The expert lookup counted a hit identically whether the pinned hot-store or the
LRU ecache served it — both Phase C branches bumped the single `m->hits`. With a
warm pin profile the pin tier absorbs most hot experts, so the ecache could be
serving anywhere from ~0% to most hits and the logs couldn't tell which. That
made every cache-policy question unanswerable, including #223's "at what cap
does the eviction policy start to win?" (a flat A/B can mean "pin absorbed
everything" or "genuine floor" and the lumped counter can't distinguish them).
Two counters `hit_pin`/`hit_ecache` bumped at the two lookup branches (the
existing `m->hits++` stays, so all existing math is unchanged), snapshotted in
ProfBase and reset alongside `hits`. Surfaced in the human-readable summaries:
decode: expert hit rate 31.3% (pin 22.1% + lru 9.2%)
[PROF]: hit 31.3% (X pin + Y lru / Z load)
tiny: hit rate 88.1% (0 pin + 74 lru / 10 miss)
The serve-mux STAT protocol line is untouched (openai_server.py parses it
positionally).
Invariant hit_pin + hit_ecache == hits holds by construction — exactly two
sites bump hits and each also bumps one split counter, nothing else mutates
hits. Verified numerically on the tiny oracle: 0 pin + 74 lru = 74 hits, 88.1%.
Zero cost: two increments on paths that already increment.
Reported-by: KingIcyCreamProjects <#336>
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
JustVugg
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