[Feature] support reward model (#5301)

* Your commit message here

* add test

* update develop

* support reward

* support enable_chunk_prefill

* support bingfa

* support convert is reward

* update test

* delete print

* fix enable_thinking

* add document

* fix place

* fix test

* fix

* support enable_prefix_caching

* add no-enable_prefix-caching test

* fix

* support enable_prefix_caching

* delete print

* fix document

* fix

* fix test

* fix document and delete chinese

* udpate

* enable_thinking

* fix test

docs/features/pooling_models.md

@@ -0,0 +1,175 @@
+[简体中文](../zh/features//pooling_models.md)
+
+# Pooling Models
+
+FastDeploy also supports pooling models, such as embedding models.
+
+In FastDeploy, pooling models implement the `FdModelForPooling` interface.
+These models use a `Pooler` to extract the final hidden states of the input
+before returning them.
+
+## Configuration
+
+### Model Runner
+
+Run a model in pooling mode via the option `--runner pooling`.
+
+!!! tip<br>
+    There is no need to set this option in the vast majority of cases as Fastdeploy can automatically
+    detect the appropriate model runner via `--runner auto`.
+
+### Model Conversion
+
+FastDeploy can adapt models for various pooling tasks via the option `--convert <type>`.
+
+If `--runner pooling` has been set (manually or automatically) but the model does not implement the
+`FdModelForPooling` interface,
+vLLM will attempt to automatically convert the model according to the architecture names
+shown in the table below.
+
+| Architecture                                    | `--convert` | Supported pooling tasks               |
+|-------------------------------------------------|-------------|---------------------------------------|
+| `*ForTextEncoding`, `*EmbeddingModel`, `*Model`  `*ForProcessRewardModel`   | `embed`     |         `embed`                       |
+
+!!! tip<br>
+    You can explicitly set `--convert <type>` to specify how to convert the model.
+
+### Pooler Configuration
+
+#### Predefined models
+
+If the `Pooler` defined by the model accepts `pooler_config`,
+you can override some of its attributes via the `--pooler-config` option.
+
+#### Converted models
+
+If the model has been converted via `--convert` (see above),
+the pooler assigned to each task has the following attributes by default:
+
+| Task       | Pooling Type | Normalization | Softmax |
+|------------|--------------|---------------|---------|
+| `embed`    | `LAST`       | ✅︎            | ❌      |
+
+When loading [Sentence Transformers](https://huggingface.co/sentence-transformers) models,
+its Sentence Transformers configuration file (`modules.json`) takes priority over the model's defaults and It can also be specified during model network construction through @default_pooling_type("LAST").
+
+##### Pooling Type
+
+1.LastPool(PoolingType.LAST)
+
+Purpose:Extracts the hidden state of the last token in each sequence
+
+2.AllPool(PoolingType.ALL)
+
+Purpose:Returns the hidden states of all tokens in each sequence
+
+3.CLSPool(PoolingType.CLS)
+
+Purpose:Returns the hidden state of the first token in each sequence (CLS token)
+
+4.MeanPool(PoolingType.MEAN)
+
+Purpose:Computes the average of all token hidden states in each sequence
+
+## Online Serving
+
+FastDeploy's OpenAI-compatible server provides API endpoints and custom reward interfaces.
+
+[Embeddings API], supports text and multi-modal inputs
+
+[Reward API], scores specific content
+
+### Embedding Model:
+```python
+model_path=Qwen/Qwen3-Embedding-0.6B
+
+python -m fastdeploy.entrypoints.openai.api_server --model ${model_path} \
+    --max-num-seqs 256 --max-model-len 32768 \
+    --port 9412 --engine-worker-queue-port 7142 \
+    --metrics-port 7211 --tensor-parallel-size 1 \
+    --gpu-memory-utilization 0.9 \
+    --runner pooling
+```
+
+Request Methods:
+A. EmbeddingCompletionRequest Example (Standard Text Input)
+
+```bash
+curl -X POST 'YOUR_SERVICE_URL/v1/embeddings' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "text-embedding-chat-model",
+    "input": [
+      "This is a sentence for pooling embedding.",
+      "Another input text."
+    ],
+    "user": "test_client"
+  }'
+```
+
+B. EmbeddingChatRequest Example (Message Sequence Input)
+
+```bash
+curl -X POST 'YOUR_SERVICE_URL/v1/embeddings' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "text-embedding-chat-model",
+    "messages": [
+      {"role": "user", "content": "Generate embedding for user query."}
+    ]
+  }'
+```
+
+### Pooling Model and reward score
+```python
+model_path=RM_v1008
+python -m fastdeploy.entrypoints.openai.api_server \
+    --model ${model_path} \
+    --max-num-seqs 256 \
+    --max-model-len 8192 \
+    --port 13351 \
+    --engine-worker-queue-port 7562 \
+    --metrics-port 7531 \
+    --tensor-parallel-size 8 \
+    --gpu-memory-utilization 0.9 \
+    --runner pooling \
+    --convert embed
+```
+Request Method: ChatRewardRequest
+```bash
+curl --location 'http://xxxx/v1/chat/reward' \
+--header 'Content-Type: application/json' \
+--data '{
+  "model": "",
+  "messages": [
+    {
+      "role": "user",
+      "content": [
+        {
+          "type": "image_url",
+          "image_url": {
+            "url": "https://xxx/a.png"
+          }
+        }
+      ]
+    },
+    {
+      "role": "assistant",
+      "content": [
+        {
+          "type": "text",
+          "text": "图里有几个人"
+        }
+      ]
+    }
+  ],
+  "user": "user-123",
+  "chat_template": null,
+  "chat_template_kwargs": {
+    "custom_var": "value"
+  },
+  "mm_processor_kwargs": {
+    "image_size": 224
+  }
+}'
+```

docs/zh/features/pooling_models.md

@@ -0,0 +1,168 @@
+[English](../../features/pooling_models.md)
+
+# Pooling Models
+
+FastDeploy也支持pooling模型，例如嵌入(embedding)模型。
+
+在FastDeploy中,池化模型通过`FdModelForPooling`接口。这些模型使用一个`Pooler`来提取输入的最终隐藏状态并返回。
+
+## Configuration
+
+### Model Runner
+
+通过`--runner pooling`选项以池化模型运行模型。
+
+!!! 提示<br>
+    在绝大多数情况下无需手动设置该选项，因此Fastdeploy可以通过--runner auto(默认值)自动检测合适的runner。
+
+### Model Conversion
+
+如果模型未实现FdModelForPooling接口但你希望以池化模式运行，FastDeploy可通过`--convert <type>`自动转换模型。
+
+当设置了`--runner pooling`(手动或自动)但模型不符合接口时，FastDeploy会根据模型架构名称自动转换:
+
+| Architecture                                    | `--convert` | 支持的池化类型               |
+|-------------------------------------------------|-------------|---------------------------------------|
+| `*ForTextEncoding`, `*EmbeddingModel`, `*Model` `**ForProcessRewardModel`  | `embed`     |  `embed`                              |
+
+!!! 提示<br>
+    你可以显示设置`--convert <type>`来制定模型转换方式。
+
+### Pooler Configuration
+
+#### Predefined models
+
+如果模型定义的`Pooler`接受pooler_config，你可以通过--pooler_config覆盖部分属性。
+
+#### Converted models
+
+如果模型通过--convert转换，各任务默认的池化配置如下:
+
+| Task       | Pooling Type | Normalization | Softmax |
+|------------|--------------|---------------|---------|
+| `embed`    | `LAST`       | ✅︎            | ❌      |
+
+加载[Sentence Transformers](https://huggingface.co/sentence-transformers)模型时，其`modules.json`配置优于默认值，也可以通过@default_pooling_type("LAST")在模型组网时指定。
+
+#### Pooling Type
+
+1.LastPool(PoolingType.LAST)
+
+作用:提取每个序列的最后一个token的隐藏状态
+
+2.AllPool(PoolingType.ALL)
+
+作用:返回每个序列的所有token的隐藏状态
+
+3.CLSPool(PoolingType.CLS)
+
+作用:返回每个序列的第一个token(CLS token)的隐藏状态
+
+4.MeanPool(PoolingType.MEAN)
+
+作用:计算每个序列所有token隐藏状态的平均值
+
+## Online Serving
+
+FastDeploy的OpenAI兼容服务器提供了API的端点和自定义的reward接口
+
+- `Embeddings API`，支持文本和多模态输入
+- `Reward API`,给指定的内容打分
+
+### Embedding模型:
+```python
+model_path=Qwen/Qwen3-Embedding-0.6B
+
+python -m fastdeploy.entrypoints.openai.api_server --model ${model_path} \
+    --max-num-seqs 256 --max-model-len 32768 \
+    --port 9412 --engine-worker-queue-port 7142 \
+    --metrics-port 7211 --tensor-parallel-size 1 \
+    --gpu-memory-utilization 0.9 \
+    --runner pooling \
+
+```
+
+请求方式:<br>
+A. EmbeddingCompletionRequest 示例（标准文本输入）
+
+```bash
+curl -X POST 'YOUR_SERVICE_URL/v1/embeddings' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "text-embedding-chat-model",
+    "input": [
+      "This is a sentence for pooling embedding.",
+      "Another input text."
+    ],
+    "user": "test_client"
+  }'
+```
+
+B. EmbeddingChatRequest 示例（消息序列输入）
+
+```bash
+curl -X POST 'YOUR_SERVICE_URL/v1/embeddings' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "text-embedding-chat-model",
+    "messages": [
+      {"role": "user", "content": "Generate embedding for user query."}
+    ]
+  }'
+```
+
+### Pooling模型和打分机制
+```python
+model_path=RM_v1008
+python -m fastdeploy.entrypoints.openai.api_server \
+    --model ${model_path} \
+    --max-num-seqs 256 \
+    --max-model-len 8192 \
+    --port 13351 \
+    --engine-worker-queue-port 7562 \
+    --metrics-port 7531 \
+    --tensor-parallel-size 8 \
+    --gpu-memory-utilization 0.9 \
+    --runner pooling \
+    --convert embed \
+```
+
+请求方式: ChatRewardRequest
+
+```bash
+curl --location 'http://xxxx/v1/chat/reward' \
+--header 'Content-Type: application/json' \
+--data '{
+  "model": "",
+  "messages": [
+    {
+      "role": "user",
+      "content": [
+        {
+          "type": "image_url",
+          "image_url": {
+            "url": "https://xxx/a.png"
+          }
+        }
+      ]
+    },
+    {
+      "role": "assistant",
+      "content": [
+        {
+          "type": "text",
+          "text": "图里有几个人"
+        }
+      ]
+    }
+  ],
+  "user": "user-123",
+  "chat_template": null,
+  "chat_template_kwargs": {
+    "custom_var": "value"
+  },
+  "mm_processor_kwargs": {
+    "image_size": 224
+  }
+}'
+```

fastdeploy/config.py

@@ -299,7 +299,7 @@ class ModelConfig:
             self.tensor_parallel_size = self.infer_model_mp_num
             del self.infer_model_mp_num
 
-        if hasattr(self, "num_hidden_layers"):
+        if hasattr(self, "num_hidden_layers") and self.runner != "pooling":
             if hasattr(self, "remove_tail_layer"):
                 if self.remove_tail_layer is True:
                     self.num_hidden_layers -= 1

fastdeploy/engine/pooling_params.py

@@ -164,7 +164,7 @@ class PoolingParams(
                 self.softmax = True
         elif self.task == "reward":
             if self.normalize is None:
-                self.normalize = True
+                self.normalize = False
         else:
             raise ValueError(f"Unknown pooling task: {self.task}")
 

fastdeploy/engine/request.py

@@ -187,6 +187,7 @@ class Request:
             pooling_params = PoolingParams.from_dict(d["pooling_params"])
         else:
             sampling_params = SamplingParams.from_dict(d)
+
         if (
             isinstance(d.get("multimodal_inputs"), dict)
             and isinstance(d["multimodal_inputs"].get("mm_positions"), list)
@@ -202,7 +203,6 @@ class Request:
                 data_processor_logger.error(
                     f"Convert mm_positions to ImagePosition error: {e}, {str(traceback.format_exc())}"
                 )
-
         return cls(
             request_id=d["request_id"],
             prompt=d.get("prompt"),

fastdeploy/entrypoints/openai/protocol.py

@@ -920,16 +920,6 @@ class EmbeddingChatRequest(BaseModel):
     user: Optional[str] = None
     truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None
 
-    # --8<-- [start:chat-embedding-extra-params]
-    add_generation_prompt: bool = Field(
-        default=False,
-        description=(
-            "If true, the generation prompt will be added to the chat template. "
-            "This is a parameter used by chat template in tokenizer config of the "
-            "model."
-        ),
-    )
-
     add_special_tokens: bool = Field(
         default=False,
         description=(
@@ -1013,9 +1003,9 @@ PoolingChatRequest = EmbeddingChatRequest
 
 
 class ChatRewardRequest(BaseModel):
-    model: Optional[str] = None  # 指定模型，例如 "default" 或支持 embedding 的 chat 模型
+    model: Optional[str] = None
-    messages: Union[List[Any], List[int]]  # 聊天消息列表（必选）
+    messages: Union[List[Any], List[int]]
-    user: Optional[str] = None  # 调用方标识符
+    user: Optional[str] = None
 
     dimensions: Optional[int] = None
     truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None
@@ -1084,15 +1074,15 @@ class ChatRewardRequest(BaseModel):
 
 
 class ChatRewardData(BaseModel):
-    index: Optional[int] = None  # 数据索引（可选）
+    index: Optional[int] = None
-    object: str = "reward"  # 固定为 "reward"
+    object: str = "reward"
-    score: List[float]  # reward 分数（浮点数列表）
+    score: List[float]
 
 
 class ChatRewardResponse(BaseModel):
-    id: str  # 响应 ID，例如 chat-reward-<uuid>
+    id: str
-    object: str = "object"  # 固定为 "object"
+    object: str = "object"
-    created: int  # 创建时间（Unix 时间戳）
+    created: int
-    model: str  # 使用的模型名
+    model: str
-    data: List[ChatRewardData]  # reward 结果列表
+    data: List[ChatRewardData]
-    usage: Optional[UsageInfo] = None  # Token 使用情况
+    usage: Optional[UsageInfo] = None

fastdeploy/entrypoints/openai/serving_engine.py

@@ -256,7 +256,6 @@ class ZmqOpenAIServing(OpenAIServing):
         chat_template_kwargs.update(
             {
                 "chat_template": request_dict.get("chat_template"),
-                "add_generation_prompt": request_dict.get("add_generation_prompt"),
                 "add_stop_sequences": request_dict.get("add_stop_sequences"),
             }
         )

fastdeploy/model_executor/layers/pool/metadata.py

@@ -15,12 +15,14 @@
 """
 
 from dataclasses import dataclass
-from typing import Optional
+from typing import Optional, Union
 
 import paddle
 
 from fastdeploy.engine.pooling_params import PoolingParams
 
+Device = Union[paddle.CPUPlace, paddle.CUDAPlace, paddle.XPUPlace]
+
 
 @dataclass
 class PoolingCursor:
@@ -60,21 +62,21 @@ class PoolingMetadata:
             pooling_cursor=None if self.pooling_cursor is None else self.pooling_cursor[indices],
         )
 
-    def build_pooling_cursor(self, num_scheduled_tokens: list[int], device: str):
+    def build_pooling_cursor(self, num_scheduled_tokens: list[int], device: Device):
         self.pooling_cursor = build_pooling_cursor(num_scheduled_tokens, self.prompt_lens, device)
 
 
-def build_pooling_cursor(num_scheduled_tokens: list[int], prompt_lens: paddle.Tensor, device: str):
+def build_pooling_cursor(num_scheduled_tokens: list[int], prompt_lens: paddle.Tensor, device: Device):
     assert len(prompt_lens) == len(num_scheduled_tokens)
 
     n_seq = len(num_scheduled_tokens)
     index = list(range(n_seq))
-    num_scheduled_tokens = paddle.to_tensor(num_scheduled_tokens)
+    num_scheduled_tokens = paddle.to_tensor(num_scheduled_tokens, dtype="int64")
     cumsum = paddle.zeros([n_seq + 1], dtype="int64")
 
     paddle.cumsum(num_scheduled_tokens, axis=0, out=cumsum[1:])
-    if device == "gpu":
+    if isinstance(device, paddle.CUDAPlace):
-        cumsum_device = cumsum.cuda()
+        cumsum_device = paddle.assign(cumsum).cuda(device.get_device_id())
     else:
         cumsum_device = cumsum
     return PoolingCursor(

fastdeploy/model_executor/layers/pooler.py

@@ -78,7 +78,6 @@ def get_pooling_params(pooling_metadata: PoolingMetadata) -> list[PoolingParams]
 
 def get_tasks(pooling_metadata: PoolingMetadata) -> list[PoolingTask]:
     pooling_params = get_pooling_params(pooling_metadata)
-
     tasks: list[PoolingTask] = [task for pooling_param in pooling_params if (task := pooling_param.task) is not None]
     assert len(pooling_params) == len(tasks)
 
@@ -108,7 +107,7 @@ class Pooler(nn.Layer, ABC):
     @staticmethod
     def for_encode(pooler_config: PoolerConfig, model_config: Optional["ModelConfig"] = None):
         if pooler_config.pooling_type == "STEP":
-            return StepPooler()
+            return StepPooler(model_config)
 
         resolved_config = ResolvedPoolingConfig(task="encode", pooling_type=PoolingType.ALL)
         return SimplePooler.from_config(resolved_config, model_config)
@@ -121,6 +120,14 @@ class Pooler(nn.Layer, ABC):
         )
         return SimplePooler.from_config(resolved_config, model_config)
 
+    @staticmethod
+    def for_reward(pooler_config: PoolerConfig, model_config: Optional["ModelConfig"] = None):
+        resolved_config = ResolvedPoolingConfig.from_config(
+            task="reward",
+            pooler_config=pooler_config,
+        )
+        return SimplePooler.from_config(resolved_config, model_config)
+
     @staticmethod
     def for_classify(
         pooler_config: PoolerConfig,
@@ -274,6 +281,7 @@ class EmbeddingPoolerHead(PoolerHead):
                 pooled_data = [vecs if d is None else vecs[..., :d] for vecs, d in zip(pooled_data, dimensions_list)]
         # for normalize
         flags = [p.normalize for p in pooling_params]
+
         if len(set(flags)) == 1:
             if flags[0]:
                 pooled_data = self.activation(pooled_data)
@@ -293,7 +301,6 @@ class RewardPoolerHead(PoolerHead):
     def forward(self, pooled_data: Union[list[paddle.Tensor], paddle.Tensor], pooling_metadata: PoolingMetadata):
         pooling_params = get_pooling_params(pooling_metadata)
 
-        # for softmax
         flags = [p.softmax for p in pooling_params]
         if len(set(flags)) == 1:
             if flags[0]:
@@ -345,7 +352,7 @@ class PoolingMethod(nn.Layer, ABC):
 class LastPool(PoolingMethod):
 
     def get_supported_tasks(self) -> Set[PoolingTask]:
-        return {"encode", "embed", "classify", "score"}
+        return {"encode", "embed", "classify", "score", "reward"}
 
     def forward_all(
         self,
@@ -366,8 +373,8 @@ class AllPool(PoolingMethod):
     ) -> Union[list[paddle.Tensor], paddle.Tensor]:
 
         assert not pooling_cursor.is_partial_prefill(), "partial prefill not supported with ALL pooling"
-
         hidden_states_lst = list(hidden_states.split(pooling_cursor.num_scheduled_tokens_cpu.tolist()))
+
         return [hidden_states_lst[i] for i in pooling_cursor.index]
 
 
@@ -416,11 +423,12 @@ class CLSPool(PoolingMethod):
 class StepPooler(Pooler):
     def __init__(
         self,
+        model_config: ModelConfig,
     ) -> None:
         super().__init__()
 
         self.pooling = AllPool()
-        self.head = RewardPoolerHead()
+        self.head = RewardPoolerHead(model_config)
 
     def extract_states(
         self,
@@ -455,14 +463,11 @@ class StepPooler(Pooler):
 
     def forward(
         self,
-        hidden_states: Union[paddle.Tensor, list[paddle.Tensor]],
+        hidden_states: paddle.Tensor | list[paddle.Tensor],
         pooling_metadata: PoolingMetadata,
     ) -> PoolerOutput:
         pooled_data = self.extract_states(hidden_states, pooling_metadata)
-        pooling_params = get_pooling_params(pooling_metadata)
+        pooled_data = self.head(pooled_data, pooling_metadata)
-        assert len(pooled_data) == len(pooling_params)
-
-        pooled_data = [self.head(d, p) for d, p in zip(pooled_data, pooling_params)]
         return pooled_data
 
 
@@ -484,7 +489,7 @@ class SimplePooler(Pooler):
         pooling = PoolingMethod.from_pooling_type(pooler_config.pooling_type)
         if pooler_config.task == "embed":
             head = EmbeddingPoolerHead(model_config)
-        elif pooler_config.task == "encode":
+        elif pooler_config.task == "encode" or pooler_config.task == "reward":
             head = RewardPoolerHead(model_config)
         else:
             raise NotImplementedError(f"Unknown task: {pooler_config.task}")

fastdeploy/model_executor/models/adapters.py

@@ -166,6 +166,7 @@ def as_embedding_model(cls: _T) -> _T:
                 {
                     "encode": Pooler.for_encode(pooler_config, fd_config.model_config),
                     "embed": Pooler.for_embed(pooler_config, fd_config.model_config),
+                    "reward": Pooler.for_reward(pooler_config, fd_config.model_config),
                 },
             )
 

fastdeploy/model_executor/models/ernie4_5_vl/ernie4_5_vl_moe.py

@@ -348,12 +348,17 @@ class Ernie4_5_VLDecoderLayer(nn.Layer):
                 prefix=f"{prefix}.mlp",
             )
 
+        norm_dtype = None
+        if fd_config.model_config.architectures[0] == "Ernie4_5_VLMoeForProcessRewardModel":
+            norm_dtype = "float32"
+
         self.input_layernorm = RMSNorm(
             fd_config,
             hidden_size=fd_config.model_config.hidden_size,
             eps=fd_config.model_config.rms_norm_eps,
             prefix=f"{prefix}.input_layernorm",
             layer_id=layer_id,
+            dtype=norm_dtype,
         )
 
         self.post_attention_layernorm = RMSNorm(
@@ -362,6 +367,7 @@ class Ernie4_5_VLDecoderLayer(nn.Layer):
             eps=fd_config.model_config.rms_norm_eps,
             prefix=f"{prefix}.post_attention_layernorm",
             layer_id=layer_id,
+            dtype=norm_dtype,
         )
 
     def load_state_dict(self, state_dict):
@@ -542,7 +548,6 @@ class Ernie4_5_VLModel(nn.Layer):
             )
 
         out = self.norm(hidden_states, residual, forward_meta=forward_meta)[0]
-
         return out
 
 

fastdeploy/model_executor/models/ernie_vl_rm.py

@@ -57,6 +57,7 @@ class Ernie4_5_VLMoeRewardBaseModel(nn.Layer):
         )
         self.ernie = Ernie4_5_VLModel(fd_config=fd_config)
         self.head_dtype = paddle.bfloat16
+        self.fd_config = fd_config
 
         # Persistent buffers for CUDA graphs.
         if fd_config.graph_opt_config.use_cudagraph:
@@ -111,7 +112,7 @@ class Ernie4_5_VLMoeRewardBaseModel(nn.Layer):
         input_embeddings = self.get_input_embeddings(
             ids_remove_padding=ids_remove_padding,
             image_features=image_features,
-            image_token_num=vl_moe_meta.image_token_num.item(),
+            image_token_num=vl_moe_meta.num_image_patch_id.item(),
         )
 
         if forward_meta.step_use_cudagraph:
@@ -124,18 +125,22 @@ class Ernie4_5_VLMoeRewardBaseModel(nn.Layer):
             forward_meta=forward_meta,
             vl_moe_meta=vl_moe_meta,
         )
+
+        if isinstance(hidden_states, tuple):
+            hidden_states = hidden_states[0]
+
         hidden_states = hidden_states.to(self.head_dtype)
         logits = self.rm_head(hidden_states)
-        return logits
+        return logits.cast("float32")
 
 
 @ModelRegistry.register_model_class(
     architecture="Ernie4_5_VLMoeForProcessRewardModel",
     module_name="ernie_vl_rm",
-    category=[ModelCategory.REWARD],
+    category=ModelCategory.REWARD | ModelCategory.MULTIMODAL,
-    primary_use=ModelCategory.REWARD,
+    primary_use=ModelCategory.REWARD | ModelCategory.MULTIMODAL,
 )
-@default_pooling_type("ALL")
+@default_pooling_type("LAST")
 class Ernie4_5_VLMoeForProcessRewardModel(Ernie4_5_VLMoeRewardBaseModel):
 
     def __init__(self, fd_config: FDConfig):
@@ -147,7 +152,13 @@ class Ernie4_5_VLMoeForProcessRewardModel(Ernie4_5_VLMoeRewardBaseModel):
         pooler_config = fd_config.model_config.pooler_config
         assert pooler_config is not None
 
-        self.pooler = DispatchPooler({"encode": Pooler.for_encode(pooler_config)})
+        self.pooler = DispatchPooler(
+            {
+                "encode": Pooler.for_encode(pooler_config, fd_config.model_config),
+                "embed": Pooler.for_embed(pooler_config, fd_config.model_config),
+                "reward": Pooler.for_reward(pooler_config, fd_config.model_config),
+            },
+        )
 
         self.process_weights_before_loading_fn = process_weights_before_loading(skip_prefixes=["lm_head"])
 
@@ -159,4 +170,5 @@ class Ernie4_5_VLMoeForProcessRewardModel(Ernie4_5_VLMoeRewardBaseModel):
     @paddle.no_grad()
     def load_weights(self, weights_iterator):
         # Filter out lm_head weights of Ernie4_5_VLMoeForConditionalGeneration
+
         Ernie4_5_VLMoeForConditionalGeneration.load_weights(self, weights_iterator)

fastdeploy/model_executor/models/model_base.py

@@ -58,7 +58,7 @@ class ModelInfo:
             is_text_generation=ModelCategory.TEXT_GENERATION in category,
             is_multimodal=ModelCategory.MULTIMODAL in category,
             is_reasoning=ModelCategory.REASONING in category,
-            is_pooling=ModelCategory.EMBEDDING in category,
+            is_pooling=(ModelCategory.EMBEDDING in category) or (ModelCategory.REWARD in category),
             default_pooling_type=get_default_pooling_type(model_cls),
             module_path=module_path,
         )

fastdeploy/model_executor/pre_and_post_process.py

@@ -298,6 +298,8 @@ def _build_stream_transfer_data(
             stream_transfer_datas.append(stream_transfer_data)
     elif pooler_outputs is not None:
         for bid, pooler_output in enumerate(pooler_outputs):
+            if pooler_output is None:
+                continue
             if pooler_output.dtype == paddle.bfloat16:
                 pooler_output = pooler_output.astype("float32")
 

fastdeploy/worker/gpu_model_runner.py

@@ -542,7 +542,11 @@ class GPUModelRunner(ModelRunnerBase):
         rope_3d_position_ids["position_ids_offset"].append(
             position_ids.shape[0] + rope_3d_position_ids["position_ids_offset"][-1]
         )
-        rope_3d_position_ids["max_tokens_lst"].append(request.get("max_tokens", 2048))
+
+        if self.is_pooling_model:
+            rope_3d_position_ids["max_tokens_lst"].append(0)
+        else:
+            rope_3d_position_ids["max_tokens_lst"].append(request.get("max_tokens", 2048))
 
     def insert_tasks_v1(self, req_dicts: List[Request], num_running_requests: int = None):
         """
@@ -2428,7 +2432,6 @@ class GPUModelRunner(ModelRunnerBase):
     def _pool(self, hidden_states: paddle.Tensor, num_running_requests: int) -> Optional[ModelRunnerOutput]:
 
         num_scheduled_tokens = int(self.share_inputs["seq_lens_this_time"][:num_running_requests].sum())
-
         hidden_states = hidden_states[:num_scheduled_tokens]
 
         prompt_lens = self.share_inputs["prompt_lens"][:num_running_requests]
@@ -2446,11 +2449,23 @@ class GPUModelRunner(ModelRunnerBase):
         pooling_metadata.build_pooling_cursor(num_scheduled_tokens_list, device=device_str)
 
         raw_pooler_output = self.model.pooler(hidden_states=hidden_states, pooling_metadata=pooling_metadata)
+
         seq_lens_cpu = self.share_inputs["seq_lens_this_time"][:num_running_requests]
         pooler_output: list[Optional[paddle.Tensor]] = []
-        for raw_output, seq_len, prompt_len in zip(raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens):
+
-            output = raw_output.data if int(seq_len) == int(prompt_len) else None
+        seq_lens_decoder_batch = self.share_inputs["seq_lens_decoder"][:num_running_requests]
-            pooler_output.append(output)
+
+        for i, (seq_len, prompt_len) in enumerate(zip(seq_lens_cpu, pooling_metadata.prompt_lens)):
+            if not self.cache_config.enable_prefix_caching:
+                output = raw_pooler_output[i].data if int(seq_len) == int(prompt_len) else None
+                pooler_output.append(output)
+            else:
+                current_seq_len_decoder = seq_lens_decoder_batch[i]
+                if int(current_seq_len_decoder) + int(seq_len) == int(prompt_len):
+                    output = raw_pooler_output[i].data
+                else:
+                    output = None
+                pooler_output.append(output)
 
         pooler_output = PoolerOutput(
             outputs=pooler_output,

tests/pooling/test_Ernie4_5_reward_serving.py

@@ -0,0 +1,204 @@
+# 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.
+
+import json
+import os
+import signal
+import subprocess
+import sys
+import time
+
+import pytest
+import requests
+from e2e.utils.serving_utils import (
+    FD_API_PORT,
+    FD_CACHE_QUEUE_PORT,
+    FD_ENGINE_QUEUE_PORT,
+    FD_METRICS_PORT,
+    clean_ports,
+    is_port_open,
+)
+
+# ==========================
+# Shared Helper Functions
+# ==========================
+
+
+def _start_server_process(enable_caching: bool, log_filename: str):
+
+    print(f"\n[Server Setup] Cleaning ports before starting (Caching={'ON' if enable_caching else 'OFF'})...")
+    clean_ports()
+
+    base_path = os.getenv("MODEL_PATH")
+    if base_path:
+        model_path = os.path.join(base_path, "RM_v1008_5")
+    else:
+        model_path = "./RM_v1008_5"
+
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(f"Model path not found: {model_path}")
+
+    cmd = [
+        sys.executable,
+        "-m",
+        "fastdeploy.entrypoints.openai.api_server",
+        "--model",
+        model_path,
+        "--port",
+        str(FD_API_PORT),
+        "--tensor-parallel-size",
+        "2",
+        "--engine-worker-queue-port",
+        str(FD_ENGINE_QUEUE_PORT),
+        "--metrics-port",
+        str(FD_METRICS_PORT),
+        "--cache-queue-port",
+        str(FD_CACHE_QUEUE_PORT),
+        "--max-model-len",
+        "8192",
+        "--max-num-seqs",
+        "256",
+        "--runner",
+        "pooling",
+        "--convert",
+        "embed",
+    ]
+
+    if enable_caching:
+        cmd.append("--enable-prefix-caching")
+    else:
+        cmd.append("--no-enable-prefix-caching")
+
+    print(f"[Server Setup] Command: {' '.join(cmd)}")
+
+    with open(log_filename, "w") as logfile:
+        process = subprocess.Popen(
+            cmd,
+            stdout=logfile,
+            stderr=subprocess.STDOUT,
+            start_new_session=True,
+        )
+
+    # Wait for server to start
+    for _ in range(300):
+        if is_port_open("127.0.0.1", FD_API_PORT):
+            print(f"[Server Setup] Server is up on port {FD_API_PORT}")
+            break
+        time.sleep(1)
+    else:
+        print("[Server Setup] Server failed to start. Cleaning up...")
+        try:
+            os.killpg(process.pid, signal.SIGTERM)
+        except Exception:
+            pass
+        if os.path.exists(log_filename):
+            with open(log_filename, "r") as f:
+                print(f"Server Log Tail ({log_filename}):\n{f.read()[-500:]}")
+        raise RuntimeError(f"Server did not start on port {FD_API_PORT}")
+
+    return process
+
+
+@pytest.fixture(scope="function")
+def reward_api_url():
+    """Returns the API endpoint URL for reward."""
+    return f"http://0.0.0.0:{FD_API_PORT}/v1/reward"
+
+
+@pytest.fixture(scope="function")
+def headers():
+    """Returns common HTTP request headers."""
+    return {"Content-Type": "application/json"}
+
+
+@pytest.fixture(scope="function")
+def server_default_caching():
+    _start_server_process(enable_caching=True, log_filename="reward_server_caching_on.log")
+
+
+@pytest.fixture(scope="function")
+def server_no_caching():
+    _start_server_process(enable_caching=False, log_filename="reward_server_caching_off.log")
+
+
+def save_score_baseline(score: float, baseline_file: str):
+    """Save reward score to baseline file."""
+    baseline_data = {"score": score}
+    with open(baseline_file, "w", encoding="utf-8") as f:
+        json.dump(baseline_data, f, indent=2)
+    print(f"Baseline saved to: {baseline_file}")
+
+
+def check_score_against_baseline(current_score: float, baseline_file: str, threshold: float = 0.01):
+    """Check reward score against baseline file."""
+    try:
+        with open(baseline_file, "r", encoding="utf-8") as f:
+            baseline_data = json.load(f)
+            baseline_score = baseline_data["score"]
+    except FileNotFoundError:
+        print(f"Baseline file not found: {baseline_file}. Saving current as baseline.")
+        save_score_baseline(current_score, baseline_file)
+        return
+
+    diff = abs(current_score - baseline_score)
+    print(f"Score Difference: {diff:.6f} (Current: {current_score}, Baseline: {baseline_score})")
+
+    if diff >= threshold:
+        temp_file = f"{baseline_file}.current"
+        save_score_baseline(current_score, temp_file)
+        raise AssertionError(
+            f"Score differs from baseline by too much (diff={diff:.6f} >= {threshold}):\n"
+            f"Current score saved to: {temp_file}"
+        )
+
+
+def _run_test_logic(reward_api_url, headers, baseline_filename):
+    payload = {
+        "model": "default",
+        "messages": [
+            {"role": "user", "content": [{"type": "text", "text": "北京天安门在哪里？"}]},
+            {"role": "assistant", "content": [{"type": "text", "text": "北京天安门在中国北京故宫的前面。"}]},
+        ],
+        "user": "user-123",
+        "enable_thinking": False,
+    }
+
+    print(f"\n=== Sending request to {reward_api_url} ===")
+    response = requests.post(reward_api_url, headers=headers, json=payload, timeout=30)
+    assert response.status_code == 200, f"API request failed with status {response.status_code}: {response.text}"
+
+    result = response.json()
+    print(f"Response: {json.dumps(result, indent=2, ensure_ascii=False)}")
+
+    assert "data" in result and len(result["data"]) > 0
+    score = float(result["data"][0]["score"][0])
+    print(f"✓ Reward Score: {score}")
+
+    base_path = os.getenv("MODEL_PATH", "")
+    if base_path:
+        baseline_file = os.path.join(base_path, baseline_filename)
+    else:
+        baseline_file = baseline_filename
+
+    check_score_against_baseline(score, baseline_file, threshold=0.01)
+
+
+def test_reward_model_with_caching(server_default_caching, reward_api_url, headers):
+    print("\n>>> Running Test: WITH Prefix Caching")
+    _run_test_logic(reward_api_url, headers, baseline_filename="reward_score_baseline.json")
+
+
+def test_reward_model_without_caching(server_no_caching, reward_api_url, headers):
+    print("\n>>> Running Test: WITHOUT Prefix Caching")
+    _run_test_logic(reward_api_url, headers, baseline_filename="reward_score_baseline_no_caching.json")

tests/pooling/test_Qwen3-Embedding_serving.py

@@ -79,7 +79,7 @@ def setup_and_run_embedding_server():
         model_path = "./Qwen3-Embedding-0.6B"
 
     if not os.path.exists(model_path):
-        pytest.skip(f"Model path not found: {model_path}")
+        raise FileNotFoundError(f"Model path not found: {model_path}")
 
     log_path = "embedding_server.log"
     cmd = [