[Model] Add multi_label_classification support (#23173)

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

tests/conftest.py

@@ -456,7 +456,15 @@ class HfRunner:
         outputs = []
         for inputs in all_inputs:
             output = self.model(**self.wrap_device(inputs))
-            logits = output.logits.softmax(dim=-1)[0].tolist()
+
+            problem_type = getattr(self.config, "problem_type", "")
+
+            if problem_type == "regression":
+                logits = output.logits[0].tolist()
+            elif problem_type == "multi_label_classification":
+                logits = output.logits.sigmoid()[0].tolist()
+            else:
+                logits = output.logits.softmax(dim=-1)[0].tolist()
             outputs.append(logits)
 
         return outputs

tests/models/language/pooling/test_multilabel_classification_support.py

@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+from transformers import AutoModelForSequenceClassification
+
+
+@pytest.mark.parametrize(
+    "model",
+    ["Rami/multi-label-class-classification-on-github-issues"],
+)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_classify_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+) -> None:
+    with vllm_runner(model, max_model_len=512, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.classify(example_prompts)
+
+    with hf_runner(model,
+                   dtype=dtype,
+                   auto_cls=AutoModelForSequenceClassification) as hf_model:
+        hf_outputs = hf_model.classify(example_prompts)
+
+    for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
+        hf_output = torch.tensor(hf_output)
+        vllm_output = torch.tensor(vllm_output)
+
+        assert torch.allclose(hf_output, vllm_output,
+                              1e-3 if dtype == "float" else 1e-2)

vllm/model_executor/layers/pooler.py

@@ -172,6 +172,15 @@ def get_tasks(pooling_metadata: PoolingMetadata) -> list[PoolingTask]:
 
 
 def get_classification_activation_function(config: PretrainedConfig):
+    # Implement alignment with transformers ForSequenceClassificationLoss
+    # https://github.com/huggingface/transformers/blob/57bb6db6ee4cfaccc45b8d474dfad5a17811ca60/src/transformers/loss/loss_utils.py#L92
+    problem_type = getattr(config, "problem_type", "")
+    if problem_type == "regression":
+        return PoolerIdentity()
+    if problem_type == "single_label_classification":
+        return PoolerClassify()
+    if problem_type == "multi_label_classification":
+        return PoolerMultiLabelClassify()
     return PoolerClassify()
 
 
@@ -409,6 +418,12 @@ class PoolerNormalize(PoolerActivation):
         return x.to(pooled_data.dtype)
 
 
+class PoolerMultiLabelClassify(PoolerActivation):
+
+    def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
+        return F.sigmoid(pooled_data.float()).to(pooled_data.dtype)
+
+
 class PoolerClassify(PoolerActivation):
 
     def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor: