Workload-conditioned operator profiling on patched vLLM 0.24.0 +
Qwen3-30B-A3B/H20. H1b PASS (irregular patterns carry +23-45pp R64
raggedness, 8-45% token-efficiency loss vs rectangular controls);
mechanism decomposition kills the padding narrative and finds the
arrival-uniformization artifact (-12.9%); cross-version churn surface
shows TP2/MNS64 -29.4% across vLLM 0.20->0.24 while the argmax held.
Raw Layer-1 JSONL streams (507 MB) stay on disk, git-ignored; footer
sidecars and metrics are tracked.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Full naive run (dash1) reached the same TP4=0.34 optimum as the harness but took 6
iters (vs 2), never stopped (full budget), and spent trials 2-5 on worse TP2+runtime
detours. The other naive run (dash0) wandered runtime-only on TP1, found nothing, and
crashed the engine. Refined conclusion (matches paper §7.3): a strong model can
sometimes find the right knob unaided, so the harness's value is reliability + speed +
stop discipline, not that naive always fails. Harness: 2 iters-to-best, stopped at 4,
no regression. Naive: 3x slower at best, no stop, failed at worst.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Controlled use_harness on/off on dense 27B (same workload/SLO/substrate, only the flag
differs). Harness ON: TP2 -> TP4 (0.34 req/s/GPU) in 2 iters, rejected two worse
refinements, premature LLM stop vetoed then honored -> converged, no regression.
Naive OFF: kept TP=1 and cranked runtime knobs (mbt 16k->65k, seqs, caching), all 5
trials infeasible (same TPOT/TTFT compute bottleneck), one engine OOM crash, no feasible
config found. The bottleneck is compute; the harness steered to the knob family that
adds compute (TP) while naive wandered in knobs that cannot. Reproduces the paper's
Fig-18 finding. Substrate is compressed (process comparison, not peak-rate); naive run
was infra-interrupted at trial-5 (already conclusive). Read from cpfs via dash1.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Sets up the controlled use_harness ON-vs-OFF ablation on dense 27B:
- both configs committed and validated on dash0 (differ only in
use_harness + study_id), LLM auth + clean engine launch confirmed;
- characterizes exactly what the harness toggles (Harnesses: prompt
section with ranked bottleneck hypotheses + knob-family steering,
deterministic guided/stop proposals, Stop-B validator/veto) vs naive;
- substrate calibration from a real harness-ON run: at scale=0.2 the
180s elapsed cap fires correctly but TP1 is uniformly infeasible even
at u=0.125 (pass=0, elapsed-capped) -> recommend scale 0.4-0.5 for a
real baseline; comparability caveat documented.
Honest status: full two-run sweep NOT completed in-session (~5-6
GPU-hours, sequential); GPUs left clean (all 0 MiB, no orphans; SIGTERM
teardown re-validated). Includes a precise continuation recipe and the
scripts/ablation_trajectory.py helper (validated against a prior store).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
At replay_time_scale=0.2 the 600s arrival window compresses to 120s, so
the inherited 900s wall-clock elapsed cap let overloaded TP1 probes burn
~15min each (the tractability hazard the brief flagged). Scale the caps
proportionately to the time axis: early_stop_max_elapsed_s 900->180,
early_stop_max_lag_s 120->30. Feasible probes (~120s arrival + drain)
finish well inside 180s; overloaded probes die in ~3min. Both configs
still differ only in use_harness + study_id. Adds the ablation doc
skeleton and a read-only trajectory-extraction helper.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Real gpt-5.4 agentic loop raised per-GPU TP1 0.123 -> TP2 0.2925 -> TP4 1.0012 (8.1x),
each a correctly-diagnosed real gain; then a TP4 runtime tweak measured 0.942 < 1.00
and was correctly rejected (no regression). With the 30B run (validator stop + LLM-stop
veto), all Stop-B behaviors are now validated end-to-end. The SIGTERM-teardown fix was
validated in practice (clean engine teardown, no GPU leak on stop). Efficiency finding:
at scale=1.0, infeasible high-theta probes burn the 900s elapsed cap, so a practical
loop needs a lower cap; this is why the run was stopped after iter-4 rather than driven
to an explicit Stop-B firing.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Under the length-aware TTFT SLO (4s + L_in/8k), dense Qwen3.5-27B per-GPU throughput:
TP1=0.065, TP2=0.2925 (4.5x), TP4>=0.908 (>=14x, ceiling-saturated). TP1 is TPOT-bound
(one H20 can't decode a 27B under 50ms/token once batched); loosening TTFT didn't move
TP1, confirming TPOT is the binding constraint. Opposite of MoE 30B-A3B where TP1 was
best per-GPU. Validates the harness + length-aware SLO produce meaningful, non-saturated
measurements (TP1/TP2). TP4 saturated -> lower bound.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The reported trajectory validates the Stop-B mechanics only. TP2-DP2/TP4 saturated
the trace ceiling (best_sampling_u~0.98) so their per-GPU peak is underestimated, and
the run used the smoke regime (scale=0.1 + 512 cap). The TP1>TP2 ordering may be real
for the small-active MoE but this run cannot establish it; the 27B TP A/B is the valid
follow-up.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
