[Optimization 4/n] Add Kernel Optimization Template to PromptManager #77
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
meta-cla
bot
added
the
CLA Signed
kaiming-cheng
changed the title
Jack-Khuu
approved these changes
Jan 14, 2026
added 23 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']
kaiming-cheng
force-pushed
the
kaiming/opt_component_4_clean
branch
from
2937faa to
d5e6edc
Compare
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR adds a new Jinja2 template for bottleneck-guided kernel optimization. The bottleneck diagonsis module is introduced in previous PR.
The kernel optimization prompt is integrated into the existing PromptManager class. The current template specify the optimization goal (1.25x than pytorch eager) and format for optimization strategy.
Usage