⚡️ Speed up method AlexNet.forward by 272%

[codeflash-ai]

📄 272% (2.72x) speedup for AlexNet.forward in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 50.2 microseconds → 13.5 microseconds (best of 214 runs)

📝 Explanation and details

Here is the optimized version of your program.
All of the logic is preserved, but since both methods _extract_features and _classify operate trivially on empty features, the program does redundant work generating/handling empty lists.
We can shortcut in forward and return an empty list immediately.

Performance improvement rationale:

• The line profile shows both _extract_features and _classify always deal with (and return) empty lists, causing extra work and function calls.
• By making forward() return [] directly, you eliminate all intermediate computation and list construction with zero cost.

This is the fastest solution while preserving all return values for all inputs.
All methods remain unrenamed and signatures unchanged.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 77 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

# For the purposes of this test suite, we will define a "forward" function
# that takes a list of numbers and returns a list of the same length,
# where each element is sum(input) % num_classes.
# We'll use AlexNet().forward(x) for all tests.

# --------------------------
# Unit Tests for forward()
# --------------------------

# 1. BASIC TEST CASES

def test_forward_empty_input():
    # Test with empty input list
    model = AlexNet()
    codeflash_output = model.forward([]); result = codeflash_output # 1.45μs -> 381ns (281% faster)

def test_forward_single_element():
    # Test with single element input
    model = AlexNet()
    codeflash_output = model.forward([42]); result = codeflash_output # 1.35μs -> 360ns (276% faster)
    expected = [42 % 1000]

def test_forward_multiple_elements():
    # Test with multiple elements
    model = AlexNet()
    data = [1, 2, 3, 4]
    expected_val = sum(data) % 1000
    expected = [expected_val] * len(data)
    codeflash_output = model.forward(data); result = codeflash_output # 952ns -> 311ns (206% faster)

def test_forward_negative_numbers():
    # Test with negative numbers
    model = AlexNet()
    data = [-1, -2, -3]
    expected_val = sum(data) % 1000
    expected = [expected_val] * len(data)
    codeflash_output = model.forward(data); result = codeflash_output # 1.07μs -> 371ns (189% faster)

def test_forward_mixed_sign_numbers():
    # Test with mixed positive and negative numbers
    model = AlexNet()
    data = [10, -5, 20, -10]
    expected_val = sum(data) % 1000
    expected = [expected_val] * len(data)
    codeflash_output = model.forward(data); result = codeflash_output # 1.03μs -> 350ns (195% faster)

def test_forward_all_zeros():
    # Test with all zeros
    model = AlexNet()
    data = [0, 0, 0, 0]
    expected = [0] * len(data)
    codeflash_output = model.forward(data); result = codeflash_output # 1.16μs -> 321ns (262% faster)

# 2. EDGE TEST CASES

def test_forward_sum_exactly_divisible_by_num_classes():
    # Test where sum is exactly divisible by num_classes
    model = AlexNet(num_classes=10)
    data = [2, 3, 5]  # sum = 10, 10 % 10 == 0
    expected = [0, 0, 0]
    codeflash_output = model.forward(data); result = codeflash_output # 1.30μs -> 330ns (295% faster)

def test_forward_sum_one_less_than_num_classes():
    # Test where sum is just below num_classes
    model = AlexNet(num_classes=10)
    data = [3, 6]  # sum = 9, 9 % 10 == 9
    expected = [9, 9]
    codeflash_output = model.forward(data); result = codeflash_output # 1.29μs -> 340ns (280% faster)

def test_forward_sum_one_more_than_num_classes():
    # Test where sum is just above num_classes
    model = AlexNet(num_classes=10)
    data = [7, 5]  # sum = 12, 12 % 10 == 2
    expected = [2, 2]
    codeflash_output = model.forward(data); result = codeflash_output # 1.29μs -> 331ns (290% faster)

def test_forward_large_numbers():
    # Test with very large numbers to check for overflow
    model = AlexNet()
    data = [10**12, 10**12]
    expected_val = (10**12 + 10**12) % 1000
    expected = [expected_val] * 2
    codeflash_output = model.forward(data); result = codeflash_output # 1.16μs -> 320ns (263% faster)

def test_forward_num_classes_one():
    # Test with num_classes = 1, should always return 0
    model = AlexNet(num_classes=1)
    data = [100, 200, 300]
    expected = [0, 0, 0]
    codeflash_output = model.forward(data); result = codeflash_output # 1.30μs -> 321ns (306% faster)

def test_forward_num_classes_large():
    # Test with a large num_classes value
    model = AlexNet(num_classes=10**6)
    data = [123, 456, 789]
    expected_val = sum(data) % 10**6
    expected = [expected_val] * 3
    codeflash_output = model.forward(data); result = codeflash_output # 1.02μs -> 310ns (230% faster)


def test_forward_input_with_float():
    # Test with float input (should work, as sum() works with floats)
    model = AlexNet()
    data = [1.5, 2.5, 3.0]
    expected_val = sum(data) % 1000
    expected = [expected_val] * 3
    codeflash_output = model.forward(data); result = codeflash_output # 1.35μs -> 441ns (207% faster)

def test_forward_input_with_bool():
    # Test with boolean values (True=1, False=0)
    model = AlexNet()
    data = [True, False, True]
    expected_val = sum(data) % 1000
    expected = [expected_val] * 3
    codeflash_output = model.forward(data); result = codeflash_output # 1.12μs -> 350ns (221% faster)


def test_forward_large_input_size():
    # Test with large input size (1000 elements)
    model = AlexNet()
    data = [i for i in range(1000)]
    expected_val = sum(data) % 1000
    expected = [expected_val] * 1000
    codeflash_output = model.forward(data); result = codeflash_output # 1.34μs -> 441ns (205% faster)

def test_forward_large_random_input():
    # Test with large random input
    model = AlexNet()
    data = [random.randint(-10000, 10000) for _ in range(999)]
    expected_val = sum(data) % 1000
    expected = [expected_val] * 999
    codeflash_output = model.forward(data); result = codeflash_output # 1.23μs -> 390ns (216% faster)

def test_forward_large_input_all_zeros():
    # Test with large input of all zeros
    model = AlexNet()
    data = [0] * 1000
    expected = [0] * 1000
    codeflash_output = model.forward(data); result = codeflash_output # 1.30μs -> 360ns (262% faster)

def test_forward_large_input_all_same_number():
    # Test with large input of the same number
    model = AlexNet()
    data = [7] * 1000
    expected_val = sum(data) % 1000
    expected = [expected_val] * 1000
    codeflash_output = model.forward(data); result = codeflash_output # 1.06μs -> 351ns (203% faster)

def test_forward_large_input_mixed_sign():
    # Test with large input of alternating positive and negative numbers
    model = AlexNet()
    data = [i if i % 2 == 0 else -i for i in range(1000)]
    expected_val = sum(data) % 1000
    expected = [expected_val] * 1000
    codeflash_output = model.forward(data); result = codeflash_output # 1.11μs -> 360ns (209% 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 inputs

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

# ---- UNIT TESTS ----

# 1. BASIC TEST CASES

def test_forward_empty_input():
    # Test with empty input list
    model = AlexNet(num_classes=10)
    codeflash_output = model.forward([]); result = codeflash_output # 1.54μs -> 371ns (316% faster)

def test_forward_single_zero():
    # Test with a single zero
    model = AlexNet(num_classes=5)
    codeflash_output = model.forward([0]); result = codeflash_output # 1.38μs -> 341ns (306% faster)

def test_forward_single_positive():
    # Test with a single positive value
    model = AlexNet(num_classes=3)
    codeflash_output = model.forward([42]); result = codeflash_output # 1.41μs -> 341ns (314% faster)

def test_forward_multiple_values():
    # Test with multiple values
    model = AlexNet(num_classes=7)
    codeflash_output = model.forward([1, 2, 3, 4]); result = codeflash_output # 1.37μs -> 331ns (315% faster)

def test_forward_negative_values():
    # Test with negative values
    model = AlexNet(num_classes=4)
    codeflash_output = model.forward([-1, -2, -3]); result = codeflash_output # 1.30μs -> 330ns (295% faster)

# 2. EDGE TEST CASES

def test_forward_all_zeros():
    # All inputs are zero
    model = AlexNet(num_classes=2)
    codeflash_output = model.forward([0, 0, 0, 0]); result = codeflash_output # 1.34μs -> 320ns (319% faster)

def test_forward_large_positive_and_negative():
    # Large positive and negative numbers
    model = AlexNet(num_classes=100)
    codeflash_output = model.forward([999999999, -999999999]); result = codeflash_output # 1.32μs -> 321ns (312% faster)

def test_forward_non_integer_values():
    # Non-integer input (floats)
    model = AlexNet(num_classes=10)
    codeflash_output = model.forward([1.5, 2.5, -3.5]); result = codeflash_output # 1.29μs -> 330ns (292% faster)

def test_forward_string_input():
    # Non-numeric input (should raise TypeError in realistic scenario, but as per current code, just returns [])
    model = AlexNet(num_classes=10)
    codeflash_output = model.forward(['a', 'b', 'c']); result = codeflash_output # 1.35μs -> 301ns (349% faster)

def test_forward_mixed_types():
    # Mixed types in input
    model = AlexNet(num_classes=10)
    codeflash_output = model.forward([1, 'a', 3.0, None]); result = codeflash_output # 1.32μs -> 311ns (325% faster)

def test_forward_num_classes_one():
    # num_classes is 1, all mod results should be zero if features were not empty
    model = AlexNet(num_classes=1)
    codeflash_output = model.forward([5, 10, 15]); result = codeflash_output # 1.33μs -> 310ns (330% faster)

def test_forward_features_size_unused():
    # features_size is not used in forward, but test that class initializes without error
    model = AlexNet(num_classes=3)

# 3. LARGE SCALE TEST CASES

def test_forward_large_input():
    # Test with a large input list
    model = AlexNet(num_classes=500)
    large_input = [random.randint(-1000, 1000) for _ in range(1000)]
    codeflash_output = model.forward(large_input); result = codeflash_output # 1.48μs -> 351ns (323% faster)

def test_forward_large_input_all_same():
    # Large input, all values the same
    model = AlexNet(num_classes=100)
    large_input = [42] * 1000
    codeflash_output = model.forward(large_input); result = codeflash_output # 1.23μs -> 331ns (272% faster)

def test_forward_large_input_all_zero():
    # Large input, all zeros
    model = AlexNet(num_classes=10)
    large_input = [0] * 1000
    codeflash_output = model.forward(large_input); result = codeflash_output # 1.18μs -> 311ns (280% faster)

def test_forward_large_input_edge_num_classes():
    # Large input, num_classes at upper edge
    model = AlexNet(num_classes=1000)
    large_input = [i for i in range(1000)]
    codeflash_output = model.forward(large_input); result = codeflash_output # 1.37μs -> 341ns (302% faster)

# 4. ADDITIONAL EDGE CASES



def test_forward_input_is_dict():
    # Input is a dictionary
    model = AlexNet(num_classes=10)
    codeflash_output = model.forward({'a': 1, 'b': 2}); result = codeflash_output # 1.65μs -> 391ns (323% faster)

# 5. FUNCTIONALITY CHECKS

def test__extract_features_directly():
    # Directly test _extract_features
    model = AlexNet()
    features = model._extract_features([1, 2, 3])

def test__classify_directly_empty():
    # Directly test _classify with empty features
    model = AlexNet(num_classes=10)
    output = model._classify([])

def test__classify_directly_nonempty():
    # Directly test _classify with non-empty features
    model = AlexNet(num_classes=5)
    features = [1, 2, 3]
    expected_mod = sum(features) % 5
    expected_output = [expected_mod] * len(features)
    output = model._classify(features)

# 6. STABILITY AND DETERMINISM

def test_forward_determinism():
    # The output should always be the same for the same input
    model = AlexNet(num_classes=10)
    input_data = [1, 2, 3]
    codeflash_output = model.forward(input_data); result1 = codeflash_output # 1.49μs -> 381ns (292% faster)
    codeflash_output = model.forward(input_data); result2 = codeflash_output # 521ns -> 160ns (226% 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-mccvhfim and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #419 by codeflash-ai[bot] was closed.