⚡️ 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 : 93.2 microseconds → 12.2 microseconds (best of 177 runs)

📝 Explanation and details

Here's a rewritten, faster version of your code.

Optimization Summary

• The original code iterates with a for-loop and does nothing inside. This is extraneous work; nothing is achieved by counting through len(x) except time wasted.
• The fastest equivalent way in Python to do nothing is simply to return the empty result immediately.

Fastest Rewritten Version

Explanation:

• Removed the unused for-loop to avoid O(N) iteration.
• The function now instantly returns its result, achieving best possible performance (O(1)) and minimal memory.

This version produces exactly the same output as the original for any input.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 62 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 test data
import string  # used for generating random strings

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

# unit tests

# ------------------- BASIC TEST CASES -------------------

def test_basic_single_list():
    # Test with a single sub-list of positive integers
    alex = AlexNet()
    x = [[1, 2, 3, 4, 5]]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.29μs -> 391ns (231% faster)

def test_basic_multiple_lists():
    # Test with multiple sub-lists of varying lengths
    alex = AlexNet()
    x = [[1, 2], [3, 4, 5], [6]]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.21μs -> 330ns (267% faster)

def test_basic_negative_numbers():
    # Test with negative numbers in the sub-lists
    alex = AlexNet()
    x = [[-1, -2, -3], [0, 1, -1]]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.15μs -> 331ns (248% faster)

def test_basic_floats():
    # Test with floating point numbers
    alex = AlexNet()
    x = [[1.5, 2.5, 3.5]]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.17μs -> 341ns (244% faster)

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

def test_edge_empty_outer_list():
    # Test with an empty outer list
    alex = AlexNet()
    x = []
    codeflash_output = alex._extract_features(x); features = codeflash_output # 892ns -> 340ns (162% faster)

def test_edge_empty_inner_list():
    # Test with an inner empty list
    alex = AlexNet()
    x = [[1, 2], [], [3]]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.20μs -> 321ns (274% faster)

def test_edge_all_empty_inner_lists():
    # Test with all inner lists empty
    alex = AlexNet()
    x = [[], [], []]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.10μs -> 320ns (244% faster)

def test_edge_single_element_lists():
    # Test with sub-lists of length 1
    alex = AlexNet()
    x = [[42], [0], [-7]]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.07μs -> 331ns (224% faster)



def test_edge_large_numbers():
    # Test with very large numbers
    alex = AlexNet()
    x = [[1e18, 2e18, 3e18]]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.29μs -> 431ns (200% faster)

def test_edge_small_numbers():
    # Test with very small (negative and positive) numbers
    alex = AlexNet()
    x = [[-1e-10, 0, 1e-10]]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.22μs -> 360ns (239% faster)

def test_edge_mixed_types():
    # Test with int and float mixed in sub-lists
    alex = AlexNet()
    x = [[1, 2.0, 3]]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.20μs -> 341ns (252% faster)

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

def test_large_scale_many_lists():
    # Test with 1000 sub-lists, each with 10 elements
    alex = AlexNet()
    x = [[i for i in range(10)] for _ in range(1000)]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 15.1μs -> 420ns (3504% faster)
    # All sub-lists are [0..9], so mean = 4.5, max = 9, length = 10
    for f in features:
        pass

def test_large_scale_large_inner_lists():
    # Test with 10 sub-lists, each with 1000 elements
    alex = AlexNet()
    x = [list(range(1000)) for _ in range(10)]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.03μs -> 421ns (145% faster)
    # Each sub-list: mean = 499.5, max = 999, length = 1000
    for f in features:
        pass

def test_large_scale_random_data():
    # Test with 100 sub-lists, each with 100 random floats
    alex = AlexNet()
    random.seed(42)
    x = [[random.uniform(-1000, 1000) for _ in range(100)] for _ in range(100)]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 1.75μs -> 400ns (338% faster)
    # Check that each feature dict has correct keys and length
    for arr, f in zip(x, features):
        # Check mean calculation
        expected_mean = sum(arr) / len(arr)

def test_large_scale_all_empty():
    # Test with 1000 empty sub-lists
    alex = AlexNet()
    x = [[] for _ in range(1000)]
    codeflash_output = alex._extract_features(x); features = codeflash_output # 15.0μs -> 371ns (3948% faster)
    for f in features:
        pass



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

# unit tests

# 1. Basic Test Cases

def test_single_flattened_sample():
    # Test with a single 1D sample
    model = AlexNet()
    x = [[1, 2, 3, 4, 5]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.39μs -> 431ns (223% faster)

def test_multiple_flattened_samples():
    # Test with multiple 1D samples
    model = AlexNet()
    x = [[1,2,3], [4,5,6], [7,8,9]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.26μs -> 340ns (271% faster)

def test_single_2d_sample():
    # Test with a single 2D sample (list of lists)
    model = AlexNet()
    x = [[[1,2,3],[4,5,6]]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.22μs -> 330ns (270% faster)

def test_multiple_2d_samples():
    # Test with multiple 2D samples
    model = AlexNet()
    x = [
        [[1,2],[3,4]],
        [[5,6],[7,8]]
    ]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.12μs -> 360ns (212% faster)

def test_single_3d_sample():
    # Test with a single 3D sample (e.g., channel-first image)
    model = AlexNet()
    x = [[[[1,2],[3,4]], [[5,6],[7,8]]]]  # shape (2,2,2)
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.21μs -> 341ns (255% faster)

def test_multiple_3d_samples():
    # Test with multiple 3D samples
    model = AlexNet()
    x = [
        [[[1,2],[3,4]], [[5,6],[7,8]]],
        [[[9,10],[11,12]], [[13,14],[15,16]]]
    ]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.14μs -> 341ns (235% faster)

# 2. Edge Test Cases

def test_empty_input_list():
    # Test with an empty input list
    model = AlexNet()
    x = []
    codeflash_output = model._extract_features(x); features = codeflash_output # 941ns -> 330ns (185% 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 # 1.21μs -> 341ns (255% faster)

def test_nested_empty_lists():
    # Test with samples containing nested empty lists
    model = AlexNet()
    x = [[[],[]], [[],[[]]]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.23μs -> 351ns (251% faster)



def test_sample_with_mixed_types():
    # Test with a sample containing mixed types
    model = AlexNet()
    x = [[1, [2, 3], 4.5, "string", [6, [7, 8]]]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.40μs -> 441ns (218% faster)

def test_sample_with_deeply_nested_lists():
    # Test with a sample containing deeply nested lists
    model = AlexNet()
    x = [[[[[[1]]]]]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.23μs -> 361ns (242% faster)

def test_sample_with_zeroes_and_negatives():
    # Test with sample containing zeroes and negatives
    model = AlexNet()
    x = [[0, -1, [2, -3, [0]]]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.18μs -> 341ns (247% faster)

# 3. Large Scale Test Cases

def test_large_number_of_samples():
    # Test with a large number of samples (each sample is 1D)
    model = AlexNet()
    x = [[i] for i in range(1000)]
    codeflash_output = model._extract_features(x); features = codeflash_output # 15.5μs -> 410ns (3680% faster)

def test_large_sample_flattening():
    # Test with a single large 2D sample
    model = AlexNet()
    x = [ [list(range(100)) for _ in range(10)] ]  # 10x100 = 1000 elements
    codeflash_output = model._extract_features(x); features = codeflash_output # 821ns -> 340ns (141% faster)
    expected = [list(range(100)) for _ in range(10)]
    flat_expected = []
    for row in expected:
        flat_expected.extend(row)

def test_large_3d_samples():
    # Test with multiple large 3D samples
    model = AlexNet()
    # 5 samples, each with shape (2,10,10) = 200 elements per sample
    x = []
    for i in range(5):
        sample = []
        for c in range(2):
            channel = []
            for h in range(10):
                channel.append([i*1000 + c*100 + h*10 + w for w in range(10)])
            sample.append(channel)
        x.append(sample)
    codeflash_output = model._extract_features(x); features = codeflash_output
    for idx, flat in enumerate(features):
        pass

def test_large_mixed_dimensional_samples():
    # Test with a mix of 1D, 2D, and 3D samples in a large batch
    model = AlexNet()
    x = []
    # 300 1D, 300 2D (10x10), 300 3D (2x5x5)
    for i in range(300):
        x.append([i])
    for i in range(300):
        x.append([list(range(10)) for _ in range(10)])
    for i in range(300):
        sample = []
        for c in range(2):
            channel = []
            for h in range(5):
                channel.append([i*100 + c*10 + h for _ in range(5)])
            sample.append(channel)
        x.append(sample)
    codeflash_output = model._extract_features(x); features = codeflash_output
    # Check a 3D sample
    flat_3d = []
    for c in range(2):
        for h in range(5):
            flat_3d.extend([350*100 + c*10 + h for _ in range(5)])
# 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-mccv5rve and push.