Separate attention-map capture from gradient hooks

[LIU-kaiyu]

Fixes #1166.

AttentionRollout previously enabled backward hooks despite using only attention maps, causing it to fail inside torch.no_grad(). This change separates map capture from gradient capture in Transformer and StageAttentionNet.

Introduces AttentionInterpretable and its subclass GradientInterpretable, retaining CheferInterpretable as an alias. Rollout explicitly requests forward-only capture and runs without autograd; Chefer explicitly requests gradients. Attribution calculations remain unchanged.

Existing positional register_hook arguments and concrete models’ one-argument hook behavior are preserved. Disabling capture stops both modes while retaining the latest results; the next attention forward clears stale gradients and replaces or clears the map. Chefer now raises clear errors for empty layers, missing tensors, and mismatched map/gradient shapes.

Compatibility: custom models must explicitly inherit the appropriate interface and accept the capture_gradients keyword. The alias does not adapt obsolete method signatures. Overlapping captured forwards/backwards on one model remain unsupported and are documented.

Validation: 36 focused tests passed in Pixi, including 11 new tests covering no-grad execution, unchanged parameter gradients, interpreter switching, cache lifecycle, positional compatibility, migration errors, and failure cleanup. All changed executable statements were covered. The synthetic example, new docstring examples, and #1166-specific contribution checks passed.