ponder-lab / Hybridize-Functions-Refactoring / #2841

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Decline hybridizing a function called with a Keras symbolic tensor (#889)

* Decline hybridizing a function called with a Keras symbolic tensor

`tf.function` is one of the APIs a `KerasTensor` explicitly refuses, so
decorating a function whose call site passes one turns a working program
into one that raises a `TypeError` on the first call, before anything is
traced (#887, observed on TensorFlow2.0-Examples' YOLOv3, whose only
call site is Functional model construction in `train.py`). The hazard is
independent of `input_signature`: a bare decorator fails the same way,
and the shape and dtype the tool infers there are exactly right.

The marker is syntactic, so no dynamic reasoning is needed: every
spelling of the producing API reads the one summarized
`tensorflow/keras/layers/Input` object, which makes the question the
provenance of the argument value in the caller's own IR. A new
caller-direction analysis resolves it backwards — an `Input` result is
symbolic, a built-in layer application propagates symbolicness from its
inputs (`Dense(...)(kt)` is a `KerasTensor`; the same layer on an eager
tensor is not), and a phi is symbolic only when every operand is, since
a merge makes the answer path-dependent, which is ignorance under this
analysis's allow-on-unknown polarity. Across call sites the quantifier
is existential: one site passing a symbolic tensor breaks the decorated
function.

Code 23 joins the safety family in `check()`, before the benefit signal,
and the evaluator gains the matching `alwaysCheckKerasSymbolicArguments`
knob and column. Validated two-sided: pre-fix `symbolic` and `derived`
both hybridize (P1) with the verdict unset, post-fix both decline while
`eager` still converts, and all three arms were runtime-verified on the
pinned TF 2.9.3 — decorating either of the first two raises the
`TypeError`, decorating `eager` runs. The class runs 632/632.

Fixes #887.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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