[Frontend][4/N] Improve all pooling task | Add plugin pooling task (#26973)

Signed-off-by: wang.yuqi <noooop@126.com>
Signed-off-by: Christian Pinto <christian.pinto@ibm.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Christian Pinto <christian.pinto@ibm.com>

examples/offline_inference/prithvi_geospatial_mae_io_processor.py

@@ -6,14 +6,14 @@ import os
 import torch
 
 from vllm import LLM
-from vllm.pooling_params import PoolingParams
 
 # This example shows how to perform an offline inference that generates
 # multimodal data. In this specific case this example will take a geotiff
 # image as input, process it using the multimodal data processor, and
 # perform inference.
-# Requirement - install plugin at:
-#   https://github.com/christian-pinto/prithvi_io_processor_plugin
+# Requirements:
+# - install TerraTorch v1.1 (or later):
+#   pip install terratorch>=v1.1
 
 
 def main():
@@ -36,16 +36,12 @@ def main():
         # to avoid the model going OOM.
         # The maximum number depends on the available GPU memory
         max_num_seqs=32,
-        io_processor_plugin="prithvi_to_tiff",
+        io_processor_plugin="terratorch_segmentation",
         model_impl="terratorch",
         enable_mm_embeds=True,
     )
 
-    pooling_params = PoolingParams(task="token_classify", activation=False)
-    pooler_output = llm.encode(
-        img_prompt,
-        pooling_params=pooling_params,
-    )
+    pooler_output = llm.encode(img_prompt, pooling_task="plugin")
     output = pooler_output[0].outputs
 
     print(output)