File size: 7,303 Bytes
73bd12f | 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 | """Isolated-process CPU/MPS parity and synchronized warm latency study.
Run after the reference evaluation finishes for uncontended timing. Every schema/input
gets one untimed warm call on each device before repeated measurements.
"""
import argparse
import gc
import json
from pathlib import Path
import numpy as np
import torch
from sealcell.config import config
from sealcell.model import LocalModel, schema_spec
from sealcell.policies import policy
from sealcell.serialization import render
from sealcell.simulator import Simulator
from sealcell.supervisor import Supervisor
def episode_parity(model):
reference = {}
source = Path("reports/runs/final-primary/decisions.jsonl")
if not source.exists():
return {"status": "not run; CPU reference traces missing"}
with source.open() as f:
for line in f:
row = json.loads(line)
if row["episode_id"] > "final-00012":
break
if row["policy"] in {"hybrid", "direct"}:
reference.setdefault((row["episode_id"], row["policy"]), []).append(row)
settings = config("evaluation")
failures, agreements = [], 0
with Path("reports/mps-episode-parity.jsonl").open("w") as output:
for (episode_id, name), cpu_trace in reference.items():
index = int(episode_id.split("-")[1])
s = Simulator(
settings["scenarios"][index % len(settings["scenarios"])],
settings["splits"]["final"]["seed_start"] + index,
settings["splits"]["final"]["recipe"],
settings["splits"]["final"]["text_family"],
)
sup = Supervisor(
s,
policy(
name,
model=model,
issue_threshold=settings["issue_threshold"],
evidence_threshold=settings["evidence_threshold"],
filter_actions=settings.get("filter_actions", False),
**settings["conditions"]["primary"],
),
).run()
def signature(trace):
return [
(
r["proposal"]["action"],
r["executed"],
r["gate_rejection"],
r["stage_after"],
r["time_after"],
(r["proposal"]["model"] or {}).get("input_hash"),
{
h: v["selected_label"]
for h, v in (r["proposal"]["model"] or {}).get("heads", {}).items()
},
)
for r in trace
]
equal = signature(cpu_trace) == signature(sup.trace)
agreements += equal
if not equal:
failures.append({"episode_id": episode_id, "policy": name})
output.write(
json.dumps(
{
"episode_id": episode_id,
"policy": name,
"matches_cpu": equal,
"trace": sup.trace,
}
)
+ "\n"
)
return {"episodes": len(reference), "matching_trajectories": agreements, "failures": failures}
parser = argparse.ArgumentParser()
parser.add_argument("--repeats", type=int, default=3)
parser.add_argument("--output", default="reports/device-benchmark.json")
args = parser.parse_args()
if not torch.backends.mps.is_available():
raise SystemExit("MPS unavailable")
source = json.loads(Path("data/fixtures/playground-probes.json").read_text())
cases = [{"id": c["id"], "text": c["text"], "schema": source["schema"]} for c in source["cases"]]
for line in Path("data/fixtures/development.jsonl").read_text().splitlines():
case = json.loads(line)
cases.append({"id": case["id"], "text": case["text"], "schema": schema_spec()})
for name in config("scenarios"):
observation = Simulator(name, 42).observe()
for direct in [False, True]:
cases.append(
{
"id": f"initial-{name}-{direct}",
"text": render(observation),
"schema": schema_spec(direct),
}
)
result = {
"precision": "float32",
"fallback_enabled": False,
"repeats": args.repeats,
"timing": "synchronized, one untimed warm call for each exact input/schema shape",
"records": {},
"metadata": {},
}
for device in ["cpu", "mps"]:
model = LocalModel(device=device)
result["metadata"][device] = model.metadata
records = []
for i, case in enumerate(cases):
warm = model.classify(case["text"], case["schema"])
if warm["error"]:
raise RuntimeError(f"{device}/{case['id']}: {warm['error']}")
for repeat in range(args.repeats):
record = model.classify(case["text"], case["schema"])
records.append({"case_id": case["id"], "repeat": repeat, **record})
if i % 10 == 0:
print(device, i + 1, "/", len(cases), flush=True)
result["records"][device] = records
if device == "mps":
result["episode_parity"] = episode_parity(model)
result["mps_memory_bytes"] = {
"allocated": torch.mps.current_allocated_memory(),
"driver": torch.mps.driver_allocated_memory(),
}
del model
gc.collect()
if device == "mps":
torch.mps.empty_cache()
cpu, mps = result["records"]["cpu"], result["records"]["mps"]
max_difference = 0.0
max_logit_difference = 0.0
agree = head_count = errors = 0
for a, b in zip(cpu, mps):
assert (a["case_id"], a["repeat"], a["schema_hash"], a["input_hash"]) == (
b["case_id"],
b["repeat"],
b["schema_hash"],
b["input_hash"],
)
if a["error"] or b["error"]:
errors += 1
continue
for name, head in a["heads"].items():
other = b["heads"][name]
head_count += 1
agree += head["selected_label"] == other["selected_label"]
for label, value in head["probabilities"].items():
max_difference = max(max_difference, abs(value - other["probabilities"][label]))
max_logit_difference = max(
max_logit_difference, abs(head["logits"][label] - other["logits"][label])
)
summary = {
"cases": len(cases),
"head_comparisons": head_count,
"selected_label_agreements": agree,
"errors": errors,
"max_absolute_probability_difference": max_difference,
"max_absolute_logit_difference": max_logit_difference,
}
for device, records in result["records"].items():
summary[device] = {
key: dict(
zip(
["p50", "p95"],
[float(v) for v in np.quantile([r[key] for r in records], [0.5, 0.95])],
)
)
for key in ["total_ms", "inference_ms"]
}
summary["median_total_speedup"] = (
summary["cpu"]["total_ms"]["p50"] / summary["mps"]["total_ms"]["p50"]
)
summary["parity_passed"] = errors == 0 and agree == head_count and max_difference < 1e-3
result["summary"] = summary
Path(args.output).write_text(json.dumps(result, indent=2) + "\n")
print(json.dumps(summary, indent=2))
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