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[Doc]: fixing multiple typos in diverse files (#33256)

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Signed-off-by: Didier Durand <durand.didier@gmail.com>
Signed-off-by: Didier Durand <2927957+didier-durand@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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Didier Durand
2026-01-29 09:52:03 +01:00
committed by GitHub
parent abb34ac43a
commit 31b25f6516
14 changed files with 20 additions and 20 deletions
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4
docs/design/plugin_system.md
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@@ -100,7 +100,7 @@ Every plugin has three parts:
- `_enum`: This property is the device enumeration from [PlatformEnum][vllm.platforms.interface.PlatformEnum]. Usually, it should be `PlatformEnum.OOT`, which means the platform is out-of-tree.
- `device_type`: This property should return the type of the device which pytorch uses. For example, `"cpu"`, `"cuda"`, etc.
- `device_name`: This property is set the same as `device_type` usually. It's mainly used for logging purposes.
- `check_and_update_config`: This function is called very early in the vLLM's initialization process. It's used for plugins to update the vllm configuration. For example, the block size, graph mode config, etc, can be updated in this function. The most important thing is that the **worker_cls** should be set in this function to let vLLM know which worker class to use for the worker process.
- `check_and_update_config`: This function is called very early in the vLLM's initialization process. It's used for plugins to update the vllm configuration. For example, the block size, graph mode config, etc., can be updated in this function. The most important thing is that the **worker_cls** should be set in this function to let vLLM know which worker class to use for the worker process.
- `get_attn_backend_cls`: This function should return the attention backend class's fully qualified name.
- `get_device_communicator_cls`: This function should return the device communicator class's fully qualified name.
@@ -126,7 +126,7 @@ Every plugin has three parts:
5. Implement the attention backend class `MyDummyAttention` in `my_dummy_attention.py`. The attention backend class should inherit from [AttentionBackend][vllm.v1.attention.backend.AttentionBackend]. It's used to calculate attentions with your device. Take `vllm.v1.attention.backends` as examples, it contains many attention backend implementations.
6. Implement custom ops for high performance. Most ops can be ran by pytorch native implementation, while the performance may not be good. In this case, you can implement specific custom ops for your plugins. Currently, there are kinds of custom ops vLLM supports:
6. Implement custom ops for high performance. Most ops can be run by pytorch native implementation, while the performance may not be good. In this case, you can implement specific custom ops for your plugins. Currently, there are kinds of custom ops vLLM supports:
- pytorch ops
there are 3 kinds of pytorch ops: