⚡️ Speed up method AlexNet._extract_features by 623%

[codeflash-ai]

📄 623% (6.23x) speedup for AlexNet._extract_features in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 36.1 microseconds → 5.00 microseconds (best of 386 runs)

📝 Explanation and details

Here is an optimized version of your program.

• I've replaced the for i in range(len(x)): with a more Pythonic and efficient way using range only if needed, or better, used no-ops directly since the for loop is entirely a no-op (pass inside).
• If x is large, looping does use CPU; result is always an empty list, so the function may as well skip unnecessary looping.

If the logic is meant to be a placeholder, it can be reduced to a minimal form that does the same work.

If you expect to implement something in the loop later, but for now want it fast, this is optimal.

If you are actually meant to process x later, let me know what it should do, but as written, your function just always returns an empty list, and the for loop is just wasted runtime.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 22 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 random data

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

# unit tests

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

def test_single_vector():
    # Test with a single feature vector
    model = AlexNet()
    x = [[1, 2, 3, 4]]
    expected = [10]
    codeflash_output = model._extract_features(x) # 1.25μs -> 411ns (205% faster)

def test_multiple_vectors():
    # Test with multiple feature vectors
    model = AlexNet()
    x = [[1, 2], [3, 4], [5, 6]]
    expected = [3, 7, 11]
    codeflash_output = model._extract_features(x) # 1.16μs -> 361ns (222% faster)

def test_empty_batch():
    # Test with empty input list (no feature vectors)
    model = AlexNet()
    x = []
    expected = []
    codeflash_output = model._extract_features(x) # 892ns -> 340ns (162% faster)

def test_negative_and_float_values():
    # Test with negative and float values in feature vectors
    model = AlexNet()
    x = [[-1, -2, 3.5], [0.5, 0.5, -1]]
    expected = [0.5, 0.0]
    codeflash_output = model._extract_features(x) # 1.23μs -> 360ns (242% faster)

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

def test_empty_vectors_in_batch():
    # Test with some empty feature vectors in the input
    model = AlexNet()
    x = [[], [1, 2, 3], []]
    expected = [0, 6, 0]
    codeflash_output = model._extract_features(x) # 1.16μs -> 311ns (274% faster)




def test_large_numbers():
    # Test with very large numbers
    model = AlexNet()
    x = [[1e100, 2e100], [-1e100, 1e100]]
    expected = [3e100, 0.0]
    codeflash_output = model._extract_features(x) # 1.32μs -> 411ns (222% faster)

def test_all_zeros():
    # Test with feature vectors of all zeros
    model = AlexNet()
    x = [[0, 0, 0], [0], []]
    expected = [0, 0, 0]
    codeflash_output = model._extract_features(x) # 1.27μs -> 340ns (274% faster)

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

def test_large_batch_size():
    # Test with a large batch size (1000 vectors)
    model = AlexNet()
    x = [[i for i in range(10)] for _ in range(1000)]  # Each vector: sum 0+1+...+9 = 45
    expected = [45] * 1000
    codeflash_output = model._extract_features(x) # 15.4μs -> 430ns (3488% faster)

def test_large_vector_size():
    # Test with a single very large feature vector (1000 elements)
    model = AlexNet()
    x = [list(range(1000))]
    expected = [sum(range(1000))]
    codeflash_output = model._extract_features(x) # 962ns -> 361ns (166% faster)

def test_large_batch_and_vector():
    # Test with 100 vectors, each of size 100
    model = AlexNet()
    x = [list(range(100)) for _ in range(100)]
    expected = [sum(range(100))] * 100
    codeflash_output = model._extract_features(x) # 1.73μs -> 351ns (394% faster)

def test_random_large_batch():
    # Test with random values in a large batch
    model = AlexNet()
    random.seed(42)
    x = [[random.randint(-1000, 1000) for _ in range(20)] for _ in range(500)]
    expected = [sum(vec) for vec in x]
    codeflash_output = model._extract_features(x) # 7.66μs -> 421ns (1718% 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-mccvaepv and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #413 by codeflash-ai[bot] was closed.