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@codeflash-ai codeflash-ai bot commented Oct 23, 2025

📄 8% (0.08x) speedup for UniversalBaseModel.json in src/deepgram/core/pydantic_utilities.py

⏱️ Runtime : 3.62 milliseconds 3.35 milliseconds (best of 53 runs)

📝 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 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.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 1518 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 2 Passed
📊 Tests Coverage 75.0%
🌀 Generated Regression Tests and Runtime
import datetime as dt import sys import pydantic # imports import pytest # used for our unit tests from deepgram.core.pydantic_utilities import UniversalBaseModel # function to test # (Paste the full implementation from above, unchanged) IS_PYDANTIC_V2 = pydantic.VERSION.startswith("2.") def serialize_datetime(dt_obj): # Simple ISO format serialization for test purposes # (the actual deepgram.core.datetime_utils.serialize_datetime may differ) return dt_obj.isoformat() from deepgram.core.pydantic_utilities import UniversalBaseModel # ------------------------- # Unit tests for json method # ------------------------- # 1. Basic Test Cases #------------------------------------------------ import datetime as dt # Helper: parse JSON string to dict for easier comparison import json import sys import pydantic # imports import pytest from deepgram.core.pydantic_utilities import UniversalBaseModel # --- Begin function to test (from deepgram/core/pydantic_utilities.py) --- # Simulate the serialize_datetime utility as used in the config def serialize_datetime(dt_obj): # ISO format with 'Z' if UTC, else with offset if dt_obj.tzinfo is not None and dt_obj.tzinfo.utcoffset(dt_obj) is not None: # Use isoformat, ensure 'Z' for UTC iso = dt_obj.isoformat() if iso.endswith("+00:00"): iso = iso[:-6] + "Z" return iso else: return dt_obj.isoformat() IS_PYDANTIC_V2 = pydantic.VERSION.startswith("2.") from deepgram.core.pydantic_utilities import UniversalBaseModel # --- End function to test --- # ------------------- UNIT TESTS ------------------- # 1. BASIC TEST CASES def test_json_with_invalid_kwarg(): # Passing an invalid kwarg should raise TypeError class Foo(UniversalBaseModel): x: int foo = Foo(x=1) with pytest.raises(TypeError): foo.json(not_a_kwarg=True) # 2.63μs -> 2.86μs (7.95% slower) #------------------------------------------------ from deepgram.core.pydantic_utilities import UniversalBaseModel def test_UniversalBaseModel_json(): UniversalBaseModel.json(UniversalBaseModel())
🔎 Concolic Coverage Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
codeflash_concolic_d0k9fm5y/tmp7by5v067/test_concolic_coverage.py::test_UniversalBaseModel_json 47.9μs 48.3μs -0.912%⚠️

To edit these changes git checkout codeflash/optimize-UniversalBaseModel.json-mh2te39v and push.

Codeflash

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.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 23, 2025 02:40
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Oct 23, 2025
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⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash

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