
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
try:
import hydra
except ImportError as e:
raise ImportError("Please install hydra-core via 'pip install hydra-core' and retry.") from e
@hydra.main(config_path="../verl/trainer/config", config_name="ppo_trainer", version_base=None)
def main(config):
"""Main entry point for PPO training with Hydra configuration management.
Args:
config_dict: Hydra configuration dictionary containing training parameters.
"""
print(config)
from verl.utils.config import omega_conf_to_dataclass
profiler_config = omega_conf_to_dataclass(config.critic.profiler)
print(profiler_config)
if __name__ == "__main__":
main()