⚡️ Speed up method AlexNet._classify by 379%

[codeflash-ai]

📄 379% (3.79x) speedup for AlexNet._classify in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 232 microseconds → 48.5 microseconds (best of 355 runs)

📝 Explanation and details

Here is an optimized version of your program.
Improvements focus on avoiding redundant computation in the list comprehension by pre-computing the result that does not depend on the loop variable.

Summary of optimizations:

• Compute mod_val only once instead of in every loop iteration.
• Use [mod_val] * len(features) to quickly create the result list.
• Handle empty features efficiently.

✅ 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 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():
    # Basic: positive integers, num_classes=1000
    net = AlexNet(num_classes=1000)
    features = [1, 2, 3]
    total = sum(features)
    expected = [total % 1000] * 3
    codeflash_output = net._classify(features) # 1.35μs -> 871ns (55.3% faster)

def test_classify_basic_negative_integers():
    # Basic: negative integers, num_classes=1000
    net = AlexNet(num_classes=1000)
    features = [-1, -2, -3]
    total = sum(features)
    expected = [total % 1000] * 3
    codeflash_output = net._classify(features) # 1.37μs -> 871ns (57.6% faster)

def test_classify_basic_mixed_integers():
    # Basic: mixed positive and negative integers
    net = AlexNet(num_classes=10)
    features = [5, -2, 8, -3]
    total = sum(features)
    expected = [total % 10] * 4
    codeflash_output = net._classify(features) # 1.37μs -> 842ns (63.1% faster)

def test_classify_basic_floats():
    # Basic: floats
    net = AlexNet(num_classes=5)
    features = [1.5, 2.5, 3.0]
    total = sum(features)
    expected = [total % 5] * 3
    codeflash_output = net._classify(features) # 1.53μs -> 932ns (64.5% faster)

def test_classify_basic_single_element():
    # Basic: single element
    net = AlexNet(num_classes=7)
    features = [42]
    expected = [42 % 7]
    codeflash_output = net._classify(features) # 1.36μs -> 1.14μs (19.3% faster)

def test_classify_basic_zeroes():
    # Basic: all zeroes
    net = AlexNet(num_classes=3)
    features = [0, 0, 0]
    expected = [0] * 3
    codeflash_output = net._classify(features) # 1.54μs -> 1.02μs (51.0% faster)

# --- Edge Test Cases ---

def test_classify_empty_input():
    # Edge: empty input should return empty list
    net = AlexNet(num_classes=100)
    features = []
    codeflash_output = net._classify(features) # 1.01μs -> 390ns (159% faster)

def test_classify_large_numbers():
    # Edge: very large numbers
    net = AlexNet(num_classes=1000)
    features = [10**12, 10**12]
    total = sum(features)
    expected = [total % 1000] * 2
    codeflash_output = net._classify(features) # 1.36μs -> 882ns (54.5% faster)

def test_classify_small_numbers():
    # Edge: very small (negative) numbers
    net = AlexNet(num_classes=100)
    features = [-10**10, -10**10]
    total = sum(features)
    expected = [total % 100] * 2
    codeflash_output = net._classify(features) # 1.29μs -> 901ns (43.4% faster)

def test_classify_non_integer_num_classes():
    # Edge: num_classes=1 (all outputs should be 0)
    net = AlexNet(num_classes=1)
    features = [5, 10, 15]
    expected = [0, 0, 0]
    codeflash_output = net._classify(features) # 1.56μs -> 1.18μs (32.2% faster)

def test_classify_non_iterable_input():
    # Edge: features is not iterable
    net = AlexNet(num_classes=10)
    with pytest.raises(TypeError):
        net._classify(42)  # Not iterable


def test_classify_zero_num_classes():
    # Edge: 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_float_num_classes():
    # Edge: num_classes is float (should work, but modulo with float)
    net = AlexNet(num_classes=2.5)
    features = [1, 2]
    total = sum(features)
    expected = [total % 2.5] * 2
    codeflash_output = net._classify(features) # 1.45μs -> 971ns (49.6% faster)


def test_classify_large_list():
    # Large: 1000 elements, all ones, num_classes=100
    net = AlexNet(num_classes=100)
    features = [1] * 1000
    total = sum(features)
    expected = [total % 100] * 1000
    codeflash_output = net._classify(features) # 41.4μs -> 5.60μs (640% faster)

def test_classify_large_random_list():
    # Large: 1000 random integers, num_classes=500
    net = AlexNet(num_classes=500)
    random.seed(42)
    features = [random.randint(-1000, 1000) for _ in range(1000)]
    total = sum(features)
    expected = [total % 500] * 1000
    codeflash_output = net._classify(features) # 43.3μs -> 9.36μs (363% faster)

def test_classify_large_float_list():
    # Large: 1000 random floats, num_classes=123
    net = AlexNet(num_classes=123)
    random.seed(99)
    features = [random.uniform(-1000.0, 1000.0) for _ in range(1000)]
    total = sum(features)
    expected = [total % 123] * 1000
    codeflash_output = net._classify(features); result = codeflash_output # 48.9μs -> 5.71μs (757% faster)
    # Use allclose for floats, but since we can't use numpy, compare manually
    for r, e in zip(result, expected):
        pass

def test_classify_large_all_zeroes():
    # Large: 1000 zeroes
    net = AlexNet(num_classes=10)
    features = [0] * 1000
    expected = [0] * 1000
    codeflash_output = net._classify(features) # 37.7μs -> 5.55μs (579% faster)

def test_classify_large_alternating_signs():
    # Large: alternating +1, -1, num_classes=2
    net = AlexNet(num_classes=2)
    features = [1 if i % 2 == 0 else -1 for i in range(1000)]
    total = sum(features)
    expected = [total % 2] * 1000
    codeflash_output = net._classify(features) # 37.8μs -> 6.00μs (529% 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-mccuy06t and push.