Fix the incompatibility issue caused by top_p=0 when using vllm to inference (#1265)

[akawincent]

Fixes #1265.

Summary

Fix a vLLM backend compatibility issue where task configs that set top_p: 0 crash during request construction.

This change normalizes top_p=0 to top_p=1.0 before building vLLM SamplingParams, which preserves the intended greedy decoding behavior while satisfying vLLM's validation rules.

Root Cause

Several task configs in lmms-eval use:

• temperature: 0
• top_p: 0
• do_sample: false

This pattern is commonly used to express greedy decoding and works in Hugging Face-style generation paths.

However, the vLLM wrappers were forwarding top_p directly into SamplingParams, and vLLM requires top_p to be in (0, 1]. As a result, tasks using top_p: 0 failed before generation started.

What Changed

• Added a small normalization step in the shared vLLM wrapper to convert top_p=0 to 1.0
• Reused that logic in:
  • vllm
  • vllm_chat
  • vllm_generate
• Added a lightweight regression test covering the normalization path

Impact

Tasks that rely on greedy-style generation settings with top_p: 0 can now run on vLLM without crashing.

This keeps task YAML unchanged and limits the fix to the vLLM compatibility layer.

Validation

Ran:

• python -m unittest test.models.test_vllm_sampling_params -v
• python -m unittest test.models.test_model_registry_v2 -v

[akawincent]

Hi @Luodian

Could you review this PR please?

The code changes are minimal: it mainly converts top_p=0 to top_p=1 when calling the vLLM backend, so that vLLM's greedy decoding can start.

Additionally, I added a regression test.