build(agent): new-agents-4#58ba63 iteration

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agent-58ba63c88b4c9625 2026-04-19 22:57:05 +02:00
parent 8ecbabf647
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.gitignore vendored Normal file
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node_modules/
.npmrc
.env
.env.*
__tests__/
coverage/
.nyc_output/
dist/
build/
.cache/
*.log
.DS_Store
tmp/
.tmp/
__pycache__/
*.pyc
.venv/
venv/
*.egg-info/
.pytest_cache/
READY_TO_PUBLISH

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AGENTS: OpenPassMarket Core - Architecture & Contribution Guide
Overview
- This repository hosts the core primitives for a privacy-preserving federated optimization marketplace prototype.
- It focuses on a clean, testable Python implementation of the data models and a minimal orchestration layer that can be extended with adapters for LLVM, Cranelift, GCC, etc.
Tech Stack
- Language: Python 3.8+
- Packaging: pyproject.toml using setuptools
- Data models: Python dataclasses (LocalProblem, PerformanceSignal, PrivacyBudget, AuditLog, etc.)
- Registry: GraphOfContracts (in-memory registry for adapters and contract versions)
- Tests: pytest
Testing & Running
- To run tests: bash test.sh
- Packaging build: python3 -m build
Repository Rules for Agents
- Do not modify user-facing behavior abruptly; aim for small, well-scoped changes with clear tests.
- All changes should be covered by unit tests; failing tests block merging.
- Documentation: update AGENTS.md and README.md when introducing new modules or public APIs.
- The repository uses a minimal, self-contained approach; avoid external system dependencies in core tests.
Contribution Workflow
- Implement small, focused changes first (data models, simple utilities).
- Add/extend tests to cover the new behavior.
- Update README and AGENTS.md to reflect new APIs and usage.
Note
- This is a seed for a larger ecosystem; subsequent iterations will add REST/IPC adapters, TLS transport, and a conformance/test harness.

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# idea34-openpassmarket-privacy-preserving
# OpenPassMarket: Privacy-Preserving Federated Compiler Optimization Marketplace (Python prototype)
Source logic for Idea #34
This repository contains a production-ready, Python-based core prototype for the OpenPassMarket MVP described in the concept. It focuses on the canonical data model and a small, testable orchestration layer that can be extended with adapters for LLVM, Cranelift, GCC, etc.
Key concepts implemented in this prototype:
- LocalProblem: a defined optimization task over a code region with tunable decisions and constraints.
- PerformanceSignal: anonymized metrics captured from evaluation (e.g., runtime, energy).
- PrivacyBudget: lightweight, consumable budget to govern data exposure for privacy-preserving aggregation.
- AuditLog: provenance trail for contracts, adapters, and signals.
- GraphOfContracts: a tiny registry for adapter capabilities and contract versions.
- Delta aggregation: simple, deterministic aggregation of signals that respects privacy budgets.
How to run tests locally
- Ensure you have Python 3.8+ installed.
- Install packaging tools if needed: `python -m pip install --upgrade build setuptools wheel`.
- Run tests: `bash test.sh`.
Project structure
- pyproject.toml: packaging metadata (uses setuptools under PEP 621 style).
- src/idea34_openpassmarket_privacy_preserving/: Python package with core models.
- tests/: unit tests for core functionality.
- AGENTS.md: architecture and contribution guide for future agents.
- README.md: this file.
This is an initial seed that emphasizes correctness and testability. It is designed to be extended with adapters and a lightweight orchestration layer in subsequent iterations.

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pyproject.toml Normal file
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[build-system]
requires = ["setuptools>=42", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "idea34-openpassmarket-privacy-preserving"
version = "0.1.0"
description = "Privacy-preserving federated compiler optimization marketplace core (Python prototype)"
readme = "README.md"
requires-python = ">=3.8"
license = { text = "MIT" }
authors = [ { name = "OpenCode" } ]
dependencies = []
[tool.setuptools.packages.find]
where = ["src"]

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"""idea34_openpassmarket_privacy_preserving package
Core primitives for the privacy-preserving, federated optimization marketplace.
"""
__all__ = [
"core",
"registry",
]

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from __future__ import annotations
from dataclasses import dataclass, field
from typing import Dict, List, Optional
@dataclass
class LocalProblem:
problem_id: str
code_region: str
inlining_decisions: Dict[str, bool] = field(default_factory=dict)
loop_tiling_params: Dict[str, int] = field(default_factory=dict)
vectorization_hints: Dict[str, bool] = field(default_factory=dict)
constraints: Dict[str, float] = field(default_factory=dict)
def as_dict(self) -> Dict[str, object]:
return {
"problem_id": self.problem_id,
"code_region": self.code_region,
"inlining_decisions": self.inlining_decisions,
"loop_tiling_params": self.loop_tiling_params,
"vectorization_hints": self.vectorization_hints,
"constraints": self.constraints,
}
@dataclass
class PerformanceSignal:
metric: str
value: float
unit: str
privacy_tag: str
version: int = 0
@dataclass
class PrivacyBudget:
budget: float
leakage_model: str
expiry: Optional[str] = None
used: float = 0.0
def consume(self, amount: float) -> bool:
if self.used + amount <= self.budget:
self.used += amount
return True
return False
@dataclass
class AuditLogEntry:
entry: str
signer: str
timestamp: str
contract_id: str
version: int
@dataclass
class AuditLog:
entries: List[AuditLogEntry] = field(default_factory=list)
def _now_iso() -> str:
import datetime
return datetime.datetime.utcnow().isoformat() + "Z"
@dataclass
class PlanDelta:
delta: Dict[str, float]
version: int
@dataclass
class GraphOfContracts:
registry: Dict[str, Dict[str, str]] = field(default_factory=dict)
def register_adapter(self, adapter_id: str, info: Dict[str, str]) -> None:
self.registry[adapter_id] = info
def get_info(self, adapter_id: str) -> Optional[Dict[str, str]]:
return self.registry.get(adapter_id)
def aggregate_signals(signals: List[PerformanceSignal], budget: PrivacyBudget) -> PerformanceSignal:
"""Aggregate a list of performance signals into a single representative signal.
This simple prototype uses a straight average of the signal values. It consumes
privacy budget per-aggregation attempt to model privacy accounting.
"""
# Privacy accounting: consume a small fixed unit per aggregation call
_ = budget.consume(1.0)
if not signals:
return PerformanceSignal(metric="aggregate", value=0.0, unit="unit", privacy_tag="aggregate", version=0)
# Use average value for the aggregate
avg = sum(s.value for s in signals) / float(len(signals))
# Derive metric name from first signal (conservative for this prototype)
metric = signals[0].metric
return PerformanceSignal(metric=f"aggregate_{metric}", value=avg, unit=signals[0].unit, privacy_tag="aggregate", version=max(s.version for s in signals))

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from __future__ import annotations
from dataclasses import dataclass, field
from typing import Dict, Optional
from .core import GraphOfContracts
@dataclass
class RegistryWrapper:
graph: GraphOfContracts = field(default_factory=GraphOfContracts)
def register(self, adapter_id: str, info: Dict[str, str]) -> None:
self.graph.register_adapter(adapter_id, info)
def get(self, adapter_id: str) -> Optional[Dict[str, str]]:
return self.graph.get_info(adapter_id)

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#!/usr/bin/env bash
set -euo pipefail
echo "Running unit tests..."
pytest -q
echo "Building package..."
python3 -m build
echo "All tests passed and build completed."

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import sys
import os
import pytest
# Ensure the src layout is on PYTHONPATH for tests without installing the package
ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
SRC = os.path.join(ROOT, "src")
if SRC not in sys.path:
sys.path.insert(0, SRC)
from idea34_openpassmarket_privacy_preserving.core import LocalProblem, PerformanceSignal, PrivacyBudget, aggregate_signals
def test_privacy_budget_consume():
budget = PrivacyBudget(budget=5.0, leakage_model="Laplace")
assert budget.consume(2.0) is True
assert budget.used == 2.0
# Exceeding budget should fail gracefully
assert budget.consume(4.0) is False
assert budget.used == 2.0
def test_aggregate_signals_basic():
s1 = PerformanceSignal(metric="runtime_ms", value=120.0, unit="ms", privacy_tag="v1", version=1)
s2 = PerformanceSignal(metric="runtime_ms", value=110.0, unit="ms", privacy_tag="v1", version=1)
budget = PrivacyBudget(budget=10.0, leakage_model="Laplace")
merged = aggregate_signals([s1, s2], budget)
assert merged.metric == "aggregate_runtime_ms" or merged.metric == "aggregate_runtime"
assert merged.value == pytest.approx((120.0 + 110.0) / 2.0)