[Optimization 6/n] Introduce Optimization Worker #80
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added 30 commits
January 15, 2026 11:44
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']
…lity to the worker_util.py
added 2 commits
January 18, 2026 13:10
…. remove class that can be directly imported
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… auxiliary kernels
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This PR introduces
OptimizationWorkerfromopt_worker.py. TheOptimizationWorkerintegrated class fromopt_worker_components, demonstrating the end-to-end usage of the optimization pipeline.Changes
opt_worker.pyintroducesOptimizationWorker- a hardware-aware optimization worker that orchestrates the full optimization pipeline:bottleneck_analyzer.py: We also add a new class to interface with the modular components inopt_worker_component/diagnose_prompt, which wraps the Judge LLM workflow for dual-bottleneck analysisworker_util.pyextracts shared utility functions used by bothVerificationWorkerandOptimizationWorker:Test