Add QAT support and pass hooks to QuantizationRecipe.

[MartinPavella]

Summary

Extend QuantizationRecipe with QAT support and four ordered pass hooks, then wire them into QuantizeStage.

QuantizationRecipe gains is_qat, train_fn, calibration_inputs_fn, and four pass-list fields (pre_prepare_passes, post_prepare_passes, pre_convert_passes, post_convert_passes). When is_qat=True, QuantizeStage calls prepare_qat_pt2e instead of prepare_pt2e, invokes train_fn on the prepared model, and skips calibration. When is_qat=False (default), PTQ proceeds as before, but callers may now supply a calibration_inputs_fn to supply their own calibration data. If omitted, the existing example-inputs fallback is used. The four pass hooks are applied at the appropriate points in both flows and compose cleanly with _combine_recipes.

_combine_recipes is overhauled to handle all previously dropped fields: the four pass lists are concatenated, is_qat must agree across all combined recipes, at most one train_fn is allowed, and multiple calibration_inputs_fn values are chained into a single factory. The strict, mode, pipeline_stages, and source_transform_in_place scalar fields are now also validated for agreement and propagated to the combined recipe. edge_manager_transform_passes from LoweringRecipe is similarly merged.

Test Plan

pytest -q export/tests/test_export_recipe.py export/tests/test_export_stages.py

[pytorch-bot]

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