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ce2d64b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 | from __future__ import annotations
import math
import random
from copy import deepcopy
from statistics import mean, median
from .models import SimulationConfig
from .simulator import run_simulation
STUDIES = {
"prefix_cache": {
"label": "Prefix reuse: off vs on",
"baseline": "Prefix reuse off",
"treatment": "Prefix reuse on",
},
"pd_vs_colocated": {
"label": "Topology: colocated vs P/D",
"baseline": "Colocated",
"treatment": "P/D disaggregated",
},
"chunked_vs_fcfs": {
"label": "Scheduling: FCFS vs chunked prefill",
"baseline": "Continuous FCFS",
"treatment": "Chunked prefill + SLO",
},
"slo_vs_fcfs": {
"label": "Scheduling: FCFS vs least-slack",
"baseline": "Continuous FCFS",
"treatment": "Continuous SLO",
},
}
METRICS = {
"goodput_rps": {"direction": 1, "label": "Goodput", "unit": "req/s"},
"p95_ttft_ms": {"direction": -1, "label": "p95 TTFT", "unit": "ms"},
"p95_e2e_ms": {"direction": -1, "label": "p95 E2E", "unit": "ms"},
"slo_attainment": {"direction": 1, "label": "SLO attainment", "unit": "fraction"},
}
def _study_configs(base: SimulationConfig, study: str) -> tuple[SimulationConfig, SimulationConfig]:
if study not in STUDIES:
raise ValueError(f"Unknown paired study: {study}")
a = deepcopy(base)
b = deepcopy(base)
if study == "prefix_cache":
a.prefix_cache_enabled = False
b.prefix_cache_enabled = True
elif study == "pd_vs_colocated":
a.topology = "colocated"
b.topology = "disaggregated_pd"
if b.scheduler == "static_fcfs":
b.scheduler = "continuous_fcfs"
elif study == "chunked_vs_fcfs":
a.topology = "colocated"
b.topology = "colocated"
a.scheduler = "continuous_fcfs"
b.scheduler = "chunked_slo"
elif study == "slo_vs_fcfs":
a.topology = "colocated"
b.topology = "colocated"
a.scheduler = "continuous_fcfs"
b.scheduler = "continuous_slo"
return a, b
def _extract(result: dict) -> dict[str, float]:
return {
"goodput_rps": float(result["summary"]["goodput_rps"]),
"p95_ttft_ms": float(result["latency"]["ttft_ms"]["p95"]),
"p95_e2e_ms": float(result["latency"]["e2e_ms"]["p95"]),
"slo_attainment": float(result["summary"]["slo_attainment"]),
}
def _percentile(values: list[float], q: float) -> float:
if not values:
return 0.0
ordered = sorted(values)
if len(ordered) == 1:
return ordered[0]
pos = min(max(q, 0.0), 1.0) * (len(ordered) - 1)
lo = int(math.floor(pos))
hi = int(math.ceil(pos))
if lo == hi:
return ordered[lo]
frac = pos - lo
return ordered[lo] * (1.0 - frac) + ordered[hi] * frac
def _bootstrap_ci(deltas: list[float], samples: int, seed: int) -> tuple[float, float]:
if not deltas:
return (0.0, 0.0)
rng = random.Random(seed)
n = len(deltas)
boot = []
for _ in range(max(samples, 50)):
boot.append(mean(deltas[rng.randrange(n)] for _ in range(n)))
return _percentile(boot, 0.025), _percentile(boot, 0.975)
def paired_study(config: dict, study: str = "prefix_cache", repetitions: int = 12, bootstrap_samples: int = 500) -> dict:
"""Run a paired Monte Carlo A/B study using common random numbers.
Baseline and treatment share the same seed on every repetition. This reduces
workload-noise variance and makes the delta attributable to the controlled
system change rather than to different synthetic request traces.
"""
base = SimulationConfig.from_dict(config)
repetitions = max(2, min(int(repetitions), 64))
a_cfg, b_cfg = _study_configs(base, study)
pairs: list[dict] = []
for rep in range(repetitions):
seed = base.seed + rep * 1009
a_cfg.seed = seed
b_cfg.seed = seed
a_result = run_simulation(a_cfg.to_dict())
b_result = run_simulation(b_cfg.to_dict())
a_metrics = _extract(a_result)
b_metrics = _extract(b_result)
pairs.append({"rep": rep + 1, "seed": seed, "baseline": a_metrics, "treatment": b_metrics})
metrics = []
for key, meta in METRICS.items():
baseline = [row["baseline"][key] for row in pairs]
treatment = [row["treatment"][key] for row in pairs]
deltas = [b - a for a, b in zip(baseline, treatment, strict=True)]
relative = [((b - a) / abs(a) * 100.0) if abs(a) > 1e-12 else 0.0 for a, b in zip(baseline, treatment, strict=True)]
metric_seed = sum((idx + 1) * ord(ch) for idx, ch in enumerate(key))
ci_low, ci_high = _bootstrap_ci(deltas, bootstrap_samples, base.seed ^ metric_seed)
direction = int(meta["direction"])
wins = sum(1 for delta in deltas if delta * direction > 0)
ties = sum(1 for delta in deltas if abs(delta) <= 1e-12)
metrics.append(
{
"metric": key,
"label": meta["label"],
"unit": meta["unit"],
"baseline_mean": mean(baseline),
"treatment_mean": mean(treatment),
"delta_mean": mean(deltas),
"delta_median": median(deltas),
"delta_ci95_low": ci_low,
"delta_ci95_high": ci_high,
"relative_change_pct": mean(relative),
"treatment_win_rate": wins / repetitions,
"tie_rate": ties / repetitions,
"preferred_direction": "higher" if direction > 0 else "lower",
"ci_excludes_zero": ci_low > 0 or ci_high < 0,
}
)
return {
"study": study,
"label": STUDIES[study]["label"],
"baseline_label": STUDIES[study]["baseline"],
"treatment_label": STUDIES[study]["treatment"],
"repetitions": repetitions,
"bootstrap_samples": max(bootstrap_samples, 50),
"protocol": "paired-common-random-numbers",
"metrics": metrics,
"pairs": pairs,
}
def robustness_study(
config: dict,
study: str = "pd_vs_colocated",
samples: int = 32,
uncertainty: float = 0.20,
) -> dict:
"""Stress-test an A/B conclusion under analytical latency uncertainty.
Each sample draws shared prefill/decode/transfer scale factors and applies
them to both alternatives. The goal is not a probability statement about
real hardware; it is a sensitivity analysis showing whether a conclusion is
fragile to plausible multiplicative error in the reference latency model.
"""
base = SimulationConfig.from_dict(config)
samples = max(4, min(int(samples), 96))
uncertainty = min(max(float(uncertainty), 0.0), 0.75)
rng = random.Random(base.seed ^ 0x51514A)
rows = []
for idx in range(samples):
prefill_scale = rng.uniform(1.0 - uncertainty, 1.0 + uncertainty)
decode_scale = rng.uniform(1.0 - uncertainty, 1.0 + uncertainty)
transfer_scale = rng.uniform(1.0 - uncertainty, 1.0 + uncertainty)
a_cfg, b_cfg = _study_configs(base, study)
seed = base.seed + idx * 1009
for cfg in (a_cfg, b_cfg):
cfg.seed = seed
cfg.prefill_time_scale = prefill_scale
cfg.decode_time_scale = decode_scale
cfg.transfer_time_scale = transfer_scale
a = run_simulation(a_cfg.to_dict())
b = run_simulation(b_cfg.to_dict())
am = _extract(a)
bm = _extract(b)
rows.append(
{
"sample": idx + 1,
"prefill_scale": prefill_scale,
"decode_scale": decode_scale,
"transfer_scale": transfer_scale,
"baseline": am,
"treatment": bm,
"baseline_slo_pass": am["slo_attainment"] >= base.slo_attainment_target,
"treatment_slo_pass": bm["slo_attainment"] >= base.slo_attainment_target,
}
)
def win_fraction(metric: str, direction: int) -> float:
return mean(
1.0 if (row["treatment"][metric] - row["baseline"][metric]) * direction > 0 else 0.0
for row in rows
)
goodput_deltas = [row["treatment"]["goodput_rps"] - row["baseline"]["goodput_rps"] for row in rows]
ttft_deltas = [row["treatment"]["p95_ttft_ms"] - row["baseline"]["p95_ttft_ms"] for row in rows]
e2e_deltas = [row["treatment"]["p95_e2e_ms"] - row["baseline"]["p95_e2e_ms"] for row in rows]
return {
"study": study,
"label": STUDIES[study]["label"],
"baseline_label": STUDIES[study]["baseline"],
"treatment_label": STUDIES[study]["treatment"],
"samples": samples,
"uncertainty": uncertainty,
"method": "shared-multiplicative-latency-perturbation",
"summary": {
"treatment_goodput_win_fraction": win_fraction("goodput_rps", 1),
"treatment_ttft_win_fraction": win_fraction("p95_ttft_ms", -1),
"treatment_e2e_win_fraction": win_fraction("p95_e2e_ms", -1),
"baseline_slo_pass_fraction": mean(1.0 if row["baseline_slo_pass"] else 0.0 for row in rows),
"treatment_slo_pass_fraction": mean(1.0 if row["treatment_slo_pass"] else 0.0 for row in rows),
"median_goodput_delta": median(goodput_deltas),
"median_ttft_delta_ms": median(ttft_deltas),
"median_e2e_delta_ms": median(e2e_deltas),
},
"rows": rows,
}
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