[Doc] Add Granite 3.1 series to batch invariance tested models

[ShengleiFu]

Purpose

Add the Granite 3.1 series to the list of models validated for batch invariance.

This expands model coverage to both GraniteMoeForCausalLM (MoE, top-8 routing) and GraniteForCausalLM (dense).

Related to #27433.

Duplicate check

I searched the open issues and pull requests and found no existing batch-invariance validation for the Granite 3.1 series.

Test Plan

• Hardware: NVIDIA H800 (SM90)
• vLLM commit: 6010c4013 (main @ 2026-08-22)
• Tensor parallel size: 1
• Dtype: bfloat16
• Backends: FLASH_ATTN, TRITON_ATTN, FLEX_ATTENTION

for M in \
  ibm-granite/granite-3.1-1b-a400m-instruct \
  ibm-granite/granite-3.1-3b-a800m-instruct \
  ibm-granite/granite-3.1-2b-instruct \
  ibm-granite/granite-3.1-8b-instruct ; do
  VLLM_TEST_MODEL="$M" \
  VLLM_USE_FLASHINFER_SAMPLER=0 \
  VLLM_TEST_TP_SIZE=1 \
  VLLM_TP_SIZE=1 \
  .venv/bin/python -m pytest tests/v1/determinism/test_batch_invariance.py -v -s
done

Test Result

Model	Arch	Result
granite-3.1-1b-a400m-instruct	MoE	18 passed, 1 failed
granite-3.1-3b-a800m-instruct	MoE	18 passed, 1 failed
granite-3.1-2b-instruct	Dense	19 passed
granite-3.1-8b-instruct	Dense	19 passed

For all four models, every test that validates behavior with batch invariance enabled passes across FLASH_ATTN, TRITON_ATTN, and FLEX_ATTENTION (generation across batch sizes, BS=1 vs. BS=N bitwise logprobs, simple generation, decode-vs-prefill logprobs).

The dense models pass the full suite (19/19). For the two MoE models the only failing node is the inverse control:

test_logprobs_without_batch_invariance_should_fail[FLEX_ATTENTION]

This control disables batch invariance and expects to observe a difference between BS=1 and BS=N. The MoE models remain bitwise identical on FLEX_ATTENTION even with batch invariance disabled, so the control reports a failure. This is a model-dependent property of the negative control — the dense models, which are non-deterministic on FLEX_ATTENTION without batch invariance, pass it — not a batch-invariance failure. Similar model-dependent sensitivity in this inverse test was previously addressed in #27054.

Final pytest summaries:

granite-3.1-1b-a400m-instruct:  1 failed, 18 passed in 882.55s
granite-3.1-3b-a800m-instruct:  1 failed, 18 passed in 759.47s
granite-3.1-2b-instruct:        19 passed in 1284.50s
granite-3.1-8b-instruct:        19 passed in 2084.55s

AI assistance

AI assistance was used to run the validation and prepare the documentation update. I reviewed every changed line and personally ran and inspected the tests above.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[github-actions]

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[mergify]

Documentation preview: https://vllm--53650.org.readthedocs.build/en/53650/

[yewentao256]

Thanks! Could you test the model series instead of one specifc model?

[ShengleiFu]

@yewentao256 thanks for the review, I have covered the whole Granite 3.1 series.

[yewentao256]

LGTM, thanks for the work!

[github-actions]

✅ @ShengleiFu, CI is now available for this PR.

• /ci run starts upstream CI; /amd-ci run starts AMD CI only.
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[ShengleiFu]

/ci run

[github-actions]

✅ Triggered Buildkite CI #85708 for commit d0f264b4bab5.