[Feature] support pool (#3827)

* support pool

* update pooling

* add pooler_config and check

* update

* support AutoWeightsLoader load weight

* fix

* update

* delete print

* update pre-commit

* fix

* fix xpu

* fix ModelRegistry->model_registry

* fix Copilot review

* fix pooler.py

* delete StepPooler

* fix abstract

* fix default_loader_v1

* fix Pre Commit

* support torch qwen3 dense

* add test and fix torch-qwen

* fix

* fix

* adapter ci:

* fix review

* fix pooling_params.py

* fix

* fix tasks.py 2025

* fix print and logger

* Modefy ModelRegistry and delete AutoWeightsLoader

* fix logger

* fix test_embedding

* fix ci bug

* ernie4_5 model_registry

* fix test

* support Qwen3-Embedding-0.6B tp=1 load

* fix extra code

* fix

* delete fix vocab_size

* delete prepare_params_dict

* fix:

fastdeploy/model_executor/models/interfaces_base.py

@@ -0,0 +1,54 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Type
+
+from paddle import nn
+
+
+def is_text_generation_model(model_cls: Type[nn.Layer]) -> bool:
+    from .model_base import ModelForCasualLM
+
+    return issubclass(model_cls, ModelForCasualLM)
+
+
+def is_pooling_model(model_cls: Type[nn.Layer]) -> bool:
+    class_name = model_cls.__name__
+    pooling_indicators = ["Embedding", "ForSequenceClassification"]
+    return (
+        any(indicator in class_name for indicator in pooling_indicators)
+        or hasattr(model_cls, "is_embedding_model")
+        and model_cls.is_embedding_model
+    )
+
+
+def is_multimodal_model(class_name: str) -> bool:
+    multimodal_indicators = ["VL", "Vision", "ConditionalGeneration"]
+    return any(indicator in class_name for indicator in multimodal_indicators)
+
+
+def determine_model_category(class_name: str):
+    from fastdeploy.model_executor.models.model_base import ModelCategory
+
+    if any(pattern in class_name for pattern in ["VL", "Vision", "ConditionalGeneration"]):
+        return ModelCategory.MULTIMODAL
+    elif any(pattern in class_name for pattern in ["Embedding", "ForSequenceClassification"]):
+        return ModelCategory.EMBEDDING
+    return ModelCategory.TEXT_GENERATION
+
+
+def get_default_pooling_type(model_cls: Type[nn.Layer] = None) -> str:
+    if model_cls is not None:
+        return getattr(model_cls, "default_pooling_type", "LAST")
+    return "LAST"