⚡️ Speed up method LogUniformDistribution._asdict by 2,304%

[codeflash-ai]

📄 2,304% (23.04x) speedup for LogUniformDistribution._asdict in optuna/distributions.py

⏱️ Runtime : 844 microseconds → 35.1 microseconds (best of 988 runs)

📝 Explanation and details

The optimization replaces copy.deepcopy(self.__dict__) with self.__dict__.copy() in the _asdict method. This change delivers a 23x speedup (2303%) because:

What changed:

• copy.deepcopy(self.__dict__) → self.__dict__.copy()

Why it's faster:

• deepcopy() recursively traverses and copies all nested objects, creating completely independent copies of every attribute value
• copy() performs a shallow copy, only copying the dictionary structure while keeping references to the same attribute objects
• Since the method immediately removes the "log" and "step" keys anyway, the deep copying of all other attributes was unnecessary overhead

Performance characteristics:

• Small objects: 4-6x faster (basic test cases show ~500% improvements)
• Large objects: Dramatic improvements - objects with 1000 attributes see ~37x speedup (3875%)
• Complex attributes: Objects with mixed data types (strings, lists, dicts) benefit significantly (1026% faster)

Safety preserved:
The optimization maintains correctness because:

1. The returned dictionary is still independent from the original __dict__
2. Modifying keys in the returned dict won't affect the original object
3. The shallow copy is sufficient since the method only manipulates the dictionary structure, not the attribute values themselves

This optimization is particularly effective for objects with many attributes or complex attribute values, where deep copying becomes exponentially expensive.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 34 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	✅ 2 Passed
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import copy

# imports
import pytest  # used for our unit tests
from optuna.distributions import LogUniformDistribution


# function to test (copied from above)
class FloatDistribution:
    def __init__(
        self, low: float, high: float, log: bool = False, step: None | float = None
    ) -> None:
        if log and step is not None:
            raise ValueError("The parameter `step` is not supported when `log` is true.")

        if low > high:
            raise ValueError(f"`low <= high` must hold, but got ({low=}, {high=}).")

        if log and low <= 0.0:
            raise ValueError(f"`low > 0` must hold for `log=True`, but got ({low=}, {high=}).")

        if step is not None and step <= 0:
            raise ValueError(f"`step > 0` must hold, but got {step=}.")

        self.step = None
        if step is not None:
            # For testing, we don't need the actual _adjust_discrete_uniform_high implementation
            high = low + ((high - low) // step) * step
            self.step = float(step)

        self.low = float(low)
        self.high = float(high)
        self.log = log
from optuna.distributions import LogUniformDistribution

# unit tests

# 1. Basic Test Cases

def test_asdict_basic_low_high():
    # Test with typical low and high values
    dist = LogUniformDistribution(1.0, 10.0)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.37μs -> 959ns (460% faster)

def test_asdict_basic_low_equals_high():
    # Test with low == high
    dist = LogUniformDistribution(5.0, 5.0)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.41μs -> 953ns (468% faster)

def test_asdict_basic_float_values():
    # Test with float values
    dist = LogUniformDistribution(0.1, 2.5)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.44μs -> 920ns (492% faster)

# 2. Edge Test Cases

def test_asdict_low_is_min_positive_float():
    # Test with smallest positive float
    import sys
    dist = LogUniformDistribution(sys.float_info.min, 1.0)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.37μs -> 920ns (483% faster)

def test_asdict_high_is_max_float():
    # Test with maximum float value
    import sys
    dist = LogUniformDistribution(1.0, sys.float_info.max)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.33μs -> 852ns (526% faster)

def test_asdict_low_and_high_are_integers():
    # Test with integer values
    dist = LogUniformDistribution(3, 7)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.34μs -> 885ns (503% faster)

def test_asdict_mutation_safety():
    # Ensure returned dict is a copy, not a reference
    dist = LogUniformDistribution(1.0, 2.0)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.26μs -> 885ns (494% faster)
    result["low"] = 999.0

def test_asdict_removes_log_and_step():
    dist = LogUniformDistribution(1.0, 2.0)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.24μs -> 847ns (519% faster)

def test_asdict_with_unusual_values():
    # Test with very large and very small values
    dist = LogUniformDistribution(1e-10, 1e10)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.18μs -> 862ns (501% faster)

def test_asdict_with_nan_and_inf():
    # Test with NaN and Inf values
    import math
    dist = LogUniformDistribution(1.0, math.inf)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.27μs -> 862ns (511% faster)
    # NaN is not allowed for low, but let's check for inf
    dist2 = LogUniformDistribution(math.inf, math.inf)
    codeflash_output = dist2._asdict(); result2 = codeflash_output # 3.45μs -> 467ns (639% faster)

def test_asdict_does_not_return_extra_keys():
    # Add an extra attribute to the instance and check it's included
    dist = LogUniformDistribution(1.0, 2.0)
    dist.extra = "should_be_included"
    codeflash_output = dist._asdict(); result = codeflash_output # 5.57μs -> 897ns (521% faster)

def test_asdict_does_not_modify_original_dict():
    dist = LogUniformDistribution(1.0, 2.0)
    original_dict = copy.deepcopy(dist.__dict__)
    codeflash_output = dist._asdict(); _ = codeflash_output # 3.58μs -> 894ns (301% faster)

# 3. Large Scale Test Cases

def test_asdict_large_number_of_attributes():
    # Add many attributes to the distribution and check all are present
    dist = LogUniformDistribution(1.0, 2.0)
    for i in range(1000):
        setattr(dist, f"attr_{i}", i)
    codeflash_output = dist._asdict(); result = codeflash_output # 379μs -> 10.0μs (3672% faster)
    # Should contain all added attributes except 'log' and 'step'
    for i in range(1000):
        pass

def test_asdict_performance_large_scale(monkeypatch):
    # Test performance with many attributes (under 1000)
    import time
    dist = LogUniformDistribution(1.0, 2.0)
    for i in range(999):
        setattr(dist, f"x_{i}", i)
    start = time.time()
    codeflash_output = dist._asdict(); result = codeflash_output # 375μs -> 9.46μs (3875% faster)
    end = time.time()

def test_asdict_with_large_float_values():
    # Test with extremely large float values
    dist = LogUniformDistribution(1e300, 1e308)
    codeflash_output = dist._asdict(); result = codeflash_output # 5.84μs -> 2.36μs (148% faster)

def test_asdict_with_multiple_types_of_attributes():
    # Add attributes of different types
    dist = LogUniformDistribution(1.0, 2.0)
    dist.string_attr = "hello"
    dist.list_attr = [1, 2, 3]
    dist.dict_attr = {"a": 1}
    dist.none_attr = None
    codeflash_output = dist._asdict(); result = codeflash_output # 11.5μs -> 1.02μs (1026% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
from optuna.distributions import LogUniformDistribution

def test_LogUniformDistribution__asdict():
    LogUniformDistribution._asdict(LogUniformDistribution(1.0, 1.0))

🔎 Concolic Coverage Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
codeflash_concolic_qluqolhr/tmpn0mhx_v_/test_concolic_coverage.py::test_LogUniformDistribution__asdict	5.91μs	1.02μs	477%✅

To edit these changes git checkout codeflash/optimize-LogUniformDistribution._asdict-mhbhxu5j and push.