Files
verl/recipe/prime/main_prime.py
Shawn/Yuxuan Tong b00f77d855 [dev] feat: immigrate from yapf & pylint to ruff based on pre-commit (#1010)
> [!WARNING]
> We are [immigrating to `ruff` as the linter and formatter and
`pre-commit` as the managing
tool](https://github.com/volcengine/verl/pull/1010).
>
> If your branch is based on a previous commit using `yapf` and
`pylint`, simply merging might trigger overwhelming linting errors,
while **you are only expected to resolve ones in the files related to
your PR**.
>
> To resolve this issue, please try the following workaround to only
include the files you **really changed** in the PR:
>
> 1. In your branch, fix linting and format with `ruff`: `ruff check
--fix && ruff-format`
> 2. Squash into a single commit in a new branch: `git reset --soft
$(git merge-base main HEAD) && git add -A && git commit -m "feat: ..."`
> 3. Merge with the latest main: `git merge origin/main`
> 4. Force push to your branch: `git push --force`

We add the reminder above to the documentation to tell contributors how
to avoid overwhelming linting errors.

### Motivation

According to dicussion in #896, this PR immigrates from yapf & pylint to
ruff based on pre-commit, which allows unified version control and
automatic hook on committing.

### Summary

The `pre-commit` hook and CI

- checks staged / committed files in commits / PR's
- checks all files each month (This should fail before we fix all the
files by the ruff standard)

### Explanation for the Failing CI Workflow `pre-commit`

For now, we only apply `ruff format` and `ruff check --fix` **without
resolving all the errors**, since there are too many errors to resolve,
which causes the CI workflow `pre-commit` fails.

For resolving the remaining errors, we leave to future commits.
Specifically, the `pre-commit` hook and CI will require every commit to
fix its related files with `ruff`, which will fix all the files
incrementally.

### Reviewing Suggestion

The commit
3d93f51ba8
is huge since we apply `ruff` to all the files. To review the main
changes, please check the commits before and after it.
2025-04-18 07:49:31 -07:00

149 lines
5.1 KiB
Python

# Copyright 2024 PRIME team 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.
# Copyright 2024 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.
"""
Note that we don't combine the main with ray_trainer as ray_trainer is used by other main.
"""
import hydra
import ray
from .prime_ray_trainer import RayPRIMETrainer
@hydra.main(config_path="config", config_name="prime_trainer", version_base=None)
def main(config):
run_prime(config)
def run_prime(config, compute_score=None):
if not ray.is_initialized():
# this is for local ray cluster
ray.init(
runtime_env={"env_vars": {"TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN"}},
)
ray.get(main_task.remote(config, compute_score))
@ray.remote(num_cpus=1) # please make sure main_task is not scheduled on head
def main_task(config, compute_score=None):
# print initial config
from pprint import pprint
from omegaconf import OmegaConf
from verl.utils.fs import copy_local_path_from_hdfs
pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values
OmegaConf.resolve(config)
# download the checkpoint from hdfs
local_path = copy_local_path_from_hdfs(config.actor_rollout_ref.model.path)
# instantiate tokenizer
from verl.utils import hf_tokenizer
tokenizer = hf_tokenizer(local_path)
# define worker classes
if config.actor_rollout_ref.actor.strategy == "fsdp":
assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
from verl.single_controller.ray import RayWorkerGroup
from verl.workers.fsdp_workers import ActorRolloutRefWorker
ray_worker_group_cls = RayWorkerGroup
elif config.actor_rollout_ref.actor.strategy == "megatron":
assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
from verl.single_controller.ray.megatron import NVMegatronRayWorkerGroup
from verl.workers.megatron_workers import ActorRolloutRefWorker
ray_worker_group_cls = NVMegatronRayWorkerGroup
else:
raise NotImplementedError
from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role
role_worker_mapping = {
Role.ActorRollout: ray.remote(ActorRolloutRefWorker),
}
global_pool_id = "global_pool"
resource_pool_spec = {
global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes,
}
mapping = {
Role.ActorRollout: global_pool_id,
}
# use reference model
if config.algorithm.use_kl_in_reward or config.actor_rollout_ref.actor.use_kl_loss:
role_worker_mapping[Role.RefPolicy] = ray.remote(ActorRolloutRefWorker)
mapping[Role.RefPolicy] = global_pool_id
if config.reward_model.enable:
from .prime_fsdp_workers import PRIMERewardModelWorker
role_worker_mapping[Role.RewardModel] = ray.remote(PRIMERewardModelWorker)
mapping[Role.RewardModel] = global_pool_id
reward_manager_name = config.reward_model.get("reward_manager", "naive")
if reward_manager_name == "naive":
from verl.workers.reward_manager import NaiveRewardManager
reward_manager_cls = NaiveRewardManager
elif reward_manager_name == "prime":
from verl.workers.reward_manager import PrimeRewardManager
reward_manager_cls = PrimeRewardManager
else:
raise NotImplementedError
reward_fn = reward_manager_cls(tokenizer=tokenizer, num_examine=0, compute_score=compute_score)
# Note that we always use function-based RM for validation
val_reward_fn = reward_manager_cls(tokenizer=tokenizer, num_examine=1, compute_score=compute_score)
resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping)
trainer = RayPRIMETrainer(
config=config,
tokenizer=tokenizer,
role_worker_mapping=role_worker_mapping,
resource_pool_manager=resource_pool_manager,
ray_worker_group_cls=ray_worker_group_cls,
reward_fn=reward_fn,
val_reward_fn=val_reward_fn,
)
trainer.init_workers()
trainer.fit()
if __name__ == "__main__":
main()