Cache template features per query in TemplateHitFeaturizer

[ssiddhantsharma]

TemplateHitFeaturizer.get_templates runs the full template pipeline, prefilter, dedup, remote mmCIF fetch (PDBe), and kalign — on every call. In iterative / optimization-in-the-loop inference (e.g. hallucination), the same fixed target chain is featurized on every optimization step, so this identical work (including the network fetch) repeats hundreds of times per trajectory.

With fetch_remote=True a single step can block for tens of minutes when PDBe is slow (up to ~20 candidate requests.get(..., timeout=30) + kalign, re-run each step). Observed in a run as a 34-minute gap between two consecutive featurization steps of the same trajectory:

[11:47:45] opendde.data.inference.infer_dataloader: Featurizing design...
[12:21:27] opendde.data.inference.infer_dataloader: Featurizing design...

Fix

Memoize get_templates on (sequence_uid, query_sequence) — computed once per unique query, reused thereafter. Single-shot inference is unaffected (each sequence is featurized once, so the cache never hits).

After the change, featurization steps run in seconds:

[19:22:55] infer_dataloader: Featurizing design...
[19:23:09] infer_dataloader: Featurizing design...
[19:23:19] infer_dataloader: Featurizing design...

Notes

• The cache lives on the featurizer instance and is bounded by the number of unique query sequences it sees.
• make_template_feature calls get_templates with max_template_date=None; a caller that varies max_template_date per call for the same sequence should add it to the key.
• Orthogonal to Template search silently no-ops in several cases (empty templates, no error raised) #20 (silent no-op on empty templates): this only avoids recomputation and does not change the empty-template path.