deps: bump Opacus to 1.6.0#278
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Raise the Opacus floor and refresh uv.lock so installs resolve to 1.6.0, which adds non-wrapping mode, FSDP/mixed-precision DP improvements, and assorted accountant/clipping fixes while maintaining torch>=2.6 alignment. Co-authored-by: Michi Platzer <michael.platzer@gmail.com>
Opacus 1.x per-sample gradient hooks hit NotImplementedError on NestedTensorCPU (aten::new_empty). For DP training, collate CTXSEQ as padded dense tensors with -1 padding; SequentialContextEmbedders already masks -1 and maps to embedding index 0. Non-DP sequential training keeps nested CTXSEQ collate for unchanged behavior. Co-authored-by: Michi Platzer <michael.platzer@gmail.com>
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Summary
This change raises the Opacus dependency from
>=1.5.4to>=1.6.0and updatesuv.lockso resolution picks Opacus 1.6.0.CI fix (nested tensors + DP)
CI failed on
run-tests-cpu-unit-sequentialwith:test_tabular_sequential.py::TestTabularTrainingStrategy::test_training_strategy[differential_privacy1]→
NotImplementedError: aten::new_emptyonNestedTensorCPUOpacus per-sample gradient computation does not support nested tensors for sequential CTXSEQ batches. When DP is enabled, the tabular
BatchCollatornow collates CTXSEQ as padded dense tensors (-1padding, matchingSequentialContextEmbedders), andSequentialContextEmbedders.forwardonly callstorch.nested.to_padded_tensorwhen the input is actually nested (so dense DP batches work). Non-DP sequential training keeps nested CTXSEQ collate.Verification
pytest tests/end_to_end/test_tabular_sequential.py::TestTabularTrainingStrategy::test_training_strategy(both DP and non-DP parametrizations)pytest tests/unit— 236 passedOptional follow-ups
Evaluating
wrap_model=Falsefor language DP is handled in a separate stacked PR (#279).