Two layers of efficiency coverage for the engine, both parsing the telemetry
glm.c already emits (REPLAY/PROFILE/[PROF]/CUDA-tier) but nothing previously
asserted on:
1. test_inefficiency.py — tiny-model asserted regression tests (8 tests, run
in make test via test-python). Gate on: throughput floor, PROFILE phase
accounting sanity, disk-wait not dominant on a resident model, CPU greedy
determinism, and (when a CUDA build is present) CUDA init, dense VRAM
upload, and CPU-vs-CUDA argmax agreement >= 70%. CUDA tests auto-skip with
a clear build hint on CPU-only binaries.
2. test_efficiency_report.py — opt-in optimization dossier for a real model.
Turns on every instrumentation flag (PROF, COLI_CUDA_PROFILE, CACHE_ROUTE,
DISK_SPLIT, LOOKA) and prints 9 sections (provenance, throughput + tail
latency, where-time-goes, attention breakdown, expert cache, disk I/O +
phase split, routing quality + predictability, speculation, GPU tiers),
each flagging inefficiency with the concrete knob to move tok/s. Never
fails CI.
tools/efficiency.py is the shared harness: parse_run() captures every signal,
run_engine() wraps the subprocess. Reuses PROFILE_RE/SPEED_RE from
tools/benchmark_cuda_fixture.py and extends the tok/s regex to also catch the
run_text (parenthesized) format the full-model PROMPT path uses.
Makefile adds: efficiency / efficiency-cuda / efficiency-report targets.
Verified end-to-end on the full glm52_i4_g64 model (CPU + CUDA).
woolcoxm