⚡️ Speed up method AlexNet.forward by 279%

[codeflash-ai]

📄 279% (2.79x) speedup for AlexNet.forward in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 25.2 microseconds → 6.65 microseconds (best of 388 runs)

📝 Explanation and details

Here is the optimized version of your program.
Optimizations applied.

• Since _extract_features always returns an empty list, and _classify just iterates over that, the result is always an empty list—no need to call both or pass any argument data around.
• Skip the allocation and pass-through steps entirely, and just directly return the empty list in forward, which is the invariant result for any input.

All functions are preserved to match signatures, and comments are preserved unless code was changed.

This version has identical input/output but requires no memory allocations or iterations in .forward.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 32 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

# unit tests

# 1. Basic Test Cases

def test_forward_basic_small_positive_integers():
    # x = [1, 2, 3], features = [1, 4, 9], output = [1, 4, 9]
    model = AlexNet(num_classes=1000)
    codeflash_output = model.forward([1, 2, 3]); result = codeflash_output # 1.53μs -> 391ns (292% faster)

def test_forward_basic_small_negative_integers():
    # x = [-1, -2, -3], features = [1, 4, 9], output = [1, 4, 9]
    model = AlexNet(num_classes=1000)
    codeflash_output = model.forward([-1, -2, -3]); result = codeflash_output # 1.36μs -> 361ns (278% faster)

def test_forward_basic_floats():
    # x = [1.5, 2.0], features = [2.25, 4.0], output = [2, 4]
    model = AlexNet(num_classes=1000)
    codeflash_output = model.forward([1.5, 2.0]); result = codeflash_output # 1.31μs -> 350ns (275% faster)

def test_forward_basic_empty_input():
    # x = [], features = [], output = []
    model = AlexNet(num_classes=1000)
    codeflash_output = model.forward([]); result = codeflash_output # 1.31μs -> 350ns (275% faster)

def test_forward_basic_different_num_classes():
    # x = [32, 33], features = [1024, 1089], output = [24, 39] for num_classes=100
    model = AlexNet(num_classes=100)
    codeflash_output = model.forward([32, 33]); result = codeflash_output # 1.29μs -> 341ns (279% faster)


# 2. Edge Test Cases

def test_forward_edge_zero():
    # x = [0], features = [0], output = [0]
    model = AlexNet(num_classes=1000)
    codeflash_output = model.forward([0]); result = codeflash_output # 1.26μs -> 341ns (270% faster)

def test_forward_edge_large_number():
    # x = [99999], features = [9999800001], output = [9999800001 % 1000]
    model = AlexNet(num_classes=1000)
    codeflash_output = model.forward([99999]); result = codeflash_output # 1.27μs -> 330ns (285% faster)

def test_forward_edge_negative_and_positive_mix():
    # x = [-2, 0, 2], features = [4, 0, 4], output = [4, 0, 4]
    model = AlexNet(num_classes=1000)
    codeflash_output = model.forward([-2, 0, 2]); result = codeflash_output # 1.31μs -> 340ns (286% faster)



def test_forward_edge_num_classes_one():
    # x = [1, 2, 3], num_classes=1, all outputs should be 0
    model = AlexNet(num_classes=1)
    codeflash_output = model.forward([1, 2, 3]); result = codeflash_output # 1.60μs -> 421ns (281% faster)

def test_forward_edge_num_classes_negative():
    # x = [2], num_classes=-3, should work as Python's % works with negatives
    model = AlexNet(num_classes=-3)
    codeflash_output = model.forward([2]); result = codeflash_output # 1.40μs -> 331ns (324% faster)

def test_forward_edge_float_num_classes():
    # x = [2], num_classes=2.5, should work as Python's % works with floats
    model = AlexNet(num_classes=2.5)
    codeflash_output = model.forward([2]); result = codeflash_output # 1.32μs -> 341ns (288% faster)


def test_forward_large_scale_1000_elements():
    # x = list(range(1000)), features = [i*i for i in range(1000)], output = [i*i % 1000 for i in range(1000)]
    model = AlexNet(num_classes=1000)
    x = list(range(1000))
    expected = [i * i % 1000 for i in x]
    codeflash_output = model.forward(x); result = codeflash_output # 1.62μs -> 441ns (268% faster)

def test_forward_large_scale_random_floats():
    # Random floats between -100 and 100, 1000 elements
    model = AlexNet(num_classes=1000)
    random.seed(42)
    x = [random.uniform(-100, 100) for _ in range(1000)]
    expected = [int(item ** 2) % 1000 for item in x]
    codeflash_output = model.forward(x); result = codeflash_output # 1.50μs -> 380ns (296% faster)

def test_forward_large_scale_large_numbers():
    # Large numbers, to check for overflow or performance issues
    model = AlexNet(num_classes=999983)
    x = [10**6 + i for i in range(1000)]
    expected = [((10**6 + i) ** 2) % 999983 for i in range(1000)]
    codeflash_output = model.forward(x); result = codeflash_output # 1.38μs -> 381ns (263% faster)

def test_forward_large_scale_all_zeros():
    # Large input, all zeros
    model = AlexNet(num_classes=1000)
    x = [0] * 1000
    expected = [0] * 1000
    codeflash_output = model.forward(x); result = codeflash_output # 1.32μs -> 360ns (267% faster)

def test_forward_large_scale_all_same_number():
    # Large input, all same number
    model = AlexNet(num_classes=1000)
    x = [7] * 1000
    expected = [49 % 1000] * 1000
    codeflash_output = model.forward(x); result = codeflash_output # 1.25μs -> 321ns (290% 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-mccuz7bu and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #387 by codeflash-ai[bot] was closed.