⚡️ Speed up function prod by 165%

[codeflash-ai]

📄 165% (1.65x) speedup for prod in python/sglang/srt/layers/dp_attention.py

⏱️ Runtime : 632 microseconds → 239 microseconds (best of 250 runs)

📝 Explanation and details

The optimization replaces a custom functools.reduce() implementation with Python's built-in math.prod() function, achieving a 164% speedup (from 632μs to 239μs).

Key Changes:

• Removed functools.reduce(lambda a, b: a * b, x, 1)
• Added import math and used math.prod(x) directly

Why This is Faster:

1. Native C Implementation: math.prod() is implemented in C within Python's standard library, eliminating the overhead of Python function calls and lambda execution that occurs with functools.reduce()
2. Reduced Function Call Overhead: The original version creates a lambda function and calls it for each multiplication, while math.prod() performs all operations at the C level
3. Optimized Algorithm: The built-in function can use more efficient multiplication strategies and memory management

Performance Impact Based on Function References:
The prod() function is called within memcpy_triton() to calculate chunk_size = prod(src.shape[1:]), which appears to be in a memory copying operation for tensor operations. This suggests it's likely called frequently in machine learning workloads where tensor shapes need to be calculated.

Test Case Analysis:
The optimization shows consistent improvements across all test scenarios:

• Small lists (2-5 elements): 43-113% faster
• Large lists (1000 elements): 568-795% faster - the most dramatic improvements
• Edge cases (zeros, negatives, floats): 17-92% faster

The performance gains are most pronounced with larger input sizes, making this optimization particularly valuable for tensor operations where shape calculations involve multiple dimensions.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 85 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from __future__ import annotations

import functools
import math  # used for float('inf'), nan, etc.
import operator

# imports
import pytest  # used for our unit tests
from sglang.srt.layers.dp_attention import prod

# unit tests

# === BASIC TEST CASES ===

def test_prod_empty_list():
    # The product of an empty list should be 1 (neutral element for multiplication)
    codeflash_output = prod([]) # 674ns -> 483ns (39.5% faster)

def test_prod_single_element():
    # The product of a single-element list should be the element itself
    codeflash_output = prod([7]) # 819ns -> 484ns (69.2% faster)
    codeflash_output = prod([0]) # 388ns -> 240ns (61.7% faster)
    codeflash_output = prod([-3]) # 316ns -> 193ns (63.7% faster)

def test_prod_two_elements():
    # The product of two elements
    codeflash_output = prod([2, 3]) # 819ns -> 495ns (65.5% faster)
    codeflash_output = prod([-2, 3]) # 515ns -> 315ns (63.5% faster)
    codeflash_output = prod([-2, -3]) # 353ns -> 209ns (68.9% faster)
    codeflash_output = prod([0, 5]) # 378ns -> 199ns (89.9% faster)

def test_prod_multiple_elements():
    # The product of more than two elements
    codeflash_output = prod([2, 3, 4]) # 841ns -> 471ns (78.6% faster)
    codeflash_output = prod([1, 2, 3, 4, 5]) # 630ns -> 313ns (101% faster)
    codeflash_output = prod([1, -1, 1, -1]) # 457ns -> 250ns (82.8% faster)
    codeflash_output = prod([1, 0, 2, 3]) # 462ns -> 217ns (113% faster)

def test_prod_with_floats():
    # The product with floating point numbers
    codeflash_output = prod([1.5, 2]) # 1.10μs -> 870ns (26.1% faster)
    codeflash_output = prod([0.5, 0.5, 4]) # 571ns -> 347ns (64.6% faster)
    codeflash_output = prod([2.0, -0.5]) # 367ns -> 256ns (43.4% faster)

def test_prod_with_mixed_int_float():
    # The product with a mix of ints and floats
    codeflash_output = prod([2, 3.0, 4]) # 1.02μs -> 711ns (43.9% faster)
    codeflash_output = prod([1, 2.5, 4]) # 555ns -> 292ns (90.1% faster)

# === EDGE TEST CASES ===

def test_prod_with_zero():
    # Any product with zero should be zero
    codeflash_output = prod([0, 1, 2, 3]) # 931ns -> 490ns (90.0% faster)
    codeflash_output = prod([1, 2, 0, 3]) # 495ns -> 257ns (92.6% faster)
    codeflash_output = prod([0]) # 310ns -> 210ns (47.6% faster)

def test_prod_with_negative_numbers():
    # Product with negative numbers should flip sign accordingly
    codeflash_output = prod([-1, 2, 3]) # 964ns -> 512ns (88.3% faster)
    codeflash_output = prod([-1, -2, 3]) # 477ns -> 282ns (69.1% faster)
    codeflash_output = prod([-1, -2, -3]) # 419ns -> 244ns (71.7% faster)

def test_prod_with_large_numbers():
    # Product with large numbers should not overflow (Python ints are arbitrary-precision)
    codeflash_output = prod([10**10, 10**10]) # 1.03μs -> 874ns (17.4% faster)
    codeflash_output = prod([2]*20) # 1.17μs -> 368ns (218% faster)

def test_prod_with_one():
    # Product with 1 should not affect the result
    codeflash_output = prod([1, 1, 1, 1]) # 899ns -> 486ns (85.0% faster)
    codeflash_output = prod([1, 2, 3, 4, 1]) # 511ns -> 256ns (99.6% faster)

def test_prod_with_bool_values():
    # Bools are ints in Python: True==1, False==0
    codeflash_output = prod([True, True, True]) # 899ns -> 727ns (23.7% faster)
    codeflash_output = prod([True, False, True]) # 523ns -> 314ns (66.6% faster)
    codeflash_output = prod([False]) # 319ns -> 241ns (32.4% faster)

def test_prod_with_nan_and_inf():
    codeflash_output = prod([1, float('-inf'), 3]) # 1.17μs -> 793ns (47.5% faster)


def test_prod_with_non_numeric_elements_raises():
    # Should raise TypeError if any element is not numeric
    with pytest.raises(TypeError):
        prod(['a', 'b', 'c'])
    with pytest.raises(TypeError):
        prod([1, 'b', 3])
    with pytest.raises(TypeError):
        prod([None])


def test_prod_with_no_arguments():
    # Should raise TypeError if called with no argument
    with pytest.raises(TypeError):
        prod() # 2.51μs -> 2.50μs (0.360% faster)

def test_prod_with_dict():
    # Should multiply the keys if passed a dict (since iter(dict) yields keys)
    codeflash_output = prod({2: 10, 3: 20, 4: 30}) # 1.27μs -> 892ns (42.9% faster)

# === LARGE SCALE TEST CASES ===

def test_prod_large_list_of_ones():
    # Product of 1000 ones should be 1
    codeflash_output = prod([1]*1000) # 27.0μs -> 3.70μs (630% faster)

def test_prod_large_list_of_zeros():
    # Product of 1000 zeros should be 0
    codeflash_output = prod([0]*1000) # 26.9μs -> 4.02μs (568% faster)

def test_prod_large_list_of_twos():
    # Product of 1000 twos should be 2**1000
    codeflash_output = prod([2]*1000) # 46.5μs -> 24.0μs (93.6% faster)

def test_prod_large_mixed_positive_negative():
    # 500 -1's and 500 1's: product should be 1 if even number of -1's
    codeflash_output = prod([-1]*500 + [1]*500) # 27.4μs -> 3.85μs (611% faster)
    # 501 -1's and 499 1's: product should be -1
    codeflash_output = prod([-1]*501 + [1]*499) # 27.0μs -> 3.51μs (668% faster)

def test_prod_large_range():
    # Product of range(1, 11) == 10!
    codeflash_output = prod(range(1, 11)) # 1.49μs -> 712ns (110% faster)


def test_prod_large_list_with_zero_in_middle():
    # Product should be zero if any element is zero, even in large lists
    lst = [2]*499 + [0] + [3]*500
    codeflash_output = prod(lst) # 35.2μs -> 12.9μs (173% faster)

def test_prod_large_float_values():
    # Product of 1000 floats (all 1.1)
    codeflash_output = prod([1.1]*1000); result = codeflash_output # 28.1μs -> 3.15μs (795% faster)

def test_prod_large_list_performance():
    # Test that prod does not take too long (should be fast for 1000 elements)
    import time
    lst = [2]*1000
    start = time.time()
    codeflash_output = prod(lst); result = codeflash_output # 46.3μs -> 24.0μs (93.3% faster)
    duration = 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.
#------------------------------------------------
from __future__ import annotations

import functools

# imports
import pytest  # used for our unit tests
from sglang.srt.layers.dp_attention import prod

# unit tests

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

def test_prod_empty_list():
    # The product of an empty list should be 1 (neutral element for multiplication)
    codeflash_output = prod([]) # 703ns -> 548ns (28.3% faster)

def test_prod_single_element():
    # The product of a single element list should be the element itself
    codeflash_output = prod([5]) # 810ns -> 525ns (54.3% faster)
    codeflash_output = prod([0]) # 388ns -> 283ns (37.1% faster)
    codeflash_output = prod([-3]) # 314ns -> 199ns (57.8% faster)

def test_prod_multiple_positive_integers():
    # Product of multiple positive integers
    codeflash_output = prod([2, 3, 4]) # 919ns -> 466ns (97.2% faster)
    codeflash_output = prod([1, 2, 3, 4, 5]) # 570ns -> 306ns (86.3% faster)

def test_prod_multiple_negative_integers():
    # Product of multiple negative integers
    codeflash_output = prod([-2, -3, -4]) # 948ns -> 553ns (71.4% faster)
    codeflash_output = prod([-1, -2, -3, -4]) # 591ns -> 325ns (81.8% faster)

def test_prod_mixed_sign_integers():
    # Product of mixed positive and negative integers
    codeflash_output = prod([2, -3, 4]) # 975ns -> 547ns (78.2% faster)
    codeflash_output = prod([-2, 3, -4]) # 529ns -> 311ns (70.1% faster)

def test_prod_contains_zero():
    # Product containing zero should always be zero
    codeflash_output = prod([2, 0, 4]) # 869ns -> 507ns (71.4% faster)
    codeflash_output = prod([0, 0, 0]) # 466ns -> 289ns (61.2% faster)

def test_prod_floats():
    # Product of floats
    codeflash_output = prod([0.5, 2.0, 4.0]) # 1.14μs -> 837ns (36.3% faster)
    codeflash_output = prod([-1.5, 2.0]) # 488ns -> 297ns (64.3% faster)

def test_prod_mixed_int_float():
    # Product of mixed int and float
    codeflash_output = prod([2, 2.5, 4]) # 1.08μs -> 719ns (49.8% faster)

def test_prod_boolean_values():
    # Product of booleans (True = 1, False = 0)
    codeflash_output = prod([True, True, True]) # 934ns -> 687ns (36.0% faster)
    codeflash_output = prod([True, False, True]) # 493ns -> 334ns (47.6% faster)

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

def test_prod_large_numbers():
    # Product of large numbers to check for overflow
    codeflash_output = prod([10**5, 10**5]) # 934ns -> 523ns (78.6% faster)
    codeflash_output = prod([10**6, -10**6]) # 489ns -> 301ns (62.5% faster)

def test_prod_negative_and_zero():
    # Product with negative and zero
    codeflash_output = prod([-1, 0, 2]) # 886ns -> 494ns (79.4% faster)

def test_prod_all_ones():
    # Product of all ones should be one
    codeflash_output = prod([1, 1, 1, 1, 1, 1, 1]) # 1.01μs -> 526ns (92.2% faster)

def test_prod_all_neg_ones_even():
    # Product of even number of -1s should be 1
    codeflash_output = prod([-1, -1]) # 854ns -> 526ns (62.4% faster)
    codeflash_output = prod([-1, -1, -1, -1]) # 578ns -> 295ns (95.9% faster)

def test_prod_all_neg_ones_odd():
    # Product of odd number of -1s should be -1
    codeflash_output = prod([-1, -1, -1]) # 880ns -> 475ns (85.3% faster)

def test_prod_iterable_types():
    # Test with tuple and set as input
    codeflash_output = prod((2, 3, 4)) # 976ns -> 583ns (67.4% faster)
    codeflash_output = prod({2, 3, 4}) # 736ns -> 505ns (45.7% faster)




def test_prod_nan_inf():
    # Product with float('nan') and float('inf')
    import math
    codeflash_output = prod([2, float('inf'), 4]) # 1.68μs -> 1.17μs (43.6% faster)
    codeflash_output = prod([2, float('-inf'), 4]) # 487ns -> 253ns (92.5% faster)
    codeflash_output = prod([float('inf')]) # 382ns -> 241ns (58.5% faster)
    codeflash_output = prod([float('-inf')]) # 283ns -> 183ns (54.6% faster)

def test_prod_boolean_and_numbers():
    # Mixing booleans and numbers
    codeflash_output = prod([True, 2, 3]) # 1.04μs -> 717ns (45.7% faster)
    codeflash_output = prod([False, 2, 3]) # 474ns -> 322ns (47.2% faster)

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

def test_prod_large_list_of_ones():
    # Product of 1000 ones should be one
    codeflash_output = prod([1]*1000) # 27.0μs -> 3.65μs (639% faster)

def test_prod_large_list_of_twos():
    # Product of 1000 twos should be 2**1000
    codeflash_output = prod([2]*1000) # 46.3μs -> 24.1μs (91.8% faster)

def test_prod_large_list_with_zero():
    # Product of large list with a single zero should be zero
    codeflash_output = prod([2]*999 + [0]) # 46.5μs -> 23.9μs (94.3% faster)

def test_prod_large_list_negative_and_positive():
    # Large list of alternating -1 and 1, even count should be 1, odd should be -1
    codeflash_output = prod([-1, 1]*500) # 27.8μs -> 4.01μs (593% faster)
    codeflash_output = prod([-1, 1]*499 + [-1]) # 27.2μs -> 3.76μs (625% faster)

def test_prod_large_list_range():
    # Product of range(1, 11) == 10! == 3628800
    codeflash_output = prod(range(1, 11)) # 1.37μs -> 680ns (101% faster)

def test_prod_large_float_list():
    # Product of 1000 floats (all 1.1)
    codeflash_output = prod([1.1]*1000); result = codeflash_output # 28.1μs -> 3.15μs (791% faster)
    # Should be 1.1**1000, allow small relative error due to floating point arithmetic
    expected = 1.1**1000

To edit these changes git checkout codeflash/optimize-prod-mholsiqg and push.