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b00f77d855
> [!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.
112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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os.environ["RAY_DEDUP_LOGS"] = "0"
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os.environ["NCCL_DEBUG"] = "WARN"
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import ray
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import torch
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import torch.distributed
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from verl.single_controller.base.worker import Worker
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from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup
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@ray.remote
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class TestAllGatherActor(Worker):
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def __init__(self, size) -> None:
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super().__init__()
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self.size = size
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def init(self):
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torch.distributed.init_process_group()
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self.tensor = torch.zeros(size=(self.size,), dtype=torch.int64, device="cuda")
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self.tensor += self.rank
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def all_gather(self):
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world_size = self._world_size
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output = torch.zeros(
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size=(self.tensor.shape[0] * world_size,), dtype=self.tensor.dtype, device=self.tensor.device
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)
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torch.distributed.all_gather_into_tensor(output, self.tensor, async_op=False)
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return output
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@ray.remote
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class TestAllGatherActorV2(Worker):
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def __init__(self, size) -> None:
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super().__init__()
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self.size = size
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torch.distributed.init_process_group()
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self.tensor = torch.zeros(size=(self.size,), dtype=torch.int64, device="cuda")
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self.tensor += self.rank
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def all_gather(self):
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world_size = self._world_size
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output = torch.zeros(
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size=(self.tensor.shape[0] * world_size,), dtype=self.tensor.dtype, device=self.tensor.device
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)
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torch.distributed.all_gather_into_tensor(output, self.tensor, async_op=False)
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return output
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def test_all_gather_torch():
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"""
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In this test, we instantiate 4 GPUs in a group and test the all_gather
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"""
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ray.init()
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# create 4 workers, each hold a GPU
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resource_pool = RayResourcePool([4], use_gpu=True)
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class_with_args = RayClassWithInitArgs(cls=TestAllGatherActor, size=2)
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worker_group = RayWorkerGroup(resource_pool, class_with_args, name_prefix="worker_group_torch")
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worker_group.execute_all_sync("init")
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output = worker_group.execute_all_sync("all_gather")
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for i in range(1, len(output)):
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assert torch.all(output[i] == output[0])
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output = output[0].cpu()
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print(output)
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assert torch.all(output == torch.tensor([0, 0, 1, 1, 2, 2, 3, 3], dtype=torch.int64))
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ray.shutdown()
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def test_all_gather_torch_v2():
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"""
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In this test, we instantiate 4 GPUs in a group and test the all_gather
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"""
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ray.init()
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# create 4 workers, each hold a GPU
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resource_pool = RayResourcePool([4], use_gpu=True)
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class_with_args = RayClassWithInitArgs(cls=TestAllGatherActorV2, size=2)
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worker_group = RayWorkerGroup(resource_pool, class_with_args, name_prefix="worker_group_torch")
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output = worker_group.execute_all_sync("all_gather")
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for i in range(1, len(output)):
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assert torch.all(output[i] == output[0])
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output = output[0].cpu()
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print(output)
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assert torch.all(output == torch.tensor([0, 0, 1, 1, 2, 2, 3, 3], dtype=torch.int64))
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ray.shutdown()
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