Add OptimizationOrchestrator for hardware-guided kernel optimization #92
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Consolidates previous kernel_benchmark.py and pytorch_benchmark.py into a streamlined 3-file architecture with clear separation of concerns: Architecture: - benchmark.py (299 lines): Main Benchmark class with simplified API - benchmark_kernel(): Always uses subprocess for crash protection - benchmark_pytorch(): Always uses direct mode for stable code - BenchmarkLockManager: GPU lock management for multi-worker scenarios - timing.py (437 lines): Complete timing infrastructure - Timing: time_with_cuda_events(), time_with_triton_do_bench() - Loading: prepare_pytorch_model(), load_kernel_function() - Stats: compute_timing_stats() with essential metrics (mean/std/min/max) - kernel_subprocess.py (442 lines): Subprocess runner for kernel isolation - Crash protection for potentially buggy kernels - Clean CUDA state between runs - Timeout handling Key improvements: - Eliminated string code generation (was generating Python as strings) - Removed unnecessary statistics (median, p25/p75/p95/p99) - Removed confusing use_subprocess parameter (behavior now deterministic) - Fixed dtype bug causing incorrect speedup measurements - Reduced from 5 files to 3 files with clearer naming - Code reduction: ~1,400 lines → 1,178 lines Simple API: bench = Benchmark(logger, temp_dir, lock, worker_id) pytorch_result = bench.benchmark_pytorch(problem_file) kernel_result = bench.benchmark_kernel(kernel_file, problem_file) speedup = pytorch_result['stats']['mean'] / kernel_result['time_ms']
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February 1, 2026 23:42
kaiming-cheng
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Jack-Khuu
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Feb 6, 2026
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Reviewed just OptOrchest since i assume that is the main delta
| from kernel_perf_agent.kernel_opt.roofline.ncu_roofline import RooflineAnalyzer | ||
| from triton_kernel_agent.prompt_manager import PromptManager | ||
| from triton_kernel_agent.worker import VerificationWorker | ||
| from triton_kernel_agent.worker_util import ( |
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Ditto this file doesn't exist anymore
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| # Fallback: return first kernel if no Triton kernel found | ||
| return next(iter(ncu_metrics.values()), {}) |
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Why would we return the first kernel if there are no Triton kernels? This seems like unexpected behavior?
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| if self.pytorch_baseline_time is not None: | ||
| pytorch_baseline_time = self.pytorch_baseline_time | ||
| if pytorch_baseline_time != float("inf"): | ||
| self.logger.info( | ||
| f"📊 PyTorch baseline: {pytorch_baseline_time:.4f} ms (pre-computed)" | ||
| ) | ||
| else: | ||
| pytorch_baseline_time = None |
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Suggested change
| if self.pytorch_baseline_time is not None: | |
| pytorch_baseline_time = self.pytorch_baseline_time | |
| if pytorch_baseline_time != float("inf"): | |
| self.logger.info( | |
| f"📊 PyTorch baseline: {pytorch_baseline_time:.4f} ms (pre-computed)" | |
| ) | |
| else: | |
| pytorch_baseline_time = None | |
| if self.pytorch_baseline_time is not None and self.pytorch_baseline_time != float("inf"): | |
| self.logger.info( | |
| f"📊 PyTorch baseline: {pytorch_baseline_time:.4f} ms (pre-computed)" | |
| ) | |
| else: | |
| pytorch_baseline_time = None |
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Summary: