[Performance] V1 Classify Models E2E Performance Optimization (#23541)

Signed-off-by: wang.yuqi <noooop@126.com>

tests/entrypoints/llm/test_classify.py

@@ -62,3 +62,9 @@ def test_encode_api(llm: LLM):
     err_msg = "pooling_task must be one of.+"
     with pytest.raises(ValueError, match=err_msg):
         llm.encode(prompts, use_tqdm=False)
+
+
+def test_score_api(llm: LLM):
+    err_msg = "Score API is only enabled for num_labels == 1."
+    with pytest.raises(ValueError, match=err_msg):
+        llm.score("ping", "pong", use_tqdm=False)

tests/entrypoints/openai/test_classification.py

@@ -226,3 +226,33 @@ def test_pooling(server: RemoteOpenAIServer, model_name: str):
         },
     )
     assert response.json()["error"]["type"] == "BadRequestError"
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+def test_score(server: RemoteOpenAIServer, model_name: str):
+    # score api is only enabled for num_labels == 1.
+    response = requests.post(
+        server.url_for("score"),
+        json={
+            "model": model_name,
+            "text_1": "ping",
+            "text_2": "pong",
+        },
+    )
+    assert response.json()["error"]["type"] == "BadRequestError"
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+def test_rerank(server: RemoteOpenAIServer, model_name: str):
+    # rerank api is only enabled for num_labels == 1.
+    response = requests.post(
+        server.url_for("rerank"),
+        json={
+            "model": model_name,
+            "query": "ping",
+            "documents": ["pong"],
+        },
+    )
+    assert response.json()["error"]["type"] == "BadRequestError"

vllm/entrypoints/openai/api_server.py

@@ -1805,17 +1805,13 @@ async def init_app_state(
         request_logger=request_logger,
         log_error_stack=args.log_error_stack,
     ) if "classify" in supported_tasks else None
-
-    enable_serving_reranking = ("classify" in supported_tasks and getattr(
-        model_config.hf_config, "num_labels", 0) == 1)
     state.openai_serving_scores = ServingScores(
         engine_client,
         model_config,
         state.openai_serving_models,
         request_logger=request_logger,
         log_error_stack=args.log_error_stack,
-    ) if ("embed" in supported_tasks or enable_serving_reranking) else None
-
+    ) if ("embed" in supported_tasks or "score" in supported_tasks) else None
     state.openai_serving_tokenization = OpenAIServingTokenization(
         engine_client,
         model_config,

vllm/model_executor/layers/pooler.py

@@ -13,12 +13,15 @@ import torch.nn.functional as F
 from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig, PoolerConfig
+from vllm.logger import init_logger
 from vllm.pooling_params import PoolingParams
 from vllm.sequence import PoolerOutput, PoolingSequenceGroupOutput
 from vllm.tasks import PoolingTask
 from vllm.utils import current_stream, resolve_obj_by_qualname
 from vllm.v1.pool.metadata import PoolingCursor, PoolingMetadata
 
+logger = init_logger(__name__)
+
 PoolingFn = Callable[
     [Union[torch.Tensor, list[torch.Tensor]], PoolingMetadata],
     Union[torch.Tensor, list[torch.Tensor]]]
@@ -183,7 +186,7 @@ def get_cross_encoder_activation_function(config: PretrainedConfig):
         fn = resolve_obj_by_qualname(function_name)()
         return PoolerActivation.wraps(fn)
 
-    return PoolerScore()
+    return PoolerClassify()
 
 
 def build_output(
@@ -371,22 +374,29 @@ class PoolerMultiLabelClassify(PoolerActivation):
 
 class PoolerClassify(PoolerActivation):
 
-    def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
-        num_labels = pooled_data.shape[-1]
-        if num_labels < 2:
-            return F.sigmoid(pooled_data.float()).to(pooled_data.dtype)
-
-        return F.softmax(pooled_data.float(), dim=-1).to(pooled_data.dtype)
-
+    def __init__(self, *, static_num_labels: bool = True) -> None:
+        super().__init__()
 
-class PoolerScore(PoolerActivation):
+        if static_num_labels:
+            from vllm.config import get_current_vllm_config
+            vllm_config = get_current_vllm_config()
+            self.num_labels = getattr(vllm_config.model_config.hf_config,
+                                      "num_labels", 0)
+            if self.num_labels == 0:
+                logger.warning("num_labels should be > 0 for classification"
+                               "models, falling back to softmax. "
+                               "Please check if the configuration is correct.")
+        else:
+            self.num_labels = None
 
     def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
-        num_labels = pooled_data.shape[-1]
+        num_labels = (self.num_labels if self.num_labels is not None else
+                      pooled_data.shape[-1])
+
         if num_labels < 2:
             return F.sigmoid(pooled_data.float()).to(pooled_data.dtype)
 
-        return pooled_data
+        return F.softmax(pooled_data.float(), dim=-1).to(pooled_data.dtype)
 
 
 class LambdaPoolerActivation(PoolerActivation):
@@ -428,6 +438,10 @@ class EmbeddingPoolerHead(PoolerHead):
     def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor],
                 pooling_metadata: PoolingMetadata):
 
+        if isinstance(pooled_data, list):
+            pooled_data = torch.stack(pooled_data)
+        # pooled_data shape: [batchsize, hidden_dimension]
+
         # Apply ST projector
         if self.projector is not None:
             projector = cast(nn.Module, self.projector)
@@ -437,17 +451,11 @@ class EmbeddingPoolerHead(PoolerHead):
                 y = projector(x.to(torch.float32))
                 return y.to(orig_dtype)
 
-            if isinstance(pooled_data, torch.Tensor):
-                pooled_data = _proj(pooled_data)
-            else:
-                pooled_data = [_proj(t) for t in pooled_data]
+            pooled_data = _proj(pooled_data)
+        # pooled_data shape: [batchsize, embedding_dimension]
 
         pooling_params = get_pooling_params(pooling_metadata)
 
-        if isinstance(pooled_data, list):
-            pooled_data = torch.stack(pooled_data)
-        # pooled_data shape: [batchsize, embedding_dimension]
-
         # for matryoshka representation
         dimensions_list = [
             pooling_param.dimensions for pooling_param in pooling_params
@@ -477,13 +485,14 @@ class EmbeddingPoolerHead(PoolerHead):
                 for vecs, f in zip(pooled_data, flags)
             ]
 
+        # pooled_data shape: [batchsize, embedding_dimension]
         return pooled_data
 
 
 class RewardPoolerHead(PoolerHead):
 
     def __init__(self) -> None:
-        super().__init__(activation=PoolerClassify())
+        super().__init__(activation=PoolerClassify(static_num_labels=False))
 
     def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor],
                 pooling_metadata: PoolingMetadata):
@@ -637,19 +646,13 @@ class ClassifierPooler(Pooler):
         pooling_metadata: PoolingMetadata,
     ) -> PoolerOutput:
         pooled_data = self.pooling(hidden_states, pooling_metadata)
-
         if isinstance(pooled_data, list):
             pooled_data = torch.stack(pooled_data)
         # pooled_data shape: [batchsize, hidden_size]
 
         if self.classifier is not None:
-            # apply classifier once on the full batch if possible
-            if isinstance(pooled_data, torch.Tensor):
-                pooled_data = self.classifier(pooled_data)
-            elif len({data.shape for data in pooled_data}) <= 1:
-                pooled_data = self.classifier(torch.stack(pooled_data))
-            else:
-                pooled_data = [self.classifier(data) for data in pooled_data]
+            pooled_data = self.classifier(pooled_data)
+        # pooled_data shape: [batchsize, num_labels]
 
         pooling_params = get_pooling_params(pooling_metadata)
         flags = [p.activation for p in pooling_params]
@@ -662,6 +665,7 @@ class ClassifierPooler(Pooler):
                 for vecs, f in zip(pooled_data, flags)
             ]
 
+        # scores shape: [batchsize, num_labels]
         return build_output(scores)
 
 

vllm/v1/worker/gpu_model_runner.py

@@ -1248,10 +1248,17 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 and "encode" in supported_tasks):
             supported_tasks.remove("encode")
 
-            logger.info_once("Chunked prefill is not supported with "
-                             "encode task which using ALL pooling. "
-                             "Please turn off chunked prefill by "
-                             "`--no-enable-chunked-prefill` before using it.")
+            logger.debug_once("Chunked prefill is not supported with "
+                              "encode task which using ALL pooling. "
+                              "Please turn off chunked prefill by "
+                              "`--no-enable-chunked-prefill` before using it.")
+
+        if "score" in supported_tasks:
+            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
+            if num_labels != 1:
+                supported_tasks.remove("score")
+                logger.debug_once(
+                    "Score API is only enabled for num_labels == 1.")
 
         return supported_tasks