[Model] Pooling model activation supports per request control by PoolingParams

[noooop]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

Purpose

Pooling model “activation” supports per request control by PoolingParams

• Consider only the dimensions and normalize parameters for embed models,
• Consider only the activation parameter for classification (score) models
• Consider only the softmax parameter for PoolingType.ALL reward models
• Consider only the softmax/step_tag_id/returned_token_ids parameter for PoolingType.STEP reward models

Config priority, from low to high

1. vllm default in vllm/pooling_params.py _set_default_parameters
2. get_pooling_config (from sentencetransformer config) in vllm/config.py _init_pooler_config
3. verify_and_update_config in vllm/model_executor/models/config.py
4. override_pooler_config in vllm/config.py _init_pooler_config
5. PoolingParams.init in vllm/pooling_params.py

To make the control logic simpler and more intuitive, we need to refactor the entire pooler control logic as follows:

1. override_pooler_config is used to modify the default parameters of pooler_config (_init_pooler_config & verify_and_update_config in vllm/config.py)
2. Allow users to set Pooler parameters via PoolingParams per request. All default parameters are merged from pooler_config into PoolingParams (PoolingParams.merge_default_parameters + PoolingParams.verify in vllm/pooling_params.py)
3. PoolingParams is used to per request control Pooler ( PoolingMethod& PoolerActivation) ( in vllm/model_executor/layers/pooler.py)

More specific Pooler

• EmbeddingPoolerHead + SimplePooler for embed models,
• ClassifierPooler for classification (score) models
• RewardPoolerHead + SimplePooler for PoolingType.ALL reward models
• RewardPoolerHead + StepPooler for PoolingType.STEP reward models

Test Plan

• override_pooler_config -> pooler_config -> PoolingParams

pytest -s -vvv tests/test_pooling_params.py

• normalize for embed api

pytest -s -vvv tests/models/language/pooling/test_override_pooler_config.py::test_embed_models_using_normalize
pytest -s -vvv tests/entrypoints/llm/test_embedding.py::test_pooling_params
pytest -s -vvv tests/entrypoints/openai/test_embedding.py::test_normalize

• activation for classify api

pytest -s -vvv tests/models/language/pooling/test_override_pooler_config.py::test_classify_models_using_activation
pytest -s -vvv tests/entrypoints/llm/test_classification.py::test_pooling_params
pytest -s -vvv tests/entrypoints/openai/test_classification.py::test_activation

• activation for score api

pytest -s -vvv tests/entrypoints/llm/test_score.py::test_pooling_params
pytest -s -vvv tests/entrypoints/openai/test_score.py::test_activation
pytest -s -vvv tests/entrypoints/openai/test_rerank.py::test_activation

• softmax for reward api

pytest -s -vvv tests/models/language/pooling/test_override_pooler_config.py::test_reward_models_using_softmax
pytest -s -vvv tests/entrypoints/llm/test_reward.py::test_pooling_params

Test Result

pass

(Optional) Documentation Update

Known Issues

We may need to refactor vllm/entrypoints/openai/serving_pooling.py because it uses EmbeddingCompletionRequest, which may deviate from the requirements of reward models.

PoolingCompletionRequest = EmbeddingCompletionRequest
PoolingChatRequest = EmbeddingChatRequest
PoolingRequest = Union[PoolingCompletionRequest, PoolingChatRequest]

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[gemini-code-assist]

Summary of Changes

Hello @noooop, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new test suite to ensure the proper functioning of the override_pooler_config mechanism for sequence classification models. It specifically focuses on validating how the softmax option within the pooler configuration influences the model's classification outputs.

Highlights

• New Test Coverage: I've added a new pytest file (tests/models/language/pooling/test_classification_pooler_config.py) to verify that ForSequenceClassification models correctly respect the override_pooler_config parameter.
• Pooler Configuration Validation: The new test specifically validates the softmax setting within PoolerConfig, asserting that classification outputs differ when softmax is enabled versus disabled, and that applying softmax to the raw output matches the softmax-enabled output.

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[gemini-code-assist]

Code Review

This pull request adds a new test to verify that override_pooler_config is correctly applied to ForSequenceClassification models. The test logic appears sound. I've found a minor issue in the test implementation regarding an assertion, which includes dead code and could be made clearer. My feedback focuses on improving the correctness and maintainability of the test.

[noooop]

@DarkLight1337

after #20527

Bert like ForSequenceClassification, for example, papluca/xlm-roberta-base-language-detection is still not controlled by override_pooler_config.

On the other hand, the model converted using as_seq_cls_model, for example, jason9693/Qwen2.5-1.5B-apeach is controlled by override_pooler_config.

Do we need to unify this?

[DarkLight1337]

Yes, override_pooler_config should be applied in all cases

[maxdebayser]

I wonder how that relates to the context extension PR. Would it perhaps make a difference to disable the normalize and softmax for the chunk processing and then apply them on the normalized results?

Another question is whether this would prevent us from using cuda graphs on the pooler.

[noooop]

I wonder how that relates to the context extension PR. Would it perhaps make a difference to disable the normalize and softmax for the chunk processing and then apply them on the normalized results?

Another question is whether this would prevent us from using cuda graphs on the pooler.

This PR is unrelated to the context extension PR.

The pooler uses too many branch computations, making it inherently unsuitable for CUDA graphs. Using CUDA graphs on the backbone network is already efficient enough.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @noooop.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[noooop]

@DarkLight1337

Language Models Test (Extended Pooling) pass. Luck