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v1.1.0

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@automatosx automatosx released this 03 Aug 07:12
· 90 commits to main since this release
v1.1.0
c04c22f

AXQuant v1.1.0 adds GPTQ Hessian error-compensated quantization and completes the AWQ calibration path end to end. Certification remains evidence-gated; this release does not claim certified public model releases.

Added

  • GPTQ (src/axquant/gptq.py) — classic second-order error compensation (damped Cholesky, LazyBatchUpdates) as a weight pre-transform before portable MLX-LM affine packing; bits 2/3/4/6/8, group sizes 32/64/128. Registered as GptqPlugin in the numpy reference registry.
  • capture-activations — new pipeline stage that replays a verified tokenized calibration cache through the BF16 model and records per-module Linear input activations into a checksum-bound ActivationCaptureManifest (axquant.activation-capture.v1). load_capture_activations fails closed on model identity, shape, or checksum drift.
  • Measured AWQ/GPTQ probing — analyze --methods awq,gptq --calibration-activations <dir> produces measured AWQ/GPTQ candidates, with hardware costs normalized against the affine packing control.
  • Convert-time AWQ/GPTQ from the CLI — convert --calibration-activations <dir> loads the capture artifact and executes refinement before affine packing, with per-module execution records. This makes AWQ plans convertible end to end for the first time.
  • Planner integration — GPTQ in the default hardware profile, gptq-hessian scale strategy, role-policy ranking after AWQ for attention/MLP, GPTQ in the refine-awq-dwq ladder, and a GPTQ release-audit evidence kind.

Changed

  • The conversion calibration mapping is method-agnostic: convert_model(awq_activations=...) is renamed to calibration_activations= and shared by AWQ and GPTQ.
  • MLX probe backend version bumped (axquant-mlx-isolated-probe-v4 → v5); prior probe resume state is invalidated.
  • Packaging classifier reconsidered for the 1.x line: Beta is kept, as certified Hub model release evidence (MTP speed, size floors) remains open.

Verification

  • 516 tests passing, including MLX-gated end-to-end coverage of capture, measured AWQ/GPTQ probing, and GPTQ conversion; ruff check, ruff format --check, and strict mypy src clean.
  • On synthetic correlated-activation fixtures, GPTQ reduces output reconstruction error by roughly half versus plain RTN at 4-bit.
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