⚡️ Speed up method AlexNet._classify by 430%

[codeflash-ai]

📄 430% (4.30x) speedup for AlexNet._classify in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 248 microseconds → 46.7 microseconds (best of 297 runs)

📝 Explanation and details

Here is an optimized version of your AlexNet class.
Optimizations made:

• In _classify, replace repeated computation of total % self.num_classes in a list comprehension with a single multiplication (which is much faster in Python for large lists).

Explanation:

• sum(features) is called once, then modulated, then reused for all outputs.
• The resultant list is built with [total_mod] * len(features) which is much faster than a list comprehension.
• The return value remains the same in all cases.
• All function names, return values, and comments are preserved in logic.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 36 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 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]
    total = sum(features)
    expected = [total % 10] * 3
    codeflash_output = net._classify(features) # 1.37μs -> 792ns (73.4% 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) # 1.42μs -> 821ns (73.3% faster)

def test_classify_basic_floats():
    # Test with floating point numbers
    net = AlexNet(num_classes=5)
    features = [1.5, 2.5, 3.0]
    total = sum(features)
    expected = [total % 5] * 3
    codeflash_output = net._classify(features) # 1.56μs -> 952ns (64.2% faster)

def test_classify_basic_mixed_types():
    # Test with a mix of ints and floats
    net = AlexNet(num_classes=4)
    features = [1, 2.0, 3]
    total = sum(features)
    expected = [total % 4] * 3
    codeflash_output = net._classify(features) # 1.49μs -> 891ns (67.6% faster)

def test_classify_basic_single_element():
    # Test with a single-element list
    net = AlexNet(num_classes=6)
    features = [42]
    total = sum(features)
    expected = [total % 6]
    codeflash_output = net._classify(features) # 1.18μs -> 891ns (32.7% faster)

# 2. Edge Test Cases

def test_classify_empty_list():
    # Test with an empty list (should return empty list)
    net = AlexNet(num_classes=10)
    features = []
    expected = []
    codeflash_output = net._classify(features) # 1.09μs -> 1.15μs (5.21% slower)

def test_classify_all_zeros():
    # Test with all zeros
    net = AlexNet(num_classes=3)
    features = [0, 0, 0, 0]
    total = sum(features)
    expected = [total % 3] * 4
    codeflash_output = net._classify(features) # 1.35μs -> 742ns (82.3% faster)

def test_classify_large_numbers():
    # Test with very large numbers
    net = AlexNet(num_classes=100)
    features = [10**12, 10**12, 10**12]
    total = sum(features)
    expected = [total % 100] * 3
    codeflash_output = net._classify(features) # 1.44μs -> 872ns (65.5% faster)

def test_classify_with_negative_and_positive():
    # Test with a mix of positive and negative numbers
    net = AlexNet(num_classes=8)
    features = [5, -3, 10, -12]
    total = sum(features)
    expected = [total % 8] * 4
    codeflash_output = net._classify(features) # 1.39μs -> 771ns (80.5% faster)

def test_classify_non_iterable_input():
    # Test with a non-iterable input (should raise TypeError)
    net = AlexNet()
    with pytest.raises(TypeError):
        net._classify(12345)


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

def test_classify_negative_num_classes():
    # Test with negative num_classes (should still work mathematically)
    net = AlexNet(num_classes=-3)
    features = [1, 2, 3]
    total = sum(features)
    expected = [total % -3] * 3
    codeflash_output = net._classify(features) # 1.44μs -> 771ns (87.2% faster)

def test_classify_tuple_input():
    # Test with tuple input instead of list
    net = AlexNet(num_classes=7)
    features = (1, 2, 3)
    total = sum(features)
    expected = [total % 7] * 3
    codeflash_output = net._classify(features) # 1.31μs -> 792ns (65.7% faster)


def test_classify_large_list():
    # Test with a large list of 1000 elements
    net = AlexNet(num_classes=123)
    features = list(range(1000))
    total = sum(features)
    expected = [total % 123] * 1000
    codeflash_output = net._classify(features) # 42.7μs -> 5.79μs (637% faster)

def test_classify_large_random_floats():
    # Test with a large list of random floats
    net = AlexNet(num_classes=77)
    random.seed(42)
    features = [random.uniform(-1000, 1000) for _ in range(999)]
    total = sum(features)
    expected = [total % 77] * 999
    codeflash_output = net._classify(features) # 49.2μs -> 5.51μs (794% faster)

def test_classify_large_negative_numbers():
    # Test with a large list of negative numbers
    net = AlexNet(num_classes=256)
    features = [-i for i in range(1000)]
    total = sum(features)
    expected = [total % 256] * 1000
    codeflash_output = net._classify(features) # 42.9μs -> 6.35μs (575% faster)

def test_classify_large_all_zeros():
    # Test with a large list of all zeros
    net = AlexNet(num_classes=11)
    features = [0] * 1000
    total = 0
    expected = [0] * 1000
    codeflash_output = net._classify(features) # 37.9μs -> 5.42μs (600% faster)

def test_classify_large_mixed_types():
    # Test with a large list of alternating ints and floats
    net = AlexNet(num_classes=100)
    features = [i if i % 2 == 0 else float(i) for i in range(1000)]
    total = sum(features)
    expected = [total % 100] * 1000
    codeflash_output = net._classify(features) # 50.3μs -> 6.72μs (648% 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-mccv1szm and push.