⚡️ Speed up method AlexNet._classify by 359%

[codeflash-ai]

📄 359% (3.59x) speedup for AlexNet._classify in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 499 microseconds → 109 microseconds (best of 390 runs)

📝 Explanation and details

Here is an optimized version of your code. The main bottleneck is the list comprehension, which recalculates total % self.num_classes for every element in features, even though this value never changes within a single call. By computing it once and multiplying it with [1]*len(features) (to create the repeated list quickly), we save significant computation time. Also, using len(features) instead of iterating over features is slightly faster for large lists.

Here's the rewritten code.

Changes made:

• Compute total % self.num_classes only once and store in mod_val.
• Replace the list comprehension with a single multiplication: [mod_val] * len(features).

This avoids both redundant modulo operations and Python's slower list comprehension for repeating a single value. The result and output remain exactly the same. The function is now allocation and compute efficient.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 82 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_empty_input():
    """Test with empty input list."""
    model = AlexNet()
    codeflash_output = model._classify([]); result = codeflash_output # 1.07μs -> 1.09μs (1.83% slower)

def test_classify_single_element():
    """Test with a single-element list."""
    model = AlexNet()
    codeflash_output = model._classify([42]); result = codeflash_output # 1.36μs -> 1.09μs (24.8% faster)

def test_classify_multiple_elements():
    """Test with a small list of 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 # 1.34μs -> 701ns (91.6% faster)

def test_classify_negative_numbers():
    """Test with negative numbers in the list."""
    model = AlexNet(num_classes=7)
    features = [-3, -2, -1, 6]
    total = sum(features)
    expected = [total % 7] * 4
    codeflash_output = model._classify(features); result = codeflash_output # 1.36μs -> 841ns (62.1% faster)

def test_classify_zeroes():
    """Test with a list of zeroes."""
    model = AlexNet(num_classes=5)
    features = [0, 0, 0, 0]
    expected = [0] * 4
    codeflash_output = model._classify(features); result = codeflash_output # 1.49μs -> 1.02μs (46.1% faster)

def test_classify_all_same_number():
    """Test with all elements the same."""
    model = AlexNet(num_classes=4)
    features = [7, 7, 7, 7]
    total = 28
    expected = [28 % 4] * 4
    codeflash_output = model._classify(features); result = codeflash_output # 1.56μs -> 982ns (59.2% faster)

# 2. Edge Test Cases

def test_classify_large_numbers():
    """Test with very large integers."""
    model = AlexNet(num_classes=1001)
    features = [10**18, 10**18, 10**18]
    total = 3 * 10**18
    expected = [total % 1001] * 3
    codeflash_output = model._classify(features); result = codeflash_output # 1.74μs -> 1.16μs (50.0% faster)

def test_classify_negative_and_positive_mix():
    """Test with a mix of positive and negative numbers."""
    model = AlexNet(num_classes=9)
    features = [100, -50, 25, -75]
    total = 0
    expected = [0] * 4
    codeflash_output = model._classify(features); result = codeflash_output # 1.64μs -> 1.00μs (64.0% faster)

def test_classify_num_classes_one():
    """Test with num_classes set to 1 (everything mod 1 is 0)."""
    model = AlexNet(num_classes=1)
    features = [5, 10, 15]
    expected = [0, 0, 0]
    codeflash_output = model._classify(features); result = codeflash_output # 1.50μs -> 1.05μs (42.8% faster)

def test_classify_num_classes_equals_sum():
    """Test when sum(features) is exactly num_classes."""
    model = AlexNet(num_classes=10)
    features = [3, 7]
    expected = [0, 0]
    codeflash_output = model._classify(features); result = codeflash_output # 1.35μs -> 1.00μs (35.0% faster)

def test_classify_negative_sum():
    """Test when the sum is negative and num_classes > abs(sum)."""
    model = AlexNet(num_classes=20)
    features = [-5, -10]
    total = -15
    expected = [total % 20] * 2
    codeflash_output = model._classify(features); result = codeflash_output # 1.45μs -> 972ns (49.5% faster)

def test_classify_sum_is_zero():
    """Test when the sum of features is zero."""
    model = AlexNet(num_classes=13)
    features = [7, -7, 3, -3]
    total = 0
    expected = [0] * 4
    codeflash_output = model._classify(features); result = codeflash_output # 1.59μs -> 1.02μs (55.9% faster)

def test_classify_non_integer_input():
    """Test with non-integer (float) values."""
    model = AlexNet(num_classes=10)
    features = [1.5, 2.5, 3.5]
    total = sum(features)
    expected = [total % 10] * 3
    codeflash_output = model._classify(features); result = codeflash_output # 1.55μs -> 951ns (63.3% faster)

def test_classify_large_negative_numbers():
    """Test with very large negative numbers."""
    model = AlexNet(num_classes=100)
    features = [-10**10, -10**10]
    total = -2 * 10**10
    expected = [total % 100] * 2
    codeflash_output = model._classify(features); result = codeflash_output # 1.63μs -> 1.12μs (45.5% faster)

# 3. Large Scale Test Cases

def test_classify_large_input_size():
    """Test with a large input list (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); result = codeflash_output # 44.4μs -> 8.73μs (409% faster)

def test_classify_large_input_all_ones():
    """Test with a large input list of all ones."""
    model = AlexNet(num_classes=500)
    features = [1] * 1000
    total = 1000
    expected = [1000 % 500] * 1000
    codeflash_output = model._classify(features); result = codeflash_output # 41.0μs -> 5.73μs (615% faster)

def test_classify_large_input_all_zero():
    """Test with a large input list of all zeroes."""
    model = AlexNet(num_classes=77)
    features = [0] * 1000
    expected = [0] * 1000
    codeflash_output = model._classify(features); result = codeflash_output # 37.4μs -> 5.52μs (578% faster)

def test_classify_large_input_pattern():
    """Test with a large input list with a repeating pattern."""
    model = AlexNet(num_classes=256)
    pattern = [1, -1, 2, -2, 3, -3]
    features = pattern * (1000 // len(pattern))
    total = sum(features)
    expected = [total % 256] * len(features)
    codeflash_output = model._classify(features); result = codeflash_output # 38.7μs -> 5.86μs (560% faster)

def test_classify_performance_on_large_input():
    """Test that function runs within reasonable time for large input."""
    import time
    model = AlexNet(num_classes=999)
    features = [random.randint(-10000, 10000) for _ in range(1000)]
    start = time.time()
    codeflash_output = model._classify(features); result = codeflash_output # 54.1μs -> 11.5μs (373% faster)
    end = time.time()
    total = sum(features)
    expected = [total % 999] * 1000
# 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 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 simple positive integers
    model = AlexNet(num_classes=10)
    features = [1, 2, 3]
    # sum = 6, 6 % 10 = 6, repeated for each element
    codeflash_output = model._classify(features) # 1.75μs -> 1.26μs (38.9% faster)

def test_classify_basic_negative_integers():
    # Test with negative integers
    model = AlexNet(num_classes=7)
    features = [-1, -2, -3]
    # sum = -6, -6 % 7 = 1
    codeflash_output = model._classify(features) # 1.73μs -> 1.25μs (38.4% faster)

def test_classify_basic_mixed_integers():
    # Test with mixed positive and negative integers
    model = AlexNet(num_classes=5)
    features = [10, -3, -2]
    # sum = 5, 5 % 5 = 0
    codeflash_output = model._classify(features) # 1.57μs -> 1.12μs (40.1% faster)

def test_classify_basic_floats():
    # Test with float values
    model = AlexNet(num_classes=4)
    features = [1.5, 2.5, 3.0]
    # sum = 7.0, 7.0 % 4 = 3.0
    codeflash_output = model._classify(features) # 2.33μs -> 1.76μs (31.9% faster)

def test_classify_basic_single_element():
    # Test with a single element
    model = AlexNet(num_classes=3)
    features = [7]
    # sum = 7, 7 % 3 = 1
    codeflash_output = model._classify(features) # 1.39μs -> 1.07μs (30.1% faster)

# 2. Edge Test Cases

def test_classify_empty_input():
    # Test with empty input list
    model = AlexNet(num_classes=10)
    features = []
    # Should return an empty list
    codeflash_output = model._classify(features) # 1.11μs -> 1.06μs (4.80% faster)

def test_classify_all_zeros():
    # Test with all zeros
    model = AlexNet(num_classes=5)
    features = [0, 0, 0, 0]
    # sum = 0, 0 % 5 = 0
    codeflash_output = model._classify(features) # 1.67μs -> 1.15μs (45.2% faster)

def test_classify_large_positive_numbers():
    # Test with very large positive numbers
    model = AlexNet(num_classes=100)
    features = [10**9, 10**9]
    # sum = 2*10^9, 2*10^9 % 100 = 0
    codeflash_output = model._classify(features) # 1.70μs -> 1.22μs (39.4% faster)

def test_classify_large_negative_numbers():
    # Test with very large negative numbers
    model = AlexNet(num_classes=50)
    features = [-10**8, -10**8, -10**8]
    # sum = -3*10^8, -3*10^8 % 50 = 0
    codeflash_output = model._classify(features) # 1.67μs -> 1.17μs (42.7% faster)

def test_classify_sum_exact_multiple_of_num_classes():
    # Test where sum is an exact multiple of num_classes
    model = AlexNet(num_classes=12)
    features = [6, 6, 0]
    # sum = 12, 12 % 12 = 0
    codeflash_output = model._classify(features) # 1.53μs -> 1.11μs (38.0% faster)

def test_classify_sum_negative_not_multiple():
    # Test where sum is negative and not a multiple of num_classes
    model = AlexNet(num_classes=8)
    features = [-5, -6]
    # sum = -11, -11 % 8 = 5
    codeflash_output = model._classify(features) # 1.53μs -> 1.16μs (31.9% faster)

def test_classify_num_classes_one():
    # Test with num_classes=1 (all results should be 0)
    model = AlexNet(num_classes=1)
    features = [1, 2, 3]
    # sum = 6, 6 % 1 = 0
    codeflash_output = model._classify(features) # 1.54μs -> 1.05μs (46.8% faster)

def test_classify_num_classes_large():
    # Test with a large num_classes
    model = AlexNet(num_classes=10**6)
    features = [123456, 654321]
    # sum = 777777, 777777 % 10^6 = 777777
    codeflash_output = model._classify(features) # 1.48μs -> 1.09μs (35.9% faster)

def test_classify_non_integer_num_classes():
    # Test with float features and num_classes
    model = AlexNet(num_classes=7)
    features = [2.5, 3.5]
    # sum = 6.0, 6.0 % 7 = 6.0
    codeflash_output = model._classify(features) # 2.16μs -> 1.67μs (29.3% faster)

def test_classify_sum_zero_negative_modulo():
    # Test with sum zero and negative num_classes (should raise ValueError)
    model = AlexNet(num_classes=-5)
    features = [2, -2]
    # sum = 0, 0 % -5 is 0 in Python, but negative num_classes is likely invalid
    # However, Python allows negative modulus, so let's check the behavior
    codeflash_output = model._classify(features) # 1.58μs -> 1.16μs (36.2% faster)

def test_classify_non_numeric_input():
    # Test with non-numeric input (should raise TypeError)
    model = AlexNet(num_classes=10)
    features = ['a', 'b', 'c']
    with pytest.raises(TypeError):
        model._classify(features)

def test_classify_mixed_numeric_types():
    # Test with a mix of int, float, and bool
    model = AlexNet(num_classes=4)
    features = [1, 2.0, True, False]
    # sum = 1 + 2.0 + 1 + 0 = 4.0, 4.0 % 4 = 0.0
    codeflash_output = model._classify(features) # 2.37μs -> 1.89μs (25.3% faster)

# 3. Large Scale Test Cases

def test_classify_large_input_list():
    # Test with a large input list of 1000 elements
    model = AlexNet(num_classes=100)
    features = [1] * 1000
    # sum = 1000, 1000 % 100 = 0
    codeflash_output = model._classify(features) # 41.4μs -> 5.79μs (615% faster)

def test_classify_large_random_input():
    # Test with 1000 random integers
    model = AlexNet(num_classes=500)
    features = [random.randint(-1000, 1000) for _ in range(1000)]
    total = sum(features)
    expected = [total % 500] * 1000
    codeflash_output = model._classify(features) # 55.2μs -> 9.00μs (514% faster)

def test_classify_large_random_floats():
    # Test with 1000 random floats
    model = AlexNet(num_classes=200)
    features = [random.uniform(-1000, 1000) for _ in range(1000)]
    total = sum(features)
    expected = [total % 200] * 1000
    codeflash_output = model._classify(features); result = codeflash_output # 51.2μs -> 5.45μs (840% faster)

def test_classify_large_input_all_same():
    # Test with 999 elements, all the same value
    model = AlexNet(num_classes=999)
    features = [7] * 999
    total = 7 * 999
    expected = [total % 999] * 999
    codeflash_output = model._classify(features) # 41.0μs -> 5.64μs (627% faster)

def test_classify_large_input_alternating_signs():
    # Test with alternating positive and negative values
    model = AlexNet(num_classes=123)
    features = [(-1) ** i * i for i in range(1000)]
    total = sum(features)
    expected = [total % 123] * 1000
    codeflash_output = model._classify(features) # 41.7μs -> 6.09μs (585% 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-mcl4jixc and push.