[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:

Author: lizexu123

docs/features/plugins.md

@@ -18,7 +18,7 @@ Assuming you have a custom model class `MyModelForCasualLM` and a pretrained cla
 
 ```python
 # File: fd_add_dummy_model/__init__.py or fd_add_dummy_model/register.py
-from fastdeploy.model_registry import ModelRegistry
+from fastdeploy.model_executor.models.model_base import ModelRegistry
 from my_custom_model import MyModelForCasualLM, MyPretrainedModel
 from fastdeploy.config import ErnieArchitectures
 

docs/zh/features/plugins.md

@@ -18,7 +18,7 @@ FastDeploy 利用 Python 的 `entry_points` 机制来发现并加载插件。开
 
 ```python
 # 文件：fd_add_dummy_model/__init__.py
-from fastdeploy.model_registry import ModelRegistry
+from fastdeploy.model_executor.models.model_base import ModelRegistry
 from my_custom_model import MyModelForCasualLM, MyPretrainedModel
 
 def register():

fastdeploy/config.py

@@ -18,24 +18,83 @@
 
 import json
 import os
+from dataclasses import field
 from enum import Enum
 from typing import Any, Dict, List, Literal, Optional, Union
 
 import paddle
 import paddle.distributed as dist
 from paddleformers.transformers.configuration_utils import PretrainedConfig
+from typing_extensions import assert_never
 
 import fastdeploy
 from fastdeploy import envs
 from fastdeploy.model_executor.layers.quantization.quant_base import QuantConfigBase
 from fastdeploy.multimodal.registry import MultimodalRegistry
 from fastdeploy.platforms import current_platform
 from fastdeploy.scheduler import SchedulerConfig
+from fastdeploy.transformer_utils.config import get_pooling_config
 from fastdeploy.utils import ceil_div, check_unified_ckpt, get_host_ip, get_logger
 
 logger = get_logger("config", "config.log")
 
-TaskOption = Literal["generate"]
+TaskOption = Literal["auto", "generate", "embedding", "embed"]
+
+RunnerType = Literal["generate", "pooling"]
+
+RunnerOption = Literal["auto", "generate", "pooling"]
+
+ConvertOption = Literal["auto", "none", "embed"]
+
+ConvertType = Literal["none", "embed"]
+
+_ResolvedTask = Literal["generate", "encode", "embed"]
+
+_RUNNER_CONVERTS: dict[RunnerType, list[ConvertType]] = {
+    "generate": [],
+    "pooling": ["embed"],
+}
+
+# Some model suffixes are based on auto classes from Transformers:
+# https://huggingface.co/docs/transformers/en/model_doc/auto
+# NOTE: Items higher on this list priority over lower ones
+_SUFFIX_TO_DEFAULTS: list[tuple[str, tuple[RunnerType, ConvertType]]] = [
+    ("ForCausalLM", ("generate", "none")),
+    ("ForConditionalGeneration", ("generate", "none")),
+    ("ChatModel", ("generate", "none")),
+    ("LMHeadModel", ("generate", "none")),
+    ("ForTextEncoding", ("pooling", "embed")),
+    ("EmbeddingModel", ("pooling", "embed")),
+    ("ForSequenceClassification", ("pooling", "classify")),
+    ("ForAudioClassification", ("pooling", "classify")),
+    ("ForImageClassification", ("pooling", "classify")),
+    ("ForVideoClassification", ("pooling", "classify")),
+    ("ClassificationModel", ("pooling", "classify")),
+    ("ForRewardModeling", ("pooling", "reward")),
+    ("RewardModel", ("pooling", "reward")),
+    # Let other `*Model`s take priority
+    ("Model", ("pooling", "embed")),
+]
+
+
+def iter_architecture_defaults():
+    yield from _SUFFIX_TO_DEFAULTS
+
+
+def try_match_architecture_defaults(
+    architecture: str,
+    *,
+    runner_type: Optional[RunnerType] = None,
+    convert_type: Optional[ConvertType] = None,
+):
+    for suffix, (default_runner_type, default_convert_type) in iter_architecture_defaults():
+        if (
+            (runner_type is None or runner_type == default_runner_type)
+            and (convert_type is None or convert_type == default_convert_type)
+            and architecture.endswith(suffix)
+        ):
+            return suffix, (default_runner_type, default_convert_type)
+    return None
 
 
 class MoEPhase:
@@ -133,6 +192,12 @@ def __init__(
         self.eos_tokens_lens: int = 2
         self.lm_head_fp32: bool = False
         self.model_format = "auto"
+        self.runner = "auto"
+        self.convert = "auto"
+        self.pooler_config: Optional["PoolerConfig"] = field(init=False)
+        self.override_pooler_config: Optional[Union[dict, "PoolerConfig"]] = None
+        self.revision = None
+
         self.partial_rotary_factor: float = 1.0
         self.num_nextn_predict_layers = 0
         for key, value in args.items():
@@ -161,6 +226,7 @@ def __init__(
         self.ori_vocab_size = args.get("ori_vocab_size", self.vocab_size)
 
         architectures = self.architectures[0]
+
         if MultimodalRegistry.contains_model(architectures):
             self.enable_mm = True
         else:
@@ -171,6 +237,43 @@ def __init__(
         self.override_name_from_config()
         self.read_from_env()
         self.read_model_config()
+        self.runner_type = self._get_runner_type(self.architectures, self.runner)
+        self.convert_type = self._get_convert_type(self.architectures, self.runner_type, self.convert)
+
+        registry = self.registry
+        is_generative_model = registry.is_text_generation_model(self.architectures, self)
+        is_pooling_model = registry.is_pooling_model(self.architectures, self)
+        is_multimodal_model = registry.is_multimodal_model(self.architectures, self)
+
+        if self.runner_type == "generate" and not is_generative_model:
+            if is_multimodal_model:
+                pass
+            else:
+                generate_converts = _RUNNER_CONVERTS["generate"]
+                if self.convert_type not in generate_converts:
+                    raise ValueError("This model does not support '--runner generate.")
+        if self.runner_type == "pooling" and not is_pooling_model:
+            pooling_converts = _RUNNER_CONVERTS["pooling"]
+            if self.convert_type not in pooling_converts:
+                convert_option = "<" + "|".join(pooling_converts) + ">"
+                raise ValueError(
+                    "This model does not support `--runner pooling`. "
+                    f"You can pass `--convert {convert_option} to adapt "
+                    "it into a pooling model."
+                )
+
+        self.supported_tasks = self._get_supported_tasks(self.architectures, self.runner_type, self.convert_type)
+        model_info, arch = registry.inspect_model_cls(self.architectures, self)
+        self._model_info = model_info
+        self._architecture = arch
+
+        self.pooler_config = self._init_pooler_config()
+
+    @property
+    def registry(self):
+        from fastdeploy.model_executor.models.model_base import ModelRegistry
+
+        return ModelRegistry()
 
     def override_name_from_config(self):
         """
@@ -194,7 +297,6 @@ def override_name_from_config(self):
     def read_from_env(self):
         """
         Read configuration information from environment variables and update the object's attributes.
-
         If an attribute is not present or is an empty string in the environment variables, use the default value.
         """
         self.max_stop_seqs_num = int(envs.FD_MAX_STOP_SEQS_NUM)
@@ -235,6 +337,165 @@ def read_model_config(self):
                     f"Config file path: {config_path}"
                 )
 
+    def _get_default_runner_type(
+        self,
+        architectures: list[str],
+    ) -> RunnerType:
+        registry = self.registry
+        if get_pooling_config(self.model, self.revision):
+            return "pooling"
+        for arch in architectures:
+            if arch in registry.get_supported_archs():
+                if registry.is_pooling_model(architectures, self):
+                    return "pooling"
+                if registry.is_text_generation_model(architectures, self):
+                    return "generate"
+            match = try_match_architecture_defaults(arch)
+            if match:
+                _, (runner_type, _) = match
+                return runner_type
+        return "generate"
+
+    def _get_default_convert_type(
+        self,
+        architectures: list[str],
+        runner_type: RunnerType,
+    ) -> ConvertType:
+        registry = self.registry
+
+        for arch in architectures:
+            if arch in registry.get_supported_archs():
+                if runner_type == "generate" and registry.is_text_generation_model(architectures, self):
+                    return "none"
+                if runner_type == "pooling" and registry.is_pooling_model(architectures, self):
+                    return "none"
+            match = try_match_architecture_defaults(arch, runner_type=runner_type)
+            if match:
+                _, (_, convert_type) = match
+                return convert_type
+
+        # This is to handle Sentence Transformers models that use *ForCausalLM
+        # and also multi-modal pooling models which are not defined as
+        # Sentence Transformers models
+        if runner_type == "pooling":
+            return "embed"
+
+        return "none"
+
+    def _get_runner_type(
+        self,
+        architectures: list[str],
+        runner: RunnerOption,
+    ) -> RunnerType:
+        if runner != "auto":
+            return runner
+
+        runner_type = self._get_default_runner_type(architectures)
+        if runner_type != "generate":
+            logger.info(
+                "Resolved `--runner auto` to `--runner %s`. " "Pass the value explicitly to silence this message.",
+                runner_type,
+            )
+
+        return runner_type
+
+    def _get_convert_type(
+        self,
+        architectures: list[str],
+        runner_type: RunnerType,
+        convert: ConvertOption,
+    ) -> ConvertType:
+        if convert != "auto":
+            return convert
+
+        convert_type = self._get_default_convert_type(architectures, runner_type)
+
+        if convert_type != "none":
+            logger.info(
+                "Resolved `--convert auto` to `--convert %s`. " "Pass the value explicitly to silence this message.",
+                convert_type,
+            )
+
+        return convert_type
+
+    def _get_supported_generation_tasks(
+        self,
+        architectures: list[str],
+        convert_type: ConvertType,
+    ) -> list[_ResolvedTask]:
+        registry = self.registry
+
+        supported_tasks = list[_ResolvedTask]()
+        if registry.is_text_generation_model(architectures, self) or convert_type in _RUNNER_CONVERTS["generate"]:
+            supported_tasks.append("generate")
+
+        # TODO:Temporarily does not support transcription.
+        return supported_tasks
+
+    def _get_default_pooling_task(
+        self,
+        architectures: list[str],
+    ) -> Literal["embed"]:
+        # Temporarily does not support classification and reward.
+        for arch in architectures:
+            match = try_match_architecture_defaults(arch, runner_type="pooling")
+            if match:
+                _, (_, convert_type) = match
+                assert convert_type != "none"
+                return convert_type
+
+        return "embed"
+
+    def _get_supported_pooling_tasks(
+        self,
+        architectures: list[str],
+        convert_type: ConvertType,
+    ) -> list[_ResolvedTask]:
+        registry = self.registry
+
+        supported_tasks = list[_ResolvedTask]()
+        if registry.is_pooling_model(architectures, self) or convert_type in _RUNNER_CONVERTS["pooling"]:
+            supported_tasks.append("encode")
+
+            extra_task = self._get_default_pooling_task(architectures) if convert_type == "none" else convert_type
+            supported_tasks.append(extra_task)
+
+        return supported_tasks
+
+    def _get_supported_tasks(
+        self,
+        architectures: list[str],
+        runner_type: RunnerType,
+        convert_type: ConvertType,
+    ) -> list[_ResolvedTask]:
+        if runner_type == "generate":
+            return self._get_supported_generation_tasks(architectures, convert_type)
+        if runner_type == "pooling":
+            return self._get_supported_pooling_tasks(architectures, convert_type)
+
+        assert_never(runner_type)
+
+    def _init_pooler_config(self) -> Optional["PoolerConfig"]:
+        if self.runner_type == "pooling":
+            if isinstance(self.override_pooler_config, dict):
+                self.override_pooler_config = PoolerConfig(**self.override_pooler_config)
+
+            pooler_config = self.override_pooler_config or PoolerConfig()
+
+            base_config = get_pooling_config(self.model, self.revision)
+            if base_config is not None:
+                for k, v in base_config.items():
+                    if getattr(pooler_config, k) is None:
+                        setattr(pooler_config, k, v)
+
+            default_pooling_type = self._model_info.default_pooling_type
+            if pooler_config.pooling_type is None:
+                pooler_config.pooling_type = default_pooling_type
+
+            return pooler_config
+
+        return None
+
     def _get_download_model(self, model_name, model_type="default"):
         # TODO: Provide dynamic graph for self-downloading and save to the specified download directory.
         pass
@@ -846,6 +1107,41 @@ def __init__(
                 setattr(self, key, value)
 
 
+class PoolerConfig:
+    """Controls the behavior of output pooling in pooling models."""
+
+    pooling_type: Optional[str] = None
+    """
+    The pooling method of the pooling model.
+    """
+    # for embeddings models
+    normalize: Optional[bool] = None
+    """
+    Whether to normalize the embeddings outputs. Defaults to True.
+    """
+    dimensions: Optional[int] = None
+    """
+    Reduce the dimensions of embeddings if model
+    support matryoshka representation. Defaults to None.
+    """
+    enable_chunked_processing: Optional[bool] = None
+    """
+    Whether to enable chunked processing for long inputs that exceed the model's
+    maximum position embeddings. When enabled, long inputs will be split into
+    chunks, processed separately, and then aggregated using weighted averaging.
+    This allows embedding models to handle arbitrarily long text without CUDA
+    errors. Defaults to False.
+    """
+    max_embed_len: Optional[int] = None
+    """
+    Maximum input length allowed for embedding generation. When set, allows
+    inputs longer than max_embed_len to be accepted for embedding models.
+    When an input exceeds max_embed_len, it will be handled according to
+    the original max_model_len validation logic.
+    Defaults to None (i.e. set to max_model_len).
+    """
+
+
 class LoRAConfig:
     """LoRA Config"""