⚡️ Speed up function load_backbone by 11%
#102
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Outdated suggestions cannot be applied.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.
📄 11% (0.11x) speedup for
load_backboneinsrc/transformers/utils/backbone_utils.py⏱️ Runtime :
39.1 microseconds→35.3 microseconds(best of6runs)📝 Explanation and details
The optimized code achieves a 10% speedup through three key micro-optimizations:
1. Conditional assignment optimization: Changed
backbone_kwargs = {} if backbone_kwargs is None else backbone_kwargsto a conditional block that only executes the assignment when needed. This eliminates the ternary operator overhead and redundant assignment whenbackbone_kwargsis already defined.2. Early returns instead of variable assignments: Replaced intermediate variable assignments with direct returns in the
use_timm_backboneanduse_pretrained_backbonebranches. This eliminates unnecessary variable creation and reduces stack operations.3. Changed elif to if for independent conditions: The
use_pretrained_backbonecheck was changed fromeliftoifsince these conditions are mutually exclusive anyway, allowing for slightly more efficient branching logic.These optimizations are particularly effective for error-handling test cases (showing 2-18% improvements in the annotated tests) where the function exits early through validation checks. The improvements stem from reducing Python bytecode operations - fewer variable assignments, eliminated ternary operations, and more direct control flow paths. While the gains are modest, they compound in high-frequency model loading scenarios typical in ML workflows.
✅ Correctness verification report:
🌀 Generated Regression Tests and Runtime
To edit these changes
git checkout codeflash/optimize-load_backbone-mhjso1ysand push.