⚡️ Speed up method AlexNet._classify by 340%

[codeflash-ai]

📄 340% (3.40x) speedup for AlexNet._classify in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 510 microseconds → 116 microseconds (best of 295 runs)

📝 Explanation and details

Here is an optimized version of your AlexNet class. Your original code recalculates total % self.num_classes for every item in features, but this value does not change within the list comprehension. We can compute it once and repeat it, making the code faster and also a bit more memory efficient.

No comments were present to preserve.
This version calculates sum(features) % self.num_classes only once and multiplies into a list, which is faster than using a list comprehension that repeats the calculation.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 90 Passed
⏪ Replay Tests	✅ 1 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random data

# imports
import pytest  # used for our unit tests
from workload import AlexNet

# unit tests

# 1. Basic Test Cases

def test_classify_empty_list():
    # Test with empty input: should return an empty list
    model = AlexNet(num_classes=10)
    codeflash_output = model._classify([]) # 1.14μs -> 1.16μs (1.72% slower)

def test_classify_single_element():
    # Test with a single-element feature list
    model = AlexNet(num_classes=5)
    codeflash_output = model._classify([7]) # 1.45μs -> 1.09μs (32.9% faster)

def test_classify_small_positive_integers():
    # Test with a small list of positive integers
    model = AlexNet(num_classes=6)
    features = [1, 2, 3]
    total = sum(features)  # 6
    expected = [total % 6] * 3  # [0, 0, 0]
    codeflash_output = model._classify(features) # 1.37μs -> 732ns (87.6% faster)

def test_classify_small_negative_integers():
    # Test with negative integers
    model = AlexNet(num_classes=4)
    features = [-1, -2, -3]
    total = sum(features)  # -6
    expected = [(-6) % 4] * 3  # [2, 2, 2]
    codeflash_output = model._classify(features) # 1.62μs -> 932ns (74.1% faster)

def test_classify_mixed_integers():
    # Test with a mix of positive and negative integers
    model = AlexNet(num_classes=7)
    features = [10, -3, 2, -5]
    total = sum(features)  # 4
    expected = [4 % 7] * 4  # [4, 4, 4, 4]
    codeflash_output = model._classify(features) # 1.56μs -> 862ns (81.3% faster)

def test_classify_floats():
    # Test with floating point numbers
    model = AlexNet(num_classes=8)
    features = [1.5, 2.5, 3.0]
    total = sum(features)  # 7.0
    expected = [7.0 % 8] * 3  # [7.0, 7.0, 7.0]
    codeflash_output = model._classify(features) # 1.84μs -> 1.14μs (61.5% faster)

def test_classify_large_num_classes():
    # Test with a large num_classes value
    model = AlexNet(num_classes=10000)
    features = [100, 200, 300]
    total = sum(features)  # 600
    expected = [600 % 10000] * 3  # [600, 600, 600]
    codeflash_output = model._classify(features) # 1.50μs -> 852ns (76.4% faster)

# 2. Edge Test Cases

def test_classify_all_zeros():
    # All features are zero
    model = AlexNet(num_classes=3)
    features = [0, 0, 0, 0]
    expected = [0 % 3] * 4  # [0, 0, 0, 0]
    codeflash_output = model._classify(features) # 1.69μs -> 981ns (72.6% faster)

def test_classify_all_same_value():
    # All features have the same non-zero value
    model = AlexNet(num_classes=5)
    features = [7, 7, 7, 7, 7]
    total = sum(features)  # 35
    expected = [35 % 5] * 5  # [0, 0, 0, 0, 0]
    codeflash_output = model._classify(features) # 1.54μs -> 752ns (105% faster)

def test_classify_num_classes_one():
    # num_classes is 1, so everything should be 0
    model = AlexNet(num_classes=1)
    features = [10, 20, 30]
    expected = [0, 0, 0]
    codeflash_output = model._classify(features) # 1.41μs -> 1.04μs (35.5% faster)

def test_classify_large_negative_sum():
    # The sum is a large negative number
    model = AlexNet(num_classes=13)
    features = [-1000, -2000, -3000]
    total = sum(features)  # -6000
    expected = [(-6000) % 13] * 3
    codeflash_output = model._classify(features) # 1.48μs -> 851ns (74.3% faster)

def test_classify_sum_multiple_of_num_classes():
    # The sum is an exact multiple of num_classes
    model = AlexNet(num_classes=9)
    features = [9, 9, 9]
    total = sum(features)  # 27
    expected = [27 % 9] * 3  # [0, 0, 0]
    codeflash_output = model._classify(features) # 1.39μs -> 772ns (80.4% faster)


def test_classify_non_iterable_features():
    # features is not iterable (should raise TypeError)
    model = AlexNet(num_classes=10)
    with pytest.raises(TypeError):
        model._classify(5)

def test_classify_features_with_non_numeric():
    # features contains a non-numeric value (should raise TypeError)
    model = AlexNet(num_classes=10)
    with pytest.raises(TypeError):
        model._classify([1, 2, "three"])

def test_classify_zero_num_classes():
    # num_classes is zero (should raise ZeroDivisionError)
    model = AlexNet(num_classes=0)
    with pytest.raises(ZeroDivisionError):
        model._classify([1, 2, 3])

def test_classify_negative_num_classes():
    # num_classes is negative (should work as with Python's modulo)
    model = AlexNet(num_classes=-5)
    features = [1, 2, 3]
    total = sum(features)  # 6
    expected = [6 % -5] * 3  # [ -4, -4, -4 ]
    codeflash_output = model._classify(features) # 1.52μs -> 932ns (63.4% faster)

def test_classify_large_negative_and_positive():
    # Large positive and negative numbers that cancel out
    model = AlexNet(num_classes=11)
    features = [1_000_000, -1_000_000, 5]
    total = sum(features)  # 5
    expected = [5 % 11] * 3  # [5, 5, 5]
    codeflash_output = model._classify(features) # 1.43μs -> 801ns (78.9% faster)

# 3. Large Scale Test Cases

def test_classify_large_list_of_zeros():
    # Large list of zeros (performance and correctness)
    model = AlexNet(num_classes=7)
    features = [0] * 1000
    expected = [0] * 1000
    codeflash_output = model._classify(features) # 38.2μs -> 5.47μs (598% faster)

def test_classify_large_random_integers():
    # Large list of random integers
    model = AlexNet(num_classes=100)
    features = [random.randint(-1000, 1000) for _ in range(1000)]
    total = sum(features)
    expected = [total % 100] * 1000
    codeflash_output = model._classify(features) # 45.3μs -> 9.45μs (379% faster)

def test_classify_large_floats():
    # Large list of random floats
    model = AlexNet(num_classes=123)
    features = [random.uniform(-1e6, 1e6) for _ in range(999)]
    total = sum(features)
    expected_val = total % 123
    codeflash_output = model._classify(features); result = codeflash_output # 51.1μs -> 5.69μs (797% faster)

def test_classify_large_alternating_signs():
    # Large list with alternating positive and negative values
    model = AlexNet(num_classes=50)
    features = [i if i % 2 == 0 else -i for i in range(1, 1000)]
    total = sum(features)
    expected = [total % 50] * 999
    codeflash_output = model._classify(features) # 41.7μs -> 5.99μs (596% faster)

def test_classify_performance_large_sum():
    # Test with large numbers to check for overflow
    model = AlexNet(num_classes=97)
    features = [10**12] * 999
    total = sum(features)
    expected = [total % 97] * 999
    codeflash_output = model._classify(features) # 54.6μs -> 11.3μs (382% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import pytest  # used for our unit tests
from workload import AlexNet

# unit tests

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

def test_classify_basic_positive_integers():
    """Test with a small list of positive integers."""
    net = AlexNet(num_classes=10)
    features = [1, 2, 3]
    # total = 6, 6 % 10 = 6, so output should be [6, 6, 6]
    codeflash_output = net._classify(features) # 1.56μs -> 1.17μs (33.4% faster)

def test_classify_basic_negative_integers():
    """Test with a small list of negative integers."""
    net = AlexNet(num_classes=10)
    features = [-1, -2, -3]
    # total = -6, -6 % 10 = 4 (Python's modulo), so output should be [4, 4, 4]
    codeflash_output = net._classify(features) # 1.63μs -> 1.18μs (38.2% faster)

def test_classify_basic_mixed_sign_integers():
    """Test with a small list of mixed positive and negative integers."""
    net = AlexNet(num_classes=10)
    features = [5, -2, 7, -3]
    # total = 5-2+7-3 = 7, 7 % 10 = 7
    codeflash_output = net._classify(features) # 1.61μs -> 1.15μs (40.0% faster)

def test_classify_basic_zeros():
    """Test with a list of zeros."""
    net = AlexNet(num_classes=10)
    features = [0, 0, 0]
    # total = 0, 0 % 10 = 0
    codeflash_output = net._classify(features) # 1.58μs -> 1.15μs (37.4% faster)

def test_classify_basic_single_element():
    """Test with a single element list."""
    net = AlexNet(num_classes=10)
    features = [42]
    # total = 42, 42 % 10 = 2
    codeflash_output = net._classify(features) # 1.39μs -> 1.07μs (29.8% faster)

def test_classify_basic_empty_list():
    """Test with an empty list."""
    net = AlexNet(num_classes=10)
    features = []
    # Should return an empty list
    codeflash_output = net._classify(features) # 1.03μs -> 1.07μs (3.73% slower)

def test_classify_basic_float_inputs():
    """Test with floating point numbers."""
    net = AlexNet(num_classes=10)
    features = [1.5, 2.5, 3.0]
    # total = 7.0, 7.0 % 10 = 7.0
    codeflash_output = net._classify(features) # 2.08μs -> 1.59μs (30.7% faster)

def test_classify_basic_large_num_classes():
    """Test with num_classes larger than sum."""
    net = AlexNet(num_classes=100)
    features = [1, 2, 3]
    # total = 6, 6 % 100 = 6
    codeflash_output = net._classify(features) # 1.53μs -> 1.10μs (39.1% faster)

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

def test_classify_edge_all_same_elements():
    """Test with all elements the same."""
    net = AlexNet(num_classes=5)
    features = [2, 2, 2, 2, 2]
    # total = 10, 10 % 5 = 0
    codeflash_output = net._classify(features) # 1.63μs -> 1.06μs (53.8% faster)

def test_classify_edge_large_numbers():
    """Test with very large numbers."""
    net = AlexNet(num_classes=100000)
    features = [10**12, 10**12, 10**12]
    # total = 3*10^12 = 3,000,000,000,000 % 100,000 = 0
    codeflash_output = net._classify(features) # 1.88μs -> 1.37μs (37.3% faster)

def test_classify_edge_negative_and_positive_large():
    """Test with both large positive and large negative numbers."""
    net = AlexNet(num_classes=1000)
    features = [10**10, -10**10, 10**10, -10**10]
    # total = 0, 0 % 1000 = 0
    codeflash_output = net._classify(features) # 1.78μs -> 1.27μs (40.2% faster)

def test_classify_edge_num_classes_one():
    """Test with num_classes = 1 (should always return 0)."""
    net = AlexNet(num_classes=1)
    features = [1, 2, 3, 4]
    # Any number % 1 = 0
    codeflash_output = net._classify(features) # 1.56μs -> 1.13μs (38.1% faster)

def test_classify_edge_num_classes_equals_sum():
    """Test with num_classes equal to sum of features."""
    net = AlexNet(num_classes=15)
    features = [5, 5, 5]
    # total = 15, 15 % 15 = 0
    codeflash_output = net._classify(features) # 1.47μs -> 1.08μs (36.1% faster)

def test_classify_edge_num_classes_zero_raises():
    """Test with num_classes = 0 (should raise ZeroDivisionError)."""
    net = AlexNet(num_classes=0)
    features = [1, 2, 3]
    with pytest.raises(ZeroDivisionError):
        net._classify(features)

def test_classify_edge_non_integer_num_classes():
    """Test with non-integer num_classes (should work, as modulo works with floats)."""
    net = AlexNet(num_classes=2.5)
    features = [1, 2, 3]
    # total = 6, 6 % 2.5 = 1.0
    codeflash_output = net._classify(features) # 1.97μs -> 1.48μs (33.1% faster)

def test_classify_edge_non_numeric_features():
    """Test with non-numeric features should raise TypeError."""
    net = AlexNet(num_classes=10)
    features = [1, 'a', 3]
    with pytest.raises(TypeError):
        net._classify(features)

def test_classify_edge_nan_in_features():
    """Test with NaN in features (should propagate NaN in output)."""
    import math
    net = AlexNet(num_classes=10)
    features = [1, float('nan'), 3]
    codeflash_output = net._classify(features); result = codeflash_output # 2.20μs -> 1.73μs (27.2% faster)

def test_classify_edge_inf_in_features():
    """Test with inf in features (should propagate inf in output)."""
    import math
    net = AlexNet(num_classes=10)
    features = [1, float('inf'), 3]
    codeflash_output = net._classify(features); result = codeflash_output # 2.29μs -> 1.69μs (35.6% faster)

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

def test_classify_large_scale_1000_elements():
    """Test with a large list (1000 elements)."""
    net = AlexNet(num_classes=100)
    features = [1] * 1000  # sum = 1000
    # 1000 % 100 = 0
    codeflash_output = net._classify(features) # 41.1μs -> 5.74μs (616% faster)

def test_classify_large_scale_alternating_signs():
    """Test with 1000 elements, alternating +1 and -1."""
    net = AlexNet(num_classes=100)
    features = [1 if i % 2 == 0 else -1 for i in range(1000)]
    # sum = 0, 0 % 100 = 0
    codeflash_output = net._classify(features) # 37.9μs -> 6.36μs (496% faster)

def test_classify_large_scale_increasing_numbers():
    """Test with 1000 elements, increasing sequence."""
    net = AlexNet(num_classes=500)
    features = list(range(1000))  # sum = 0+1+...+999 = 499500
    # 499500 % 500 = 0
    codeflash_output = net._classify(features) # 41.9μs -> 6.11μs (585% faster)

def test_classify_large_scale_floats():
    """Test with 1000 float elements."""
    net = AlexNet(num_classes=100)
    features = [0.1] * 1000  # sum = 100.0, 100.0 % 100 = 0.0
    codeflash_output = net._classify(features) # 45.0μs -> 5.65μs (696% faster)

def test_classify_large_scale_large_values():
    """Test with 1000 elements, each a large number."""
    net = AlexNet(num_classes=999)
    features = [10**8] * 1000  # sum = 10**11, 10**11 % 999
    expected = (10**11) % 999
    codeflash_output = net._classify(features) # 48.2μs -> 5.73μs (741% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-AlexNet._classify-mcl4vfqk and push.