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[Plugin] Simplify IO Processor Plugin interface (#34236)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-02-11 11:47:39 +08:00
committed by GitHub
parent b482f71e9f
commit c9a1923bb4
9 changed files with 164 additions and 148 deletions
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40
docs/design/io_processor_plugins.md
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@@ -14,8 +14,26 @@ IOProcessorOutput = TypeVar("IOProcessorOutput")
class IOProcessor(ABC, Generic[IOProcessorInput, IOProcessorOutput]):
def __init__(self, vllm_config: VllmConfig):
super().__init__()
self.vllm_config = vllm_config
@abstractmethod
def parse_data(self, data: object) -> IOProcessorInput:
raise NotImplementedError
def merge_sampling_params(
self,
params: SamplingParams | None = None,
) -> SamplingParams:
return params or SamplingParams()
def merge_pooling_params(
self,
params: PoolingParams | None = None,
) -> PoolingParams:
return params or PoolingParams()
@abstractmethod
def pre_process(
self,
@@ -55,29 +73,13 @@ class IOProcessor(ABC, Generic[IOProcessorInput, IOProcessorOutput]):
[(i, item) async for i, item in model_output], key=lambda output: output[0]
)
collected_output = [output[1] for output in sorted_output]
return self.post_process(collected_output, request_id, **kwargs)
@abstractmethod
def parse_request(self, request: Any) -> IOProcessorInput:
raise NotImplementedError
def validate_or_generate_params(
self, params: SamplingParams | PoolingParams | None = None
) -> SamplingParams | PoolingParams:
return params or PoolingParams()
@abstractmethod
def output_to_response(
self, plugin_output: IOProcessorOutput
) -> IOProcessorResponse:
raise NotImplementedError
return self.post_process(collected_output, request_id=request_id, **kwargs)
```
The `parse_request` method is used for validating the user prompt and converting it into the input expected by the `pre_process`/`pre_process_async` methods.
The `parse_data` method is used for validating the user data and converting it into the input expected by the `pre_process*` methods.
The `merge_sampling_params` and `merge_pooling_params` methods merge input `SamplingParams` or `PoolingParams` (if any) with the default one.
The `pre_process*` methods take the validated plugin input to generate vLLM's model prompts for regular inference.
The `post_process*` methods take `PoolingRequestOutput` objects as input and generate a custom plugin output.
The `validate_or_generate_params` method is used for validating with the plugin any `SamplingParameters`/`PoolingParameters` received with the user request, or to generate new ones if none are specified. The function always returns the validated/generated parameters.
The `output_to_response` method is used only for online serving and converts the plugin output to the `IOProcessorResponse` type that is then returned by the API Server. The implementation of the `/pooling` serving endpoint is available here [vllm/entrypoints/openai/serving_pooling.py](../../vllm/entrypoints/pooling/pooling/serving.py).
An example implementation of a plugin that enables generating geotiff images with the PrithviGeospatialMAE model is available [here](https://github.com/IBM/terratorch/tree/main/terratorch/vllm/plugins/segmentation). Please, also refer to our online ([examples/pooling/plugin/prithvi_geospatial_mae_online.py](../../examples/pooling/plugin/prithvi_geospatial_mae_online.py)) and offline ([examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py](../../examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py)) inference examples.