⚡️ Speed up method AlexNet._classify by 391%

[codeflash-ai]

📄 391% (3.91x) speedup for AlexNet._classify in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 514 microseconds → 105 microseconds (best of 235 runs)

📝 Explanation and details

Certainly! Below is a faster version of your program. It mainly optimizes the _classify method by avoiding recomputation of the same expression in the list comprehension and leveraging local variables to reduce attribute lookup overhead (Python does this faster).

Explanation:

• Precompute sum(features) % self.num_classes once instead of computing sum(features) and % self.num_classes for each element in the list comprehension.
• Use list multiplication ([total_mod] * features_len) which is much faster than list comprehension for repeating the same value.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 83 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_single_zero():
    # Test with a single zero in features
    model = AlexNet()
    codeflash_output = model._classify([0]) # 1.40μs -> 1.13μs (23.9% faster)

def test_classify_single_positive():
    # Test with a single positive number
    model = AlexNet()
    codeflash_output = model._classify([7]) # 1.34μs -> 1.06μs (26.4% faster)

def test_classify_single_negative():
    # Test with a single negative number
    model = AlexNet()
    codeflash_output = model._classify([-5]) # 1.45μs -> 1.09μs (33.0% faster)

def test_classify_multiple_positives():
    # Test with multiple positive numbers
    model = AlexNet()
    features = [1, 2, 3]
    expected = [6, 6, 6]  # sum=6, 6%1000=6
    codeflash_output = model._classify(features) # 1.51μs -> 1.08μs (39.8% faster)

def test_classify_mixed_signs():
    # Test with both positive and negative numbers
    model = AlexNet()
    features = [10, -5, 20]
    total = sum(features)  # 25
    expected = [25, 25, 25]
    codeflash_output = model._classify(features) # 1.31μs -> 951ns (38.1% faster)

def test_classify_all_zeros():
    # Test with all zeros
    model = AlexNet()
    features = [0, 0, 0, 0]
    expected = [0, 0, 0, 0]
    codeflash_output = model._classify(features) # 1.48μs -> 1.14μs (29.9% faster)

def test_classify_num_classes_change():
    # Test with a different num_classes
    model = AlexNet(num_classes=5)
    features = [2, 3, 4]
    total = sum(features)  # 9
    expected = [9 % 5] * 3  # [4, 4, 4]
    codeflash_output = model._classify(features) # 1.31μs -> 781ns (68.1% faster)

# 2. Edge Test Cases

def test_classify_large_numbers():
    # Test with very large numbers
    model = AlexNet()
    features = [10**12, 10**12]
    total = sum(features)
    expected_value = total % 1000
    codeflash_output = model._classify(features) # 1.35μs -> 972ns (39.2% faster)

def test_classify_negative_sum():
    # Test where the sum is negative
    model = AlexNet()
    features = [-100, -900]
    total = sum(features)  # -1000
    # Python's % operator always returns a non-negative remainder
    expected_value = (-1000) % 1000  # 0
    codeflash_output = model._classify(features) # 1.39μs -> 1.00μs (39.0% faster)

def test_classify_sum_exactly_multiple_of_num_classes():
    # Test where sum is exactly a multiple of num_classes
    model = AlexNet(num_classes=7)
    features = [7, 14, 0, -7, -7]
    total = sum(features)  # 7+14+0-7-7=7
    expected_value = 7 % 7  # 0
    codeflash_output = model._classify(features) # 1.54μs -> 942ns (63.8% faster)

def test_classify_sum_just_below_zero():
    # Test where sum is just below zero
    model = AlexNet(num_classes=10)
    features = [-1]
    expected = [9]  # -1 % 10 == 9
    codeflash_output = model._classify(features) # 1.31μs -> 1.06μs (23.6% faster)

def test_classify_sum_just_above_num_classes():
    # Test where sum is just above num_classes
    model = AlexNet(num_classes=10)
    features = [11]
    expected = [1]  # 11 % 10 == 1
    codeflash_output = model._classify(features) # 1.23μs -> 1.01μs (21.9% faster)

def test_classify_non_integer_features():
    # Test with float features
    model = AlexNet(num_classes=10)
    features = [1.5, 2.5, 3.0]
    total = sum(features)  # 7.0
    expected = [7.0 % 10] * 3  # [7.0, 7.0, 7.0]
    codeflash_output = model._classify(features); result = codeflash_output # 1.77μs -> 1.08μs (63.9% faster)

def test_classify_empty_with_nonzero_num_classes():
    # Test empty input with non-default num_classes
    model = AlexNet(num_classes=17)
    codeflash_output = model._classify([]) # 1.06μs -> 1.05μs (0.951% faster)

def test_classify_large_negative_and_positive():
    # Test with very large negative and positive numbers
    model = AlexNet(num_classes=100)
    features = [10**18, -10**18 + 1]
    total = sum(features)  # 1
    expected = [1, 1]
    codeflash_output = model._classify(features) # 1.30μs -> 962ns (35.3% faster)

# 3. Large Scale Test Cases

def test_classify_large_input_size_all_ones():
    # Test with a large number of features, all ones
    model = AlexNet()
    features = [1] * 1000
    total = 1000
    expected_value = total % 1000  # 0
    codeflash_output = model._classify(features) # 45.3μs -> 5.74μs (689% faster)

def test_classify_large_input_random_integers():
    # Test with a large number of random integers
    model = AlexNet(num_classes=123)
    random.seed(42)
    features = [random.randint(-1000, 1000) for _ in range(999)]
    total = sum(features)
    expected_value = total % 123
    codeflash_output = model._classify(features); result = codeflash_output # 45.0μs -> 9.63μs (368% faster)

def test_classify_large_input_random_floats():
    # Test with a large number of random floats
    model = AlexNet(num_classes=500)
    random.seed(123)
    features = [random.uniform(-1000, 1000) for _ in range(800)]
    total = sum(features)
    expected_value = total % 500
    codeflash_output = model._classify(features); result = codeflash_output # 41.1μs -> 4.84μs (750% faster)

def test_classify_large_input_alternating_signs():
    # Test with large input, alternating positive and negative numbers
    model = AlexNet(num_classes=100)
    features = [i if i % 2 == 0 else -i for i in range(1, 1001)]
    total = sum(features)
    expected_value = total % 100
    codeflash_output = model._classify(features); result = codeflash_output # 41.8μs -> 6.33μs (560% faster)

def test_classify_large_input_sum_zero():
    # Test with large input whose sum is zero
    model = AlexNet(num_classes=50)
    features = [1] * 500 + [-1] * 500
    total = sum(features)
    expected = [0] * 1000
    codeflash_output = model._classify(features) # 44.8μs -> 6.12μs (632% 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 random test data

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

# unit tests

# 1. BASIC TEST CASES

def test_classify_single_element():
    # Single positive integer
    alex = AlexNet(num_classes=10)
    codeflash_output = alex._classify([7]); result = codeflash_output # 1.44μs -> 1.19μs (21.1% faster)

def test_classify_multiple_elements():
    # Multiple elements, sum less than num_classes
    alex = AlexNet(num_classes=20)
    codeflash_output = alex._classify([1, 2, 3]); result = codeflash_output # 1.54μs -> 1.14μs (35.1% faster)

def test_classify_sum_over_num_classes():
    # Sum greater than num_classes
    alex = AlexNet(num_classes=5)
    codeflash_output = alex._classify([3, 4, 5]); result = codeflash_output # 1.50μs -> 1.11μs (35.1% faster)

def test_classify_negative_numbers():
    # Negative numbers in features
    alex = AlexNet(num_classes=10)
    codeflash_output = alex._classify([-1, -2, -3]); result = codeflash_output # 1.64μs -> 1.17μs (40.2% faster)

def test_classify_floats():
    # Floating point numbers
    alex = AlexNet(num_classes=7)
    codeflash_output = alex._classify([1.5, 2.5, 3.0]); result = codeflash_output # 2.16μs -> 1.55μs (39.3% faster)

def test_classify_mixed_int_float():
    # Mixed integers and floats
    alex = AlexNet(num_classes=4)
    codeflash_output = alex._classify([1, 2.5, 3]); result = codeflash_output # 2.04μs -> 1.43μs (42.6% faster)

def test_classify_all_zeros():
    # All zeros
    alex = AlexNet(num_classes=3)
    codeflash_output = alex._classify([0, 0, 0]); result = codeflash_output # 1.58μs -> 1.11μs (42.4% faster)

def test_classify_large_num_classes():
    # num_classes is large, sum less than num_classes
    alex = AlexNet(num_classes=10000)
    codeflash_output = alex._classify([1, 2, 3]); result = codeflash_output # 1.48μs -> 1.04μs (42.3% faster)

# 2. EDGE TEST CASES


def test_classify_none_features():
    # None as features should raise TypeError
    alex = AlexNet(num_classes=10)
    with pytest.raises(TypeError):
        alex._classify(None)

def test_classify_string_features():
    # String as features should raise TypeError
    alex = AlexNet(num_classes=10)
    with pytest.raises(TypeError):
        alex._classify("abc")

def test_classify_non_numeric_elements():
    # Non-numeric element in features should raise TypeError
    alex = AlexNet(num_classes=10)
    with pytest.raises(TypeError):
        alex._classify([1, "a", 3])

def test_classify_tuple_input():
    # Tuple input should work like list
    alex = AlexNet(num_classes=7)
    codeflash_output = alex._classify((2, 3, 4)); result = codeflash_output # 1.62μs -> 1.30μs (24.7% faster)


def test_classify_negative_num_classes():
    # Negative num_classes should still work (modulo negative)
    alex = AlexNet(num_classes=-5)
    codeflash_output = alex._classify([2, 3]); result = codeflash_output # 1.48μs -> 1.34μs (10.4% faster)

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

def test_classify_large_negative_numbers():
    # Large negative numbers
    alex = AlexNet(num_classes=100)
    codeflash_output = alex._classify([-1000, -2000, -3000]); result = codeflash_output # 1.66μs -> 1.33μs (24.8% faster)

def test_classify_features_size_not_used():
    # features_size attribute is not used in _classify, but should exist
    alex = AlexNet()

# 3. LARGE SCALE TEST CASES

def test_classify_large_input_list():
    # Large list of 1000 elements
    alex = AlexNet(num_classes=500)
    features = [random.randint(1, 100) for _ in range(1000)]
    expected = [sum(features) % 500] * 1000
    codeflash_output = alex._classify(features); result = codeflash_output # 45.7μs -> 5.65μs (708% faster)

def test_classify_large_input_all_same():
    # Large list of same element
    alex = AlexNet(num_classes=123)
    features = [7] * 1000
    expected_mod = (7 * 1000) % 123
    codeflash_output = alex._classify(features) # 41.2μs -> 5.73μs (619% faster)

def test_classify_large_input_floats():
    # Large list of floats
    alex = AlexNet(num_classes=77)
    features = [float(i) for i in range(1000)]
    expected_mod = (sum(features) % 77)
    codeflash_output = alex._classify(features); result = codeflash_output # 58.8μs -> 5.63μs (944% faster)

def test_classify_performance_large_scale():
    # Performance: Should not take too long for 1000 elements
    alex = AlexNet(num_classes=999)
    features = [random.random() for _ in range(1000)]
    codeflash_output = alex._classify(features); result = codeflash_output # 49.9μs -> 5.72μs (772% faster)
    expected = [sum(features) % 999] * 1000

def test_classify_large_negative_and_positive():
    # Large scale with mix of negative and positive
    alex = AlexNet(num_classes=256)
    features = [(-1)**i * i for i in range(1000)]
    expected_mod = (sum(features) % 256)
    codeflash_output = alex._classify(features); result = codeflash_output # 45.6μs -> 6.24μs (631% 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-mccv1kzo and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #389 by codeflash-ai[bot] was closed.