fix(training): use singular candidate kwargs in test_step - #433
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test_step called predict_with_candidates with candidates_x/candidates_y, but TabR and ModernNCA both define the method as (self, *data, candidate_x, candidate_y) with no **kwargs, so trainer.test() on any candidate model raised TypeError. predict_step already used the correct names. Part of #413 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
compute_loss is typed as returning float, and estimator is typed nn.Module; cast for the stub's recorded kwargs. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Part of #413 (the wiring half; the retrieval-leakage half of that issue is not addressed here).
Problem
TaskModel.test_stepcalls:but both implementations —
deeptab/architectures/tabr.pyanddeeptab/architectures/experimental/modern_nca.py— are declared asdef predict_with_candidates(self, *data, candidate_x, candidate_y)with no**kwargs. Sotrainer.test(...)on any TabR/ModernNCA model always raisesTypeError: predict_with_candidates() got an unexpected keyword argument.predict_step(a few lines below) already uses the singular names, which is what the intended spelling is.Fix
Rename the two kwargs at the call site.
Tests
New
tests/test_candidate_models.pydrivestest_stepandpredict_stepwith a minimal stub estimator that has TabR's signature and records what it received. Verified the newtest_steptest fails onmainand passes with the fix.🤖 Generated with Claude Code