[Doc] Add EXAONE-4.0-1.2B to batch invariance tested models

[cogniera]

Purpose

Validate the EXAONE 4.0 series under VLLM_BATCH_INVARIANT=1 and add it to the
tested models list in docs/features/batch_invariance.md. Part of #27433.

Updated from the original single-model PR to cover the full series, per review
feedback. This is the first EXAONE-family entry, and the first on the list with
per-layer hybrid sliding/global attention.

Test Plan

• EXAONE-4.0-1.2B - RTX 4090 (SM 8.9), driver 590.48.01, vLLM 0.1.dev20408+gf6130145c @ f6130145c
• EXAONE-4.0-1.2B - H200 NVL (SM 9.0), driver 595.84, vLLM 0.26.1rc1.dev1245+g17da48596 @ 17da48596
• EXAONE-4.0-32B - H200 NVL, same build
• EXAONE-4.0.1-32B - H200 NVL, same build

All bf16, TP=1, torch 2.13.0+cu130, CUDA 13.1, Python 3.12.

cd tests && VLLM_WORKER_MULTIPROC_METHOD=spawn VLLM_BATCH_INVARIANT=1 \
  VLLM_TEST_MODEL=<model> pytest -v -rf v1/determinism/test_batch_invariance.py

Test Result

Model	GPU	Result
EXAONE-4.0-1.2B	RTX 4090	19 passed (1080s)
EXAONE-4.0-1.2B	H200 NVL	19 passed (1058s)
EXAONE-4.0-32B	H200 NVL	14 passed, 5 failed (2467s)
EXAONE-4.0.1-32B	H200 NVL	14 passed, 5 failed (2368s)

Every test that validates behavior with batch invariance enabled passes on
FLASH_ATTN and TRITON_ATTN in all four runs and generation across batch sizes,
BS=1 vs BS=N bitwise logprobs, simple generation, and decode-vs-prefill logprobs.

The 1.2B passes the full suite on both GPUs, including all FLEX_ATTENTION tests.
For the two 32B checkpoints, the five failing nodes are all FLEX_ATTENTION and all
fail at engine initialization, not on a determinism assertion:

torch._dynamo.exc.FailOnRecompileLimitHit: Hard failure due to fullgraph=True
torch._dynamo.exc.Unsupported: Dynamo recompile limit exceeded
  ... exceeding the recompile_limit cache size limit (currently set to 16)

The traceback runs through capture_model() → cudagraph_manager.capture() →
flex_attention.py:forward → flex_attention_compiled. flex_attention.py:54
sets torch._dynamo.config.recompile_limit = 16, while this configuration captures
35 CUDA-graph sizes.

This does not appear to be a batch-invariance failure:

• It reproduces with batch invariance disabled because test_logprobs_without_batch_invariance_should_fail[FLEX_ATTENTION], which sets VLLM_BATCH_INVARIANT=0, fails identically.
• test_simple_generation[FLEX_ATTENTION] passes on both 32B models (max_num_seqs=1, max_model_len=2048); only the larger configurations fail.
• EXAONE-4.0-1.2B passes every FLEX_ATTENTION test on the same GPU and the same commit, so this is not a regression in this build. Unlike the 32B, the 1.2B uses uniform full attention (layer_types all full_attention, no sliding window), whereas the 32B interleaves sliding-window and full-attention layers (sliding_window_pattern: "LLLG", 48 sliding / 16 full). The extra mask configuration appears to multiply flex_attention specializations across CUDA-graph capture shapes, exceeding recompile_limit = 16.
• The traceback goes through the native vllm/model_executor/models/exaone4.py, not the Transformers fallback backend.

I'll file this separately as a FLEX_ATTENTION issue. Happy to drop the 32B entries
from the doc list, or annotate them, if you'd rather wait on a fix.

Claude was used to help identify candidate models and draft this PR description. The validation runs, results, and the documentation change were performed and reviewed by me.

[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--53839.org.readthedocs.build/en/53839/

[cogniera]

exaone4_batch_invariance_full.txt
Here is the full test log

[yewentao256]

Thanks for the work! Could you test this series of model instead of the specific one?

[cogniera]

Updated to cover the full series and added EXAONE-4.0-32B and EXAONE-4.0.1-32B, validated on an H200 NVL . Both 32B checkpoints pass on FLASH_ATTN and TRITON_ATTN; FLEX_ATTENTION fails at engine init with a torch._dynamo recompile-limit error that also reproduces with VLLM_BATCH_INVARIANT=0, so it looks unrelated to batch invariance. Full details in the description, logs attached below.

1.2B parameter model logs : 12b-h200.log
4.0.1 32B parameter model logs : 401-32b.log
4.0.0 32B parameter model logs : 32b-run2.log

Here is the environment details if additional details regarding the environment are required :
collect_env_h200.txt

[mergify]

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

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

[yewentao256]

LGTM, thanks for the wokr!

[yewentao256]

/ci run

[github-actions]

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

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[github-actions]

✅ Triggered Buildkite CI #85859 for commit 616b67d2db66.