File size: 8,475 Bytes
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from __future__ import annotations
from dataclasses import asdict
from hashlib import sha256
import importlib.metadata
import json
import platform
from pathlib import Path
import shutil
import subprocess
import sys
import time
from typing import Any
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.specs import (
load_embeddings,
load_models,
load_repositories,
validate_configuration_tree,
)
from agent_harness.study2_experiment import tokenizer_for
PACKAGES = (
"faiss-cpu",
"numpy",
"pytest",
"scipy",
"statsmodels",
"tokenizers",
"tree-sitter",
"tree-sitter-go",
"tree-sitter-python",
)
def _command(arguments: list[str], cwd: Path | None = None) -> dict[str, Any]:
result = subprocess.run(
arguments,
cwd=cwd,
text=True,
capture_output=True,
check=False,
timeout=120,
)
return {
"command": arguments,
"returncode": result.returncode,
"stdout": result.stdout,
"stderr": result.stderr,
}
def _tool_probe(client: LMStudioClient, model_key: str) -> dict[str, Any]:
response = client.chat_completions(
model_key,
[
{
"role": "system",
"content": "This is a runtime preflight. Use the required tool exactly once.",
},
{
"role": "user",
"content": "Call preflight_echo with marker MODEL_TOOL_OK. Do not answer in prose.",
},
],
tools=[
{
"type": "function",
"function": {
"name": "preflight_echo",
"description": "Return the requested preflight marker.",
"parameters": {
"type": "object",
"properties": {"marker": {"type": "string"}},
"required": ["marker"],
"additionalProperties": False,
},
},
}
],
max_tokens=2_048,
seed=0,
)
try:
message = response["choices"][0]["message"]
calls = message["tool_calls"]
function = calls[0]["function"]
arguments = function["arguments"]
decoded = arguments if isinstance(arguments, dict) else json.loads(arguments)
except (KeyError, IndexError, TypeError, json.JSONDecodeError) as exc:
raise RuntimeError("model did not return a valid preflight tool call") from exc
if function.get("name") != "preflight_echo" or decoded != {"marker": "MODEL_TOOL_OK"}:
raise RuntimeError(f"unexpected tool preflight payload: {function}")
serialized = json.dumps(response, sort_keys=True, separators=(",", ":"))
return {
"tool_name": function["name"],
"arguments": decoded,
"finish_reason": response["choices"][0].get("finish_reason"),
"usage": response.get("usage", {}),
"response_sha256": sha256(serialized.encode()).hexdigest(),
}
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}")
disk = shutil.disk_usage(root)
if disk.free < 50 * 1024**3:
raise RuntimeError(f"less than 50 GiB free before Study 2: {disk.free} bytes")
models = load_models(root)
embedding = load_embeddings(root)["EMB002"]
repositories = load_repositories(root)
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 2",
"outcome_blind": True,
"research_code_revision": revision,
"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
},
"repositories": {},
"models": {},
"embedding": {},
}
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(),
}
for model_id in ("M002", "M003"):
model = models[model_id]
transition = residency.ensure_exclusive(
model.expected_inference_key, model.context_length
)
client = LMStudioClient(model, timeout_seconds=1_800)
discovery, resolved = 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(client, resolved.inference_key),
}
report["models"][model_id]["unload"] = residency.unload_all().to_dict()
transition = residency.ensure_exclusive(
embedding.model_key, embedding.loaded_context_length
)
embedding_client = LMStudioEmbeddingClient(embedding, timeout_seconds=1_800)
record = embedding_client.resolve()
probe = embedding_client.probe()
report["embedding"] = {
"spec": asdict(embedding),
"config_hash": embedding.config_hash,
"transition": transition.to_dict(),
"resolved": record,
"probe": probe.to_dict(),
"unload": residency.unload_all().to_dict(),
}
report["passed"] = True
return report
finally:
cleanup_errors: list[str] = []
try:
report["final_unload"] = residency.unload_all().to_dict()
except Exception as cleanup_error:
cleanup_errors.append(f"unload_all: {cleanup_error}")
try:
status = server.status()
report["server_stop"] = (
server.stop()
if status["running"]
else {"action": "already_stopped", "status": status}
)
except Exception as cleanup_error:
cleanup_errors.append(f"server_stop: {cleanup_error}")
report["cleanup_errors"] = cleanup_errors
report["finished_unix"] = time.time()
def main() -> None:
root = Path(__file__).resolve().parents[1]
report: dict[str, Any] = {}
output = root / "results" / "reports" / "study2_preflight.json"
output.parent.mkdir(parents=True, exist_ok=True)
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",
encoding="utf-8",
)
raise
output.write_text(
json.dumps(report, indent=2, sort_keys=True, default=str) + "\n",
encoding="utf-8",
)
print(json.dumps({"passed": True, "report": str(output)}, indent=2))
if __name__ == "__main__":
main()
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