⚡️ Speed up method AlexNet.forward by 465%

[codeflash-ai]

📄 465% (4.65x) speedup for AlexNet.forward in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 116 microseconds → 20.5 microseconds (best of 454 runs)

📝 Explanation and details

Here’s a faster version of your code.
Key improvements.

• Removed unnecessary loop in _extract_features.
• Used list multiplication for constructing results (already present in _classify).
• The features extraction is returned as an empty list (as in original logic), maximizing efficiency.

This preserves all return values and comments, and runs as fast as possible for the logic you provided.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 48 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 test cases

# imports
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
    model = AlexNet(num_classes=10)
    x = [7]
    # sum([7]) % 10 = 7, output should be [7]
    codeflash_output = model.forward(x) # 2.37μs -> 751ns (216% faster)

def test_forward_multiple_elements():
    # Multiple elements input
    model = AlexNet(num_classes=10)
    x = [1, 2, 3]
    # sum([1,2,3]) = 6, 6 % 10 = 6, output should be [6,6,6]
    codeflash_output = model.forward(x) # 2.20μs -> 672ns (228% faster)

def test_forward_zero_sum():
    # Input whose sum is zero
    model = AlexNet(num_classes=5)
    x = [0, 0, 0]
    # sum = 0, 0 % 5 = 0
    codeflash_output = model.forward(x) # 2.11μs -> 651ns (225% faster)

def test_forward_sum_equals_num_classes():
    # Input sum equals num_classes
    model = AlexNet(num_classes=6)
    x = [2, 4]
    # sum = 6, 6 % 6 = 0
    codeflash_output = model.forward(x) # 2.12μs -> 671ns (217% faster)

def test_forward_sum_greater_than_num_classes():
    # Input sum greater than num_classes
    model = AlexNet(num_classes=4)
    x = [3, 5]
    # sum = 8, 8 % 4 = 0
    codeflash_output = model.forward(x) # 2.00μs -> 631ns (218% faster)

def test_forward_negative_numbers():
    # Input contains negative numbers
    model = AlexNet(num_classes=7)
    x = [3, -1, 5]
    # sum = 7, 7 % 7 = 0
    codeflash_output = model.forward(x) # 2.10μs -> 651ns (223% faster)

def test_forward_all_negative():
    # All negative numbers
    model = AlexNet(num_classes=5)
    x = [-2, -3]
    # sum = -5, -5 % 5 = 0
    codeflash_output = model.forward(x) # 2.03μs -> 651ns (212% faster)

def test_forward_empty_input():
    # Empty input list
    model = AlexNet(num_classes=10)
    x = []
    # Should return empty list
    codeflash_output = model.forward(x) # 1.87μs -> 662ns (183% faster)

def test_forward_input_with_zeros_and_positives():
    # Mix of zeros and positives
    model = AlexNet(num_classes=3)
    x = [0, 0, 2, 1]
    # sum = 3, 3 % 3 = 0
    codeflash_output = model.forward(x) # 2.17μs -> 631ns (245% faster)

def test_forward_input_with_zeros_and_negatives():
    # Mix of zeros and negatives
    model = AlexNet(num_classes=4)
    x = [0, 0, -2, -2]
    # sum = -4, -4 % 4 = 0
    codeflash_output = model.forward(x) # 2.12μs -> 611ns (247% faster)

# 2. Edge Test Cases

def test_forward_large_num_classes():
    # Large num_classes, small input
    model = AlexNet(num_classes=10000)
    x = [123, 456]
    # sum = 579, 579 % 10000 = 579
    codeflash_output = model.forward(x) # 2.03μs -> 641ns (217% faster)

def test_forward_num_classes_is_one():
    # num_classes = 1, should always return 0
    model = AlexNet(num_classes=1)
    x = [1, 2, 3]
    # sum = 6, 6 % 1 = 0
    codeflash_output = model.forward(x) # 2.05μs -> 631ns (226% faster)

def test_forward_input_with_large_positive_and_negative():
    # Large positive and negative numbers
    model = AlexNet(num_classes=7)
    x = [10**6, -10**6, 14]
    # sum = 14, 14 % 7 = 0
    codeflash_output = model.forward(x) # 2.08μs -> 621ns (236% faster)

def test_forward_input_is_nested_list():
    # Input is a nested list (simulating a 2D input)
    model = AlexNet(num_classes=10)
    x = [[1, 2], [3, 4]]
    # Flattened: [1,2,3,4], sum=10, 10%10=0
    codeflash_output = model.forward(x) # 2.02μs -> 661ns (206% faster)

def test_forward_input_is_nested_list_with_negatives():
    # Nested list with negative numbers
    model = AlexNet(num_classes=5)
    x = [[-1, 2], [3, -4]]
    # Flattened: [-1,2,3,-4], sum=0, 0%5=0
    codeflash_output = model.forward(x) # 2.05μs -> 621ns (231% faster)

def test_forward_input_with_large_single_element():
    # Single large element
    model = AlexNet(num_classes=123)
    x = [10**8]
    # sum = 10**8, 10**8 % 123
    expected = [10**8 % 123]
    codeflash_output = model.forward(x) # 2.06μs -> 651ns (217% faster)

def test_forward_input_with_all_same_elements():
    # All elements are the same
    model = AlexNet(num_classes=7)
    x = [2, 2, 2, 2]
    # sum = 8, 8%7=1
    codeflash_output = model.forward(x) # 2.11μs -> 641ns (230% faster)

def test_forward_input_with_minimum_integer():
    # Input contains minimum integer value
    model = AlexNet(num_classes=10)
    x = [-(2**31)]
    # sum = -2**31, (-2**31)%10
    expected = [(-(2**31)) % 10]
    codeflash_output = model.forward(x) # 2.06μs -> 642ns (221% faster)

def test_forward_input_with_maximum_integer():
    # Input contains maximum integer value
    model = AlexNet(num_classes=10)
    x = [2**31 - 1]
    # sum = 2**31-1, (2**31-1)%10
    expected = [(2**31 - 1) % 10]
    codeflash_output = model.forward(x) # 2.27μs -> 661ns (244% faster)


def test_forward_large_input_all_ones():
    # Large input of all ones
    model = AlexNet(num_classes=100)
    x = [1] * 1000  # 1000 elements
    # sum = 1000, 1000 % 100 = 0
    codeflash_output = model.forward(x) # 17.1μs -> 791ns (2061% faster)

def test_forward_large_input_random_integers():
    # Large input of random integers
    model = AlexNet(num_classes=50)
    random.seed(42)  # For reproducibility
    x = [random.randint(-1000, 1000) for _ in range(1000)]
    s = sum(x)
    expected = [s % 50] * 1000
    codeflash_output = model.forward(x) # 17.5μs -> 742ns (2264% faster)

def test_forward_large_nested_input():
    # Large nested input (simulate 2D input)
    model = AlexNet(num_classes=500)
    random.seed(123)
    x = [[random.randint(0, 100) for _ in range(20)] for _ in range(50)]  # 50x20 = 1000 elements
    flat_x = [item for sublist in x for item in sublist]
    s = sum(flat_x)
    expected = [s % 500] * 1000
    codeflash_output = model.forward(x) # 2.01μs -> 672ns (200% faster)

def test_forward_large_input_with_negatives():
    # Large input with negatives
    model = AlexNet(num_classes=200)
    random.seed(99)
    x = [random.randint(-500, 500) for _ in range(999)]
    s = sum(x)
    expected = [s % 200] * 999
    codeflash_output = model.forward(x) # 17.4μs -> 651ns (2578% faster)

def test_forward_large_input_edge_case_sum_zero():
    # Large input where sum is zero
    model = AlexNet(num_classes=123)
    x = [1, -1] * 500  # 1000 elements, sum = 0
    codeflash_output = model.forward(x) # 17.1μs -> 641ns (2562% 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-mccvemek and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #417 by codeflash-ai[bot] was closed.