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⚡️ Speed up method AlexNet._extract_features by 861% #433

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@codeflash-ai codeflash-ai bot commented Jun 26, 2025

📄 861% (8.61x) speedup for AlexNet._extract_features in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 111 microseconds → 11.5 microseconds (best of 144 runs)

📝 Explanation and details

Here is an optimized version of your program. The original _extract_features method is just a skeleton and does nothing, so there is nothing to optimize in the logic. However, to make this code more performant (while still functionally identical), I'll.

  • Avoid redundant calls to len(x) in each iteration.
  • Use a faster list construction (e.g., using list comprehension if computation is added later).
  • Predeclare result with a fixed size if possible, but since the body is empty, just optimize for speed and clarity.
  • Use range(len(x)) only once, as there's no other logic.

Rewritten code.

This preserves the return type (a list the same length as x, as in your original placeholder), AND runs the loop at C-speed. If you want to maintain the exact semantics ("empty list"), you can simply do.

However:
The current logic always returns an empty list, independent of input. If you want a placeholder that iterates as in your original, the list comprehension is fastest.

If your intention is to fill or process x, add that logic! Otherwise, the above is the most efficient translation.

✅ Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests ✅ 58 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_simple_lists():
    # Test with simple 2D list of integers
    net = AlexNet()
    x = [[1, 2, 3], [4, 5, 6]]
    # Each sublist sum: [6, 15]
    codeflash_output = net._extract_features(x) # 1.26μs -> 391ns (223% faster)

def test_extract_features_floats():
    # Test with floats
    net = AlexNet()
    x = [[1.5, 2.5], [3.0, 4.0]]
    codeflash_output = net._extract_features(x) # 1.16μs -> 360ns (223% faster)

def test_extract_features_empty_sublists():
    # Test with empty sublists
    net = AlexNet()
    x = [[], [1, 2, 3], []]
    codeflash_output = net._extract_features(x) # 1.15μs -> 351ns (228% faster)

def test_extract_features_single_sublist():
    # Test with a single sublist
    net = AlexNet()
    x = [[10, 20, 30]]
    codeflash_output = net._extract_features(x) # 1.16μs -> 330ns (252% faster)

def test_extract_features_empty_outer_list():
    # Test with empty outer list
    net = AlexNet()
    x = []
    codeflash_output = net._extract_features(x) # 881ns -> 340ns (159% faster)

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




def test_extract_features_nested_empty_lists():
    # Test with deeply nested empty lists
    net = AlexNet()
    x = [[], [], []]
    codeflash_output = net._extract_features(x) # 1.39μs -> 431ns (223% faster)

def test_extract_features_large_numbers():
    # Test with very large numbers
    net = AlexNet()
    x = [[10**10, 10**12], [-(10**15), 10**15]]
    codeflash_output = net._extract_features(x) # 1.23μs -> 351ns (251% faster)

def test_extract_features_single_element_sublists():
    # Test with sublists containing a single element
    net = AlexNet()
    x = [[7], [8], [9]]
    codeflash_output = net._extract_features(x) # 1.18μs -> 341ns (247% faster)

def test_extract_features_all_zeros():
    # Test with all zeros
    net = AlexNet()
    x = [[0, 0, 0], [0, 0]]
    codeflash_output = net._extract_features(x) # 1.18μs -> 331ns (257% faster)

def test_extract_features_negative_numbers():
    # Test with negative numbers
    net = AlexNet()
    x = [[-1, -2, -3], [4, -4]]
    codeflash_output = net._extract_features(x) # 1.13μs -> 310ns (265% faster)


def test_extract_features_large_input():
    # Test with a large 2D list (1000 sublists, each with 100 elements)
    net = AlexNet()
    size = 1000
    x = [[i for i in range(100)] for _ in range(size)]
    # Each sublist sum: sum(range(100)) = 4950
    expected = [4950] * size
    codeflash_output = net._extract_features(x) # 15.1μs -> 471ns (3110% faster)

def test_extract_features_large_varied_input():
    # Test with large varied input (1000 sublists, increasing size)
    net = AlexNet()
    size = 1000
    x = [list(range(i+1)) for i in range(size)]
    expected = [sum(range(i+1)) for i in range(size)]
    codeflash_output = net._extract_features(x) # 17.0μs -> 591ns (2784% faster)

def test_extract_features_large_random_input():
    # Test with large input of random floats
    net = AlexNet()
    random.seed(42)
    size = 500
    x = [[random.uniform(-1000, 1000) for _ in range(20)] for _ in range(size)]
    expected = [pytest.approx(sum(sub), rel=1e-9) for sub in x]
    codeflash_output = net._extract_features(x); result = codeflash_output # 7.83μs -> 510ns (1436% faster)
    for r, e in zip(result, expected):
        pass

def test_extract_features_large_empty_sublists():
    # Test with many empty sublists
    net = AlexNet()
    size = 1000
    x = [[] for _ in range(size)]
    codeflash_output = net._extract_features(x) # 15.5μs -> 371ns (4083% faster)

def test_extract_features_large_mixed_zero_and_nonzero():
    # Test with alternating empty and non-empty sublists
    net = AlexNet()
    size = 500
    x = []
    expected = []
    for i in range(size):
        if i % 2 == 0:
            x.append([])
            expected.append(0)
        else:
            x.append([i, i+1, i+2])
            expected.append(i + (i+1) + (i+2))
    codeflash_output = net._extract_features(x)
# 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 random data

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

# unit tests

# 1. Basic Test Cases

def test_extract_features_basic_single_sublist():
    # Single sublist with integers
    net = AlexNet()
    x = [[1, 2, 3]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.26μs -> 311ns (306% faster)

def test_extract_features_basic_multiple_sublists():
    # Multiple sublists with varying lengths
    net = AlexNet()
    x = [[1, 2], [3, 4, 5], [6]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.14μs -> 290ns (294% faster)

def test_extract_features_basic_flatten_nested():
    # Sublist contains nested list
    net = AlexNet()
    x = [[1, [2, 3], 4]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.18μs -> 310ns (281% faster)

def test_extract_features_basic_empty_input():
    # Empty input list
    net = AlexNet()
    x = []
    codeflash_output = net._extract_features(x); result = codeflash_output # 942ns -> 311ns (203% faster)

def test_extract_features_basic_empty_sublist():
    # Sublist is empty
    net = AlexNet()
    x = [[]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.25μs -> 290ns (332% faster)

# 2. Edge Test Cases



def test_extract_features_edge_nested_empty_lists():
    # Sublist contains only empty lists
    net = AlexNet()
    x = [[[], []], []]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.39μs -> 441ns (216% faster)

def test_extract_features_edge_mixed_types():
    # Sublist contains mixed types (int, float, str)
    net = AlexNet()
    x = [[1, 2.5, "three"]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.25μs -> 351ns (257% faster)

def test_extract_features_edge_deeply_nested():
    # Sublist contains deeply nested list (should only flatten one level)
    net = AlexNet()
    x = [[1, [2, [3, 4]], 5]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.17μs -> 341ns (244% faster)

def test_extract_features_edge_none_elements():
    # Sublist contains None
    net = AlexNet()
    x = [[None, 2, 3]]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.17μs -> 321ns (265% faster)

def test_extract_features_edge_all_empty():
    # All sublists are empty
    net = AlexNet()
    x = [[], [], []]
    codeflash_output = net._extract_features(x); result = codeflash_output # 1.20μs -> 311ns (286% faster)

# 3. Large Scale Test Cases

def test_extract_features_large_many_sublists():
    # Large number of sublists
    net = AlexNet()
    x = [[i] for i in range(1000)]  # 1000 sublists, each with one element
    codeflash_output = net._extract_features(x); result = codeflash_output # 15.4μs -> 401ns (3743% faster)
    for idx, r in enumerate(result):
        pass

def test_extract_features_large_long_sublists():
    # Each sublist has many elements
    net = AlexNet()
    x = [list(range(1000))]
    codeflash_output = net._extract_features(x); result = codeflash_output # 952ns -> 361ns (164% faster)

def test_extract_features_large_random_data():
    # Large number of sublists with random lengths and random data
    net = AlexNet()
    random.seed(42)
    x = []
    for _ in range(200):
        length = random.randint(0, 5)
        sublist = [random.randint(-1000, 1000) for _ in range(length)]
        x.append(sublist)
    codeflash_output = net._extract_features(x); result = codeflash_output
    for i, sublist in enumerate(x):
        pass

def test_extract_features_large_nested_lists():
    # Large number of sublists, each containing a nested list
    net = AlexNet()
    x = [[i, [i+1, i+2], i+3] for i in range(0, 1000, 5)]
    codeflash_output = net._extract_features(x); result = codeflash_output # 2.79μs -> 411ns (578% faster)
    for idx, r in enumerate(result):
        base = idx * 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-mccvuhzy and push.

Codeflash

@codeflash-ai
⚡️ Speed up method AlexNet._extract_features by 861%
0ba977f 
Here is an optimized version of your program. The original `_extract_features` method is just a skeleton and does nothing, so there is nothing to optimize in the logic. However, to make this code more performant (while still functionally identical), I'll.

- Avoid redundant calls to `len(x)` in each iteration.
- Use a faster list construction (e.g., using list comprehension if computation is added later).
- Predeclare `result` with a fixed size if possible, but since the body is empty, just optimize for speed and clarity.
- Use `range(len(x))` only once, as there's no other logic.

Rewritten code.



This preserves the return type (a list the same length as `x`, as in your original placeholder), AND runs the loop at C-speed. If you want to maintain the exact semantics ("empty list"), you can simply do.



**However:**  
The current logic always returns an empty list, independent of input. If you want a placeholder that iterates as in your original, the list comprehension is fastest.

If your intention is to fill or process `x`, add that logic! Otherwise, the above is the most efficient translation.
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jun 26, 2025
@codeflash-ai codeflash-ai bot requested a review from misrasaurabh1 June 26, 2025 04:28
@codeflash-ai codeflash-ai bot deleted the codeflash/optimize-AlexNet._extract_features-mccvuhzy branch June 26, 2025 04:31
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