[Model] Use sigmoid for single-label classification

[22quinn]

In classification problems, two labels are often reduced to one label because they are equivalent, both representing binary classification. For a single label, we should apply sigmoid instead of softmax

Related to #18052

cc @maxdebayser @DarkLight1337 @WoosukKwon @houseroad

[github-actions]

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[DarkLight1337]

Could you explain why this is needed? I thought softmax is just a generalization of sigmoid so they should be equivalent for the binary case.

[22quinn]

Could you explain why this is needed? I thought softmax is just a generalization of sigmoid so they should be equivalent for the binary case.

@DarkLight1337 When the output has only a single class, applying softmax will always give you 1: e^x / e^x = 1. This can be verified by running the code below without this PR.
Softmax only works for >=2 classes. A binary classification problem can be represented with either two-class or one-class. I think one class is more common as two classes seem not necessary and involve more computation.

from vllm import LLM
MODEL = "22quinn/Llama-3.2-1B-1Label-dummy"
PROMPTS = ["Hello my name is Robert", "ok I got it"]
model = LLM(MODEL, task="classify", enforce_eager=True, enable_prefix_caching=True)
outputs = model.classify(PROMPTS)
for output in outputs:
    print(output.outputs.probs)

[DarkLight1337]

Oh, I get it now. Thanks for the explanation!