⚡️ Speed up method AlexNet._classify by 371%

[codeflash-ai]

📄 371% (3.71x) speedup for AlexNet._classify in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 482 microseconds → 102 microseconds (best of 319 runs)

📝 Explanation and details

Here is an optimized version of your program. The main area to optimize is the _classify method: using sum() is already efficient, but [val for _ in features] can be replaced with list multiplication, which is faster in Python. We also avoid recalculating total % self.num_classes for every element.

This runs faster, especially for large lists of features, because the modulo is computed just once and the resultant list is constructed in a single step.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 84 Passed
⏪ Replay Tests	✅ 1 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import random  # 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_empty_input():
    # Test with empty input list
    model = AlexNet()
    codeflash_output = model._classify([]); result = codeflash_output # 1.15μs -> 1.15μs (0.087% faster)

def test_classify_single_element():
    # Test with a single-element list
    model = AlexNet(num_classes=10)
    codeflash_output = model._classify([7]); result = codeflash_output # 1.40μs -> 1.12μs (25.0% faster)

def test_classify_multiple_elements():
    # Test with multiple positive integers
    model = AlexNet(num_classes=10)
    features = [1, 2, 3, 4]
    expected = [sum(features) % 10] * 4
    codeflash_output = model._classify(features); result = codeflash_output # 1.30μs -> 772ns (68.7% faster)

def test_classify_negative_numbers():
    # Test with negative numbers in features
    model = AlexNet(num_classes=10)
    features = [-1, -2, -3]
    expected = [sum(features) % 10] * 3
    codeflash_output = model._classify(features); result = codeflash_output # 1.35μs -> 742ns (82.3% faster)

def test_classify_mixed_numbers():
    # Test with mixed positive and negative numbers
    model = AlexNet(num_classes=10)
    features = [5, -3, 7, -2]
    expected = [sum(features) % 10] * 4
    codeflash_output = model._classify(features); result = codeflash_output # 1.33μs -> 781ns (70.6% faster)

def test_classify_sum_zero():
    # Test where sum is zero
    model = AlexNet(num_classes=5)
    features = [2, -2, 3, -3]
    expected = [0] * 4
    codeflash_output = model._classify(features); result = codeflash_output # 1.56μs -> 992ns (57.6% faster)

# 2. Edge Test Cases

def test_classify_large_positive_numbers():
    # Test with very large positive numbers
    model = AlexNet(num_classes=100)
    features = [10**12, 10**12, 10**12]
    expected = [sum(features) % 100] * 3
    codeflash_output = model._classify(features); result = codeflash_output # 1.41μs -> 831ns (70.0% faster)

def test_classify_large_negative_numbers():
    # Test with very large negative numbers
    model = AlexNet(num_classes=100)
    features = [-10**12, -10**12, -10**12]
    expected = [sum(features) % 100] * 3
    codeflash_output = model._classify(features); result = codeflash_output # 1.40μs -> 822ns (70.7% faster)

def test_classify_num_classes_one():
    # Test with num_classes set to 1 (all results should be 0)
    model = AlexNet(num_classes=1)
    features = [5, 10, 15]
    expected = [0, 0, 0]
    codeflash_output = model._classify(features); result = codeflash_output # 1.53μs -> 1.09μs (40.5% faster)

def test_classify_num_classes_large():
    # Test with a very large num_classes
    model = AlexNet(num_classes=10**9)
    features = [123456789, 987654321]
    expected = [sum(features) % (10**9)] * 2
    codeflash_output = model._classify(features); result = codeflash_output # 1.32μs -> 781ns (69.3% faster)

def test_classify_all_zeros():
    # Test with all elements zero
    model = AlexNet(num_classes=7)
    features = [0, 0, 0, 0, 0]
    expected = [0] * 5
    codeflash_output = model._classify(features); result = codeflash_output # 1.61μs -> 991ns (62.7% faster)

def test_classify_single_large_element():
    # Test with a single very large element
    model = AlexNet(num_classes=123)
    features = [10**18]
    expected = [10**18 % 123]
    codeflash_output = model._classify(features); result = codeflash_output # 1.57μs -> 1.22μs (28.6% faster)

def test_classify_non_integer_elements():
    # Test with float elements (should work if sum works)
    model = AlexNet(num_classes=10)
    features = [1.5, 2.5, 3.0]
    expected = [sum(features) % 10] * 3
    codeflash_output = model._classify(features); result = codeflash_output # 1.53μs -> 932ns (64.4% faster)

def test_classify_negative_num_classes():
    # Test with negative num_classes (should behave as modulo with negative)
    model = AlexNet(num_classes=-10)
    features = [1, 2, 3]
    expected = [sum(features) % -10] * 3
    codeflash_output = model._classify(features); result = codeflash_output # 1.24μs -> 722ns (72.2% faster)

def test_classify_non_list_iterable():
    # Test with a tuple input instead of a list
    model = AlexNet(num_classes=10)
    features = (1, 2, 3)
    expected = [sum(features) % 10] * 3
    codeflash_output = model._classify(features); result = codeflash_output # 1.28μs -> 741ns (73.0% faster)

def test_classify_mutation_resistance():
    # Test that the function does not mutate the input
    model = AlexNet(num_classes=10)
    features = [1, 2, 3]
    features_copy = list(features)
    codeflash_output = model._classify(features); _ = codeflash_output # 1.43μs -> 1.02μs (40.2% faster)

# 3. Large Scale Test Cases

def test_classify_large_input_size():
    # Test with a large input list (1000 elements)
    model = AlexNet(num_classes=100)
    features = [random.randint(-1000, 1000) for _ in range(1000)]
    expected_val = sum(features) % 100
    codeflash_output = model._classify(features); result = codeflash_output # 44.8μs -> 9.26μs (384% faster)

def test_classify_large_input_all_same():
    # Test with large input where all elements are the same
    model = AlexNet(num_classes=50)
    features = [7] * 1000
    expected_val = (7 * 1000) % 50
    codeflash_output = model._classify(features); result = codeflash_output # 41.5μs -> 5.86μs (608% faster)

def test_classify_large_input_all_zeros():
    # Test with large input where all elements are zero
    model = AlexNet(num_classes=33)
    features = [0] * 1000
    codeflash_output = model._classify(features); result = codeflash_output # 37.1μs -> 5.55μs (569% faster)

def test_classify_large_input_floats():
    # Test with large input of floats
    model = AlexNet(num_classes=77)
    features = [float(i) for i in range(1000)]
    expected_val = sum(features) % 77
    codeflash_output = model._classify(features); result = codeflash_output # 49.9μs -> 5.52μs (804% faster)

def test_classify_large_input_negative():
    # Test with large input of negative numbers
    model = AlexNet(num_classes=123)
    features = [-i for i in range(1000)]
    expected_val = sum(features) % 123
    codeflash_output = model._classify(features); result = codeflash_output # 42.3μs -> 6.56μs (544% 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  # for generating large scale test data

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

# unit tests

# 1. Basic Test Cases

def test_classify_empty_list():
    # Should return an empty list when input is empty
    model = AlexNet()
    codeflash_output = model._classify([]) # 1.19μs -> 1.17μs (1.79% faster)

def test_classify_single_element():
    # Single element: output should be [element % num_classes]
    model = AlexNet(num_classes=10)
    codeflash_output = model._classify([7]) # 1.43μs -> 1.11μs (28.8% faster)
    codeflash_output = model._classify([15]) # 601ns -> 380ns (58.2% faster)
    codeflash_output = model._classify([0]) # 591ns -> 401ns (47.4% faster)

def test_classify_multiple_elements():
    # Multiple elements: output should be a list of same length, all with sum(features) % num_classes
    model = AlexNet(num_classes=10)
    features = [1, 2, 3]
    total = sum(features)
    expected = [total % 10] * 3
    codeflash_output = model._classify(features) # 1.30μs -> 731ns (78.1% faster)

def test_classify_negative_numbers():
    # Negative numbers: sum may be negative, but modulo should still work
    model = AlexNet(num_classes=7)
    features = [-3, -4, 2]
    total = sum(features)
    expected = [total % 7] * 3
    codeflash_output = model._classify(features) # 1.38μs -> 811ns (70.5% faster)

def test_classify_mixed_types():
    # Floats in input: sum should work, modulo with float is allowed in Python
    model = AlexNet(num_classes=5)
    features = [1.5, 2.5, 3]
    total = sum(features)
    expected = [total % 5] * 3
    codeflash_output = model._classify(features) # 1.57μs -> 952ns (65.2% faster)

def test_classify_large_num_classes():
    # Large num_classes: test with a big modulus
    model = AlexNet(num_classes=1000000)
    features = [123456, 789012, 345678]
    total = sum(features)
    expected = [total % 1000000] * 3
    codeflash_output = model._classify(features) # 1.32μs -> 802ns (65.0% faster)

# 2. Edge Test Cases

def test_classify_all_zeros():
    # All zeros: sum is zero, result should be zeros
    model = AlexNet(num_classes=8)
    features = [0, 0, 0, 0]
    codeflash_output = model._classify(features) # 1.63μs -> 1.17μs (39.3% faster)

def test_classify_sum_exact_multiple_of_num_classes():
    # Sum is exact multiple of num_classes: result should be all zeros
    model = AlexNet(num_classes=6)
    features = [2, 4]
    total = sum(features)  # 6
    codeflash_output = model._classify(features) # 1.33μs -> 932ns (43.0% faster)

def test_classify_sum_negative_multiple_of_num_classes():
    # Sum is negative and a multiple of num_classes: result should be all zeros
    model = AlexNet(num_classes=5)
    features = [-5, 0]
    total = sum(features)  # -5
    codeflash_output = model._classify(features) # 1.38μs -> 1.01μs (36.6% faster)

def test_classify_num_classes_one():
    # num_classes = 1: result is always zero regardless of input
    model = AlexNet(num_classes=1)
    features = [100, 200, -300, 0]
    codeflash_output = model._classify(features) # 1.68μs -> 1.21μs (38.9% faster)

def test_classify_large_negative_sum():
    # Large negative sum, test correct modulo behavior
    model = AlexNet(num_classes=13)
    features = [-1000, -500, -2]
    total = sum(features)
    expected = [total % 13] * 3
    codeflash_output = model._classify(features) # 1.34μs -> 792ns (69.6% faster)


def test_classify_non_numeric_features():
    # Non-numeric features should raise an error on sum
    model = AlexNet()
    with pytest.raises(TypeError):
        model._classify(['a', 'b', 'c'])

def test_classify_features_with_nan():
    # Features containing NaN should propagate NaN in sum and modulo
    import math
    model = AlexNet(num_classes=10)
    features = [1, math.nan, 3]
    codeflash_output = model._classify(features); result = codeflash_output # 2.52μs -> 1.94μs (29.4% faster)

def test_classify_features_with_inf():
    # Features containing inf should propagate inf in sum and modulo
    import math
    model = AlexNet(num_classes=10)
    features = [1, math.inf, 3]
    codeflash_output = model._classify(features); result = codeflash_output # 2.29μs -> 1.79μs (27.9% faster)

# 3. Large Scale Test Cases

def test_classify_large_input():
    # Large input: test with 1000 elements
    model = AlexNet(num_classes=123)
    features = [random.randint(-1000, 1000) for _ in range(1000)]
    total = sum(features)
    expected = [total % 123] * 1000
    codeflash_output = model._classify(features) # 46.7μs -> 11.3μs (311% faster)

def test_classify_large_input_all_same():
    # Large input, all elements the same
    model = AlexNet(num_classes=17)
    features = [42] * 1000
    total = sum(features)
    expected = [total % 17] * 1000
    codeflash_output = model._classify(features) # 41.4μs -> 5.61μs (639% faster)

def test_classify_large_input_all_zeros():
    # Large input, all zeros
    model = AlexNet(num_classes=999)
    features = [0] * 1000
    codeflash_output = model._classify(features) # 37.7μs -> 5.57μs (576% faster)

def test_classify_large_input_floats():
    # Large input, all floats
    model = AlexNet(num_classes=50)
    features = [random.uniform(-1000, 1000) for _ in range(1000)]
    total = sum(features)
    expected = [total % 50] * 1000
    codeflash_output = model._classify(features); result = codeflash_output # 48.9μs -> 5.43μs (801% faster)

def test_classify_large_input_negative():
    # Large input, all negative numbers
    model = AlexNet(num_classes=77)
    features = [-random.randint(1, 1000) for _ in range(1000)]
    total = sum(features)
    expected = [total % 77] * 1000
    codeflash_output = model._classify(features) # 42.5μs -> 6.33μs (571% 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-mccv62j4 and push.