⚡️ Speed up method AlexNet._extract_features by 978%

[codeflash-ai]

📄 978% (9.78x) speedup for AlexNet._extract_features in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 133 microseconds → 12.3 microseconds (best of 225 runs)

📝 Explanation and details

Here's an optimized rewrite of your program. Since the body of the for-loop is pass (it does nothing), the entire loop can be removed for maximum speed, and thus, the function can return an empty list immediately. This returns the exact same result as before, but with negligible runtime.

This is now optimal: zero iterations, zero memory allocations inside a loop, and instantly returns. If later you add content inside the loop, further optimizations may be possible depending on the new code.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 66 Passed
⏪ Replay Tests	✅ 1 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import random
import string

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

# unit tests

# BASIC TEST CASES

def test_extract_features_basic_positive_integers():
    # Test with a simple list of lists of positive integers
    model = AlexNet()
    x = [[1, 2, 3], [4, 5], [6]]
    expected = [6, 9, 6]  # sums of each sublist
    codeflash_output = model._extract_features(x) # 1.34μs -> 390ns (244% faster)

def test_extract_features_basic_negative_and_zero():
    # Test with negative numbers and zeros
    model = AlexNet()
    x = [[0, -1, -2], [3, 0, 0], [-5, 5]]
    expected = [-3, 3, 0]
    codeflash_output = model._extract_features(x) # 1.17μs -> 340ns (245% faster)

def test_extract_features_basic_floats():
    # Test with floating point numbers
    model = AlexNet()
    x = [[1.5, 2.5], [0.1, 0.2, 0.3]]
    expected = [4.0, 0.6]
    codeflash_output = model._extract_features(x) # 1.22μs -> 330ns (271% faster)

def test_extract_features_basic_empty_lists():
    # Test with empty sublists
    model = AlexNet()
    x = [[], [1, 2], []]
    expected = [0, 3, 0]
    codeflash_output = model._extract_features(x) # 1.24μs -> 330ns (276% faster)

def test_extract_features_basic_single_element_lists():
    # Test with single-element sublists
    model = AlexNet()
    x = [[42], [-7], [0]]
    expected = [42, -7, 0]
    codeflash_output = model._extract_features(x) # 1.22μs -> 331ns (269% faster)

# EDGE TEST CASES

def test_extract_features_edge_empty_input():
    # Test with completely empty input
    model = AlexNet()
    x = []
    expected = []
    codeflash_output = model._extract_features(x) # 961ns -> 320ns (200% faster)

def test_extract_features_edge_non_list_elements():
    # Test with non-list elements in input
    model = AlexNet()
    x = [[1, 2], "not a list", 123, None, [3, 4]]
    expected = [3, 0, 0, 0, 7]
    codeflash_output = model._extract_features(x) # 1.32μs -> 321ns (312% faster)

def test_extract_features_edge_nested_lists():
    # Test with nested lists (lists inside lists inside lists)
    model = AlexNet()
    x = [[1, 2], [[3, 4]], [5, [6]], [7]]
    # Only [1,2] and [7] are valid (flat lists of numbers), rest get 0
    expected = [3, 0, 0, 7]
    codeflash_output = model._extract_features(x) # 1.22μs -> 310ns (294% faster)

def test_extract_features_edge_non_numeric_elements():
    # Test with non-numeric elements inside sublists
    model = AlexNet()
    x = [[1, 2, "a"], [3, None], [4.0, 5.0], ["x", "y"]]
    # Only [4.0, 5.0] is valid (all numbers), rest get 0
    expected = [0, 0, 9.0, 0]
    codeflash_output = model._extract_features(x) # 1.25μs -> 310ns (304% faster)

def test_extract_features_edge_large_and_small_numbers():
    # Test with very large and very small numbers
    model = AlexNet()
    x = [[1e100, -1e100], [1e-100, -1e-100], [1, 2, 3]]
    expected = [0.0, 0.0, 6]
    codeflash_output = model._extract_features(x) # 1.21μs -> 301ns (303% faster)

def test_extract_features_edge_mixed_types():
    # Test with a mix of valid and invalid types
    model = AlexNet()
    x = [[1, 2], [], None, 5, [3.5, 4.5], ["a", 1], [0]]
    expected = [3, 0, 0, 0, 8.0, 0, 0]
    codeflash_output = model._extract_features(x) # 1.30μs -> 301ns (333% faster)

def test_extract_features_edge_all_invalid():
    # Test with all elements invalid
    model = AlexNet()
    x = [None, "abc", 123, {}, set()]
    expected = [0, 0, 0, 0, 0]
    codeflash_output = model._extract_features(x) # 1.24μs -> 310ns (301% faster)

# LARGE SCALE TEST CASES

def test_extract_features_large_scale_all_valid():
    # Test with a large input where all sublists are valid
    model = AlexNet()
    x = [[i, i+1, i+2] for i in range(0, 1000, 3)]
    expected = [sum([i, i+1, i+2]) for i in range(0, 1000, 3)]
    codeflash_output = model._extract_features(x) # 5.09μs -> 521ns (877% faster)

def test_extract_features_large_scale_all_empty():
    # Test with a large input of empty sublists
    model = AlexNet()
    x = [[] for _ in range(1000)]
    expected = [0] * 1000
    codeflash_output = model._extract_features(x) # 16.2μs -> 351ns (4527% faster)

def test_extract_features_large_scale_mixed():
    # Test with a large input mixing valid, invalid, and empty sublists
    model = AlexNet()
    x = []
    expected = []
    for i in range(1000):
        if i % 3 == 0:
            x.append([i, i+1])  # valid
            expected.append(i + i + 1)
        elif i % 3 == 1:
            x.append([])        # empty
            expected.append(0)
        else:
            x.append("bad")     # invalid
            expected.append(0)
    codeflash_output = model._extract_features(x)

def test_extract_features_large_scale_randomized():
    # Test with a large input of randomized valid and invalid sublists
    model = AlexNet()
    x = []
    expected = []
    for _ in range(1000):
        r = random.random()
        if r < 0.4:
            vals = [random.randint(-100, 100) for _ in range(random.randint(1, 5))]
            x.append(vals)
            expected.append(sum(vals))
        elif r < 0.7:
            x.append([])
            expected.append(0)
        elif r < 0.85:
            x.append("".join(random.choices(string.ascii_letters, k=5)))
            expected.append(0)
        else:
            x.append([random.randint(-100, 100), "bad", 42])
            expected.append(0)
    codeflash_output = model._extract_features(x)

def test_extract_features_large_scale_performance():
    # Test that the function completes for large input (performance test)
    model = AlexNet()
    x = [[i] * 10 for i in range(1000)]  # 1000 sublists, each with 10 elements
    expected = [i * 10 for i in range(1000)]
    codeflash_output = model._extract_features(x) # 16.0μs -> 381ns (4102% 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
import string

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

# unit tests

# 1. Basic Test Cases

def test_single_flat_list():
    # Single list of numbers, should return the same list wrapped in a list
    net = AlexNet()
    x = [[1, 2, 3, 4]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.25μs -> 311ns (303% faster)

def test_multiple_flat_lists():
    # Multiple lists of numbers
    net = AlexNet()
    x = [[1, 2], [3, 4], [5, 6]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.21μs -> 300ns (304% faster)

def test_empty_outer_list():
    # Empty input list should return empty list
    net = AlexNet()
    x = []
    codeflash_output = net._extract_features(x); features = codeflash_output # 902ns -> 300ns (201% faster)

def test_empty_inner_list():
    # Inner empty lists should be preserved
    net = AlexNet()
    x = [[], [1, 2, 3], []]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.27μs -> 281ns (353% faster)

def test_float_and_int_mixture():
    # Accepts both ints and floats
    net = AlexNet()
    x = [[1, 2.5, 3], [4.0, 5]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.16μs -> 280ns (315% faster)

# 2. Edge Test Cases




def test_negative_and_zero_values():
    # Should handle negative and zero values correctly
    net = AlexNet()
    x = [[0, -1, -2.5, 3]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.34μs -> 421ns (219% faster)

def test_large_numbers():
    # Should handle very large numbers
    net = AlexNet()
    x = [[1e10, -1e12, 999999999]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.27μs -> 370ns (244% faster)

def test_single_element_lists():
    # Should handle lists with a single element
    net = AlexNet()
    x = [[42], [0], [-3.14]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.15μs -> 340ns (239% faster)

def test_all_empty_inner_lists():
    # All inner lists are empty
    net = AlexNet()
    x = [[], [], []]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.24μs -> 331ns (275% faster)


def test_large_number_of_lists():
    # Test with a large number of lists (e.g., 1000)
    net = AlexNet()
    x = [[i] for i in range(1000)]
    codeflash_output = net._extract_features(x); features = codeflash_output # 15.3μs -> 471ns (3156% faster)

def test_large_inner_lists():
    # Test with large inner lists (each up to 1000 elements)
    net = AlexNet()
    x = [list(range(1000)) for _ in range(5)]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.02μs -> 401ns (155% faster)

def test_large_mixed_values():
    # Test with large lists and mixed int/float values
    net = AlexNet()
    x = [[i if i % 2 == 0 else float(i) for i in range(500)] for _ in range(2)]
    codeflash_output = net._extract_features(x); features = codeflash_output # 842ns -> 370ns (128% faster)
    for sublist in features:
        for idx, val in enumerate(sublist):
            if idx % 2 == 0:
                pass
            else:
                pass

def test_large_empty_and_nonempty_mix():
    # Mix of empty and large lists
    net = AlexNet()
    x = [[], list(range(1000)), [], [1, 2, 3], []]
    codeflash_output = net._extract_features(x); features = codeflash_output # 962ns -> 341ns (182% faster)

def test_large_random_data():
    # Randomized data, but all numeric
    net = AlexNet()
    x = [[random.randint(-1000, 1000) for _ in range(100)] for _ in range(10)]
    codeflash_output = net._extract_features(x); features = codeflash_output # 862ns -> 341ns (153% faster)

# Additional edge: very large numbers of empty lists
def test_many_empty_lists():
    net = AlexNet()
    x = [[] for _ in range(1000)]
    codeflash_output = net._extract_features(x); features = codeflash_output # 15.5μs -> 371ns (4091% faster)

# Additional edge: single very large list
def test_single_very_large_list():
    net = AlexNet()
    x = [list(range(1000))]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.01μs -> 371ns (173% 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._extract_features-mccutms4 and push.