⚡️ Speed up method AlexNet._extract_features by 663%

[codeflash-ai]

📄 663% (6.63x) speedup for AlexNet._extract_features in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 96.3 microseconds → 12.6 microseconds (best of 148 runs)

📝 Explanation and details

Here's the optimized version of your code.
Your original for-loop only iterated and did nothing (contained just pass). To optimize such a case, do not loop at all—the loop is entirely unnecessary and is the biggest cost observed in the profile.
If this loop is a placeholder for future feature extraction (the "real" code), you should only optimize so far as this placeholder allows.
But based on what's given, here's the more efficient version (no-op extraction).

Explanation.

• The original method performed no computation, just created and returned an empty list after looping over input.
• The optimized version immediately returns the empty list, entirely eliminating the unnecessary loop.

This is now O(1) runtime regardless of x.
Line profile time will no longer be spent inside the unusable loop.

If in the future you add real feature extraction inside the loop, consider vectorized operations with NumPy or appropriate PyTorch/TensorFlow ops to optimize further. Let me know if you need help with that!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 64 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

# ------------------------
# 1. Basic Test Cases
# ------------------------

def test_single_sample_basic():
    # Test with a single sample of positive integers
    net = AlexNet()
    x = [[3, 4]]
    # Normalized: [3/5, 4/5]
    expected = [[0.6, 0.8]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.23μs -> 401ns (207% faster)

def test_multiple_samples_basic():
    # Test with multiple samples of various lengths
    net = AlexNet()
    x = [
        [1, 0, 0],
        [0, 2, 0],
        [0, 0, 3]
    ]
    expected = [
        [1.0, 0.0, 0.0],
        [0.0, 1.0, 0.0],
        [0.0, 0.0, 1.0]
    ]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.11μs -> 351ns (217% faster)
    for r, e in zip(result, expected):
        for rv, ev in zip(r, e):
            pass

def test_sample_with_negative_values():
    # Test normalization with negative values
    net = AlexNet()
    x = [[-3, 4]]
    norm = (9 + 16) ** 0.5
    expected = [[-3/norm, 4/norm]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.18μs -> 340ns (248% faster)

def test_sample_with_floats():
    # Test normalization with float values
    net = AlexNet()
    x = [[0.3, 0.4]]
    norm = (0.3 ** 2 + 0.4 ** 2) ** 0.5
    expected = [[0.3/norm, 0.4/norm]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.17μs -> 350ns (235% faster)

# ------------------------
# 2. Edge Test Cases
# ------------------------

def test_empty_input():
    # Test with empty input list
    net = AlexNet()
    x = []
    codeflash_output = net._extract_features(x); result = codeflash_output # 921ns -> 341ns (170% faster)

def test_sample_empty_list():
    # Test with a sample that is an empty list
    net = AlexNet()
    x = [[]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.22μs -> 341ns (258% faster)

def test_sample_all_zeros():
    # Test with a sample of all zeros
    net = AlexNet()
    x = [[0, 0, 0]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.16μs -> 351ns (231% faster)




def test_sample_with_one_element():
    # Test with a sample of a single element
    net = AlexNet()
    x = [[7]]
    expected = [[1.0]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.44μs -> 441ns (227% faster)


def test_sample_tuple_input():
    # Test with a tuple instead of a list in the sample
    net = AlexNet()
    x = [(3, 4)]
    expected = [[0.6, 0.8]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.41μs -> 430ns (229% faster)

def test_sample_mixed_types():
    # Test with int and float mixed in one sample
    net = AlexNet()
    x = [[3, 4.0]]
    norm = (3**2 + 4.0**2) ** 0.5
    expected = [[3/norm, 4.0/norm]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.22μs -> 370ns (230% faster)

# ------------------------
# 3. Large Scale Test Cases
# ------------------------

def test_large_number_of_samples():
    # Test with a large number of samples (e.g., 500 samples)
    net = AlexNet()
    num_samples = 500
    sample_length = 10
    x = [[float(i) for i in range(sample_length)] for _ in range(num_samples)]
    codeflash_output = net._extract_features(x); result = codeflash_output # 7.64μs -> 421ns (1716% faster)
    # Check that the first sample is normalized
    norm = sum(i**2 for i in range(sample_length)) ** 0.5
    for i in range(sample_length):
        pass

def test_large_sample_length():
    # Test with a single sample of large length (e.g., 1000 elements)
    net = AlexNet()
    sample_length = 1000
    x = [list(range(1, sample_length + 1))]
    norm = sum(i**2 for i in range(1, sample_length + 1)) ** 0.5
    codeflash_output = net._extract_features(x); result = codeflash_output # 982ns -> 381ns (158% faster)
    # Check normalization for a few elements
    for idx in [0, 499, 999]:
        pass



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_list_of_numbers():
    # Basic: list of ints
    model = AlexNet()
    input_data = [1, 2, 3, 4, 5]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.49μs -> 431ns (246% faster)

def test_extract_features_list_of_floats():
    # Basic: list of floats
    model = AlexNet()
    input_data = [1.1, 2.2, 3.3]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.27μs -> 360ns (253% faster)

def test_extract_features_list_of_lists_of_numbers():
    # Basic: list of lists of ints
    model = AlexNet()
    input_data = [[1, 2], [3, 4], [5]]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.18μs -> 361ns (228% faster)

def test_extract_features_list_of_lists_of_floats():
    # Basic: list of lists of floats
    model = AlexNet()
    input_data = [[1.1, 2.2], [3.3]]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.20μs -> 351ns (242% faster)

def test_extract_features_list_of_strings():
    # Basic: list of strings
    model = AlexNet()
    input_data = ["a", "abc", ""]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.17μs -> 311ns (277% faster)

def test_extract_features_list_of_dicts():
    # Basic: list of dicts
    model = AlexNet()
    input_data = [{"a": 1, "b": 2}, {"x": 3}]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.16μs -> 320ns (263% faster)

def test_extract_features_list_of_none():
    # Basic: list of None
    model = AlexNet()
    input_data = [None, None]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.16μs -> 330ns (252% faster)

# ---------------- EDGE TEST CASES ----------------

def test_extract_features_empty_list():
    # Edge: empty list
    model = AlexNet()
    input_data = []
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 961ns -> 331ns (190% faster)

def test_extract_features_single_element_list():
    # Edge: single element list
    model = AlexNet()
    input_data = [42]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.33μs -> 360ns (270% faster)

def test_extract_features_list_of_empty_lists():
    # Edge: list of empty lists
    model = AlexNet()
    input_data = [[], [], []]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.17μs -> 351ns (234% faster)

def test_extract_features_list_of_empty_strings():
    # Edge: list of empty strings
    model = AlexNet()
    input_data = ["", ""]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.15μs -> 340ns (239% faster)

def test_extract_features_list_of_empty_dicts():
    # Edge: list of empty dicts
    model = AlexNet()
    input_data = [{}, {}]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.12μs -> 330ns (240% faster)




def test_extract_features_list_of_dicts_with_non_str_keys():
    # Edge: dicts with non-string keys
    model = AlexNet()
    input_data = [{1: "a", 2: "b"}, {3: "c"}]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.36μs -> 431ns (216% faster)

def test_extract_features_list_of_lists_of_empty_lists():
    # Edge: list of lists, some of which are empty
    model = AlexNet()
    input_data = [[1, 2], [], [3]]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.27μs -> 381ns (234% faster)

# ---------------- LARGE SCALE TEST CASES ----------------

def test_extract_features_large_list_of_numbers():
    # Large scale: long list of numbers
    model = AlexNet()
    input_data = list(range(1000))
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 15.6μs -> 371ns (4118% faster)

def test_extract_features_large_list_of_lists():
    # Large scale: list of lists of numbers
    model = AlexNet()
    input_data = [list(range(10)) for _ in range(100)]
    expected = list(range(10)) * 100
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 1.74μs -> 341ns (411% faster)

def test_extract_features_large_list_of_strings():
    # Large scale: list of random strings
    model = AlexNet()
    input_data = [''.join(random.choices(string.ascii_letters, k=10)) for _ in range(500)]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 7.53μs -> 361ns (1987% faster)

def test_extract_features_large_list_of_dicts():
    # Large scale: list of dicts with random keys
    model = AlexNet()
    input_data = [{str(i): i for i in range(5)} for _ in range(200)]
    expected = [sorted([str(i) for i in range(5)]) for _ in range(200)]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 2.83μs -> 441ns (543% faster)

def test_extract_features_large_list_of_none():
    # Large scale: list of None
    model = AlexNet()
    input_data = [None] * 800
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 13.2μs -> 361ns (3550% faster)

def test_extract_features_large_list_of_empty_lists():
    # Large scale: list of empty lists
    model = AlexNet()
    input_data = [[] for _ in range(1000)]
    codeflash_output = model._extract_features(input_data); output = codeflash_output # 15.4μs -> 390ns (3861% 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-mccv9m46 and push.