| |
| """ |
| Test script to verify the complete AlphaEvolve setup works |
| """ |
|
|
| import sys |
| import json |
| from pathlib import Path |
|
|
| def test_evaluator(): |
| """Test the evaluator with a simple program""" |
| |
| print("🧪 Testing evaluator...") |
| |
| |
| test_program = ''' |
| def optimize_attention(): |
| return { |
| 'tile_size_m': 32, |
| 'tile_size_n': 64, |
| 'vectorization': 'none', |
| 'unroll_factor': 2, |
| 'loop_interchange': False, |
| 'fusion_strategy': 'none', |
| 'use_shared_memory': False, |
| 'optimize_for_latency': True, |
| 'enable_blocking': False, |
| 'enable_recomputation': False, |
| 'optimization_strategy': 'alphaevolve_test', |
| 'target_speedup': 1.32, |
| } |
| ''' |
| |
| try: |
| |
| sys.path.insert(0, '.') |
| from evaluator import evaluate_program |
| |
| print("✅ Evaluator imported successfully") |
| |
| |
| result = evaluate_program(test_program) |
| |
| if 'error' in result: |
| print(f"📊 Evaluation result: error={result['error']:.3f}") |
| if 'speedup' in result: |
| print(f"📊 Speedup: {result['speedup']:.3f}x") |
| if 'mlir_source' in result: |
| print(f"📂 MLIR source: {result['mlir_source']}") |
| |
| if result['error'] < 1000: |
| print("✅ Evaluator works!") |
| return True |
| else: |
| print(f"❌ Evaluator failed: {result}") |
| return False |
| else: |
| print(f"❌ Invalid result format: {result}") |
| return False |
| |
| except Exception as e: |
| print(f"❌ Evaluator test failed: {e}") |
| return False |
|
|
| def test_initial_program(): |
| """Test the initial program generates parameters""" |
| |
| print("\n🧪 Testing initial program...") |
| |
| try: |
| sys.path.insert(0, '.') |
| from initial_program import optimize_attention |
| |
| params = optimize_attention() |
| |
| print("✅ Initial program imported successfully") |
| print(f"📊 Generated parameters: {list(params.keys())}") |
| |
| |
| required = ['tile_size_m', 'tile_size_n', 'unroll_factor'] |
| for param in required: |
| if param in params: |
| print(f"✅ {param}: {params[param]}") |
| else: |
| print(f"❌ Missing parameter: {param}") |
| return False |
| |
| return True |
| |
| except Exception as e: |
| print(f"❌ Initial program test failed: {e}") |
| return False |
|
|
| def test_mlir_file(): |
| """Test that the MLIR file exists and is readable""" |
| |
| print("\n🧪 Testing MLIR file...") |
| |
| mlir_file = Path("./mlir/self_attn_with_consts_linalg_dialect.mlir") |
| |
| if mlir_file.exists(): |
| print(f"✅ MLIR file exists: {mlir_file}") |
| try: |
| with open(mlir_file, 'r') as f: |
| content = f.read() |
| print(f"✅ MLIR file readable: {len(content)} characters") |
| |
| |
| if 'output_shape' in content: |
| print("✅ tensor.expand_shape syntax is fixed") |
| else: |
| print("⚠️ tensor.expand_shape may need fixing") |
| |
| return True |
| except Exception as e: |
| print(f"❌ Cannot read MLIR file: {e}") |
| return False |
| else: |
| print(f"❌ MLIR file not found: {mlir_file}") |
| return False |
|
|
| def main(): |
| """Run all tests""" |
| |
| print("🚀 Testing Complete AlphaEvolve Setup\n") |
| |
| tests = [ |
| ("MLIR File", test_mlir_file), |
| ("Initial Program", test_initial_program), |
| ("Evaluator", test_evaluator), |
| ] |
| |
| results = [] |
| for test_name, test_func in tests: |
| success = test_func() |
| results.append((test_name, success)) |
| |
| |
| print(f"\n{'='*50}") |
| print("TEST SUMMARY") |
| print('='*50) |
| |
| passed = 0 |
| for test_name, success in results: |
| status = "✅ PASS" if success else "❌ FAIL" |
| print(f"{status:8} {test_name}") |
| if success: |
| passed += 1 |
| |
| print(f"\nResults: {passed}/{len(results)} tests passed") |
| |
| if passed == len(results): |
| print("\n🎉 All tests passed! Ready to run AlphaEvolve!") |
| print("\n🚀 Run evolution with:") |
| print(" python ../../openevolve-run.py initial_program.py evaluator.py --config config.yaml --iterations 10") |
| print("\n🎯 Target: Achieve 32% speedup (1.32x) like AlphaEvolve paper") |
| else: |
| print(f"\n⚠️ {len(results) - passed} test(s) failed. Fix issues before running evolution.") |
| |
| return passed == len(results) |
|
|
| if __name__ == "__main__": |
| success = main() |
| sys.exit(0 if success else 1) |
