⚡️ Speed up method UniversalBaseModel.json by 8%#9
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The optimization achieves a 7% speedup through two key improvements:
**1. Module-level caching of `IS_PYDANTIC_V2`**: The original code referenced an undefined `IS_PYDANTIC_V2` variable, which likely caused runtime lookups or errors. The optimized version computes `pydantic.VERSION.startswith("2.")` once at module import time, eliminating repeated version checks.
**2. Conditional dictionary creation**: The original code always performed dictionary merging with `**kwargs`, even when `kwargs` was empty. The optimization adds a branch to handle empty kwargs separately:
- When `kwargs` is empty (most common case): Creates a simple dict literal `{"by_alias": True, "exclude_unset": True}`
- When `kwargs` has values: Uses `dict(by_alias=True, exclude_unset=True, **kwargs)` for proper merging
From the line profiler, we see the dict creation overhead reduced from 4 lines of execution (88+44+44+44 hits = 220 total) to a more efficient 2-branch approach (44+36+8 = 88 total hits). The optimization is particularly effective when `kwargs` is empty, which appears to be the common case based on the test showing 36 hits for the empty branch vs 8 for the non-empty branch.
This optimization works best for frequent calls to `json()` without additional parameters, which is typical for serialization-heavy workloads.
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📄 8% (0.08x) speedup for
UniversalBaseModel.jsoninsrc/deepgram/core/pydantic_utilities.py⏱️ Runtime :
3.62 milliseconds→3.35 milliseconds(best of53runs)📝 Explanation and details
The optimization achieves a 7% speedup through two key improvements:
1. Module-level caching of
IS_PYDANTIC_V2: The original code referenced an undefinedIS_PYDANTIC_V2variable, which likely caused runtime lookups or errors. The optimized version computespydantic.VERSION.startswith("2.")once at module import time, eliminating repeated version checks.2. Conditional dictionary creation: The original code always performed dictionary merging with
**kwargs, even whenkwargswas empty. The optimization adds a branch to handle empty kwargs separately:kwargsis empty (most common case): Creates a simple dict literal{"by_alias": True, "exclude_unset": True}kwargshas values: Usesdict(by_alias=True, exclude_unset=True, **kwargs)for proper mergingFrom the line profiler, we see the dict creation overhead reduced from 4 lines of execution (88+44+44+44 hits = 220 total) to a more efficient 2-branch approach (44+36+8 = 88 total hits). The optimization is particularly effective when
kwargsis empty, which appears to be the common case based on the test showing 36 hits for the empty branch vs 8 for the non-empty branch.This optimization works best for frequent calls to
json()without additional parameters, which is typical for serialization-heavy workloads.✅ Correctness verification report:
🌀 Generated Regression Tests and Runtime
🔎 Concolic Coverage Tests and Runtime
codeflash_concolic_d0k9fm5y/tmp7by5v067/test_concolic_coverage.py::test_UniversalBaseModel_jsonTo edit these changes
git checkout codeflash/optimize-UniversalBaseModel.json-mh2te39vand push.