⚡️ Speed up method AlexNet.forward by 3,112%

[codeflash-ai]

📄 3,112% (31.12x) speedup for AlexNet.forward in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 807 microseconds → 25.1 microseconds (best of 318 runs)

📝 Explanation and details

Here’s an optimized version of your program. Changes.

• Removes unnecessary loop in _extract_features, returning an empty list directly (same semantics as before).
• Uses direct list multiplication in _classify rather than a list comprehension, as features is always empty, resulting always in an empty list.
• The logic and return values remain exactly the same.

This is as fast as possible given the original function logic.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 72 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 random inputs

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

# unit tests

# --------- BASIC TEST CASES ---------

def test_forward_basic_flat_input():
    # Test with a simple flat input list of length less than features_size
    model = AlexNet(num_classes=10)
    x = [1, 2, 3]
    codeflash_output = model.forward(x); output = codeflash_output # 2.47μs -> 671ns (269% faster)

def test_forward_basic_nested_input():
    # Test with a nested input list
    model = AlexNet(num_classes=5)
    x = [[1, 2], [3, 4]]
    codeflash_output = model.forward(x); output = codeflash_output # 2.22μs -> 601ns (270% faster)

def test_forward_basic_padding():
    # Test input shorter than features_size, should pad with zeros
    model = AlexNet(num_classes=3)
    x = [7]
    codeflash_output = model.forward(x); output = codeflash_output # 2.16μs -> 601ns (260% faster)

def test_forward_basic_truncation():
    # Test input longer than features_size, should truncate
    model = AlexNet(num_classes=4)
    x = [1] * (model.features_size + 10)  # 10 more than needed
    codeflash_output = model.forward(x); output = codeflash_output # 151μs -> 581ns (26018% faster)
    # Only features_size elements are used, so sum is features_size
    expected = model.features_size % 4

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

def test_forward_empty_input():
    # Test with empty input
    model = AlexNet(num_classes=2)
    x = []
    codeflash_output = model.forward(x); output = codeflash_output # 1.86μs -> 581ns (221% faster)

def test_forward_input_with_negatives():
    # Test with negative numbers
    model = AlexNet(num_classes=7)
    x = [-1, -2, -3]
    codeflash_output = model.forward(x); output = codeflash_output # 2.25μs -> 561ns (302% faster)

def test_forward_input_with_large_numbers():
    # Test with large numbers
    model = AlexNet(num_classes=13)
    x = [10**6, 10**7, 10**8]
    codeflash_output = model.forward(x); output = codeflash_output # 2.07μs -> 551ns (276% faster)
    # Sum is 111000000, 111000000 % 13
    expected = 111000000 % 13

def test_forward_input_with_non_integer():
    # Test with floats
    model = AlexNet(num_classes=6)
    x = [1.5, 2.5, 3.0]
    codeflash_output = model.forward(x); output = codeflash_output # 2.05μs -> 561ns (266% faster)

def test_forward_input_with_inner_empty_lists():
    # Test with inner empty lists
    model = AlexNet(num_classes=3)
    x = [[], [], [1, 2]]
    codeflash_output = model.forward(x); output = codeflash_output # 1.99μs -> 551ns (262% faster)


def test_forward_large_flat_input():
    # Test with a large flat input, exactly features_size elements
    model = AlexNet(num_classes=100)
    x = [1] * model.features_size
    codeflash_output = model.forward(x); output = codeflash_output # 151μs -> 711ns (21255% faster)
    # Sum is features_size, features_size % 100
    expected = model.features_size % 100

def test_forward_large_nested_input():
    # Test with a large nested input, each sublist is length 10
    model = AlexNet(num_classes=50)
    n = model.features_size // 10
    x = [[2] * 10 for _ in range(n)]  # total elements = features_size
    codeflash_output = model.forward(x); output = codeflash_output # 15.3μs -> 661ns (2221% faster)
    # Sum is features_size * 2
    expected = (model.features_size * 2) % 50

def test_forward_large_random_input():
    # Test with a large random input
    model = AlexNet(num_classes=123)
    random.seed(42)
    x = [random.randint(-1000, 1000) for _ in range(model.features_size)]
    codeflash_output = model.forward(x); output = codeflash_output # 176μs -> 692ns (25422% faster)
    expected = sum(x) % 123

def test_forward_large_input_with_padding():
    # Test with a large input, but less than features_size, should pad
    model = AlexNet(num_classes=77)
    x = [5] * 900  # much less than features_size
    codeflash_output = model.forward(x); output = codeflash_output # 15.6μs -> 601ns (2497% faster)
    expected = (5 * 900) % 77

def test_forward_large_input_with_truncation():
    # Test with a large input, more than features_size, should truncate
    model = AlexNet(num_classes=88)
    x = [3] * (model.features_size + 900)
    codeflash_output = model.forward(x); output = codeflash_output # 157μs -> 550ns (28470% faster)
    expected = (3 * model.features_size) % 88
# 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 test data

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

# unit tests

# ----------------------
# BASIC TEST CASES
# ----------------------

def test_forward_single_element():
    # Test with a single-element input
    model = AlexNet(num_classes=10)
    x = [5]
    # features = [5], total = 5, output = [5 % 10] = [5]
    codeflash_output = model.forward(x) # 2.16μs -> 531ns (308% faster)

def test_forward_multiple_elements():
    # Test with a typical small list of integers
    model = AlexNet(num_classes=10)
    x = [1, 2, 3]
    # features = [1,2,3], total = 6, output = [6%10, 6%10, 6%10] = [6,6,6]
    codeflash_output = model.forward(x) # 2.10μs -> 561ns (275% faster)

def test_forward_nested_lists():
    # Test with nested lists as elements
    model = AlexNet(num_classes=100)
    x = [[1, 2, 3], [4, 5], [6]]
    # features = [6, 9, 6], total = 21, output = [21%100, 21%100, 21%100] = [21,21,21]
    codeflash_output = model.forward(x) # 2.05μs -> 551ns (273% faster)

def test_forward_zero_elements():
    # Test with all zeros
    model = AlexNet(num_classes=7)
    x = [0, 0, 0]
    # features = [0,0,0], total = 0, output = [0,0,0]
    codeflash_output = model.forward(x) # 2.07μs -> 561ns (270% faster)

def test_forward_negative_numbers():
    # Test with negative numbers
    model = AlexNet(num_classes=10)
    x = [-1, -2, -3]
    # features = [-1,-2,-3], total = -6, output = [-6%10]*3 = [4,4,4]
    codeflash_output = model.forward(x) # 2.01μs -> 571ns (253% faster)

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

def test_forward_empty_input():
    # Test with empty input list
    model = AlexNet(num_classes=5)
    x = []
    # features = [], total = 0, output = []
    codeflash_output = model.forward(x) # 1.77μs -> 531ns (234% faster)

def test_forward_large_numbers():
    # Test with very large numbers
    model = AlexNet(num_classes=99999)
    x = [10**12, 10**12, 10**12]
    # features = [10**12, 10**12, 10**12], total = 3*10**12, output = [3*10**12 % 99999]*3
    expected = [ (3 * 10**12) % 99999 ] * 3
    codeflash_output = model.forward(x) # 2.05μs -> 541ns (280% faster)

def test_forward_mixed_types():
    # Test with a mix of ints and nested lists
    model = AlexNet(num_classes=50)
    x = [1, [2, 3], 4]
    # features = [1,5,4], total = 10, output = [10,10,10]
    codeflash_output = model.forward(x) # 1.98μs -> 531ns (274% faster)

def test_forward_all_nested_empty_lists():
    # Test with all elements as empty lists
    model = AlexNet(num_classes=3)
    x = [[], [], []]
    # features = [0,0,0], total = 0, output = [0,0,0]
    codeflash_output = model.forward(x) # 2.08μs -> 551ns (278% faster)

def test_forward_num_classes_one():
    # Test with num_classes=1 (should always output 0)
    model = AlexNet(num_classes=1)
    x = [1, 2, 3]
    # features = [1,2,3], total = 6, output = [6%1,6%1,6%1] = [0,0,0]
    codeflash_output = model.forward(x) # 2.06μs -> 551ns (275% faster)

def test_forward_num_classes_negative():
    # Test with negative num_classes (should behave as Python's % operator does)
    model = AlexNet(num_classes=-7)
    x = [1, 2, 3]
    # features = [1,2,3], total = 6, output = [6%-7]*3 = [-1,-1,-1]
    codeflash_output = model.forward(x) # 1.95μs -> 511ns (282% faster)

def test_forward_input_with_zero_and_negatives():
    # Test with zeros and negatives mixed
    model = AlexNet(num_classes=8)
    x = [0, -8, 8]
    # features = [0,-8,8], total = 0, output = [0,0,0]
    codeflash_output = model.forward(x) # 1.99μs -> 511ns (290% faster)

def test_forward_input_with_large_nested_list():
    # Test with a single large nested list
    model = AlexNet(num_classes=100)
    x = [list(range(100))]
    # features = [sum(0..99)] = [4950], total = 4950, output = [4950%100] = [50]
    codeflash_output = model.forward(x) # 1.69μs -> 531ns (219% faster)

# ----------------------
# LARGE SCALE TEST CASES
# ----------------------

def test_forward_large_flat_input():
    # Test with a large flat input list (length=1000)
    model = AlexNet(num_classes=123)
    x = list(range(1000))  # 0..999
    total = sum(x)
    expected = [total % 123] * 1000
    codeflash_output = model.forward(x) # 16.4μs -> 552ns (2862% faster)

def test_forward_large_nested_input():
    # Test with a large input of nested lists (1000 elements, each a list of 2 numbers)
    model = AlexNet(num_classes=500)
    x = [ [i, i+1] for i in range(0, 2000, 2) ]  # 1000 lists: [0,1], [2,3], ...
    features = [sum(pair) for pair in x]
    total = sum(features)
    expected = [total % 500] * 1000
    codeflash_output = model.forward(x) # 16.3μs -> 621ns (2520% faster)

def test_forward_large_random_input():
    # Test with large random input (length=1000)
    model = AlexNet(num_classes=97)
    random.seed(42)
    x = [random.randint(-10000, 10000) for _ in range(1000)]
    total = sum(x)
    expected = [total % 97] * 1000
    codeflash_output = model.forward(x) # 17.6μs -> 581ns (2926% faster)

def test_forward_large_mixed_input():
    # Test with a mix of ints and lists in a large input
    model = AlexNet(num_classes=333)
    x = []
    for i in range(500):
        x.append(i)
        x.append([i, i+1])
    # x has 1000 elements: 500 ints, 500 lists
    features = []
    for elem in x:
        if isinstance(elem, list):
            features.append(sum(elem))
        else:
            features.append(elem)
    total = sum(features)
    expected = [total % 333] * 1000
    codeflash_output = model.forward(x)

def test_forward_large_input_all_zeros():
    # Test with a large input of all zeros
    model = AlexNet(num_classes=111)
    x = [0] * 1000
    expected = [0] * 1000
    codeflash_output = model.forward(x) # 16.6μs -> 531ns (3024% faster)

# ----------------------
# ADDITIONAL EDGE CASES
# ----------------------

def test_forward_input_with_non_integer_elements():
    # Test with floats (should work as sum works for floats)
    model = AlexNet(num_classes=9)
    x = [1.5, 2.5, 3.0]
    # features = [1.5,2.5,3.0], total = 7.0, output = [7.0%9]*3 = [7.0,7.0,7.0]
    codeflash_output = model.forward(x) # 2.05μs -> 521ns (294% faster)

def test_forward_input_with_empty_and_nonempty_lists():
    # Test with a mix of empty lists and numbers
    model = AlexNet(num_classes=5)
    x = [[], 2, [3, 4], []]
    # features = [0,2,7,0], total = 9, output = [9%5]*4 = [4,4,4,4]
    codeflash_output = model.forward(x) # 2.08μs -> 521ns (300% faster)

def test_forward_input_with_single_large_nested_list():
    # Test with one nested list of 1000 elements
    model = AlexNet(num_classes=1009)
    x = [list(range(1000))]
    features = [sum(range(1000))]  # [499500]
    total = 499500
    expected = [499500 % 1009]
    codeflash_output = model.forward(x) # 1.75μs -> 551ns (218% faster)

def test_forward_input_with_varied_length_nested_lists():
    # Test with nested lists of varied lengths
    model = AlexNet(num_classes=100)
    x = [[1], [2, 3], [4, 5, 6], []]
    # features = [1,5,15,0], total = 21, output = [21,21,21,21]
    codeflash_output = model.forward(x) # 2.06μs -> 551ns (275% 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-mcdqa79j and push.