⚡️ Speed up method AlexNet._extract_features by 698%

[codeflash-ai]

📄 698% (6.98x) speedup for AlexNet._extract_features in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 90.4 microseconds → 11.3 microseconds (best of 52 runs)

📝 Explanation and details

Here is an optimized version of the program.

Explanation:

• The original function iterates through x using a for loop, but does nothing on each iteration except pass, and then returns an empty list.
• This means the loop is unnecessary and can be removed entirely for speed.
• The function's output does not depend on x, thus returning [] immediately is optimal (eliminating the loop brings it as fast as is possible for this function, reducing runtime and memory used by not needlessly looping).

If, in the future, you want to add actual feature extraction operations inside the loop:

• Consider using NumPy or other vectorized libraries for batch operations.
• If you need index access, avoid range(len(x)) in favor of direct iteration (for item in x:) unless index is absolutely needed.

But for the program as given, the above is the fastest possible result.

✅ 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

# 1. Basic Test Cases

def test_extract_features_single_sample():
    # Test with a single sample, typical case
    model = AlexNet()
    sample = {'pixels': [0, 128, 255], 'label': 1}
    codeflash_output = model._extract_features([sample]); features = codeflash_output # 1.31μs -> 421ns (212% faster)

def test_extract_features_multiple_samples():
    # Test with multiple samples
    model = AlexNet()
    samples = [
        {'pixels': [0, 255], 'label': 0},
        {'pixels': [127, 64], 'label': 1}
    ]
    codeflash_output = model._extract_features(samples); features = codeflash_output # 1.20μs -> 361ns (233% faster)

def test_extract_features_empty_pixels():
    # Test with a sample that has empty pixels
    model = AlexNet()
    sample = {'pixels': [], 'label': 2}
    codeflash_output = model._extract_features([sample]); features = codeflash_output # 1.21μs -> 321ns (278% faster)

def test_extract_features_empty_input():
    # Test with empty input list
    model = AlexNet()
    codeflash_output = model._extract_features([]); features = codeflash_output # 961ns -> 361ns (166% faster)

# 2. Edge Test Cases

def test_extract_features_missing_pixels_key():
    # Test with sample missing 'pixels' key (should treat as empty)
    model = AlexNet()
    sample = {'label': 3}
    codeflash_output = model._extract_features([sample]); features = codeflash_output # 1.25μs -> 360ns (248% faster)


def test_extract_features_pixels_with_min_max():
    # Test with pixel values at the min and max of the range
    model = AlexNet()
    sample = {'pixels': [0, 255], 'label': 5}
    codeflash_output = model._extract_features([sample]); features = codeflash_output # 1.45μs -> 431ns (237% faster)

def test_extract_features_pixels_with_negative_and_large_values():
    # Test with negative and >255 values (should still normalize as float)
    model = AlexNet()
    sample = {'pixels': [-10, 260], 'label': 6}
    codeflash_output = model._extract_features([sample]); features = codeflash_output # 1.22μs -> 380ns (222% faster)

def test_extract_features_pixels_with_non_integer_values():
    # Test with floats in 'pixels'
    model = AlexNet()
    sample = {'pixels': [0.0, 127.5, 255.0], 'label': 7}
    codeflash_output = model._extract_features([sample]); features = codeflash_output # 1.24μs -> 341ns (265% faster)

def test_extract_features_pixels_with_single_value():
    # Test with a single pixel value in the list
    model = AlexNet()
    sample = {'pixels': [42], 'label': 8}
    codeflash_output = model._extract_features([sample]); features = codeflash_output # 1.19μs -> 330ns (261% faster)

def test_extract_features_all_zero_pixels():
    # All pixels are zero
    model = AlexNet()
    sample = {'pixels': [0]*10, 'label': 9}
    codeflash_output = model._extract_features([sample]); features = codeflash_output # 1.18μs -> 341ns (247% faster)

def test_extract_features_all_max_pixels():
    # All pixels are 255
    model = AlexNet()
    sample = {'pixels': [255]*10, 'label': 10}
    codeflash_output = model._extract_features([sample]); features = codeflash_output # 1.17μs -> 351ns (234% faster)

# 3. Large Scale Test Cases

def test_extract_features_large_number_of_samples():
    # Test with 1000 samples, each with 10 pixels
    model = AlexNet()
    samples = [{'pixels': [i % 256 for i in range(10)], 'label': i} for i in range(1000)]
    codeflash_output = model._extract_features(samples); features = codeflash_output # 15.3μs -> 531ns (2787% faster)
    for i in range(1000):
        pass

def test_extract_features_large_pixel_vectors():
    # Test with a single sample with 1000 pixels
    model = AlexNet()
    pixels = [random.randint(0, 255) for _ in range(1000)]
    sample = {'pixels': pixels, 'label': 42}
    codeflash_output = model._extract_features([sample]); features = codeflash_output # 972ns -> 360ns (170% faster)

def test_extract_features_large_samples_and_pixels():
    # Test with 500 samples, each with 500 pixels
    model = AlexNet()
    samples = []
    for i in range(500):
        pixels = [random.randint(0, 255) for _ in range(500)]
        samples.append({'pixels': pixels, 'label': i})
    codeflash_output = model._extract_features(samples); features = codeflash_output
    for i in range(500):
        pass

def test_extract_features_performance_large_random():
    # Test with random data, 1000 samples of 100 pixels each
    model = AlexNet()
    samples = []
    for _ in range(1000):
        pixels = [random.randint(0, 255) for _ in range(100)]
        samples.append({'pixels': pixels, 'label': random.randint(0, 9)})
    codeflash_output = model._extract_features(samples); features = codeflash_output
    for i in range(10):  # spot check a few
        idx = random.randint(0, 999)
# 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 scale test data

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

# unit tests

# 1. Basic Test Cases
def test_extract_features_single_sample():
    # 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 # 1.25μs -> 341ns (267% faster)

def test_extract_features_multiple_samples():
    # Test with multiple samples, including negative and float values
    model = AlexNet()
    x = [
        [10, 20, 30],
        [-1, -2, -3],
        [1.5, 2.5, 3.5]
    ]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.24μs -> 300ns (314% faster)

def test_extract_features_empty_sample():
    # Test with a single empty sample
    model = AlexNet()
    x = [[]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.21μs -> 301ns (303% faster)

def test_extract_features_mixed_empty_and_nonempty():
    # Test with a mix of empty and non-empty samples
    model = AlexNet()
    x = [[], [1, 2, 3]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.18μs -> 301ns (293% faster)

# 2. Edge Test Cases

def test_extract_features_all_empty():
    # Test with all samples empty
    model = AlexNet()
    x = [[] for _ in range(5)]
    codeflash_output = model._extract_features(x); features = codeflash_output # 762ns -> 290ns (163% faster)
    for f in features:
        pass

def test_extract_features_single_element_lists():
    # Test with samples each containing a single element
    model = AlexNet()
    x = [[42], [-7], [0]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.14μs -> 290ns (294% faster)




def test_extract_features_large_numbers():
    # Test with very large numbers to check for overflow or precision issues
    model = AlexNet()
    big = 10**18
    x = [[big, big, big]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.41μs -> 431ns (228% faster)

# 3. Large Scale Test Cases

def test_extract_features_large_number_of_samples():
    # Test with a large number of samples (up to 1000)
    model = AlexNet()
    x = [[i for i in range(10)] for _ in range(1000)]
    codeflash_output = model._extract_features(x); features = codeflash_output # 15.2μs -> 461ns (3201% faster)
    for f in features:
        pass

def test_extract_features_large_samples():
    # Test with a single sample containing a large number of elements (up to 1000)
    model = AlexNet()
    x = [list(range(1000))]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.04μs -> 401ns (160% faster)

def test_extract_features_random_large_samples():
    # Test with random large samples to check for performance and correctness
    model = AlexNet()
    random.seed(42)
    x = [[random.randint(-1000, 1000) for _ in range(100)] for _ in range(500)]
    codeflash_output = model._extract_features(x); features = codeflash_output # 8.72μs -> 491ns (1675% faster)
    for i, sample in enumerate(x):
        pass

def test_extract_features_large_and_empty_mix():
    # Test with a mix of large, small, and empty samples
    model = AlexNet()
    x = [
        [],
        [1],
        [1, 2, 3],
        list(range(100)),
        [random.randint(0, 100) for _ in range(500)],
    ]
    codeflash_output = model._extract_features(x); features = codeflash_output # 972ns -> 371ns (162% faster)

# Edge case: all elements are the same
def test_extract_features_identical_elements():
    model = AlexNet()
    x = [[7]*10]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.23μs -> 370ns (233% 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-mccv7hm1 and push.