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[V0 deprecation] Remove more V0 references (#29088)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
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Cyrus Leung
2025-11-21 19:56:59 +08:00
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parent b34129bf8e
commit aab0102a26
15 changed files with 31 additions and 75 deletions
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docs/usage/reproducibility.md
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@@ -1,10 +1,7 @@
# Reproducibility
vLLM does not guarantee the reproducibility of the results by default, for the sake of performance. You need to do the following to achieve
reproducible results:
- For V1: Turn off multiprocessing to make the scheduling deterministic by setting `VLLM_ENABLE_V1_MULTIPROCESSING=0`.
- For V0: Set the global seed (see below).
vLLM does not guarantee the reproducibility of the results by default, for the sake of performance. To achieve
reproducible results, you need to turn off multiprocessing to make the scheduling deterministic by setting `VLLM_ENABLE_V1_MULTIPROCESSING=0`.
Example: [examples/offline_inference/reproducibility.py](../../examples/offline_inference/reproducibility.py)
@@ -30,8 +27,6 @@ However, in some cases, setting the seed will also [change the random state in u
### Default Behavior
In V0, the `seed` parameter defaults to `None`. When the `seed` parameter is `None`, the random states for `random`, `np.random`, and `torch.manual_seed` are not set. This means that each run of vLLM will produce different results if `temperature > 0`, as expected.
In V1, the `seed` parameter defaults to `0` which sets the random state for each worker, so the results will remain consistent for each vLLM run even if `temperature > 0`.
!!! note

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docs/usage/v1_guide.md
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@@ -2,7 +2,7 @@
!!! announcement
We have started the process of deprecating V0. Please read [RFC #18571](https://github.com/vllm-project/vllm/issues/18571) for more details.
We have fully deprecated V0. Please read [RFC #18571](https://github.com/vllm-project/vllm/issues/18571) for more details.
V1 is now enabled by default for all supported use cases, and we will gradually enable it for every use case we plan to support. Please share any feedback on [GitHub](https://github.com/vllm-project/vllm) or in the [vLLM Slack](https://inviter.co/vllm-slack).