⚡️ Speed up method `DiscreteUniformDistribution._asdict` by 702% #46

codeflash-ai · 2025-10-29T04:35:17Z

📄 702% (7.02x) speedup for `DiscreteUniformDistribution._asdict` in `optuna/distributions.py`

⏱️ Runtime : 3.09 milliseconds → 386 microseconds (best of 214 runs)

📝 Explanation and details

The optimization replaces copy.deepcopy(self.__dict__) with self.__dict__.copy() in the _asdict method. This change delivers a 702% speedup because:

What changed: Switched from deep copy to shallow copy for creating a dictionary copy.

Why it's faster: The DiscreteUniformDistribution instance dictionary only contains immutable values (floats and a boolean: low, high, step, log). Since there are no nested mutable objects to recursively copy, deepcopy performs unnecessary overhead by:

Recursively traversing each value to check for nested structures
Maintaining a memo dictionary to handle circular references
Creating new objects even when unnecessary

A shallow copy with .copy() is sufficient and much faster since it only needs to create a new dictionary with references to the same immutable values.

Performance impact: The line profiler shows the bottleneck was the copy.deepcopy call taking 95.4% of the method's time (20.98ms out of 21.9ms). The optimized version reduces this to just 27.3% (430μs out of 1.58ms).

Test case benefits: All test cases show consistent 5-7x speedups, with the optimization being particularly effective for:

High-frequency operations (the large-scale test with 1000 iterations shows 720% speedup)
Simple distributions with basic numeric parameters
Any scenario where _asdict is called repeatedly, as the per-call overhead is dramatically reduced

The optimization maintains identical behavior since both approaches return a mutable copy that doesn't affect the original instance when modified.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 2280 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 DiscreteUniformDistribution


# function to test (copied from above)
class BaseDistribution:
    pass

class FloatDistribution(BaseDistribution):
    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 simplicity, just set high to high (skip _adjust_discrete_uniform_high)
            self.step = float(step)

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

# unit tests

# --- Basic Test Cases ---

def test_asdict_basic_linear():
    # Test basic conversion for a simple distribution
    dud = DiscreteUniformDistribution(1.0, 5.0, 0.5)
    codeflash_output = dud._asdict(); result = codeflash_output # 6.40μs -> 1.08μs (493% faster)

def test_asdict_basic_integer_values():
    # Test with integer values for low, high, q
    dud = DiscreteUniformDistribution(2, 10, 2)
    codeflash_output = dud._asdict(); result = codeflash_output # 5.99μs -> 1.03μs (483% faster)

def test_asdict_basic_negative_low():
    # Test with negative low value
    dud = DiscreteUniformDistribution(-5, 5, 1)
    codeflash_output = dud._asdict(); result = codeflash_output # 5.96μs -> 987ns (504% faster)

def test_asdict_basic_zero_step():
    # Should raise ValueError for q=0
    with pytest.raises(ValueError):
        DiscreteUniformDistribution(0, 1, 0)

def test_asdict_basic_high_equals_low():
    # Test where high == low
    dud = DiscreteUniformDistribution(3, 3, 0.1)
    codeflash_output = dud._asdict(); result = codeflash_output # 6.25μs -> 976ns (540% faster)

# --- Edge Test Cases ---

def test_asdict_edge_high_less_than_low():
    # Should raise ValueError when high < low
    with pytest.raises(ValueError):
        DiscreteUniformDistribution(5, 1, 1)

def test_asdict_edge_small_step():
    # Test with very small step
    dud = DiscreteUniformDistribution(0, 1, 1e-9)
    codeflash_output = dud._asdict(); result = codeflash_output # 6.14μs -> 1.05μs (482% faster)

def test_asdict_edge_large_step():
    # Test with step larger than range (should still work)
    dud = DiscreteUniformDistribution(0, 1, 10)
    codeflash_output = dud._asdict(); result = codeflash_output # 6.13μs -> 1.01μs (509% faster)

def test_asdict_edge_float_precision():
    # Test with float values that could cause precision issues
    dud = DiscreteUniformDistribution(0.1, 0.3, 0.1)
    codeflash_output = dud._asdict(); result = codeflash_output # 6.03μs -> 973ns (519% faster)

def test_asdict_edge_step_is_none():
    # Should not be possible for DiscreteUniformDistribution, but test FloatDistribution
    fd = FloatDistribution(1, 2)

def test_asdict_edge_log_true_should_fail():
    # DiscreteUniformDistribution should never have log=True
    # But if forcibly set, should not be present in _asdict
    dud = DiscreteUniformDistribution(1, 2, 0.1)
    # forcibly set log True
    dud.log = True
    codeflash_output = dud._asdict(); result = codeflash_output # 5.99μs -> 1.01μs (494% faster)

def test_asdict_edge_mutation():
    # Ensure _asdict returns a copy, not a reference
    dud = DiscreteUniformDistribution(1, 2, 0.1)
    codeflash_output = dud._asdict(); d1 = codeflash_output # 5.92μs -> 983ns (502% faster)
    d1["low"] = 999
    codeflash_output = dud._asdict(); d2 = codeflash_output # 3.54μs -> 499ns (610% faster)

def test_asdict_edge_no_extra_keys():
    # Ensure no unexpected keys are present
    dud = DiscreteUniformDistribution(1, 2, 0.1)
    codeflash_output = dud._asdict(); result = codeflash_output # 5.87μs -> 882ns (565% faster)

# --- Large Scale Test Cases ---

def test_asdict_large_scale_many_distributions():
    # Test many instances to check for memory leaks or performance
    for i in range(1000):
        low = float(i)
        high = float(i + 10)
        q = float(i + 1)
        dud = DiscreteUniformDistribution(low, high, q)
        codeflash_output = dud._asdict(); d = codeflash_output # 2.61ms -> 318μs (720% faster)

def test_asdict_large_scale_extreme_values():
    # Test with very large float values
    dud = DiscreteUniformDistribution(1e300, 1e301, 1e299)
    codeflash_output = dud._asdict(); result = codeflash_output # 6.74μs -> 1.18μs (470% faster)

def test_asdict_large_scale_small_values():
    # Test with very small float values
    dud = DiscreteUniformDistribution(1e-300, 1e-299, 1e-301)
    codeflash_output = dud._asdict(); result = codeflash_output # 6.04μs -> 999ns (505% faster)

def test_asdict_large_scale_step_near_zero():
    # Test with step just above zero
    dud = DiscreteUniformDistribution(0, 1, 1e-16)
    codeflash_output = dud._asdict(); result = codeflash_output # 6.17μs -> 1.02μs (505% faster)

def test_asdict_large_scale_high_precision():
    # Test with many decimal places
    dud = DiscreteUniformDistribution(0.1234567890123456, 0.9876543210987654, 0.0000000000000001)
    codeflash_output = dud._asdict(); result = codeflash_output # 6.18μs -> 999ns (519% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import copy

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


# function to test (copied from above)
class BaseDistribution:
    pass

class FloatDistribution(BaseDistribution):
    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 use the identity function for _adjust_discrete_uniform_high
            high = self._adjust_discrete_uniform_high(low, high, step)
            self.step = float(step)

        self.low = float(low)
        self.high = float(high)
        self.log = log

    @staticmethod
    def _adjust_discrete_uniform_high(low, high, step):
        # For testing: mimic the behavior described in the docstring
        # Find the largest k such that low + k * step <= high
        if step is None:
            return high
        k = int((high - low) // step)
        adjusted_high = low + k * step
        if adjusted_high > high:
            adjusted_high -= step
        return adjusted_high
from optuna.distributions import DiscreteUniformDistribution

# unit tests

# ---- Basic Test Cases ----

def test_asdict_basic_positive_range():
    """Test basic usage with positive range and step."""
    dist = DiscreteUniformDistribution(0.0, 1.0, 0.1)
    codeflash_output = dist._asdict(); dct = codeflash_output # 5.99μs -> 931ns (543% faster)

def test_asdict_basic_integers():
    """Test with integer values for low, high, and q."""
    dist = DiscreteUniformDistribution(1, 5, 2)
    codeflash_output = dist._asdict(); dct = codeflash_output # 5.95μs -> 972ns (513% faster)

def test_asdict_basic_zero_step():
    """Test that zero step raises ValueError."""
    with pytest.raises(ValueError):
        DiscreteUniformDistribution(0.0, 1.0, 0.0)

def test_asdict_basic_negative_step():
    """Test that negative step raises ValueError."""
    with pytest.raises(ValueError):
        DiscreteUniformDistribution(0.0, 1.0, -0.1)

def test_asdict_basic_low_equals_high():
    """Test when low == high."""
    dist = DiscreteUniformDistribution(2.0, 2.0, 1.0)
    codeflash_output = dist._asdict(); dct = codeflash_output # 6.48μs -> 1.08μs (497% faster)

# ---- Edge Test Cases ----

def test_asdict_edge_high_not_divisible_by_q():
    """Test when (high-low) is not divisible by q, high should be adjusted."""
    dist = DiscreteUniformDistribution(0.0, 0.95, 0.3)
    codeflash_output = dist._asdict(); dct = codeflash_output # 6.79μs -> 1.18μs (476% faster)

def test_asdict_edge_high_less_than_low():
    """Test when high < low, should raise ValueError."""
    with pytest.raises(ValueError):
        DiscreteUniformDistribution(2.0, 1.0, 0.1)

def test_asdict_edge_q_greater_than_range():
    """Test when q > (high-low), only one value possible."""
    dist = DiscreteUniformDistribution(0.0, 0.5, 1.0)
    codeflash_output = dist._asdict(); dct = codeflash_output # 6.48μs -> 1.07μs (505% faster)

def test_asdict_edge_float_precision():
    """Test with values that may cause floating point precision issues."""
    dist = DiscreteUniformDistribution(0.0, 1.0, 0.3333333333333333)
    codeflash_output = dist._asdict(); dct = codeflash_output # 6.25μs -> 991ns (531% faster)
    # k = int((1.0-0.0)//0.3333333333333333) = 3, high = 0.0 + 3*0.3333333333333333
    expected_high = 0.0 + 3*0.3333333333333333

def test_asdict_edge_large_step_small_range():
    """Test with large step and small range."""
    dist = DiscreteUniformDistribution(0.0, 0.1, 10.0)
    codeflash_output = dist._asdict(); dct = codeflash_output # 6.13μs -> 981ns (524% faster)

def test_asdict_edge_negative_low_high():
    """Test with negative low and high."""
    dist = DiscreteUniformDistribution(-5.0, -1.0, 1.0)
    codeflash_output = dist._asdict(); dct = codeflash_output # 6.03μs -> 963ns (526% faster)

def test_asdict_edge_negative_step():
    """Test that negative step raises ValueError."""
    with pytest.raises(ValueError):
        DiscreteUniformDistribution(0.0, 1.0, -1.0)

def test_asdict_edge_zero_range():
    """Test when range is zero and step is positive."""
    dist = DiscreteUniformDistribution(3.0, 3.0, 1.0)
    codeflash_output = dist._asdict(); dct = codeflash_output # 6.25μs -> 1.06μs (489% faster)

def test_asdict_edge_step_is_float():
    """Test with step as a float."""
    dist = DiscreteUniformDistribution(0.0, 1.0, 0.5)
    codeflash_output = dist._asdict(); dct = codeflash_output # 6.08μs -> 935ns (550% faster)

# ---- Large Scale Test Cases ----

def test_asdict_large_scale_many_distributions():
    """Test creating many distributions and calling _asdict on each."""
    for i in range(100):
        low = float(i)
        high = float(i + 10)
        step = 0.1 + i * 0.01
        dist = DiscreteUniformDistribution(low, high, step)
        codeflash_output = dist._asdict(); dct = codeflash_output # 270μs -> 33.1μs (716% faster)
        # high is adjusted: k = int((high-low)//step), adj_high = low + k*step
        k = int((high-low)//step)
        expected_high = low + k*step

def test_asdict_large_scale_high_precision():
    """Test with high precision floats and many steps."""
    dist = DiscreteUniformDistribution(0.0, 0.999, 0.001)
    codeflash_output = dist._asdict(); dct = codeflash_output # 6.22μs -> 936ns (564% faster)

def test_asdict_large_scale_max_elements():
    """Test with range and step that produces nearly 1000 elements."""
    low = 0.0
    high = 999.0
    step = 1.0
    dist = DiscreteUniformDistribution(low, high, step)
    codeflash_output = dist._asdict(); dct = codeflash_output # 5.75μs -> 921ns (524% faster)

def test_asdict_large_scale_small_step():
    """Test with small step and moderate range."""
    dist = DiscreteUniformDistribution(0.0, 1.0, 0.001)
    codeflash_output = dist._asdict(); dct = codeflash_output # 5.94μs -> 948ns (527% faster)

def test_asdict_large_scale_varied_steps():
    """Test with varied step sizes and check adjustment."""
    for step in [0.1, 0.25, 0.5, 1.0, 2.5, 5.0]:
        dist = DiscreteUniformDistribution(0.0, 10.0, step)
        codeflash_output = dist._asdict(); dct = codeflash_output # 20.7μs -> 2.82μs (633% faster)
        k = int((10.0-0.0)//step)
        expected_high = 0.0 + k*step

# ---- Mutation Testing: Ensure that the output dict is a deep copy ----

def test_asdict_mutation_does_not_affect_original():
    """Test that mutating the returned dict does not affect the original instance."""
    dist = DiscreteUniformDistribution(1.0, 5.0, 1.0)
    codeflash_output = dist._asdict(); dct = codeflash_output # 5.47μs -> 825ns (563% faster)
    dct["low"] = 999.0

# ---- Mutation Testing: Ensure that the output dict keys are correct ----

def test_asdict_keys_are_exact():
    """Test that the output dict has exactly the keys: low, high, q."""
    dist = DiscreteUniformDistribution(0.0, 1.0, 0.1)
    codeflash_output = dist._asdict(); dct = codeflash_output # 5.72μs -> 853ns (571% faster)

# ---- Mutation Testing: Ensure that log and step are not present ----

def test_asdict_no_log_or_step():
    """Test that 'log' and 'step' are not present in the output dict."""
    dist = DiscreteUniformDistribution(0.0, 1.0, 0.1)
    codeflash_output = dist._asdict(); dct = codeflash_output # 5.70μs -> 907ns (529% 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 DiscreteUniformDistribution

def test_DiscreteUniformDistribution__asdict():
    DiscreteUniformDistribution._asdict(DiscreteUniformDistribution(1.0, 1.0, 1.0))

🔎 Concolic Coverage Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
`codeflash_concolic_qluqolhr/tmpbo9m53yg/test_concolic_coverage.py::test_DiscreteUniformDistribution__asdict`	5.81μs	957ns	507%✅

To edit these changes git checkout codeflash/optimize-DiscreteUniformDistribution._asdict-mhbi5380 and push.

The optimization replaces `copy.deepcopy(self.__dict__)` with `self.__dict__.copy()` in the `_asdict` method. This change delivers a **702% speedup** because: **What changed:** Switched from deep copy to shallow copy for creating a dictionary copy. **Why it's faster:** The `DiscreteUniformDistribution` instance dictionary only contains immutable values (floats and a boolean: `low`, `high`, `step`, `log`). Since there are no nested mutable objects to recursively copy, `deepcopy` performs unnecessary overhead by: - Recursively traversing each value to check for nested structures - Maintaining a memo dictionary to handle circular references - Creating new objects even when unnecessary A shallow copy with `.copy()` is sufficient and much faster since it only needs to create a new dictionary with references to the same immutable values. **Performance impact:** The line profiler shows the bottleneck was the `copy.deepcopy` call taking 95.4% of the method's time (20.98ms out of 21.9ms). The optimized version reduces this to just 27.3% (430μs out of 1.58ms). **Test case benefits:** All test cases show consistent 5-7x speedups, with the optimization being particularly effective for: - High-frequency operations (the large-scale test with 1000 iterations shows 720% speedup) - Simple distributions with basic numeric parameters - Any scenario where `_asdict` is called repeatedly, as the per-call overhead is dramatically reduced The optimization maintains identical behavior since both approaches return a mutable copy that doesn't affect the original instance when modified.

codeflash-ai bot requested a review from mashraf-222 October 29, 2025 04:35

codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Oct 29, 2025

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⚡️ Speed up method `DiscreteUniformDistribution._asdict` by 702% #46

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⚡️ Speed up method DiscreteUniformDistribution._asdict by 702% #46

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codeflash-ai bot commented Oct 29, 2025

📄 702% (7.02x) speedup for DiscreteUniformDistribution._asdict in optuna/distributions.py

📝 Explanation and details

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⚡️ Speed up method `DiscreteUniformDistribution._asdict` by 702% #46

⚡️ Speed up method `DiscreteUniformDistribution._asdict` by 702% #46

📄 702% (7.02x) speedup for `DiscreteUniformDistribution._asdict` in `optuna/distributions.py`