tests: efficiency suite — tiny-model regression gates + full-model optimization dossier
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).
This commit is contained in:
woolcoxm
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"""Efficiency / regression harness for the colibri engine.
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The engine already emits rich telemetry (REPLAY tok/s, PROFILE phase timings,
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[PROF] time shares + verdict, CUDA expert-tier utilization). Until now every
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consumer of that telemetry — `benchmark_cuda_fixture.py`, `bench_full.sh`,
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`bench_ux.sh` — has only *printed* it for a human to eyeball. This module turns
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each signal into a parseable field so tests can assert on it.
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Design:
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- Reuses SPEED_RE / PROFILE_RE from tools.benchmark_cuda_fixture (no drift).
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- parse_run() is pure: stdout+stderr in, dict out. Easy to unit-test against
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captured strings (like the existing test_benchmark_cuda_fixture does).
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- run_engine() is the subprocess wrapper. Captures stdout and stderr
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separately, because the engine splits them: PROFILE/REPLAY/CUDA-tier go to
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stdout, the [CUDA]/[PROF]/[prefill] banners go to stderr.
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- Floor defaults are module constants (tunable in one place, not scattered).
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No model file is required to import this module; only run_engine() invokes the
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binary. parse_run() works on any captured text, so most test surface is covered
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by string fixtures without spinning the engine at all.
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"""
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from __future__ import annotations
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import os
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import re
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import subprocess
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from pathlib import Path
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from typing import Optional
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# Reuse the validated regexes from the existing A/B benchmark harness so the
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# PROFILE field order (disk, expert_matmul, attention, lm_head, other) and the
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# tok/s capture stay identical. Drift here would silently break every consumer.
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from tools.benchmark_cuda_fixture import SPEED_RE as _SPEED_RE_REPLAY, PROFILE_RE, PROFILE_KEYS
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# SPEED_RE (from benchmark_cuda_fixture) matches the REPLAY-mode line only:
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# "REPLAY decode: ... | 12.34 tok/s | ..."
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# run_text / PROMPT mode uses a DIFFERENT format (glm.c:4682):
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# "decode N tokens in X.XXs (12.34 tok/s) | expert hit rate ..."
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# This alt regex catches the parenthesized form so the full-model report (which
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# uses PROMPT mode) gets a real tok/s instead of reporting it missing.
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SPEED_RE_TEXT = re.compile(r"decode \d+ tokens in [0-9.]+s \(([0-9.]+) tok/s\)")
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def _first_speed(stdout: str):
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"""Find tok/s in whichever run-mode format the engine used."""
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for rx in (_SPEED_RE_REPLAY, SPEED_RE_TEXT):
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m = rx.search(stdout)
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if m:
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return m
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return None
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# Public alias so existing imports keep working (tests reference SPEED_RE).
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SPEED_RE = _SPEED_RE_REPLAY
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# --- additional parsers (formats verified against glm.c printf strings) ---
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# "expert hit rate 88.1%" (summary line) | "expert hit 95.0%" (REPLAY line)
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HIT_RE = re.compile(r"expert hit(?:\s+rate)?\s+([0-9.]+)%")
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# "[PROF] time shares: expert-I/O 3% | expert-matmul 12% | attention 56% | lm_head 2% | other 27%"
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SHARES_RE = re.compile(
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r"\[PROF\] time shares: expert-I/O\s+([0-9.]+)%\s*\|\s*expert-matmul\s+([0-9.]+)%\s*"
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r"\|\s*attention\s+([0-9.]+)%\s*\|\s*lm_head\s+([0-9.]+)%\s*\|\s*other\s+([0-9.]+)%"
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)
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# "[PROF] verdict: I/O-bound — 60% of the time ..." (also compute-bound / attention-bound / balanced)
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VERDICT_RE = re.compile(r"\[PROF\] verdict:\s*(I/O-bound|compute-bound|attention-bound|balanced)")
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# "[PROF] expert I/O: ... hit 95.0% (76 hit / 4 load) | 4.0 loads/token"
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LOADS_PER_TOK_RE = re.compile(r"\|\s*([0-9.]+)\s+loads/token")
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# "CUDA expert tier: 111 resident experts (2.36 GB) | 5400 calls served from VRAM" (stdout)
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CUDA_TIER_RE = re.compile(
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r"CUDA expert tier:\s+(\d+)\s+resident experts\s+\(([0-9.]+)\s+GB\)\s*\|\s+(\d+)\s+calls served from VRAM"
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)
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# "[CUDA] device 0: NVIDIA ..., 14.4 GB VRAM, sm_120" (stderr, per device at init)
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CUDA_DEVICE_RE = re.compile(r"\[CUDA\] device\s+\d+:")
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# "[CUDA] resident set: 12 tensors, 0.45 GB VRAM" (stderr, cuda_stats_print)
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CUDA_RESIDENT_RE = re.compile(
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r"\[CUDA\] resident set:\s+(\d+)\s+tensors,\s+([0-9.]+)\s+GB\s+VRAM"
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)
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# "PREFILL (teacher-forcing) C vs oracle: 11/32 positions | 1700.4 pos/s" (TF=1 mode, stdout)
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TF_MATCH_RE = re.compile(r"PREFILL \(teacher-forcing\).*:\s+(\d+)/(\d+)\s+positions")
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# --- the six deeper signals (added after "are you gathering everything?" audit) ---
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# "ATTENTION: projection/RoPE 0.050s | score-softmax-value 0.009s | output projection 0.011s"
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# Sub-breakdown of the attention phase — answers "how is attention being read".
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ATTN_BREAKDOWN_RE = re.compile(
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r"ATTENTION: projection/RoPE\s+([0-9.]+)s\s*\|\s*score-softmax-value\s+([0-9.]+)s\s*"
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r"\|\s*output projection\s+([0-9.]+)s"
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)
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# "[PROF] decode forwards: 20 | latency p50 7.3 ms | p90 8.0 ms | p99 8.1 ms | max 8.2 ms | 1.00 tok/forward"
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# Per-forward tail latency — a p99 >> p50 means decode stalls (I/O hiccups, KV grow).
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LATENCY_RE = re.compile(
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r"\[PROF\] decode forwards:\s+(\d+)\s*\| latency p50\s+([0-9.]+)\s*ms\s*\| "
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r"p90\s+([0-9.]+)\s*ms\s*\|\s*p99\s+([0-9.]+)\s*ms\s*\|\s*max\s+([0-9.]+)\s*ms"
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)
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# "[PROF] expert I/O: 0.004 GB fetched (0.2 MB/token, 0.03 GB/s over the run) | hit 95.0% ... |
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# 4.0 loads/token | 0.0s read service / 0.0s felt wait"
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# Absolute disk throughput + the felt-wait split (the [PROF] version, more detailed than PROFILE).
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EXPERT_IO_RE = re.compile(
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r"\[PROF\] expert I/O:\s+([0-9.]+)\s+GB fetched\s+\(([0-9.]+)\s+MB/token,\s*([0-9.]+)\s+GB/s"
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r".*?\|\s*([0-9.]+)\s+loads/token\s*\|\s*([0-9.]+)s\s+read service\s*/\s*([0-9.]+)s\s+felt wait"
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)
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# "speculation: 1.05 tokens/forward (19 forwards per 20 tokens) | MTP acceptance 44% (7/16)"
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# Draft efficiency — is the speculative decoder pulling weight or dead overhead?
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SPECULATION_RE = re.compile(
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r"speculation:\s+([0-9.]+)\s+tokens/forward\s+\((\d+)\s+forwards per\s+(\d+)\s+tokens\)"
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r"\s*\|\s*MTP acceptance\s+([0-9.]+)%"
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)
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# "experts loaded/token: 450.0 (per-layer 56.25 across 8; baseline topk=8) | TOPK=0 TOPP=0.00"
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# Fuller than loads_per_tok: includes the per-layer spread + the active topk/topp.
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EXPERTS_LOADED_RE = re.compile(
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r"experts loaded/token:\s+([0-9.]+)\s+\(per-layer\s+([0-9.]+)\s+across\s+(\d+);\s*baseline topk=(\d+)\)"
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)
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# "[PROF] machine: Intel(...) | 22 cores (22 omp threads) | RAM 34.1 GB total, 27.1 GB available | backend CUDA"
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# Provenance — makes a report reproducible across machines/runs.
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MACHINE_RE = re.compile(r"\[PROF\] machine:\s*(.*)\|\s*(\d+)\s+cores.*backend\s+(\S+)")
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# "[PROF] config: RAM_GB=auto 23.9 CTX=4096 | expert cache cap 8/layer ... | DRAFT=0 PIPE=1 DIRECT=0 ..."
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# Effective resolved config (after auto-budgeting). Answers "what config actually ran".
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CONFIG_RE = re.compile(r"\[PROF\] config:\s*(.*)")
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# "[ORACLE] mismatch pos=7 expected=197 got=22" (TF=1 mode, stderr, per position)
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# Captures the engine's *actual* argmax at each position, so two backends can be
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# compared DIRECTLY (independent of how each relates to the oracle).
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TF_MISMATCH_RE = re.compile(r"\[ORACLE\] mismatch pos=(\d+) expected=\d+ got=(\d+)")
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# --- routing-quality + disk-split + cuda-groups (the deep signals) ---
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# summary suffix: " | swap 3.1% (12/384)" (CACHE_ROUTE inline)
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SWAP_RE = re.compile(r"swap\s+([0-9.]+)%\s+\((\d+)/(\d+)\)")
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# summary suffix: " | route_agree 85.0% | route_kl 0.0123" (CACHE_ROUTE/ROUTE_AGREE)
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ROUTE_AGREE_RE = re.compile(r"route_agree\s+([0-9.]+)%\s*\|\s*route_kl\s+([0-9.]+)")
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# "disk-load split: draft 8 + absorb 0 + verify/main 3 misses | MTP-layer 0 loads 0.00 GB |
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# main-layers 11 loads 0.01 GB (MTP 0.0% of bytes)" (DISK_SPLIT=1)
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DISK_SPLIT_RE = re.compile(
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r"disk-load split: draft\s+(\d+)\s+\+\s+absorb\s+(\d+)\s+\+\s+verify/main\s+(\d+)\s+misses"
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r"\s*\|\s*MTP-layer\s+(\d+)\s+loads\s+([0-9.]+)\s+GB\s*\|\s*main-layers\s+(\d+)\s+loads\s+([0-9.]+)\s+GB"
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r"(?:\s+\(MTP\s+([0-9.]+)%\s+of bytes\))?"
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)
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# "[CUDA] expert groups: 120 call, 840 expert, 1200 righe (7.00 expert/call)"
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CUDA_GROUPS_RE = re.compile(
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r"\[CUDA\] expert groups:\s+(\d+)\s+call,\s+(\d+)\s+expert,\s+(\d+)\s+righe\s+\(([0-9.]+)\s+expert/call\)"
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)
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# "[CUDA] expert groups timing: H2D 12.3 ms | kernel 45.6 ms | D2H 7.8 ms" (COLI_CUDA_PROFILE=1)
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CUDA_GROUPS_TIME_RE = re.compile(
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r"\[CUDA\] expert groups timing: H2D\s+([0-9.]+)\s+ms\s*\|\s*kernel\s+([0-9.]+)\s+ms\s*\|\s*D2H\s+([0-9.]+)\s+ms"
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)
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# LOOKAHEAD recall block — 4 named rows. (name, pct, hit, tot)
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LOOKAHEAD_RE = re.compile(
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r"^\s*(.+?)\s+([0-9.]+)%\s+\((\d+)/(\d+)\)\s*$", re.MULTILINE
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)
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# "loaded in 0.02s | resident dense: 0.21 MB | layers=5 experts=8 | MTP absent (draft=0)"
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LOAD_BANNER_RE = re.compile(
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r"loaded in\s+([0-9.]+)s\s*\|\s*resident dense:\s+([0-9.]+)\s+MB\s*\|"
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r"\s*layers=(\d+)\s+experts=(\d+)\s*\|\s*MTP\s+(\w+)\s+\(draft=(\d+)\)"
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)
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# --- tunable floors -----------------------------------------------------------
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# These are deliberately generous so they catch *regressions* (broken builds,
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# pathological configs, telemetry accounting bugs) without flapping on machine
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# noise. The tiny model is fully resident at ~200 tok/s, so a 20 tok/s floor is
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# a 10x margin. Tune per-host via env if needed (documented in README).
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TINY_TOK_S_FLOOR = float(os.environ.get("COLI_TINY_TOK_S_FLOOR", "20.0"))
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# On a fully-resident tiny model the expert-disk wait share should be tiny.
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# If it exceeds this, something regressed in the I/O accounting or cache path.
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MAX_DISK_WAIT_SHARE = float(os.environ.get("COLI_MAX_DISK_WAIT_SHARE", "0.20"))
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# decode wall-time should be roughly the sum of PROFILE phases (other = residual).
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PROFILE_SUM_TOLERANCE = float(os.environ.get("COLI_PROFILE_SUM_TOL", "0.05"))
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# Minimum direct CPU-vs-CUDA argmax agreement on the tiny TF fixture. The two
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# backends use different accumulation orders (SIMD dot vs CUDA kernel), so a
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# few near-tied positions flip argmax — that's expected numeric divergence, not
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# a kernel bug. Measured baseline ~84% (27/32) on this fixture; the 70% floor
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# leaves headroom for machine noise while still catching a catastrophic kernel
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# regression (e.g. a wrong GEMM would drop this to ~random = ~4%).
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MIN_CPU_CUDA_AGREEMENT = float(os.environ.get("COLI_MIN_CPU_CUDA_AGREE", "0.70"))
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def parse_run(stdout: str, stderr: str = "") -> dict:
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"""Parse one engine run's output into a telemetry dict.
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Returns keys: tok_s, hit_pct, profile (dict, seconds), profile_sum,
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time_shares (dict, fractions 0..1), verdict, loads_per_tok, cuda (dict),
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tf_match (tuple or None), parsed (set of field names found).
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Raises RuntimeError only if the core throughput line is missing — everything
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else is optional and absent on some run modes (e.g. [PROF] needs PROF=1,
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CUDA tier needs gpu_expert_count>0).
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"""
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out = dict(
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tok_s=None, hit_pct=None, profile=None, profile_sum=None,
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time_shares=None, verdict=None, loads_per_tok=None,
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cuda=None, tf_match=None, stderr=stderr,
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)
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parsed = set()
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blob = stdout + "\n" + stderr # [PROF]/[CUDA] live on stderr; scan both.
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m = _first_speed(stdout)
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if m:
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out["tok_s"] = float(m.group(1)); parsed.add("tok_s")
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m = HIT_RE.search(blob)
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if m:
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out["hit_pct"] = float(m.group(1)); parsed.add("hit_pct")
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m = PROFILE_RE.search(stdout)
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if m:
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service, wait, emm, attn, head, other = (float(x) for x in m.groups())
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disk = service + (wait or 0.0)
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out["profile"] = dict(zip(PROFILE_KEYS, (disk, emm, attn, head, other)))
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out["profile_sum"] = disk + emm + attn + head + other
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parsed.add("profile")
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# ATTENTION sub-breakdown: projection/RoPE | score-softmax-value | output.
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m = ATTN_BREAKDOWN_RE.search(stdout)
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if m:
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out["attn_breakdown"] = dict(zip(
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("proj_rope", "score_sm_value", "out_proj"),
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(float(x) for x in m.groups())))
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parsed.add("attn_breakdown")
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m = SHARES_RE.search(blob)
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if m:
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io, emm, attn, head, other = (float(x) / 100.0 for x in m.groups())
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out["time_shares"] = dict(io=io, matmul=emm, attention=attn, head=head, other=other)
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parsed.add("time_shares")
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m = VERDICT_RE.search(blob)
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if m:
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out["verdict"] = m.group(1); parsed.add("verdict")
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# [PROF] decode forwards + latency p50/p90/p99/max.
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m = LATENCY_RE.search(blob)
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if m:
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out["latency"] = dict(zip(
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("forwards", "p50_ms", "p90_ms", "p99_ms", "max_ms"),
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(float(x) for x in m.groups())))
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parsed.add("latency")
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# [PROF] expert I/O throughput: GB fetched, MB/token, GB/s, service vs felt wait.
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m = EXPERT_IO_RE.search(blob)
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if m:
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out["expert_io"] = dict(zip(
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("gb_fetched", "mb_per_tok", "gb_per_s", "loads_per_tok",
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"read_service_s", "felt_wait_s"),
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(float(x) for x in m.groups())))
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parsed.add("expert_io")
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m = LOADS_PER_TOK_RE.search(blob)
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if m:
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out["loads_per_tok"] = float(m.group(1)); parsed.add("loads_per_tok")
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# experts loaded/token with per-layer spread + baseline topk (run_text summary).
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m = EXPERTS_LOADED_RE.search(stdout)
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if m:
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out["experts_loaded"] = dict(
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per_tok=float(m.group(1)), per_layer=float(m.group(2)),
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n_sparse_layers=int(m.group(3)), baseline_topk=int(m.group(4)))
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parsed.add("experts_loaded")
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# speculation: tokens/forward, forwards, tokens, MTP acceptance%.
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m = SPECULATION_RE.search(stdout)
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if m:
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out["speculation"] = dict(zip(
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("tok_per_fw", "forwards", "tokens", "mtp_accept_pct"),
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(float(m.group(1)), int(m.group(2)), int(m.group(3)), float(m.group(4)))))
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parsed.add("speculation")
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# routing quality (CACHE_ROUTE inline suffixes on the summary line).
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m = SWAP_RE.search(stdout)
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if m:
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out["swap"] = dict(pct=float(m.group(1)), swaps=int(m.group(2)), slots=int(m.group(3)))
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parsed.add("swap")
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m = ROUTE_AGREE_RE.search(stdout)
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if m:
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out["route_agree"] = dict(agree_pct=float(m.group(1)), kl=float(m.group(2)))
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parsed.add("route_agree")
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# disk-load split by decode phase (DISK_SPLIT=1).
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m = DISK_SPLIT_RE.search(stdout)
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if m:
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out["disk_split"] = dict(zip(
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("draft", "absorb", "verify_main", "mtp_loads", "mtp_gb",
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"main_loads", "main_gb", "mtp_bytes_pct"),
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(int(m.group(1)), int(m.group(2)), int(m.group(3)), int(m.group(4)),
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float(m.group(5)), int(m.group(6)), float(m.group(7)),
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float(m.group(8)) if m.group(8) else None)))
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parsed.add("disk_split")
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# provenance: machine + resolved config.
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m = MACHINE_RE.search(blob)
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if m:
|
||||
out["machine"] = dict(cpu=m.group(1).strip(), cores=int(m.group(2)), backend=m.group(3))
|
||||
parsed.add("machine")
|
||||
m = CONFIG_RE.search(blob)
|
||||
if m:
|
||||
out["config_str"] = m.group(1).strip(); parsed.add("config")
|
||||
|
||||
# load banner: load time, resident dense MB, layers, experts, MTP status.
|
||||
m = LOAD_BANNER_RE.search(stdout)
|
||||
if m:
|
||||
out["load"] = dict(zip(
|
||||
("load_s", "resident_dense_mb", "layers", "experts", "mtp_status", "draft"),
|
||||
(float(m.group(1)), float(m.group(2)), int(m.group(3)),
|
||||
int(m.group(4)), m.group(5), int(m.group(6)))))
|
||||
parsed.add("load")
|
||||
|
||||
# LOOKAHEAD routing-recall block (LOOKA=1): list of {predictor, pct, hit, tot}.
|
||||
la_block = re.search(
|
||||
r"LOOKAHEAD routing.*?recall.*?:\n((?:^\s+.+?\s+[0-9.]+%\s+\(\d+/\d+\)\s*$\n?)+)",
|
||||
blob, re.MULTILINE)
|
||||
if la_block:
|
||||
out["lookahead"] = []
|
||||
for row in LOOKAHEAD_RE.finditer(la_block.group(1)):
|
||||
out["lookahead"].append(dict(
|
||||
predictor=row.group(1).strip(), pct=float(row.group(2)),
|
||||
hit=int(row.group(3)), tot=int(row.group(4))))
|
||||
parsed.add("lookahead")
|
||||
|
||||
cuda = dict(enabled=False, expert_count=None, expert_gb=None,
|
||||
calls_served=None, resident_tensors=None, resident_gb=None,
|
||||
groups=None, groups_timing=None)
|
||||
if CUDA_DEVICE_RE.search(stderr):
|
||||
cuda["enabled"] = True
|
||||
m = CUDA_TIER_RE.search(stdout)
|
||||
if m:
|
||||
cuda["expert_count"] = int(m.group(1))
|
||||
cuda["expert_gb"] = float(m.group(2))
|
||||
cuda["calls_served"] = int(m.group(3))
|
||||
m = CUDA_RESIDENT_RE.search(stderr)
|
||||
if m:
|
||||
cuda["resident_tensors"] = int(m.group(1))
|
||||
cuda["resident_gb"] = float(m.group(2))
|
||||
m = CUDA_GROUPS_RE.search(stderr)
|
||||
if m:
|
||||
cuda["groups"] = dict(zip(
|
||||
("calls", "experts", "rows", "experts_per_call"),
|
||||
(int(m.group(1)), int(m.group(2)), int(m.group(3)), float(m.group(4)))))
|
||||
m = CUDA_GROUPS_TIME_RE.search(stderr)
|
||||
if m:
|
||||
cuda["groups_timing"] = dict(zip(
|
||||
("h2d_ms", "kernel_ms", "d2h_ms"),
|
||||
(float(m.group(1)), float(m.group(2)), float(m.group(3)))))
|
||||
out["cuda"] = cuda
|
||||
|
||||
m = TF_MATCH_RE.search(stdout)
|
||||
if m:
|
||||
out["tf_match"] = (int(m.group(1)), int(m.group(2))); parsed.add("tf_match")
|
||||
|
||||
# Capture per-position argmax divergences from the oracle, keyed by position.
|
||||
mismatches = {int(mm.group(1)): int(mm.group(2))
|
||||
for mm in TF_MISMATCH_RE.finditer(blob)}
|
||||
if out["tf_match"] is not None:
|
||||
out["tf_mismatches"] = mismatches
|
||||
parsed.add("tf_mismatches")
|
||||
|
||||
out["parsed"] = parsed
|
||||
return out
|
||||
|
||||
|
||||
def run_engine(
|
||||
env_overlay: dict,
|
||||
*,
|
||||
engine: Optional[str] = None,
|
||||
cap: int = 4,
|
||||
ebits: int = 4,
|
||||
dbits: int = 4,
|
||||
timeout: float = 600.0,
|
||||
snap: Optional[str] = None,
|
||||
) -> tuple[dict, subprocess.CompletedProcess]:
|
||||
"""Run the engine with an env overlay; return (parsed_telemetry, proc).
|
||||
|
||||
`engine` defaults to ./glm.exe (colibri's Windows host). `snap` defaults to
|
||||
the bundled tiny model (glm_tiny) so callers can omit it for fast tests.
|
||||
The positional argv is `cap ebits dbits`, matching the engine's main().
|
||||
"""
|
||||
if engine is None:
|
||||
engine = str(Path(__file__).resolve().parent.parent / "glm.exe")
|
||||
env = os.environ.copy()
|
||||
# Strip CUDA vars by default so a CPU run isn't accidentally GPU-accelerated
|
||||
# by a leftover env; callers opt in by passing them in env_overlay.
|
||||
for k in ("COLI_CUDA", "COLI_GPU", "COLI_GPUS", "CUDA_DENSE", "CUDA_EXPERT_GB"):
|
||||
env.pop(k, None)
|
||||
env.update(env_overlay)
|
||||
if snap is not None:
|
||||
env["SNAP"] = snap
|
||||
elif "SNAP" not in env:
|
||||
env["SNAP"] = str(Path(__file__).resolve().parent.parent / "glm_tiny")
|
||||
|
||||
proc = subprocess.run(
|
||||
[engine, str(cap), str(ebits), str(dbits)],
|
||||
env=env, capture_output=True, text=True, timeout=timeout,
|
||||
)
|
||||
telemetry = parse_run(proc.stdout, proc.stderr)
|
||||
telemetry["returncode"] = proc.returncode
|
||||
telemetry["env"] = {k: env_overlay[k] for k in env_overlay}
|
||||
return telemetry, proc
|
||||
|
||||
|
||||
def disk_wait_share(t: dict) -> Optional[float]:
|
||||
"""Fraction of decode wall-time spent waiting on expert disk reads.
|
||||
|
||||
Preferred source: [PROF] time_shares (the engine's own accounting, which
|
||||
separates felt-wait from read-service). Falls back to PROFILE disk / sum if
|
||||
[PROF] wasn't emitted (PROF=0 runs). None if neither is available.
|
||||
"""
|
||||
if t.get("time_shares"):
|
||||
return t["time_shares"]["io"]
|
||||
if t.get("profile") and t.get("profile_sum"):
|
||||
return t["profile"]["disk"] / t["profile_sum"]
|
||||
return None
|
||||
|
||||
|
||||
def tf_agreement(cpu: dict, cuda: dict, oracle: list[int]) -> tuple[float, list[int]]:
|
||||
"""Direct CPU-vs-CUDA argmax agreement on the TF fixture.
|
||||
|
||||
Both runs prefilled the SAME oracle sequence; tf_mismatches holds each
|
||||
backend's actual argmax where it diverged from the oracle. Where a backend
|
||||
is ABSENT from the mismatch map, its prediction equals the oracle token at
|
||||
that position. So the reconstructed per-position prediction is:
|
||||
oracle[i] if i not in mismatches else mismatches[i]
|
||||
and agreement is the fraction of positions where CPU and CUDA predictions
|
||||
are identical — independent of how each relates to the oracle.
|
||||
|
||||
`oracle` is ref_glm.json's tf_pred (the per-position oracle argmax). Pass
|
||||
n_positions = len(oracle).
|
||||
|
||||
Returns (agreement_fraction, list_of_differing_positions).
|
||||
"""
|
||||
cm = cpu.get("tf_mismatches") or {}
|
||||
gm = cuda.get("tf_mismatches") or {}
|
||||
diff = []
|
||||
for i, orc in enumerate(oracle):
|
||||
cpu_tok = cm.get(i, orc) # matched oracle => oracle token
|
||||
cuda_tok = gm.get(i, orc)
|
||||
if cpu_tok != cuda_tok:
|
||||
diff.append(i)
|
||||
agree = (len(oracle) - len(diff)) / len(oracle) if oracle else 0.0
|
||||
return agree, diff
|
||||
Reference in New Issue
Block a user