[Frontend] Add LLM.reward specific to reward models

docs/models/pooling_models.md

@@ -83,21 +83,6 @@ which takes priority over both the model's and Sentence Transformers's defaults.
 The [LLM][vllm.LLM] class provides various methods for offline inference.
 See [configuration][configuration] for a list of options when initializing the model.
 
-### `LLM.encode`
-
-The [encode][vllm.LLM.encode] method is available to all pooling models in vLLM.
-It returns the extracted hidden states directly, which is useful for reward models.
-
-```python
-from vllm import LLM
-
-llm = LLM(model="Qwen/Qwen2.5-Math-RM-72B", runner="pooling")
-(output,) = llm.encode("Hello, my name is")
-
-data = output.outputs.data
-print(f"Data: {data!r}")
-```
-
 ### `LLM.embed`
 
 The [embed][vllm.LLM.embed] method outputs an embedding vector for each prompt.
@@ -106,7 +91,7 @@ It is primarily designed for embedding models.
 ```python
 from vllm import LLM
 
-llm = LLM(model="intfloat/e5-mistral-7b-instruct", runner="pooling")
+llm = LLM(model="intfloat/e5-small", runner="pooling")
 (output,) = llm.embed("Hello, my name is")
 
 embeds = output.outputs.embedding
@@ -154,6 +139,43 @@ print(f"Score: {score}")
 
 A code example can be found here: <gh-file:examples/offline_inference/basic/score.py>
 
+### `LLM.reward`
+
+The [reward][vllm.LLM.reward] method is available to all pooling models in vLLM.
+It returns the extracted hidden states directly, which is useful for reward models.
+
+```python
+from vllm import LLM
+
+llm = LLM(model="internlm/internlm2-1_8b-reward", runner="pooling", trust_remote_code=True)
+(output,) = llm.reward("Hello, my name is")
+
+data = output.outputs.data
+print(f"Data: {data!r}")
+```
+
+### `LLM.encode`
+
+The [encode][vllm.LLM.encode] method is available to all pooling models in vLLM.
+It returns the extracted hidden states directly.
+
+!!! note
+    `LLM.encode` defaults to using `pooling_task = embed`.
+    - For embeddings, use `LLM.embed(...)`.
+    - For classification logits, use `LLM.classify(...)`.
+    - For reward scores, use `LLM.reward(...)`.
+    - For similarity scores, use `LLM.score(...)`.  
+
+```python
+from vllm import LLM
+
+llm = LLM(model="intfloat/e5-small", runner="pooling")
+(output,) = llm.encode("Hello, my name is")
+
+data = output.outputs.data
+print(f"Data: {data!r}")
+```
+
 ## Online Serving
 
 Our [OpenAI-Compatible Server](../serving/openai_compatible_server.md) provides endpoints that correspond to the offline APIs:

tests/conftest.py

@@ -1049,8 +1049,8 @@ def embed(self,
         req_outputs = self.llm.embed(inputs, *args, **kwargs)
         return [req_output.outputs.embedding for req_output in req_outputs]
 
-    def encode(self, prompts: list[str]) -> list[list[float]]:
-        req_outputs = self.llm.encode(prompts)
+    def reward(self, prompts: list[str]) -> list[list[float]]:
+        req_outputs = self.llm.reward(prompts)
         return [req_output.outputs.data for req_output in req_outputs]
 
     def score(

tests/models/language/pooling/test_reward.py

@@ -95,7 +95,7 @@ def test_prm_models(
         monkeypatch.setenv("VLLM_USE_TRITON_FLASH_ATTN", "False")
 
     with vllm_runner(model, max_model_len=1024, dtype=dtype) as vllm_model:
-        vllm_outputs = vllm_model.encode(math_step_prompts)
+        vllm_outputs = vllm_model.reward(math_step_prompts)
 
     with hf_runner(model, dtype=dtype, auto_cls=AutoModel) as hf_model:
         hf_model = step_reward_patch_hf_model(hf_model)

tests/models/language/pooling/test_truncation_control.py

@@ -28,7 +28,7 @@ def test_smaller_truncation_size(vllm_runner,
 
     with vllm_runner(model_name, runner="pooling",
                      max_model_len=max_model_len) as vllm_model:
-        vllm_output = vllm_model.llm.encode(
+        vllm_output = vllm_model.llm.embed(
             input_str, truncate_prompt_tokens=truncate_prompt_tokens)
 
     prompt_tokens = vllm_output[0].prompt_token_ids
@@ -43,7 +43,7 @@ def test_max_truncation_size(vllm_runner,
 
     with vllm_runner(model_name, runner="pooling",
                      max_model_len=max_model_len) as vllm_model:
-        vllm_output = vllm_model.llm.encode(
+        vllm_output = vllm_model.llm.embed(
             input_str, truncate_prompt_tokens=truncate_prompt_tokens)
 
     prompt_tokens = vllm_output[0].prompt_token_ids
@@ -61,7 +61,7 @@ def test_bigger_truncation_size(vllm_runner,
             model_name, runner="pooling",
             max_model_len=max_model_len) as vllm_model:
 
-        llm_output = vllm_model.llm.encode(
+        llm_output = vllm_model.llm.embed(
             input_str, truncate_prompt_tokens=truncate_prompt_tokens)
 
         assert llm_output == f"""truncate_prompt_tokens value 

vllm/entrypoints/llm.py

@@ -1060,7 +1060,7 @@
         truncate_prompt_tokens: Optional[int] = None,
         use_tqdm: Union[bool, Callable[..., tqdm]] = True,
         lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-        pooling_task: PoolingTask = "encode",
+        pooling_task: Optional[PoolingTask] = None,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
     ) -> list[PoolingRequestOutput]:
         """Apply pooling to the hidden states corresponding to the input
@@ -1092,6 +1092,16 @@
             considered legacy and may be deprecated in the future. You should
             instead pass them via the `inputs` parameter.
         """
+        if pooling_task is None:
+            logger.warning(
+                "`LLM.encode` defaults to using `pooling_task = embed`.\n"
+                "Please use one of the more specific methods instead of `encode`:\n"
+                "  - For embeddings, use `LLM.embed(...)`.\n"
+                "  - For classification logits, use `LLM.classify(...)`.\n"
+                "  - For reward scores, use `LLM.reward(...)`.\n"
+                "  - For similarity scores, use `LLM.score(...)`.")
+            pooling_task = "embed"
+
         model_config = self.llm_engine.model_config
         runner_type = model_config.runner_type
         if runner_type != "pooling":
@@ -1230,6 +1240,45 @@
 
         return [ClassificationRequestOutput.from_base(item) for item in items]
 
+    def reward(
+        self,
+        prompts: Union[PromptType, Sequence[PromptType]],
+        /,
+        *,
+        truncate_prompt_tokens: Optional[int] = None,
+        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
+        pooling_params: Optional[Union[PoolingParams,
+                                       Sequence[PoolingParams]]] = None,
+        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
+    ) -> list[PoolingRequestOutput]:
+        """
+        Generate reward scores for each prompt.
+
+        Args:
+            prompts: The prompts to the LLM. You may pass a sequence of prompts
+                for batch inference. See [PromptType][vllm.inputs.PromptType]
+                for more details about the format of each prompts.
+            use_tqdm: If `True`, shows a tqdm progress bar.
+                If a callable (e.g., `functools.partial(tqdm, leave=False)`),
+                it is used to create the progress bar.
+                If `False`, no progress bar is created.
+            lora_request: LoRA request to use for generation, if any.
+            pooling_params: The pooling parameters for pooling. If None, we
+                use the default pooling parameters.
+        Returns:
+            A list of `PoolingRequestOutput` objects containing the
+            pooled hidden states in the same order as the input prompts.
+        """
+
+        return self.encode(
+            prompts,
+            use_tqdm=use_tqdm,
+            lora_request=lora_request,
+            pooling_params=pooling_params,
+            truncate_prompt_tokens=truncate_prompt_tokens,
+            pooling_task="encode",
+        )
+
     def _embedding_score(
         self,
         tokenizer: AnyTokenizer,