[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/model_loader/default_loader_v1.py

@@ -16,7 +16,7 @@
 
 import paddle
 from paddle import nn
-from paddleformers.utils.log import logger
+from typing_extensions import assert_never
 
 from fastdeploy.config import FDConfig, LoadConfig, ModelConfig
 from fastdeploy.model_executor.load_weight_utils import (
@@ -27,6 +27,7 @@ from fastdeploy.model_executor.load_weight_utils import (
     save_model,
 )
 from fastdeploy.model_executor.model_loader.base_loader import BaseModelLoader
+from fastdeploy.model_executor.models.adapters import as_embedding_model
 from fastdeploy.model_executor.models.model_base import ModelRegistry
 from fastdeploy.platforms import current_platform
 
@@ -54,11 +55,11 @@ class DefaultModelLoaderV1(BaseModelLoader):
             load_weights_form_cache(model, weights_iterator)
         else:
             model.load_weights(weights_iterator)
+
         self.clean_memory_fragments()
 
     def load_model(self, fd_config: FDConfig) -> nn.Layer:
         architectures = fd_config.model_config.architectures[0]
-        logger.info(f"Starting to load model {architectures}")
         context = paddle.LazyGuard()
         if fd_config.load_config.dynamic_load_weight:
             # register rl model
@@ -70,6 +71,14 @@ class DefaultModelLoaderV1(BaseModelLoader):
         with weight_cache_context:
             with context:
                 model_cls = ModelRegistry.get_class(architectures)
+                convert_type = fd_config.model_config.convert_type
+                if convert_type == "none":
+                    pass
+                elif convert_type == "embed":
+                    model_cls = as_embedding_model(model_cls)
+                else:
+                    assert_never(convert_type)
+
                 model = model_cls(fd_config)
 
         model.eval()