⚡️ Speed up method AlexNet._classify by 333%

[codeflash-ai]

📄 333% (3.33x) speedup for AlexNet._classify in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 510 microseconds → 118 microseconds (best of 335 runs)

📝 Explanation and details

Here is an optimized version of your program. The primary optimization opportunity is replacing the use of sum(features) and the list comprehension [total % self.num_classes for _ in features] with a more efficient approach.
Since the total and num_classes never change inside the method, we compute the modulo only once, then reuse it for all outputs with multiplication rather than generating a new list with a comprehension.

Explanation of optimizations:

• sum(features) % self.num_classes is computed once instead of computing total % self.num_classes for every element of features.
• List multiplication ([total_mod] * len(features)) is faster than a list comprehension over the same value, as it doesn't loop nor call the modulo operator repeatedly.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 96 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 scale random test data

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

# unit tests

# 1. Basic Test Cases

def test_classify_basic_positive_integers():
    # Test with a simple list of positive integers
    net = AlexNet(num_classes=10)
    features = [1, 2, 3]
    expected = [6 % 10, 6 % 10, 6 % 10]
    codeflash_output = net._classify(features) # 1.66μs -> 1.26μs (31.7% faster)

def test_classify_basic_zeroes():
    # Test with all zeroes
    net = AlexNet(num_classes=5)
    features = [0, 0, 0, 0]
    expected = [0, 0, 0, 0]
    codeflash_output = net._classify(features) # 1.57μs -> 1.23μs (27.6% faster)

def test_classify_basic_negative_integers():
    # Test with negative integers
    net = AlexNet(num_classes=7)
    features = [-1, -2, -3]
    total = sum(features)
    expected = [total % 7] * 3
    codeflash_output = net._classify(features) # 1.40μs -> 782ns (79.4% faster)

def test_classify_basic_mixed_signs():
    # Test with mixed positive and negative integers
    net = AlexNet(num_classes=8)
    features = [5, -3, 2, -1]
    total = sum(features)
    expected = [total % 8] * 4
    codeflash_output = net._classify(features) # 1.35μs -> 841ns (60.8% faster)

def test_classify_basic_floats():
    # Test with float values
    net = AlexNet(num_classes=10)
    features = [1.5, 2.5, 3.0]
    total = sum(features)
    expected = [total % 10] * 3
    codeflash_output = net._classify(features) # 1.49μs -> 952ns (56.8% faster)

# 2. Edge Test Cases

def test_classify_empty_features():
    # Test with an empty list
    net = AlexNet(num_classes=10)
    features = []
    expected = []
    codeflash_output = net._classify(features) # 1.12μs -> 1.12μs (0.000% faster)

def test_classify_single_element():
    # Test with a single element
    net = AlexNet(num_classes=3)
    features = [7]
    expected = [7 % 3]
    codeflash_output = net._classify(features) # 1.38μs -> 1.16μs (19.0% faster)

def test_classify_large_numbers():
    # Test with very large numbers
    net = AlexNet(num_classes=100000)
    features = [10**12, 10**12, 10**12]
    total = sum(features)
    expected = [total % 100000] * 3
    codeflash_output = net._classify(features) # 1.43μs -> 802ns (78.7% faster)

def test_classify_all_same_value():
    # Test with all elements the same
    net = AlexNet(num_classes=4)
    features = [7, 7, 7, 7]
    total = sum(features)
    expected = [total % 4] * 4
    codeflash_output = net._classify(features) # 1.34μs -> 802ns (67.3% faster)

def test_classify_num_classes_one():
    # Test with num_classes=1, should always return 0
    net = AlexNet(num_classes=1)
    features = [1, 2, 3]
    expected = [0, 0, 0]
    codeflash_output = net._classify(features) # 1.48μs -> 1.13μs (31.0% faster)

def test_classify_negative_num_classes():
    # Test with negative num_classes, should behave as per Python's modulo rules
    net = AlexNet(num_classes=-5)
    features = [1, 2, 3]
    total = sum(features)
    expected = [total % -5] * 3
    codeflash_output = net._classify(features) # 1.28μs -> 752ns (70.5% faster)

def test_classify_non_integer_features():
    # Test with non-integer, non-float types (should raise TypeError)
    net = AlexNet(num_classes=10)
    features = ["a", "b", "c"]
    with pytest.raises(TypeError):
        net._classify(features)

def test_classify_none_features():
    # Test with None as input, should raise TypeError
    net = AlexNet(num_classes=10)
    with pytest.raises(TypeError):
        net._classify(None)

def test_classify_features_with_nan():
    # Test with float('nan') in features
    net = AlexNet(num_classes=10)
    features = [1, float('nan'), 3]
    codeflash_output = net._classify(features); result = codeflash_output # 2.37μs -> 1.81μs (30.4% faster)

def test_classify_features_with_inf():
    # Test with float('inf') in features
    net = AlexNet(num_classes=10)
    features = [1, float('inf'), 3]
    codeflash_output = net._classify(features); result = codeflash_output # 2.23μs -> 1.73μs (28.9% faster)

# 3. Large Scale Test Cases

def test_classify_large_input_size():
    # Test with a large number of features
    net = AlexNet(num_classes=100)
    features = [i for i in range(1000)]  # 1000 elements
    total = sum(features)
    expected = [total % 100] * 1000
    codeflash_output = net._classify(features) # 41.3μs -> 5.78μs (614% faster)

def test_classify_large_random_input():
    # Test with a large random list of features
    net = AlexNet(num_classes=997)
    random.seed(42)
    features = [random.randint(-1000, 1000) for _ in range(999)]
    total = sum(features)
    expected = [total % 997] * 999
    codeflash_output = net._classify(features) # 54.3μs -> 8.89μs (511% faster)

def test_classify_large_all_zeros():
    # Test with a large list of all zeros
    net = AlexNet(num_classes=123)
    features = [0] * 1000
    expected = [0] * 1000
    codeflash_output = net._classify(features) # 37.0μs -> 5.56μs (566% faster)

def test_classify_large_all_same_value():
    # Test with a large list of the same value
    net = AlexNet(num_classes=321)
    features = [7] * 1000
    total = sum(features)
    expected = [total % 321] * 1000
    codeflash_output = net._classify(features) # 38.5μs -> 5.43μs (609% faster)

def test_classify_large_negative_values():
    # Test with a large list of negative values
    net = AlexNet(num_classes=400)
    features = [-5] * 1000
    total = sum(features)
    expected = [total % 400] * 1000
    codeflash_output = net._classify(features) # 41.8μs -> 6.10μs (585% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import random  # used for generating large scale test data

# imports
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 basic list of positive integers
    net = AlexNet(num_classes=10)
    features = [1, 2, 3]
    # sum = 6, 6 % 10 = 6, output should be [6, 6, 6]
    codeflash_output = net._classify(features) # 1.44μs -> 1.16μs (24.1% faster)

def test_classify_basic_negative_integers():
    # Test with negative integers
    net = AlexNet(num_classes=7)
    features = [-2, -3, -4]
    # sum = -9, -9 % 7 = 5 (Python's modulo)
    codeflash_output = net._classify(features) # 1.48μs -> 1.19μs (24.3% faster)

def test_classify_basic_mixed_integers():
    # Test with a mix of positive and negative integers
    net = AlexNet(num_classes=8)
    features = [10, -3, 5, -4]
    # sum = 8, 8 % 8 = 0
    codeflash_output = net._classify(features) # 1.53μs -> 1.16μs (31.9% faster)

def test_classify_basic_single_element():
    # Test with a single-element list
    net = AlexNet(num_classes=5)
    features = [7]
    # sum = 7, 7 % 5 = 2
    codeflash_output = net._classify(features) # 1.28μs -> 1.08μs (18.6% faster)

def test_classify_basic_zero_sum():
    # Test with a list whose sum is zero
    net = AlexNet(num_classes=4)
    features = [2, -2, 0]
    # sum = 0, 0 % 4 = 0
    codeflash_output = net._classify(features) # 1.55μs -> 1.19μs (30.3% faster)

def test_classify_basic_all_zeros():
    # Test with a list of all zeros
    net = AlexNet(num_classes=3)
    features = [0, 0, 0, 0]
    # sum = 0, 0 % 3 = 0
    codeflash_output = net._classify(features) # 1.48μs -> 1.14μs (29.8% faster)

def test_classify_basic_large_num_classes():
    # Test where num_classes is larger than the sum
    net = AlexNet(num_classes=100)
    features = [1, 2, 3]
    # sum = 6, 6 % 100 = 6
    codeflash_output = net._classify(features) # 1.46μs -> 1.14μs (28.1% faster)

def test_classify_basic_sum_multiple_of_num_classes():
    # Test where the sum is a multiple of num_classes
    net = AlexNet(num_classes=5)
    features = [5, 5, 5]
    # sum = 15, 15 % 5 = 0
    codeflash_output = net._classify(features) # 1.38μs -> 1.07μs (29.0% faster)

# EDGE TEST CASES

def test_classify_edge_empty_list():
    # Test with an empty list
    net = AlexNet(num_classes=10)
    features = []
    # Should return an empty list
    codeflash_output = net._classify(features) # 1.04μs -> 1.12μs (7.13% slower)

def test_classify_edge_num_classes_one():
    # Test with num_classes = 1 (all outputs should be 0)
    net = AlexNet(num_classes=1)
    features = [10, 20, 30]
    # sum = 60, 60 % 1 = 0
    codeflash_output = net._classify(features) # 1.39μs -> 1.07μs (29.9% faster)

def test_classify_edge_sum_negative_and_num_classes_positive():
    # Test with sum negative and num_classes positive
    net = AlexNet(num_classes=4)
    features = [-5, -3]
    # sum = -8, -8 % 4 = 0
    codeflash_output = net._classify(features) # 1.42μs -> 1.17μs (21.3% faster)

def test_classify_edge_sum_zero_and_num_classes_zero():
    # 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_large_negative_sum():
    # Test with a large negative sum
    net = AlexNet(num_classes=13)
    features = [-1000, -2000, -3000]
    # sum = -6000, -6000 % 13 = 2
    codeflash_output = net._classify(features) # 1.59μs -> 1.30μs (22.3% faster)

def test_classify_edge_large_positive_sum():
    # Test with a large positive sum
    net = AlexNet(num_classes=17)
    features = [1000, 2000, 3000]
    # sum = 6000, 6000 % 17 = 7
    codeflash_output = net._classify(features) # 1.48μs -> 1.13μs (31.0% faster)

def test_classify_edge_non_integer_features():
    # Test with float features
    net = AlexNet(num_classes=10)
    features = [1.5, 2.5, 3.0]
    # sum = 7.0, 7.0 % 10 = 7.0
    codeflash_output = net._classify(features) # 2.13μs -> 1.64μs (29.9% faster)

def test_classify_edge_non_integer_sum_and_integer_num_classes():
    # Test with floats that sum to a float, and integer num_classes
    net = AlexNet(num_classes=4)
    features = [1.2, 2.8, -1.0]
    # sum = 3.0, 3.0 % 4 = 3.0
    codeflash_output = net._classify(features) # 1.89μs -> 1.46μs (29.4% faster)

def test_classify_edge_features_with_zero_and_negative():
    # Test with features containing zeros and negatives
    net = AlexNet(num_classes=6)
    features = [0, -6, 6]
    # sum = 0, 0 % 6 = 0
    codeflash_output = net._classify(features) # 1.57μs -> 1.25μs (25.6% faster)

def test_classify_edge_num_classes_negative():
    # Test with negative num_classes (should behave like Python modulo)
    net = AlexNet(num_classes=-7)
    features = [5, 6, 8]
    # sum = 19, 19 % -7 = -2
    codeflash_output = net._classify(features) # 1.43μs -> 1.14μs (25.4% faster)

def test_classify_edge_features_are_non_numeric():
    # Test with non-numeric features should raise TypeError
    net = AlexNet(num_classes=5)
    features = ['a', 'b', 'c']
    with pytest.raises(TypeError):
        net._classify(features)

def test_classify_edge_features_is_none():
    # Test with features is None should raise TypeError
    net = AlexNet(num_classes=5)
    features = None
    with pytest.raises(TypeError):
        net._classify(features)

def test_classify_edge_features_is_not_iterable():
    # Test with features is not iterable (e.g., integer)
    net = AlexNet(num_classes=5)
    features = 123
    with pytest.raises(TypeError):
        net._classify(features)

# LARGE SCALE TEST CASES

def test_classify_large_scale_hundred_elements():
    # Test with 100 elements
    net = AlexNet(num_classes=50)
    features = [i for i in range(100)]  # sum = 4950
    expected = [4950 % 50] * 100  # 4950 % 50 = 0
    codeflash_output = net._classify(features) # 6.19μs -> 1.64μs (277% faster)

def test_classify_large_scale_thousand_elements():
    # Test with 1000 elements
    net = AlexNet(num_classes=123)
    features = [i for i in range(1000)]  # sum = 499500
    expected = [499500 % 123] * 1000
    codeflash_output = net._classify(features) # 41.4μs -> 6.06μs (583% faster)

def test_classify_large_scale_random_integers():
    # Test with 500 random integers between -1000 and 1000
    net = AlexNet(num_classes=77)
    random.seed(42)
    features = [random.randint(-1000, 1000) for _ in range(500)]
    total = sum(features)
    expected = [total % 77] * 500
    codeflash_output = net._classify(features) # 23.1μs -> 4.96μs (367% faster)

def test_classify_large_scale_random_floats():
    # Test with 800 random floats between -1000 and 1000
    net = AlexNet(num_classes=101)
    random.seed(123)
    features = [random.uniform(-1000, 1000) for _ in range(800)]
    total = sum(features)
    expected = [total % 101] * 800
    codeflash_output = net._classify(features) # 46.6μs -> 4.63μs (907% faster)

def test_classify_large_scale_all_zeros():
    # Test with 999 zeros
    net = AlexNet(num_classes=11)
    features = [0] * 999
    expected = [0] * 999
    codeflash_output = net._classify(features) # 37.2μs -> 5.53μs (572% faster)

def test_classify_large_scale_all_same_value():
    # Test with 1000 elements, all the same value
    net = AlexNet(num_classes=43)
    features = [7] * 1000
    total = 7 * 1000
    expected = [total % 43] * 1000
    codeflash_output = net._classify(features) # 40.9μs -> 5.68μs (619% faster)

def test_classify_large_scale_edge_large_negative_values():
    # Test with large negative values
    net = AlexNet(num_classes=89)
    features = [-1000] * 900
    total = -1000 * 900
    expected = [total % 89] * 900
    codeflash_output = net._classify(features) # 38.2μs -> 5.83μs (555% 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-mccv60x4 and push.