coreai_opt.quantization.config.QATSchedule

class coreai_opt.quantization.config.QATSchedule

Bases: BaseModel

Schedule for controlling observer and fake quantization state in QAT.

Defines step thresholds for enabling/disabling observers and fake quantization during quantization-aware training. Must be used in conjunction with the quantizer.step() API to advance the schedule.

The step values correspond to the cadence at which quantizer.step() is called. For example, if step() is called once per batch, the thresholds represent batch steps; if called once per epoch, they represent epochs.

Calling step() increments the step counter and immediately applies the corresponding observer/fake-quantization state. Where you place step() in your training loop determines when the model sees the new state.

enable_observer

Step count at which observers are enabled. Must be >= 0.

enable_fake_quant

Step count at which fake quantization is enabled. Must be >= enable_observer.

disable_observer

Step count at which observers are disabled. Must be > enable_observer and >= enable_fake_quant if provided. None means observers are never disabled by the schedule.

Example

>>> schedule = QATSchedule(
...     enable_observer=0,
...     enable_fake_quant=500,
...     disable_observer=1500,
... )

Note

In graph execution mode, when consecutive modules both quantize the intermediate edge (one via op_output_spec, the next via op_input_spec), graph mode deduplicates them into a single fake-quantize node. The schedule of the consuming module is always applied to the deduplicated node, irrespective of the choice of deduplication made by the graph preparation.

Note

When two modules share a weight parameter and have different schedules, the schedule of the first module encountered in the module tree is applied. A warning is emitted for the conflict if there is no fake-quantize node deduplication happening (in Eager execution mode).