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d61821a | 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 | #!/usr/bin/env python3
"""Outcome-blind runtime, design, repository, and executor preflight for Study 5."""
from __future__ import annotations
from dataclasses import asdict
import importlib.metadata
import json
from pathlib import Path
import platform
import shutil
import sys
import time
from typing import Any
from preflight_study3 import PACKAGES, _command, _executor_conformance, _tool_probe
from agent_harness.lm_studio import LMStudioClient
from agent_harness.lm_studio_embeddings import LMStudioEmbeddingClient
from agent_harness.lm_studio_management import LMStudioResidencyManager, LMStudioServer
from agent_harness.pilot import research_code_revision
from agent_harness.protocol_experiment import protocol_tool_definitions
from agent_harness.specs import (
load_edit_interfaces,
load_embeddings,
load_experiments,
load_harnesses,
load_models,
load_repositories,
load_task_split,
load_tasks,
validate_configuration_tree,
)
from agent_harness.study2_experiment import tokenizer_for
EXPERIMENTS = ("E13", "E14", "E15")
EXPECTED = {"E13": 1440, "E14": 540, "E15": 540}
def run(root: Path) -> dict[str, Any]:
revision = research_code_revision(root)
errors, warnings = validate_configuration_tree(root)
if errors or warnings:
raise RuntimeError(f"configuration failed: errors={errors}, warnings={warnings}")
for experiment_id in EXPERIMENTS:
raw = root / "results" / "raw" / experiment_id
if raw.exists() and any(raw.rglob("*")):
raise RuntimeError(f"{experiment_id} raw outcomes exist before preflight")
audit = json.loads((root / "docs" / "STUDY5_DESIGN_AUDIT.json").read_text())
if not audit.get("outcome_blind"):
raise RuntimeError("Study 5 design audit is not outcome blind")
manifests: dict[str, Any] = {}
for experiment_id in EXPERIMENTS:
manifest = json.loads(
(root / "configs" / "study5" / f"{experiment_id}_cells.json").read_text()
)
if manifest.get("planned_cells") != EXPECTED[experiment_id]:
raise RuntimeError(f"{experiment_id} manifest count mismatch")
if len(manifest.get("cells", [])) != EXPECTED[experiment_id]:
raise RuntimeError(f"{experiment_id} cell array mismatch")
manifests[experiment_id] = manifest
tasks = load_tasks(root)
fresh = load_task_split(root / "tasks" / "splits" / "study5_fresh.txt")
if len(fresh) != 17 or len({tasks[item].gold_commit for item in fresh}) != 17:
raise RuntimeError("Study 5 fresh split must contain 17 unique gold commits")
prior = {task.gold_commit for task_id, task in tasks.items() if task_id not in fresh}
if prior & {tasks[item].gold_commit for item in fresh}:
raise RuntimeError("Study 5 fresh split overlaps a prior task commit")
disk = shutil.disk_usage(root)
if disk.free < 20 * 1024**3:
raise RuntimeError(f"less than 20 GiB free before Study 5: {disk.free} bytes")
models = load_models(root)
experiments = load_experiments(root)
embedding = load_embeddings(root)[experiments["E13"].embedding_id]
repositories = load_repositories(root)
harnesses = load_harnesses(root)
interfaces = load_edit_interfaces(root)
first_task = tasks[manifests["E15"]["cells"][0]["task_id"]]
tool_signatures = {
harness_id: [
item["function"]["name"]
for item in protocol_tool_definitions(interfaces["P002"], first_task, harnesses[harness_id])
]
for harness_id in experiments["E15"].harness_ids
}
if tool_signatures["H008"] == tool_signatures["H011"]:
raise RuntimeError("unified and specialized search signatures did not separate")
server = LMStudioServer(port=1234)
server_state = server.ensure_running()
residency = LMStudioResidencyManager(
models["M002"].base_url, models["M002"].api_token_env, timeout_seconds=1_800
)
report: dict[str, Any] = {
"schema_version": 1,
"study": "Study 5 / E13--E15",
"outcome_blind": True,
"research_code_revision": revision,
"planned_cells": sum(EXPECTED.values()),
"design_audit": audit,
"fresh_task_count": len(fresh),
"started_unix": time.time(),
"platform": platform.platform(),
"python": sys.version,
"torch_used": False,
"disk": {"total": disk.total, "used": disk.used, "free": disk.free},
"lms_version": _command([str(server.cli_path), "--version"]),
"server_start": server_state,
"dependencies": {package: importlib.metadata.version(package) for package in PACKAGES},
"executor_conformance": _executor_conformance(root),
"tool_signatures": tool_signatures,
"repositories": {},
"embedding": {},
"models": {},
}
try:
residency.unload_all()
for repository_id, repository in sorted(repositories.items()):
observed = _command(["git", "rev-parse", "HEAD"], cwd=root / repository.local_path)
if observed["returncode"] or observed["stdout"].strip() != repository.pinned_head:
raise RuntimeError(f"repository head mismatch: {repository_id}: {observed}")
report["repositories"][repository_id] = {
"spec": asdict(repository),
"observed_head": observed["stdout"].strip(),
}
transition = residency.ensure_exclusive(
embedding.model_key, embedding.loaded_context_length
)
client = LMStudioEmbeddingClient(embedding, timeout_seconds=1_800)
report["embedding"] = {
"spec": asdict(embedding),
"config_hash": embedding.config_hash,
"transition": transition.to_dict(),
"resolved": client.resolve(),
"probe": client.probe().to_dict(),
"unload": residency.unload_all().to_dict(),
}
for model_id in experiments["E13"].model_ids:
model = models[model_id]
transition = residency.ensure_exclusive(model.expected_inference_key, model.context_length)
model_client = LMStudioClient(model, timeout_seconds=1_800)
discovery, resolved = model_client.resolve()
tokenizer = tokenizer_for(model)
report["models"][model_id] = {
"spec": asdict(model),
"config_hash": model.config_hash,
"transition": transition.to_dict(),
"resolved": resolved.to_dict(),
"discovery_errors": discovery.endpoint_errors,
"tokenizer_path": str(tokenizer.path),
"tokenizer_sha256": tokenizer.sha256,
"tool_probe": _tool_probe(model_client, resolved.inference_key),
"unload": residency.unload_all().to_dict(),
}
report["passed"] = True
return report
finally:
cleanup_errors = []
try:
report["final_unload"] = residency.unload_all().to_dict()
except Exception as exc:
cleanup_errors.append(f"unload_all: {exc}")
try:
status = server.status()
report["server_stop"] = (
server.stop() if status["running"] else {"action": "already_stopped", "status": status}
)
except Exception as exc:
cleanup_errors.append(f"server_stop: {exc}")
report["cleanup_errors"] = cleanup_errors
report["finished_unix"] = time.time()
def main() -> None:
root = Path(__file__).resolve().parents[1]
output = root / "results" / "reports" / "study5_preflight.json"
output.parent.mkdir(parents=True, exist_ok=True)
report: dict[str, Any] = {}
try:
report = run(root)
except Exception as exc:
report = {**report, "passed": False, "error": repr(exc)}
output.write_text(json.dumps(report, indent=2, sort_keys=True, default=str) + "\n")
raise
output.write_text(json.dumps(report, indent=2, sort_keys=True, default=str) + "\n")
print(json.dumps({"passed": True, "report": str(output)}, indent=2))
if __name__ == "__main__":
main()
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