Add LMMSEvaluator (integrate lmms-eval toolkit) for multimodal (vision+audio) evaluation

[DelwinKim]

Describe your changes

This PR adds multimodal (vision + audio) evaluation to Olive by integrating the lmms-eval harness as an Olive evaluator, plus the supporting pass needed to make quantized multi-component ORT-GenAI packages evaluable.

What's added

1. LMMSEvaluator (olive/evaluator/olive_evaluator.py)
A new evaluator ("type": "LMMSEvaluator") that runs lmms-eval benchmarks from within a single olive run (should also just be able run eval in a standalone script if needed). It supports two model handler types:

• ONNXModelHandler pointing at an ORT-GenAI multimodal package (genai_config.json + quantized ONNX, e.g. from MobiusBuilder + OnnxKQuantQuantization) → dispatches to the new ortgenai_mm adapter.
• HfModelHandler for HuggingFace PyTorch multimodal models → dispatches to lmms-eval's native wrapper, auto-detected from the HF model_type (overridable via model_class).

Benchmarks are selected purely by config ("tasks": ["ocrbench", "docvqa_val_lite", "librispeech_test_clean", "fleurs_en", ...]), and the harness's official scorers are used (OCRBench scoring, DocVQA ANLS, WER, etc.), so results can be more easily compared.

2. ortgenai_mm adapter (olive/evaluator/lmms_ort.py)
A lmms-eval model adapter that drives ORT-GenAI multimodal generation. Implements generate_until and loglikelihood, handles image and audio inputs (og.Images / og.Audios), per-model-type processor argument shapes (Phi-4-MM vs Whisper), and EOS/stop handling (including Whisper's BOS==EOS collision).

3. CompositeToOnnxPackage pass (olive/passes/onnx/composite_to_onnx_package.py)
MobiusBuilder emits a multi-component CompositeModelHandler (decoder / vision / audio / embedding in subdirectories). A composite can't be evaluated directly — it has no single session, and the nested layout defeats ORT-GenAI's genai_config.json auto-detection. This pass flattens the components into a single-directory ORT-GenAI package (rewriting external-data references and genai_config.json) and returns a runnable ONNXModelHandler, so the evaluator can load it.

4. Registration

• olive/olive_config.json: registers the new evaluator and pass.

Example recipe

A complete example recipe — build + quantize Gemma‑4 E2B, flatten to an ORT‑GenAI package, then evaluate vision and audio benchmarks:

{
  "input_model": { "type": "HfModel", "model_path": "google/gemma-4-E2B-it" },
  "systems": {
    "local_system": {
      "type": "LocalSystem",
      "accelerators": [
        { "device": "gpu", "execution_providers": ["CUDAExecutionProvider"] }
      ]
    }
  },
  "passes": {
    "mobius_build":  { "type": "MobiusBuilder", "precision": "fp16" },
    "int4_quantize": { "type": "OnnxKQuantQuantization", "bits": 4, "block_size": 32, "save_as_external_data": true },
    "flatten":       { "type": "CompositeToOnnxPackage" }
  },
  "evaluators": {
    "evaluator": {
      "type": "LMMSEvaluator",
      "tasks": ["ocrbench", "docvqa_val_lite", "librispeech_test_clean", "fleurs_en"],
      "batch_size": 1,
      "max_new_tokens": 256,
      "max_length": 4096,
      "limit": 10,
      "log_samples": false,
      "image_token_format": "<|image|>",
      "audio_token_format": "<|audio|>",
      "output_path": "results/gemma4_vision_audio.json"
    }
  },
  "evaluator": "evaluator",
  "evaluate_input_model": false,
  "target": "local_system",
  "output_dir": "models/gemma4_vision_audio",
  "cache_dir": "cache/gemma4_vision_audio",
  "no_artifacts": true
}

LMMSEvaluator config reference

Field	Default	Applies to	Description
tasks	(required)	both	List of lmms-eval task names, e.g. ocrbench, docvqa_val_lite, chartqa_lite, ai2d_lite, mmmu_val, textvqa_val, librispeech_test_clean, librispeech_test_other, fleurs_en.
limit	None (full set)	both	Cap the number of samples per task.
batch_size	1	both	Currently only 1 is supported (see limitations).
max_new_tokens	256	both	Max tokens generated per generate_until request.
max_length	32768	both	Total sequence budget (prompt + multimodal embeds + completion). Image/audio embeds can be 1000+ tokens, so keep this generous; lower it (e.g. 4096) to bound memory.
image_token_format	"<|image_{index}|>"	ONNX/genai	Model-specific image placeholder. The adapter pre-renders media tokens into the prompt itself, then calls apply_chat_template only for the system/user turn scaffolding \u2014 so this must match the model's expected token. Default is Phi-4-MM style (<|image_1|>); Gemma-4 requires <|image|>. Wrong value \u21d2 the processor can't bind image tokens to images and the run fails.
audio_token_format	"<|audio_{index}|>"	ONNX/genai	Model-specific audio placeholder; same mechanism/caveats as image_token_format. Gemma-4 requires <|audio|>.

Vision-only or audio-only runs are the same recipe with the corresponding subset of tasks. Audio tasks (librispeech, fleurs, covost) additionally require FFmpeg available at runtime (HF datasets decodes audio via torchcodec).

Tests (not very complete or thorough)

• test/evaluator/test_lmms_ort.py — adapter unit tests (generation, loglikelihood, image/audio handling, processor branching, EOS logic).
• test/passes/onnx/test_composite_to_onnx_package.py — flatten pass (external-data rewrite, genai_config rewrite, entry-point component selection).

Notes / limitations

• batch_size is currently fixed at 1: ORT-GenAI's per-request Generator + set_inputs flow is single-sequence, and batching ragged, per-request multimodal (image/audio) inputs isn't currently supported by runtime.
• lmms-eval is imported lazily, so non-multimodal Olive usage is unaffected if it isn't installed. Running the LMMSEvaluator (and the full adapter test suite) requires pip install lmms-eval.

Checklist before requesting a review

• Add unit tests for this change.
• Make sure all tests can pass. (Adapter tests require lmms-eval installed; mocked tests pass without it, but the tests that instantiate the real adapter need the dependency.)
• 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 LMMSEvaluator for multimodal (vision + audio) evaluation via the lmms-eval harness, and CompositeToOnnxPackage to flatten multi-component ORT-GenAI packages for evaluation.

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