Add Aumann-Shapley sensitivity scoring method to auto_quantize

CHANGELOG.rst

@@ -104,6 +104,7 @@ Changelog
 - Add a calibration-free streaming Kimi-K3 converter and checkpoint-mirror recipe for NVFP4 routed experts with ``input_scale=1.0`` and 128x128 block-FP8 KDA/MLA attention weights. The converter operates shard-by-shard on the source checkpoint's packed MXFP4 experts instead of loading the 2.8T model through the in-memory ``hf_ptq.py`` path.
 - Add end-to-end PETRv1 and PETRv2 ONNX PTQ examples covering calibration, INT8 and FP8 VoVNet backbone quantization, TensorRT deployment, and accuracy evaluation.
 - Add opt-in FP8 Vision Encoder recipes under the ``qwen3_vl`` and ``qwen3_5`` model types. The vision-only recipe keeps the language model and KV cache in high precision; the joint recipe quantizes Vision Encoder and language-model Linears and uses FP8 KV-cache cast. Both quantize primary and deepstack merger Linears where present, while leaving patch embedding and vision-attention BMMs in high precision. Exported checkpoints require an inference runtime that supports quantized Vision Encoder Linears.
+- Add ``method="aumann_shapley"`` to ``mtq.auto_quantize`` for label-free path-integral sensitivity scoring and predicted calibration damage; pass method-specific settings such as an optional damage bound in a method dictionary.
 - Add ``mtq.temporarily_fold_weights`` for repeated frozen-weight inference and ``mtq.preserve_quantizer_attributes_context`` for restoring temporary quantizer property and type changes. Temporary folding snapshots affected fake-quant weights on a configurable device and restores them with their quantizer state; retained pre-quant scales are inactive, while shared weights, shared quantizers, and ``SequentialQuantizer`` weights are unsupported.
 - Add the ``nvfp4_act_headroom`` calibration algorithm for NVFP4 **activation** global scales. Instead of setting the global scale from the largest per-block amax seen during calibration (plain ``max``, which leaves no room above it so any larger activation saturates), it anchors the scale to a low percentile of the per-block amax distribution, leaving the rest of the FP8 block-scale range as headroom: ``amax = max(rho * anchor, upper)``, where ``anchor`` and ``upper`` are the per-block amaxes at ``anchor_percentile`` (default 1) and ``upper_percentile`` (default 99.99; set to 100 to never clip calibration data), and ``rho`` (default 16384) is the headroom factor. Applies only to NVFP4 dynamic-block input quantizers; ``SequentialQuantizer`` activation quantizers raise. Weight scales are an orthogonal axis selected by a nested ``weight_scale_algorithm`` (``max`` by default, or ``mse`` / ``local_hessian``), so one recipe can combine a weight calibration with this activation policy in a single pass. Ships ``modelopt_recipes/general/ptq/nvfp4_act_headroom-kv_fp8_cast.yaml``, which mirrors ``nvfp4_default-kv_fp8_cast`` with only the calibration algorithm swapped and exports a standard NVFP4 checkpoint.
 - Add layer-wise KV-cache AutoQuantize through ``mtq.auto_quantize`` with ``cost_model="kv_cache"``. It measures isolated full-vocabulary forward KL for caller-supplied K/V formats, solves a width-weighted additive storage-constrained recipe across eligible layers, preserves search-disabled layers in their existing format, exports the selected per-attention mapping in unified HF checkpoints, returns sensitivity metadata in the search state, and supports resumable search checkpoints. A cast-mode FP8/NVFP4 recipe at 5.4 bits/scalar is included.