support loading fit checkpoints in JacobianLens.load()

[priyanka25aug]

What this does

Extends JacobianLens.load() to accept fit checkpoints (files saved by the fitting pipeline with a jacobian_sum key) in addition to the existing artifact format (files with a J key).

The conversion lives in a new _from_checkpoint_payload() classmethod:

• Divides the running Jacobian sums by n_prompts to recover the per-prompt mean
• Harvests safe scalar provenance keys (model_name, model_revision, corpus) from the flat payload namespace into metadata
• Strips fit-reserved keys (transformer_lens_fit, transformer_lens_version, model_system, etc.) so they don't leak into the converted lens
• Drops tensor-valued metadata fields that can't survive weights_only=True reload, recording their names and shapes in dropped_fields for transparency
• Sets converted_from: "jacobian_lens_checkpoint" so merge() naturally refuses to mix converted and natively TL-fitted lenses (provenance keys differ)

Raises ValueError with a clear message if the file has neither key, or if n_prompts <= 0. Tuned-lens support deferred.

Files changed

• transformer_lens/tools/analysis/jacobian_lens.py — new _FIT_RESERVED_KEYS and _CHECKPOINT_FLAT_PROVENANCE frozensets; updated load(); new _from_checkpoint_payload()
• tests/unit/tools/test_jacobian_lens_import.py — unit tests on synthetic fixtures covering artifact regression, checkpoint round-trip, dtype preservation, n_prompts=0 guard, tensor field dropping, fit-key stripping, and merge provenance rejection; no model/oracle dependency
• docs/source/content/jacobian_lens_fitting.md — "Importing an existing lens" section added

Type of change

• New feature (non-breaking change which adds functionality)
• This change requires a documentation update

Checklist

• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• I have not rewritten tests relating to key interfaces which would affect backward compatibility

Testing

uv run pytest tests/unit/tools/test_jacobian_lens_import.py -v

[priyanka25aug]

Hi @jlarson4 — all format, type, docstring, and benchmark checks are passing. The remaining long-running jobs (compatibility across Python 3.10/3.11/3.12 and full coverage) are still in progress but looking clean so far.

Would you mind taking a look when you get a chance? Happy to make any changes based on your feedback.

[jlarson4]

Hi @priyanka25aug! Thanks for taking this on. Checkpoint import is a genuinely useful capability, and the conversion math, artifact-path safety, and merge sentinel are all done right.

A couple comments below that we should address before merging

[priyanka25aug]

@jlarson4 All four of your review comments have been addressed in 08738f86 and 7aec2e7b:

1. target_layer preservation — removed from _FIT_RESERVED_KEYS so it survives checkpoint conversion and validate_model() correctly raises for non-final-target checkpoints.
2. 2. Reference fixture — added test_load_checkpoint_mirrors_fit_payload_schema using the exact payload layout fit() produces, including flat provenance keys and nested fit-reserved metadata, with assertions that verify target_layer is preserved and fit-reserved keys are stripped.
3. 3. Tuned-lens note — corrected the direction: tuned-lens translators are affine (weight + bias), the Jacobian artifact format has no bias slot to receive the translation component.
4. 4. n_done key — _from_checkpoint_payload now reads payload.get("n_done", payload.get("n_prompts", 0)), preferring n_done (the key the reference writer emits) with n_prompts as a fallback so genuine checkpoints aren't silently rejected.
      All threads are resolved. Could you please re-review and merge when you're happy? Thank you!

[jlarson4]

@priyanka25aug There is a CI failure that needs it be addressed still it looks like. The failure is an indicator of the remaining bug. n_done was one of three keys the reference writer disagrees with us about. Real checkpoints also carry no d_model (derivable from any jacobian_sum matrix's shape) and put target_layer at the top level rather than inside a nested metadata dict. With only the count fixed, a genuine checkpoint still cannot load, it just fails one line later with an unhandled KeyError. Worth opening write_checkpoint() (jlens/fitting.py:315-327 in anthropics/jacobian-lens@581d398) and working from its six keys directly rather than from my comments one at a time.

If the intent was a TransformerLens checkpoint format rather than the reference one, say so and we'll take the other fork: that would mean teaching fit() to write it (checkpoint_path / checkpoint_every, resumable), because nothing in either codebase emits the documented layout. The PR docstring describes the running-sum file "written during or after a fitting run", which reads as the reference format, so that is what I was assuming.

Suggested order:

1. Derive d_model from a jacobian_sum matrix's shape rather than requiring the key, and harvest top-level target_layer alongside the flat provenance keys.
2. Then make one fixture the verbatim six-key payload and assert both that it loads and that a checkpoint recording a non-final target_layer is refused by validate_model().
3. test_load_checkpoint_with_zero_n_prompts_raises needs a decision rather than a patch: its jacobian_sum is empty, so it can never load however the schema lands. Give it a matrix and let it assert the round trip, and test the non-positive-count guard on its own synthetic file. Whichever error the empty-sums case raises, it should name the empty sums, rather than using n_prompts=0 or a bare KeyError.
4. Update the docs schema table to the real key set – as written it documents n_prompts/d_model as required, which sends readers off to hand-build files.
5. Small doc gap from 08738f86: the "Metadata handling" section still says keys written by fit() "are not carried over ... (transformer_lens_fit, transformer_lens_version, model_system, hook_convention, etc.)". target_layer is now a deliberate exception and documented in the code comment, but not the doc.