⚡️ Speed up method AlexNet.forward by 314%

[codeflash-ai]

📄 314% (3.14x) speedup for AlexNet.forward in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 110 microseconds → 26.6 microseconds (best of 912 runs)

📝 Explanation and details

Here is a rewrite of your program for significantly improved runtime, based on your profile and the code. The main bottleneck is the _extract_features method: it currently loops through len(x), and only does pass in the loop, so the only output is result = [] regardless of x. If the real method does no processing and always returns an empty list, then you can replace the body with a simple return. This makes the function O(1) instead of O(N), and also reduces allocations.

Your _classify is already quite efficient for lists, but sum(features) will immediately return 0 if the list is empty. No further optimization needed here.

Optimized code.

Summary of changes:

• Rewrote _extract_features to simply return []. This removes the unnecessary loop and the allocation of an unused list, making it trivial in runtime.

Note:
If you planned to actually extract features in that function, you'll need to replace the pass with efficient processing, perhaps with list comprehensions or optimized numpy/PyTorch calls depending on context. But given the line profile and behavior, this is the fastest correct equivalent for the code you provided.

Let me know if you want an example rewrite assuming more realistic feature extraction!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 34 Passed
⏪ Replay Tests	✅ 1 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

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

# unit tests

# 1. BASIC TEST CASES

def test_forward_single_element():
    # Single element input
    net = AlexNet(num_classes=10)
    x = [3]
    # features = [9], sum=9, 9%10=9, output=[9]
    codeflash_output = net.forward(x) # 2.29μs -> 1.48μs (54.1% faster)

def test_forward_multiple_elements():
    # Multiple elements, normal case
    net = AlexNet(num_classes=100)
    x = [1, 2, 3]
    # features = [1,4,9], sum=14, 14%100=14, output=[14,14,14]
    codeflash_output = net.forward(x) # 2.11μs -> 1.29μs (63.6% faster)

def test_forward_negative_numbers():
    # Negative numbers in input
    net = AlexNet(num_classes=50)
    x = [-2, -3]
    # features = [4,9], sum=13, 13%50=13, output=[13,13]
    codeflash_output = net.forward(x) # 2.02μs -> 1.25μs (61.5% faster)

def test_forward_zero_input():
    # All zeros
    net = AlexNet(num_classes=7)
    x = [0, 0, 0]
    # features = [0,0,0], sum=0, 0%7=0, output=[0,0,0]
    codeflash_output = net.forward(x) # 1.99μs -> 1.21μs (64.5% faster)

def test_forward_empty_input():
    # Empty input should return empty output
    net = AlexNet(num_classes=100)
    x = []
    codeflash_output = net.forward(x) # 1.74μs -> 1.26μs (38.0% faster)

# 2. EDGE TEST CASES

def test_forward_large_numbers():
    # Very large numbers to test overflow/large ints
    net = AlexNet(num_classes=1000)
    x = [10**6, 10**6]
    # features = [10**12, 10**12], sum=2*10**12, 2*10**12%1000=0, output=[0,0]
    codeflash_output = net.forward(x) # 2.00μs -> 1.20μs (66.7% faster)

def test_forward_num_classes_one():
    # num_classes=1, all outputs should be 0
    net = AlexNet(num_classes=1)
    x = [5, -5, 2]
    # features = [25,25,4], sum=54, 54%1=0, output=[0,0,0]
    codeflash_output = net.forward(x) # 2.00μs -> 1.23μs (62.6% faster)

def test_forward_num_classes_equals_sum():
    # num_classes equal to sum of features
    net = AlexNet(num_classes=30)
    x = [3, 4, 5]
    # features = [9,16,25], sum=50, 50%30=20, output=[20,20,20]
    codeflash_output = net.forward(x) # 1.96μs -> 1.23μs (59.3% faster)

def test_forward_with_one_element_and_zero_class():
    # num_classes=0 should raise ZeroDivisionError
    net = AlexNet(num_classes=0)
    x = [1]
    with pytest.raises(ZeroDivisionError):
        net.forward(x)

def test_forward_with_non_integer_input():
    # Non-integer input (floats)
    net = AlexNet(num_classes=10)
    x = [1.5, -2.5]
    # features = [2.25, 6.25], sum=8.5, 8.5%10=8.5, output=[8.5,8.5]
    codeflash_output = net.forward(x) # 2.07μs -> 1.33μs (55.7% faster)

def test_forward_with_boolean_input():
    # Boolean input (should be treated as 1 and 0)
    net = AlexNet(num_classes=3)
    x = [True, False, True]
    # features = [1,0,1], sum=2, 2%3=2, output=[2,2,2]
    codeflash_output = net.forward(x) # 2.00μs -> 1.25μs (60.1% faster)

def test_forward_with_mixed_types():
    # Mixed int/float/bool
    net = AlexNet(num_classes=5)
    x = [True, 2, 2.0, False]
    # features = [1,4,4.0,0], sum=9.0, 9.0%5=4.0, output=[4.0,4.0,4.0,4.0]
    codeflash_output = net.forward(x) # 1.98μs -> 1.18μs (67.8% faster)

# 3. LARGE SCALE TEST CASES

def test_forward_large_input():
    # Large input list
    net = AlexNet(num_classes=1000)
    x = list(range(1000))  # 0 to 999
    # features = [i**2 for i in range(1000)]
    # sum = sum(i**2 for i in range(1000)) = n(n-1)(2n-1)/6 for n=1000
    n = 1000
    expected_sum = sum(i**2 for i in range(n))
    expected_mod = expected_sum % 1000
    codeflash_output = net.forward(x) # 15.9μs -> 1.09μs (1359% faster)

def test_forward_large_input_all_ones():
    # Large input, all ones
    net = AlexNet(num_classes=100)
    x = [1]*1000
    # features = [1]*1000, sum=1000, 1000%100=0
    codeflash_output = net.forward(x) # 16.4μs -> 1.11μs (1370% faster)

def test_forward_large_input_all_negatives():
    # Large input, all -1
    net = AlexNet(num_classes=100)
    x = [-1]*1000
    # features = [1]*1000, sum=1000, 1000%100=0
    codeflash_output = net.forward(x) # 16.4μs -> 1.10μs (1384% faster)

def test_forward_large_input_alternating():
    # Large input, alternating 1 and -1
    net = AlexNet(num_classes=100)
    x = [1 if i%2==0 else -1 for i in range(1000)]
    # features = [1]*1000, sum=1000, 1000%100=0
    codeflash_output = net.forward(x) # 15.5μs -> 1.14μs (1257% faster)

def test_forward_large_input_random():
    # Large input, random values
    import random
    random.seed(42)
    net = AlexNet(num_classes=500)
    x = [random.randint(-1000,1000) for _ in range(1000)]
    features = [v**2 for v in x]
    expected_mod = sum(features) % 500
    codeflash_output = net.forward(x) # 15.7μs -> 1.18μs (1227% 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.forward-mccvat6i and push.