Add multimodal benchmark evaluation with lmms-eval

[Delwin Kim (DelwinKim)]

Describe your changes

Add LMMSEvaluator, an Olive evaluator for protocol-defined image and audio
benchmarks from upstream
lmms-eval.

This is the focused evaluator replacement for draft PR #2531. It contains no
quantization algorithms, calibration passes, mixed-precision planning (for pytorch side quant passes), HQQ
changes/fixes, or ModelBuilder compatibility changes. Those are in PR #2531, which serves as a consolidated location for reproduction of experimental results.

Why a second multimodal evaluation path?

Olive's existing OnnxEvaluator remains the supported path for proprietary
tasks, lightweight smoke tests, and workflows that should not depend on
lmms-eval. LMMSEvaluator serves a different use case: it delegates public
benchmark task definitions, prompts, filters, aggregations, and metric
direction to lmms-eval so scores use the benchmark protocol rather than an
Olive-specific approximation.

Included changes

• Add an ortgenai_mm adapter that runs lmms-eval generate_until requests
  against deployed multimodal ORT-GenAI packages.
• Support image and audio requests, package-declared audio sample rates,
  model-native structured chat templates, task stop strings, and Gemma response
  channel parsing.
• Dispatch HfModelHandler inputs to model wrappers available in the pinned
  upstream lmms-eval revision.
• Support external task directories through include_path, preserve
  lmms-eval's metric direction, and optionally persist compact aggregates plus
  per-sample generations.
• Add CompositeToOnnxPackage so a MobiusBuilder composite package can enter
  Olive evaluation without flattening or rewriting its nested ORT-GenAI layout.
• Preserve complete ORT-GenAI package directories when saving cached models.
• Document the pinned upstream lmms-eval installation and dependency boundary.

Dependency

The test environment and documented installation pin upstream lmms-eval commit
3e675904f8cba6793de12b91979b04d91754bdf3. The latest PyPI release,
0.7.2, omits extensionless task templates from its wheel and fails while
loading the default task registry. The pinned upstream commit contains
lmms-eval PR #1390, which fixes that packaging defect. The pin should move to
the first upstream release containing #1390.

The Git reference is not placed in Olive's extras_require, because direct VCS
requirements make a distribution ineligible for PyPI. A normal
olive-ai[lmms-eval] extra can be added after upstream publishes the fix. No
personal lmms-eval fork is required. Automatic HF Gemma-4 dispatch is
intentionally omitted because upstream has no Gemma-4 wrapper.

Supported scope and limitations

• Supported ONNX input: a complete ORT-GenAI package, not a raw single-file
  ONNX model.
• ORT requests are processed individually; tensor-batched generation is not
  implemented.
• Video, multi-round/interleaved generation, beam search, multiple return
  sequences, and loglikelihood tasks are not supported.
• The adapter is registered in-process for Olive; it is not an lmms-eval CLI
  entry point.
• External task names cannot override built-in lmms-eval task names.
• HF adapters and handler load_kwargs are rejected rather than silently
  evaluating a different checkpoint; wrapper-supported options use
  hf_model_kwargs.

Fresh deployed-package probes against the pinned upstream dependency cover:

• built-in AI2D image generation and scoring;
• built-in LibriSpeech audio generation with lower-is-better WER propagation;
• external include_path task discovery and scoring.

Checklist before requesting a review

• Add unit tests for this change.
• Make sure all tests can pass.
• Update documents if necessary.
• Lint and apply fixes to your code by running lintrunner -a.
• Is this a user-facing change? If yes, give a description of this change to be included in the release notes.

Release note: Add optional lmms-eval-backed multimodal benchmark evaluation for
Hugging Face models and deployed ORT-GenAI packages.

(Optional) Issue link

• Supersedes the evaluator portion of Add multimodal evaluation and component-aware quantization support #2531.
• Multimodal quantization investigation results: Multimodal quantization investigation #2613.

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