⚡️ Speed up method AlexNet.forward by 268%

[codeflash-ai]

📄 268% (2.68x) speedup for AlexNet.forward in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 55.9 microseconds → 15.2 microseconds (best of 242 runs)

📝 Explanation and details

Here's an optimized version of your program. Since _extract_features always returns an empty list, calling _classify with this empty list results in a sum of zero and range(len(features)) is always empty, resulting in an empty list as output.

This means the entire process can be shortcut: any value of x will result in a return value of [], with no further computation. All the slow code is avoided.

Perf note:
The optimized forward function simply returns [] and does not instantiate intermediary lists or call redundant routines when it's clear from static analysis that the outputs are always empty. This is the fastest you can make this program without altering the class interface or logic.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 82 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

# ----------------------
# Basic Test Cases
# ----------------------

def test_forward_single_integer():
    # Single integer input
    model = AlexNet(num_classes=10)
    codeflash_output = model.forward(7) # 1.24μs -> 360ns (245% faster)

def test_forward_single_float():
    # Single float input
    model = AlexNet(num_classes=5)
    codeflash_output = model.forward(12.7) # 1.26μs -> 331ns (281% faster)

def test_forward_simple_list():
    # Simple list of integers
    model = AlexNet(num_classes=100)
    codeflash_output = model.forward([1, 2, 3]) # 1.23μs -> 330ns (274% faster)

def test_forward_simple_tuple():
    # Simple tuple of integers
    model = AlexNet(num_classes=4)
    codeflash_output = model.forward((5, 6, 7)) # 1.25μs -> 380ns (229% faster)

def test_forward_list_with_floats():
    # List with floats
    model = AlexNet(num_classes=6)
    codeflash_output = model.forward([1.9, 2.1, 3.7]) # 1.24μs -> 331ns (275% faster)

def test_forward_list_with_negative_numbers():
    # List with negative numbers
    model = AlexNet(num_classes=10)
    codeflash_output = model.forward([-1, -10, 9]) # 1.28μs -> 321ns (300% faster)

def test_forward_list_with_large_numbers():
    # List with large numbers
    model = AlexNet(num_classes=1000)
    codeflash_output = model.forward([1001, 2002, 3003]) # 1.23μs -> 321ns (284% faster)

# ----------------------
# Edge Test Cases
# ----------------------

def test_forward_empty_list():
    # Empty input list should return empty output
    model = AlexNet()
    codeflash_output = model.forward([]) # 1.23μs -> 331ns (272% faster)

def test_forward_nested_lists():
    # Nested lists should be flattened
    model = AlexNet(num_classes=10)
    codeflash_output = model.forward([[1, 2], [3, [4, 5]], 6]) # 1.17μs -> 320ns (266% faster)

def test_forward_deeply_nested():
    # Deeply nested structure
    model = AlexNet(num_classes=5)
    codeflash_output = model.forward([[[[[42]]]]]) # 1.24μs -> 321ns (287% faster)

def test_forward_list_with_non_numeric():
    # Non-numeric values should be ignored
    model = AlexNet(num_classes=10)
    codeflash_output = model.forward([1, 'a', None, 2.5, [3, 'b']]) # 1.25μs -> 311ns (303% faster)

def test_forward_tuple_in_list():
    # Tuples inside lists should be handled
    model = AlexNet(num_classes=3)
    codeflash_output = model.forward([1, (2, 3), [4, (5,)]]) # 1.21μs -> 321ns (278% faster)


def test_forward_non_list_non_tuple_input():
    # Input is not a list or tuple (e.g., a string)
    model = AlexNet(num_classes=100)
    # String is not numeric, so should be ignored, resulting in empty output
    codeflash_output = model.forward("hello") # 1.59μs -> 451ns (253% faster)

def test_forward_list_with_bool():
    # Booleans are instances of int in Python
    model = AlexNet(num_classes=2)
    codeflash_output = model.forward([True, False, 2]) # 1.30μs -> 351ns (271% faster)

def test_forward_list_with_none():
    # None should be ignored
    model = AlexNet(num_classes=5)
    codeflash_output = model.forward([None, 3, None]) # 1.30μs -> 350ns (272% faster)

def test_forward_all_non_numeric():
    # All elements are non-numeric
    model = AlexNet(num_classes=10)
    codeflash_output = model.forward(['a', None, {}, []]) # 1.26μs -> 340ns (271% faster)

# ----------------------
# Large Scale Test Cases
# ----------------------

def test_forward_large_flat_list():
    # Large flat list of integers
    model = AlexNet(num_classes=100)
    data = list(range(1000))
    expected = [i % 100 for i in range(1000)]
    codeflash_output = model.forward(data) # 1.27μs -> 361ns (253% faster)

def test_forward_large_nested_list():
    # Large nested list
    model = AlexNet(num_classes=500)
    data = [[i for i in range(100)] for _ in range(10)]
    flat = [i for i in range(100)] * 10
    expected = [i % 500 for i in flat]
    codeflash_output = model.forward(data) # 1.30μs -> 341ns (282% faster)

def test_forward_large_list_with_non_numeric():
    # Large list with some non-numeric elements
    model = AlexNet(num_classes=1000)
    data = list(range(500)) + ['a', None, {}, [], 999.9]
    expected = [i % 1000 for i in range(500)] + [999 % 1000]
    codeflash_output = model.forward(data) # 1.26μs -> 341ns (270% faster)

def test_forward_large_list_with_negative_and_positive():
    # Large list with negative and positive numbers
    model = AlexNet(num_classes=50)
    data = list(range(-500, 500))
    expected = [i % 50 for i in data]
    codeflash_output = model.forward(data) # 1.28μs -> 360ns (256% faster)

def test_forward_large_deeply_nested():
    # Large, deeply nested list
    model = AlexNet(num_classes=100)
    data = [[[i for i in range(10)] for _ in range(10)] for _ in range(10)]  # 1000 elements
    flat = [i for i in range(10)] * 100
    expected = [i % 100 for i in flat]
    codeflash_output = model.forward(data) # 1.38μs -> 441ns (214% faster)
# 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_forward_single_element():
    # Test with a single integer input
    net = AlexNet(num_classes=10)
    codeflash_output = net.forward([7]); result = codeflash_output # 1.52μs -> 400ns (281% faster)

def test_forward_multiple_elements():
    # Test with a simple list of numbers
    net = AlexNet(num_classes=10)
    codeflash_output = net.forward([1, 2, 3]); result = codeflash_output # 1.29μs -> 341ns (279% faster)

def test_forward_nested_list():
    # Test with a nested list
    net = AlexNet(num_classes=100)
    codeflash_output = net.forward([[1, 2], [3, 4]]); result = codeflash_output # 1.30μs -> 340ns (283% faster)

def test_forward_negative_numbers():
    # Test with negative numbers
    net = AlexNet(num_classes=10)
    codeflash_output = net.forward([-1, -2, -3]); result = codeflash_output # 1.30μs -> 361ns (261% faster)

def test_forward_zero_input():
    # Test with zeros
    net = AlexNet(num_classes=5)
    codeflash_output = net.forward([0, 0, 0]); result = codeflash_output # 1.27μs -> 331ns (284% faster)

def test_forward_single_non_list_input():
    # Test with a single non-list input
    net = AlexNet(num_classes=7)
    codeflash_output = net.forward(5); result = codeflash_output # 1.30μs -> 350ns (272% faster)

# 2. Edge Test Cases

def test_forward_empty_list():
    # Test with empty input list
    net = AlexNet(num_classes=10)
    codeflash_output = net.forward([]); result = codeflash_output # 1.25μs -> 300ns (317% faster)

def test_forward_large_numbers():
    # Test with very large numbers to check for overflow
    net = AlexNet(num_classes=100000)
    large = 10**18
    codeflash_output = net.forward([large, large]); result = codeflash_output # 1.26μs -> 331ns (282% faster)

def test_forward_all_same_value():
    # Test with all elements the same
    net = AlexNet(num_classes=3)
    codeflash_output = net.forward([2, 2, 2]); result = codeflash_output # 1.22μs -> 331ns (269% faster)

def test_forward_different_num_classes():
    # Test with different num_classes
    net = AlexNet(num_classes=2)
    codeflash_output = net.forward([1, 2, 3]); result = codeflash_output # 1.28μs -> 330ns (288% faster)

def test_forward_deeply_nested_list():
    # Test with a deeply nested list
    net = AlexNet(num_classes=10)
    codeflash_output = net.forward([[[1], [2]], [[3, [4]]]]); result = codeflash_output # 1.25μs -> 331ns (279% faster)

def test_forward_non_integer_values():
    # Test with float values
    net = AlexNet(num_classes=100)
    codeflash_output = net.forward([1.5, 2.5, 3.0]); result = codeflash_output # 1.21μs -> 321ns (278% faster)

def test_forward_mixed_int_float():
    # Test with mixed int and float
    net = AlexNet(num_classes=10)
    codeflash_output = net.forward([1, 2.5, 3]); result = codeflash_output # 1.24μs -> 320ns (288% faster)

def test_forward_large_num_classes():
    # Test with num_classes larger than sum
    net = AlexNet(num_classes=10000)
    codeflash_output = net.forward([1, 2, 3]); result = codeflash_output # 1.38μs -> 371ns (273% faster)

def test_forward_num_classes_one():
    # Test with num_classes=1 (all outputs should be 0)
    net = AlexNet(num_classes=1)
    codeflash_output = net.forward([5, 10, 15]); result = codeflash_output # 1.32μs -> 351ns (277% faster)



def test_forward_large_flat_list():
    # Test with a large flat list
    net = AlexNet(num_classes=1000)
    data = [i for i in range(1000)]  # 0..999
    expected_sum = sum(data)
    expected_mod = expected_sum % 1000
    codeflash_output = net.forward(data); result = codeflash_output # 1.47μs -> 421ns (250% faster)

def test_forward_large_nested_list():
    # Test with a large nested list
    net = AlexNet(num_classes=500)
    # Create 10 lists of 100 elements each
    data = [[j for j in range(i*100, (i+1)*100)] for i in range(10)]
    flat = [item for sublist in data for item in sublist]
    expected_sum = sum(flat)
    expected_mod = expected_sum % 500
    codeflash_output = net.forward(data); result = codeflash_output # 1.28μs -> 380ns (237% faster)

def test_forward_large_random_values():
    # Test with a large list of random values
    net = AlexNet(num_classes=100)
    random.seed(42)
    data = [random.randint(-1000, 1000) for _ in range(999)]
    expected_sum = sum(data)
    expected_mod = expected_sum % 100
    codeflash_output = net.forward(data); result = codeflash_output # 1.30μs -> 380ns (243% faster)

def test_forward_large_float_values():
    # Test with a large list of floats
    net = AlexNet(num_classes=1000)
    data = [float(i) * 0.1 for i in range(1000)]
    expected_sum = sum(data)
    expected_mod = expected_sum % 1000
    codeflash_output = net.forward(data); result = codeflash_output # 1.25μs -> 340ns (269% faster)

def test_forward_performance_large_input():
    # Test that the function completes in reasonable time for large input
    import time
    net = AlexNet(num_classes=123)
    data = [i for i in range(1000)]
    start = time.time()
    codeflash_output = net.forward(data); result = codeflash_output # 1.37μs -> 380ns (261% faster)
    elapsed = time.time() - start
# 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-mccv8xt5 and push.