selective_mixed_precision: QKV-aware overrides, AUTO memory mode, MULTI_GPU dispatch

[hanbitmyths]

This PR hardens SelectiveMixedPrecision (SMP) for real-world LLMs targeting ONNX Runtime GenAI:

1. QKV-aware quant config overrides (olive/passes/pytorch/quant_utils.py): Normalize the per-layer override dict so that the Q, K, and V projections in the same attention block always share precision. ModelBuilder's GQA fusion requires this; without it, partial overrides silently break export on Qwen-style models.

2. AUTO kld_memory_mode (olive/passes/pytorch/selective_mixed_precision.py): A new auto setting selects among full, multi_gpu, low_memory, and offload based on visible GPU memory and estimated model footprint, and logs the decision (e.g. KLD memory mode auto-selected: multi_gpu (gpus=3, full=145.14GB, multi_budget=215.86GB, ...)).

3. New multi_gpu mode: Uses accelerate.dispatch_model + infer_auto_device_map with _no_split_modules honored. After infer_auto_device_map, every model.layers.N.* entry is coalesced to the first device assigned for that layer, and a defensive check falls back to low_memory if a decoder layer still spans devices. A diagnostic info log reports the per-device layer counts.

Validation (A100 VM)

• Qwen3-0.6B old vs new export: tokens identical (124 vs 116 overrides, new_missing_qkv_partners=[]), same 657 MB output, ~301 vs 309 tok/s.
• Qwen2.5-1.5B-Instruct export + ort-genai: 1.34 GB int4, 290 tok/s.
• Qwen2.5-14B-Instruct AUTO → MULTI_GPU (3×A100), 9.44 GB int4, 95 tok/s.

MMLU 0-shot (HF fp16 vs ort-genai int4, greedy)

Model	N	PyTorch	ort-genai	Δ
Qwen3-0.6B	500	36.6%	28.6%	−8.0 pp
Qwen2.5-1.5B-Instruct	500	60.2%	54.2%	−6.0 pp
Qwen2.5-14B-Instruct	250	74.8%	77.2%	+2.4 pp (within ±5.5 pp CI)

14B is essentially lossless; the small-model deltas are inherent to int4 SMP on sub-2B parameters, not regressions introduced here.

Checklist before requesting a review

• Add unit tests for this change.
• Make sure all tests can pass. (24 passed, 1 skipped in test_selective_mixed_precision.py)
• 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: SelectiveMixedPrecision now supports an auto setting for kld_memory_mode and a new multi_gpu mode that shards the KLD-scored forward across visible GPUs via Accelerate. Quant config overrides are normalized so Q/K/V projections in the same attention block share precision, ensuring compatibility with ModelBuilder GQA fusion.