Changelog¶

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.3.0 - 2026-09-25¶

Support ignored_ops in cast_fp32_to_fp16 and cast_to_16_bit_precision to allow users to exclude sensitive operations (e.g. operations causing activation-level overflow like exp or softplus) from FP16 casting, keeping them in FP32 with appropriate boundary casts.
Add training-aware palettization to KMeansPalettizer via a training_mode() context manager and step() method, with a PATSchedule (pat_schedule on the module config) controlling when palettization activates during a training loop. PalettizationSpec gains a training_strategy_spec field for selecting pluggable training-time strategies.
Added support for torchao 0.18.0.
Added support for PyTorch 2.12 and 2.13.
Graph mode quantization support with Core AI composite ops. Added tests and documentation for usage details.
Add utility to compute analytical bits per weight (BPW) of a prepared eager-mode quantized or palettized model
Support for memory-efficient finalize in graph mode via finalize(..., mmap_dir=...), previously available only in eager mode. Supported with the Core AI backend only.
Support new coreai-torch and coreai-core releases

Replace the coremltools-based 1D k-means used by palettization with a vendored C++ core that is JIT-compiled at runtime via torch.utils.cpp_extension. coremltools is no longer a runtime dependency (it is now an optional dependency, installable via the coreml extra). This requires a C++ compiler to be available on the host at runtime.
Carry the next planned release with a .dev0 suffix on main (e.g. 0.2.2.dev0). make build strips the suffix for a clean release and make build-dev builds a unique, timestamped dev wheel (0.2.2.dev<timestamp>+<shortsha>). A repo that vendors this one can insert one extra release segment via the COREAI_OPT_VERSION_EXTENSION environment variable to extend the version scheme.
When a weight is incompatible with the configured quantization block size or palettization granularity, the warning now reports the weight’s fully qualified name and shape along with the module name to use in module_name_configs, instead of only the compression target.
Re-cluster palettization centroids from the warmed-up weights when a PATSchedule enables fake palettization mid-training (enable_fake_palettize > 0).
Remove upper bound on torch, torchao. Add untested message for untested versions on coreai-opt

Fix setting of qscheme and float_range for fixed output range ops.
Fix graph-mode Quantizer resolving a tied weight’s QAT schedule from a different module than its dtype. A shared fake-quantize module’s owning module is now picked by the same config priority that decides its dtype, instead of by graph order, so the schedule and dtype for a shared weight always come from one consistent module.
Reject per-channel activation quantization on CoreML export
Fix per-channel activation quantization crashing with a shape-mismatch RuntimeError on MaxPool/AvgPool/AdaptiveAvgPool layers whose shared observer spans an axis the pool shrinks (e.g. a spatial axis under a stride>1 pool). Axes pooling never touches (batch, channel) keep working as per-channel; only the specific unsafe axis falls back to per-tensor, with a warning explaining why and how to pick a safe axis instead.
Fix eager Quantizer.prepare failing on models that call a supported op whose inputs are all scalars or single-element tensors
Ensure KMeansPalettizer.calibration_mode cleans up temporary model checkpoints on failure and preserves caller exceptions without masking. Prevent fake palettization from remaining disabled after calibration abort, and fix sensitivity parameter name resolution for root modules.
Finalized models in graph mode no longer contain the unused original full precision weights similar to eager mode

Support palettization of ConvTranspose1d/ConvTranspose2d/ConvTranspose3d layers via KMeansPalettizer
Support for EAGER execution mode in model inspection utility

Fixed pruning mask dtype to match that of the weight being pruned
Fixes to allow better support for bfloat16 dtype in palettization and quantization

Initial release of coreai-opt. See the GitHub Releases page for release notes.