⚡ Optimize RichardsGlu::compute_gradients by removing unnecessary clones#28
Conversation
- Replaced `.cloned()` with conditional borrowing for cached fields (x1, x2, swish, gated) - Added benchmarks for RichardsGlu gradients - Achieved ~7.4% performance improvement in gradient computation
|
👋 Jules, reporting for duty! I'm here to lend a hand with this pull request. When you start a review, I'll add a 👀 emoji to each comment to let you know I've read it. I'll focus on feedback directed at me and will do my best to stay out of conversations between you and other bots or reviewers to keep the noise down. I'll push a commit with your requested changes shortly after. Please note there might be a delay between these steps, but rest assured I'm on the job! For more direct control, you can switch me to Reactive Mode. When this mode is on, I will only act on comments where you specifically mention me with New to Jules? Learn more at jules.google/docs. For security, I will only act on instructions from the user who triggered this task. |
|
Important Review skippedBot user detected. To trigger a single review, invoke the You can disable this status message by setting the Comment |
There was a problem hiding this comment.
Review by RecurseML
🔍 Review performed on 2f07c31..34ffc48
✨ No bugs found, your code is sparkling clean
✅ Files analyzed, no issues (3)
• Cargo.toml
• benches/richards_glu_bench.rs
• src/richards/richards_glu.rs
This PR optimizes
RichardsGlu::compute_gradientsby eliminating unnecessaryArray2cloning operations when using cached values.💡 What:
src/richards/richards_glu.rsto use a conditional borrowing pattern. Instead of cloning the cachedOption<Array2<f32>>, the code now borrows the cached reference if available, or creates a new owned array (stored in a local variable) and borrows it if not.&Array2<f32>references.richards_glu_benchto verify performance.🎯 Why:
.cloned().unwrap_or_else(...), which forced a full matrix copy even when the cache was present. This caused significant allocation and memory copy overhead during training.📊 Measured Improvement:
compute_gradients.PR created automatically by Jules for task 4074737496829598444 started by @ryancinsight
High-level PR Summary
This PR optimizes the
compute_gradientsmethod inRichardsGluby replacing expensive cloning operations with conditional borrowing. Instead of cloning cachedArray2<f32>matrices usingcloned().unwrap_or_else(...), the code now uses a pattern that borrows cached references when available or creates owned values in local variables when cache misses occur. This eliminates unnecessary memory allocations and copies during the training forward pass, achieving approximately 7.4% performance improvement (from ~31.46ms to ~29.13ms). A new benchmark is included to measure the optimization impact.⏱️ Estimated Review Time: 5-15 minutes
💡 Review Order Suggestion
Cargo.tomlbenches/richards_glu_bench.rssrc/richards/richards_glu.rs