⚡️ Speed up method AlexNet._extract_features by 938%

[codeflash-ai]

📄 938% (9.38x) speedup for AlexNet._extract_features in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 117 microseconds → 11.2 microseconds (best of 182 runs)

📝 Explanation and details

Here is an optimized version of your code. The original for loop is a no-op (does nothing: the body is just pass). In Python, looping with an empty body over a range is inefficient and serves no useful purpose.

• The fastest equivalent code is to simply skip the loop entirely if you're not modifying result or computing something.
• If you want to return an empty list every time (matching the original program), you can just do that directly.
• No need for range, len, or the loop at all.

Here is the rewritten program.

Explanation of optimizations:

• Removed the for loop which did nothing and was expensive in terms of runtime.
• Directly returns an empty list which matches the behavior and output of the original function.
• Kept all comments intact as required.

This version is as fast and memory-efficient as possible for the originally-defined semantics!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 54 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_single_sample():
    # Single sample, simple list of ints
    net = AlexNet()
    x = [[1, 2, 3]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.22μs -> 381ns (221% faster)

def test_extract_features_multiple_samples():
    # Multiple samples, different values
    net = AlexNet()
    x = [
        [1, 2, 3],
        [4, 5],
        [0]
    ]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.17μs -> 340ns (245% faster)

def test_extract_features_empty_sublist():
    # Sublist is empty, should fill with zeros
    net = AlexNet()
    x = [[1, 2], []]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.08μs -> 341ns (217% faster)

def test_extract_features_empty_input():
    # No samples at all
    net = AlexNet()
    x = []
    codeflash_output = net._extract_features(x); result = codeflash_output # 862ns -> 360ns (139% faster)

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




def test_extract_features_large_numbers():
    # Sublist contains very large numbers
    net = AlexNet()
    big = 10**18
    x = [[big, big]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.38μs -> 411ns (236% faster)

def test_extract_features_negative_numbers():
    # Sublist contains negative numbers
    net = AlexNet()
    x = [[-1, -2, -3]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.21μs -> 370ns (228% faster)

def test_extract_features_mixed_types_in_sublist():
    # Sublist contains floats and ints
    net = AlexNet()
    x = [[1, 2.5, 3]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.17μs -> 361ns (225% faster)

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

def test_extract_features_many_samples():
    # Test with 1000 samples, each with 2 elements
    net = AlexNet()
    x = [[i, i+1] for i in range(1000)]
    codeflash_output = net._extract_features(x); result = codeflash_output # 15.1μs -> 450ns (3262% faster)
    for i in range(1000):
        expected = x[i][0] + x[i][1]

def test_extract_features_large_sublist():
    # Sublist with 1000 elements
    net = AlexNet()
    sublist = list(range(1000))
    x = [sublist]
    codeflash_output = net._extract_features(x); result = codeflash_output # 942ns -> 381ns (147% faster)
    expected_sum = sum(sublist)

def test_extract_features_random_large_input():
    # Randomized test: 500 samples, each with 10-20 random ints
    net = AlexNet()
    random.seed(42)
    x = []
    expected_sums = []
    for _ in range(500):
        sample = [random.randint(-1000, 1000) for _ in range(random.randint(10, 20))]
        x.append(sample)
        expected_sums.append(sum(sample))
    codeflash_output = net._extract_features(x); result = codeflash_output
    for i in range(500):
        pass

def test_extract_features_all_empty_sublists_large():
    # 1000 empty sublists
    net = AlexNet()
    x = [[] for _ in range(1000)]
    codeflash_output = net._extract_features(x); result = codeflash_output # 15.2μs -> 371ns (4008% faster)
    for vec in result:
        pass

def test_extract_features_performance_limit():
    # 999 samples, each with 999 elements (all 1s)
    net = AlexNet()
    x = [[1]*999 for _ in range(999)]
    codeflash_output = net._extract_features(x); result = codeflash_output # 15.6μs -> 541ns (2789% faster)
    for vec in result:
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

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

# unit tests

# -------------------------
# Basic Test Cases
# -------------------------

def test_single_sample_basic():
    # Test with a single sample of positive integers
    model = AlexNet()
    x = [[1, 2, 3, 4, 5]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.35μs -> 431ns (214% faster)

def test_multiple_samples_basic():
    # Test with multiple samples of varying values
    model = AlexNet()
    x = [
        [1, 1, 1],
        [2, 3, 4],
        [10, 0, -10, 20]
    ]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.20μs -> 331ns (263% faster)

def test_sample_with_floats():
    # Test with floats in the sample
    model = AlexNet()
    x = [[1.5, 2.5, 3.0]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.14μs -> 321ns (256% faster)

# -------------------------
# Edge Test Cases
# -------------------------

def test_empty_input_list():
    # Test with empty input list
    model = AlexNet()
    x = []
    codeflash_output = model._extract_features(x); features = codeflash_output # 881ns -> 340ns (159% faster)

def test_empty_sample():
    # Test with a sample that is an empty list
    model = AlexNet()
    x = [[]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.15μs -> 330ns (249% faster)

def test_sample_with_negative_numbers():
    # Test with negative numbers
    model = AlexNet()
    x = [[-5, -10, -3]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.11μs -> 300ns (271% faster)

def test_sample_with_zeros():
    # Test with all zeros
    model = AlexNet()
    x = [[0, 0, 0, 0]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.07μs -> 281ns (281% faster)

def test_sample_with_large_numbers():
    # Test with large numbers
    model = AlexNet()
    x = [[1_000_000, 2_000_000, 3_000_000]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.15μs -> 280ns (311% faster)

def test_sample_with_single_element():
    # Test with a sample containing a single element
    model = AlexNet()
    x = [[42]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.12μs -> 340ns (230% faster)


def test_sample_is_not_list_or_tuple():
    # Test that a sample that is not a list or tuple returns None features
    model = AlexNet()
    x = [None, 5, {"a": 1}]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.43μs -> 431ns (232% faster)


def test_tuple_samples():
    # Test that tuples are accepted as samples
    model = AlexNet()
    x = [(1, 2, 3), (4, 5, 6)]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.20μs -> 421ns (186% faster)

# -------------------------
# Large Scale Test Cases
# -------------------------

def test_large_number_of_samples():
    # Test with 1000 samples, each with 10 elements
    model = AlexNet()
    x = [[i for i in range(10)] for _ in range(1000)]
    codeflash_output = model._extract_features(x); features = codeflash_output # 15.4μs -> 441ns (3403% faster)
    for f in features:
        pass

def test_large_sample_size():
    # Test with a single sample of 1000 elements
    model = AlexNet()
    x = [list(range(1000))]
    codeflash_output = model._extract_features(x); features = codeflash_output # 982ns -> 410ns (140% faster)
    expected_sum = sum(range(1000))
    expected_mean = expected_sum / 1000
    expected_max = 999

def test_large_varied_samples():
    # Test with 500 samples, each with increasing size
    model = AlexNet()
    x = [list(range(i)) for i in range(1, 501)]
    codeflash_output = model._extract_features(x); features = codeflash_output # 7.75μs -> 481ns (1510% faster)
    for i, f in enumerate(features):
        n = i + 1
        expected_sum = sum(range(n))
        expected_mean = expected_sum / n
        expected_max = n - 1

def test_large_samples_with_edge_cases():
    # Test with 1000 samples, some of which are empty or invalid
    model = AlexNet()
    x = [[i] for i in range(995)] + [[] for _ in range(3)] + [None, "abc"]
    codeflash_output = model._extract_features(x); features = codeflash_output # 15.1μs -> 391ns (3764% faster)
    for i in range(995):
        pass
    for i in range(995, 998):
        pass
# 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-mccvs2h6 and push.