Add Aumann-Shapley sensitivity scoring method to auto_quantize

[joshua-hill]

Paper · Overview · Implementation thread

Depends on #2231, which provides the shared AutoQuantize backward-scoring infrastructure. Until that PR merges, the focused PR B diff is available here.

What does this PR do?

Type of change: new feature

This PR adds method="aumann_shapley" to mtq.auto_quantize. It is a label-free scoring method that measures how each candidate quantization format affects the model across the path from full precision to quantized.

The new method is opt-in; existing gradient and kl_div behavior is unchanged.

For each calibration batch, the method:

1. Runs the baseline model and saves its next-token distribution.
2. Measures each candidate format at a configurable number of points along the quantization path.
3. Uses the KL-divergence gradients at those points to assign a damage contribution to every runtime group and candidate format.
4. Measures the most aggressive candidate configuration once and uses that value to calibrate the per-group damage model.

The resulting scores use the existing AutoQuantize linear-program solver. The search can either:

• choose the least damaging configuration that meets an effective_bits target; or
• choose the smallest configuration whose predicted damage stays below max_predicted_damage.

The selected recipe records predicted_damage in mean per-token KL units together with its validity and fit diagnostics.

Public API

auto_quantize gains an optional method_options dictionary. For method="aumann_shapley", it accepts:

Option	Default	Meaning
num_path_nodes	2	Number of points used to average gradients along the quantization path.
damage_link	"coverage"	How per-group scores combine. "coverage" uses damage = c * (1 - exp(-sum(b))); "additive" sums the path contributions.
max_predicted_damage	None	Replaces the bit target with a maximum predicted mean per-token KL.

Method options are validated before the model is modified. Unknown options and incompatible targets fail early.

Usage

Select a configuration for a target effective bit width:

import modelopt.torch.quantization as mtq

model, search_state = mtq.auto_quantize(
    model,
    constraints={"effective_bits": 4.8},
    quantization_formats=["NVFP4_DEFAULT_CFG", "FP8_DEFAULT_CFG"],
    data_loader=calib_loader,
    forward_step=lambda model, batch: model(**batch),
    method="aumann_shapley",
)

print(search_state["best"]["predicted_damage"])
print(search_state["best"]["predicted_damage_valid"])

Or let the search choose the bit width for a predicted-damage target:

model, search_state = mtq.auto_quantize(
    model,
    constraints={},
    quantization_formats=["NVFP4_DEFAULT_CFG", "FP8_DEFAULT_CFG"],
    data_loader=calib_loader,
    forward_step=forward_step,
    method="aumann_shapley",
    method_options={"max_predicted_damage": 0.05},
)

Implementation

• Reuses the candidate-replay and backward-scoring lifecycle introduced in Fix distributed AutoQuantize scoring and share backward setup #2231.
• Reuses the existing AutoQuantize linear-program solver for both search directions.
• Numerically integrates the coverage path when converting measured contributions into per-group damage costs.
• Preserves deterministic runtime-group and candidate ordering.
• Keeps raw measurements, solver scores, and damage-model diagnostics distinct in the search state.
• Rejects incompatible checkpoint resumes while allowing the same scores to be re-solved for a new bit budget.
• Retains the shared MoE score-module rules so routed experts are scored at their enclosing block.

Recipe integration will follow separately.

Testing

Focused tests:

pytest -q \
  tests/unit/torch/quantization/test_autoquant.py::test_backward_scoring_session_restores_partial_setup \
  tests/unit/torch/quantization/test_autoquant_shapley.py

Result: 51 passed.

The tests cover:

• end-to-end scoring and configuration generation;
• agreement between path contributions and measured quantization damage;
• exact allocation checks against exhaustive search;
• effective-bits and predicted-damage search modes;
• checkpoint resume and offline re-solving;
• custom formats and heterogeneous candidate ladders;
• distributed reductions and nested MoE score modules;
• reused score modules and model-specific backward support;
• non-finite measurements and invalid-fit reporting; and
• input validation before model conversion.

End-to-end checks with NVFP4 and FP8 candidates at a 6.0-bit target:

• Qwen/Qwen2.5-0.5B-Instruct reaches 5.998 effective bits, with the summed path contributions reproducing 98% of the directly measured lowest-precision KL.
• Qwen/Qwen3-30B-A3B reaches 6.000 effective bits and reproduces 99%, with all 48 MoE layers scored once at the sparse-MoE block rather than per expert.

Production use

We use this method in production for NVFP4 checkpoints of Kimi-K3, MiniMax-M3, and GLM-5.2.

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Summary by CodeRabbit

• New Features

  • Added label-free Aumann–Shapley scoring for automatic quantization.
  • Added configurable path sampling, damage modeling, effective-bit targets, and predicted-damage bounds.
  • Added temporary weight-folding support with automatic state restoration.
  • Added method-specific search options, checkpoint resumption, and distributed scoring.

• Bug Fixes

  • Improved cleanup and restoration of quantizer state, gradients, hooks, and forward behavior after scoring or failures.
  • Added validation and clearer handling for unsupported configurations and invalid measurements.

• Tests

  • Expanded coverage for scoring, solver behavior, distributed execution, checkpointing, and custom quantization formats.

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📝 Walkthrough

Walkthrough

Version 0.47 adds Aumann–Shapley AutoQuantize scoring, predicted-damage allocation, method-specific options, registry-based searcher selection, managed state restoration, and expanded distributed and failure-recovery tests.

Changes

AutoQuantize Aumann–Shapley support

Layer / File(s)	Summary
Public configuration and method registration modelopt/torch/quantization/model_quant.py, modelopt/torch/quantization/algorithms.py	AutoQuantize accepts the registered aumann_shapley method, validates method-specific options before conversion, and restores searcher-specific checkpoint state during re-solving.
Scoring sessions and deterministic search infrastructure modelopt/torch/quantization/algorithms.py	Shared scoring sessions manage forwards, hooks, gradients, quantizer state, module ordering, distributed reductions, candidate metadata, and LP solving.
Aumann–Shapley scoring and damage modeling modelopt/torch/quantization/_auto_quantize_shapley.py	The new searcher computes path-integrated KL attributions, fits additive or coverage damage models, persists metadata, and solves effective-bit or predicted-damage constraints.
Integration and regression coverage tests/unit/torch/quantization/test_autoquant.py, tests/unit/torch/quantization/test_autoquant_shapley.py, CHANGELOG.rst	Tests cover validation, damage models, solver correctness, fallback behavior, candidate ordering, nested modules, distributed behavior, state cleanup, and checkpoint resume. The changelog records the 0.47 additions.

Estimated code review effort: 4 (Complex) | ~60 minutes

Merge Risk: 🟠 High · up to b8cc6

This PR adds an opt-in scoring path, but unresolved issues can cause scoring to fail for valid calls, produce incorrect sensitivity results, or select an unintended quantization configuration under damage-bound search; merge should wait for fixes or explicit owner acceptance.

Sequence Diagram(s)

sequenceDiagram
  participant AutoQuantize
  participant AutoQuantizeAumannShapleySearcher
  participant QuantizedModules
  participant DistributedGroups
  participant DamageModel
  AutoQuantize->>AutoQuantizeAumannShapleySearcher: validate options and initialize search
  AutoQuantizeAumannShapleySearcher->>QuantizedModules: replay candidate outputs across path nodes
  QuantizedModules-->>AutoQuantizeAumannShapleySearcher: return path-node gradients
  AutoQuantizeAumannShapleySearcher->>DistributedGroups: reduce KL measurements and token counts
  DistributedGroups-->>AutoQuantizeAumannShapleySearcher: return synchronized scores
  AutoQuantizeAumannShapleySearcher->>DamageModel: fit damage model and solve constraints
  DamageModel-->>AutoQuantize: return selected recipe and predicted-damage metadata

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Suggested reviewers: edwardf0t1, kevalmorabia97, realasma

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tests/unit/torch/quantization/test_autoquant_shapley.py (1)

354-361: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick win

Unjustified in-function imports in the new tests. Both new test files import modules inside test bodies. None of these imports is circular or optional, and none carries a justifying comment, so an import error surfaces mid-test instead of at collection time.

• tests/unit/torch/quantization/test_autoquant_shapley.py#L354-L361: move TensorQuantizer (also at line 536), _mckp_max_value (line 581), DistributedProcessGroup (line 617), partial and spawn_multiprocess_job (lines 641-643), and modelopt.torch.quantization.model_quant (line 931) to the module-scope import block.
• tests/examples/hf_ptq/test_hf_ptq_args.py#L104-L109: move from modelopt.torch.quantization.algorithms import AUTO_QUANTIZE_SEARCHERS to the module-scope import block.

As per path instructions: "Imports inside functions or test methods without explicit justification. Imports belong at the top of the file so import errors surface at collection time, not mid-test."

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In `@tests/unit/torch/quantization/test_autoquant_shapley.py` around lines 354 -
361, Move all unjustified in-function imports to the module-level import blocks:
in tests/unit/torch/quantization/test_autoquant_shapley.py, hoist
TensorQuantizer, _mckp_max_value, DistributedProcessGroup, partial,
spawn_multiprocess_job, and modelopt.torch.quantization.model_quant; in
tests/examples/hf_ptq/test_hf_ptq_args.py, hoist AUTO_QUANTIZE_SEARCHERS. Update
the affected tests to use these module-scope imports without changing their
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In `@tests/unit/torch/quantization/test_autoquant_shapley.py`:
- Around line 354-361: Move all unjustified in-function imports to the
module-level import blocks: in
tests/unit/torch/quantization/test_autoquant_shapley.py, hoist TensorQuantizer,
_mckp_max_value, DistributedProcessGroup, partial, spawn_multiprocess_job, and
modelopt.torch.quantization.model_quant; in
tests/examples/hf_ptq/test_hf_ptq_args.py, hoist AUTO_QUANTIZE_SEARCHERS. Update
the affected tests to use these module-scope imports without changing their
behavior.

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In `@modelopt/torch/quantization/_auto_quantize_shapley.py`:
- Around line 386-401: Make the candidate replay loop around _forward_original
state-safe by resetting the module/model replay state before each base and
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modelopt/torch/quantization/algorithms.py (1)

1579-1582: 🚀 Performance & Scalability | 🔵 Trivial | 💤 Low value

Consider clearing per-invocation hook handles more often.

_register_output_grad_hook pushes one handle.remove callback onto self._stack for every scored output invocation. The stack is only unwound when the session exits. For Aumann-Shapley scoring the count is num_score_steps × recipes × num_path_nodes × score_modules, so the callback list grows through the whole scoring run.

The handles hold a weakref to the tensor hook dict, so no GPU tensor is retained and correctness is unaffected. Only host memory grows. A per-step ExitStack (or omitting the removal, since tensor hooks die with the tensor) keeps the footprint constant.

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In `@modelopt/torch/quantization/algorithms.py` around lines 1579 - 1582, The
_register_output_grad_hook method currently accumulates handle.remove callbacks
on self._stack for every scored output. Use a per-invocation or per-scoring-step
ExitStack and clear it after each step, or otherwise avoid registering these
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In `@modelopt/torch/quantization/_auto_quantize_shapley.py`:
- Around line 162-176: Update _AumannShapleyScoringSession.forward to remove the
required input parameter and accept only module, *args, and **kwargs. Pass args
and kwargs unchanged to original_forward, _run_unquantized, and
_replay_candidates so keyword-only calls such as hidden_states=... remain
supported.

---

Nitpick comments:
In `@modelopt/torch/quantization/algorithms.py`:
- Around line 1579-1582: The _register_output_grad_hook method currently
accumulates handle.remove callbacks on self._stack for every scored output. Use
a per-invocation or per-scoring-step ExitStack and clear it after each step, or
otherwise avoid registering these callbacks on the long-lived session stack,
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Also addressed the review-body hook-lifetime nitpick in #2231 (89172ef) and propagated it here. Output tensor hooks are no longer added to the session-wide ExitStack; their lifetime is naturally bounded by each output autograd graph, so callback storage does not grow across scoring steps.

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Inline comments:
In `@modelopt/torch/quantization/_auto_quantize_shapley.py`:
- Around line 423-440: Update before_search so the constraint merge handles
self.constraints being None, using the same empty-mapping fallback as
validate_search_input before unpacking constraints. Preserve the existing
effective_bits insertion and validation behavior for non-None constraints.
- Around line 1039-1055: Update the LPS damage-score cost construction and
selected_score calculation in the AutoQuantizeDamageBound flow to exclude fixed
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