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fix some typos (#24071)

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Signed-off-by: co63oc <co63oc@users.noreply.github.com>
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co63oc
2025-09-03 11:44:50 +08:00
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parent 136d853e65
commit 1bd007f234
32 changed files with 39 additions and 39 deletions
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4
docs/configuration/optimization.md
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@ -210,7 +210,7 @@ vllm serve Qwen/Qwen2.5-VL-3B-Instruct --api-server-count 4 -dp 2
!!! note
API server scale-out disables [multi-modal IPC caching](#ipc-caching)
because it requires a one-to-one correspondance between API and engine core processes.
because it requires a one-to-one correspondence between API and engine core processes.
This does not impact [multi-modal processor caching](#processor-caching).
@ -227,7 +227,7 @@ to avoid repeatedly processing the same multi-modal inputs in `BaseMultiModalPro
### IPC Caching
Multi-modal IPC caching is automatically enabled when
there is a one-to-one correspondance between API (`P0`) and engine core (`P1`) processes,
there is a one-to-one correspondence between API (`P0`) and engine core (`P1`) processes,
to avoid repeatedly transferring the same multi-modal inputs between them.
### Configuration

2
docs/design/io_processor_plugins.md
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@ -2,7 +2,7 @@
IO Processor plugins are a feature that allows pre and post processing of the model input and output for pooling models. The idea is that users are allowed to pass a custom input to vLLM that is converted into one or more model prompts and fed to the model `encode` method. One potential use-case of such plugins is that of using vLLM for generating multi-modal data. Say users feed an image to vLLM and get an image in output.
When performing an inference with IO Processor plugins, the prompt type is defined by the plugin and the same is valid for the final request output. vLLM does not perform any validation of input/output data, and it is up to the plugin to ensure the correct data is being fed to the model and returned to the user. As of now these plugins support only pooling models and can be triggerd via the `encode` method in `LLM` and `AsyncLLM`, or in online serving mode via the `/pooling` endpoint.
When performing an inference with IO Processor plugins, the prompt type is defined by the plugin and the same is valid for the final request output. vLLM does not perform any validation of input/output data, and it is up to the plugin to ensure the correct data is being fed to the model and returned to the user. As of now these plugins support only pooling models and can be triggered via the `encode` method in `LLM` and `AsyncLLM`, or in online serving mode via the `/pooling` endpoint.
## Writing an IO Processor Plugin