Consider adding sumproduct() or dotproduct() to the math module

I was reviewing the itertools recipes to see whether some were worth promoting to be builtin tools. The dotproduct() recipe was the best candidate. To non-matrix people this is known as sumproduct() and it comes up in many non-vector applications, possibly the most common being sum([price * quantity for price, quantity in zip(prices, quantities)]) and the second most common being weighted averages.

The current version of the recipe is:

   def dotproduct(vec1, vec2):
       "Compute a sum of products."
       return sum(starmap(operator.mul, zip(vec1, vec2, strict=True)))

If we offered this as part of the math module, we could make a higher quality implementation.

For float inputs or mixed int/float inputs, we could square and sum in quad precision, making a single rounding at the end. This would make a robust building block to serve as a foundation for users to construct higher level tools. It is also something that is difficult for them to do on their own.

Linked PRs

@rhettinger

@rhettinger working on implementing this feature. I'm curious, how would one be expected to use quad precision in this context? Should I just write a multiplication and addition function for a custom quad precision data structure?

how would one be expected to use quad precision in this context

Raymond's use of "quad precision" was an active link to an article spelling out what he means. It's a software technique for getting twice native precision using single native precision float operations, and for that reason is usually called "doubled precision". Since Python's "float" is IEEE-754 "double precision" on most (all?) platforms, twice that leads to "quad".

If you look at the edit history of Raymond's post, at first it had a prototype implementation in pure Python.

@rhettinger

@rhettinger working on implementing this feature

@PurityLake Please leave that for me. I've already done substantial work on it and opened this issue to get buy in before going further.

to get buy in

Not needed - it would self-evidently be nice to have 😄

Here's the logic I'm planning to use (and the tests to make sure it correct). If you all want something different or can suggest improvements, please let me know.

'Pure Python implementation of sumprod() with accuracy enhancements.'

from math import fabs, fsum, frexp, isfinite
from sys import stderr

verbose = False

def accumulate(total, frac, x):
    if fabs(total) < fabs(x):
        x, total = total, x
    t = total + x
    frac += (total - t) + x
    return t, frac

def split(x, VELTKAMP_CONSTANT=float(0x8000001)):
    t = x * VELTKAMP_CONSTANT
    hi = t - (t - x)
    lo = x - hi
    assert not isfinite(hi) or (frexp(hi)[0] * 2**26).is_integer()
    assert not isfinite(hi) or (frexp(lo)[0] * 2**26).is_integer()
    assert not isfinite(hi) or hi + lo == x
    return hi, lo

def multiply(p, q):
    hp, lp = split(p)
    hq, lq = split(q)
    parts = hp*hq, hp*lq, hq*lp, lp*lq
    assert not all(map(isfinite, parts)) or fsum(parts) == p * q
    return parts

def both_int(p, q):
    return {type(p), type(q)} == {int}

def one_float_one_int(p, q):
    return {type(p), type(q)} == {int, float}

def both_float(p, q):
    return {type(p), type(q)} == {float}

def PyLong_AsLongAndOverflow(p):
    p = int(p)
    if p.bit_length() > 63:
        raise OverflowError
    return p

def check_long_mult_overflow(p, q):
    "Multiply longs and raise if too large for a longlong."
    result = p * q
    if result.bit_length() > 63:
        raise OverflowError
    return result

def PyFloat_AsDouble(p):
    assert type(p) in {int, float}
    return float(p)  # This can raise OverflowError

NULL = object()

def sumprod(p, q, verbose=False):
    p_stopped = q_stopped = False
    int_path_enabled = flt_path_enabled = True
    int_total_in_use = flt_total_in_use = False
    int_total = 0
    flt_total = flt_frac = 0.0
    obj_total = 0

    p_it = iter(p)
    q_it = iter(q)
    while True:
        p_i = next(p_it, NULL)
        if p_i is NULL:
            p_stopped = True
        q_i = next(q_it, NULL)
        if q_i is NULL:
            q_stopped = True
        if p_stopped != q_stopped:
            raise ValueError('Inputs are not the same length')
        finished = p_stopped & q_stopped

        if int_path_enabled:

            if not finished and both_int(p_i, q_i):
                try:
                    if verbose: print('I', end='', file=stderr)
                    int_p = PyLong_AsLongAndOverflow(p_i)
                    int_q = PyLong_AsLongAndOverflow(q_i)
                    int_total += check_long_mult_overflow(int_p, int_q)
                    int_total_in_use = True
                    continue
                except OverflowError:
                    pass

           # We're finished, overflowed, or have a non-int
            int_path_enabled = False
            if int_total_in_use:
                obj_total += int(int_total)
                int_total_in_use = False

        if flt_path_enabled:

            if not finished and (both_float(p_i, q_i) or one_float_one_int(p_i, q_i)):
                try:
                    if verbose: print('F', end='', file=stderr)
                    flt_p = PyFloat_AsDouble(p_i)
                    flt_q = PyFloat_AsDouble(q_i)
                    for part in multiply(flt_p, flt_q):
                        flt_total, flt_frac = accumulate(flt_total, flt_frac, part)
                    flt_total_in_use = True
                    if isfinite(flt_total):
                        continue
                    # If a non-finite value arises, fallback to the slow path
                    flt_total = flt_frac = 0.0
                except OverflowError:
                    pass

            # We're finished, overflowed, have a non-float, or have a non-finite value
            flt_path_enabled = False
            if flt_total_in_use:
                flt_total += flt_frac
                obj_total += float(flt_total)
                flt_total_in_use = False

        assert not int_total_in_use
        assert not flt_total_in_use

        if finished:
            return obj_total

        if verbose: print('O', end='', file=stderr)
        obj_total += p_i * q_i


if __name__ == '__main__':

    from decimal import Decimal
    from fractions import Fraction
    from itertools import product
    from math import isnan
    from random import randrange

    def baseline_sumprod(p, q):
        "This defines the target behavior including expections and special values."
        total = 0
        for p_i, q_i in zip(p, q, strict=True):
            total += p_i * q_i
        return total

    def run(func, *args):
        "Make comparing functions easier. Returns error status, type, and result."
        try:
            result = func(*args)
        except Exception as e:
            return 'Error', type(e), None
        if isinstance(result, (float, Decimal)) and isnan(result):
            result = 'NaN'
        return None, type(result), result,

    pool = [3.75, 2.5, -1.5, 0.0, 1.0,
            float('inf'), -float('inf'), float('NaN'),
            3, -5, 7, 0, 1, 10*2**64, 10**1000,
            Fraction(7, 8), Fraction(21, 4), Fraction(-41, 2),
            Fraction(0), Fraction(1),
            Decimal('6.125'), Decimal('12.125'), Decimal('-2.125'),
            Decimal(0), Decimal(1),
            Decimal('Inf'), -Decimal('Inf'), Decimal('NaN'),
    ]

    args = ((), ())
    assert run(baseline_sumprod, *args) == run(sumprod, *args)
    print('Done 0')

    for a, b in product(pool, repeat=2):
        args = (a,), (b,)
        assert run(baseline_sumprod, *args) == run(sumprod, *args)
    print('Done 2')

    for m, n in product(range(5), repeat=2):
        args = [randrange(100)/16 for i in range(m)], [randrange(100)/16 for i in range(n)]
        assert run(baseline_sumprod, *args) == run(sumprod, *args)
    print('Done uneven')

    for a, b, c, d in product(pool, repeat=4):
        args = (a, b), (c, d)
        assert run(baseline_sumprod, *args) == run(sumprod, *args)
    print('Done 4')

    alt_pool = (1e200, 2e200, 5e200, 0.0, -1e200, -2e200, -5e200)
    for a, b, c, d, e, f in product(pool, repeat=6):
        args = (a, b, c), (d, e, f)
        assert run(baseline_sumprod, *args) == run(sumprod, *args)
    print('Done overflow 6')

    for a, b, c, d, e, f in product(pool, repeat=6):
        args = (a, b, c), (d, e, f)
        assert run(baseline_sumprod, *args) == run(sumprod, *args)
    print('Done regular 6')

GH-100485: Add math.sumprod() (GH-100677)

I checked in the core logic so that we could have a baseline for further improvements. The beta release is still far away. Suggestions are welcome. Here are some topics that I'm mulling over:

Determine whether 2SUM would be faster than Fast2Sum with a fabs test. The disassembly from both Intel and M1 Arm builds show that Fast2Sum is compiled without branches. The x86_64 code selects between the results with cmpnlesd and andnpd. The AArch64 code uses fcmp and fcsel to the same effect.
The API is a bit awkward for some inputs. To compute the sum of squares from an iterator input, you need sumprod(*tee(it)). Inputs consisting of 2-tuples need to be regrouped with sumprod(*zip(*paired)). Generator expression inputs work fine but should be formatted on two lines for readabilty:

sumprod(((xi - xbar) for xi in x),
        ((yi - ybar) for yi in y))

For grins, I'm experimenting with 2-fold Neumaier summation. At a cost of an extra 2Sum call, the error bound becomes proportional to ε³·n²·Σ|aᵢ|. The extra factor of ε would only be relevant in cases where large terms mostly cancel (this is typical for some use cases; for example, summing the moments on a lever).

def twofold(data):
    ce = e = b = 0.0
    for n in data:
        bb, ee = twosum(b, n)
        eee, cce = twosum(e, ee)
        b = bb
        e = eee
        ce += cce
    return cce + b + e

It might be worthwhile to support a high accuracy code path for complex numbers.
The MS Excel implementation generalizes sumprod to handle an arbitrary number of input vectors. If there were valid use cases, we could do the same:

def sumprod(*vecs):
    return sum(map(math.prod, zip(*vecs, strict=True)))

>>> sumprod([10, 20, 30, 40], [2, 4, 6, 8], [1, 2, 3, 4])
2000
>>> 10*2*1 + 20*4*2 + 30*6*3 + 40*8*4
2000

The test_sumprod_stress() unittest does too much work. Running test_math now takes 15 seconds.

GH-100485: Convert from Fast2Sum to 2Sum (GH-100836)

Determine whether 2SUM would be faster than Fast2Sum

2SUM was faster so I put it in.

For grins, I'm experimenting with 2-fold Neumaier summation.

Interestingly, this only makes the code two lines longer. The running time only increases by 12%. Worth it?

Please add @support.requires_resource('cpu') to test_sumprod_stress.

It would be good to include this in What's New.

https://docs.python.org/3.12/whatsnew/3.12.html

GH-100485: Tweaks to sumprod() (GH-100857)

@gpshead

@gpshead Done.
@hugovk Done.

Fun fact, sumprod can be used to make a single rounding fused-mulitply-add:

def fma(x, y, z):
    'Return x * y + z with a single rounding'
    return sumprod((x, z), (y, 1.0))

If we're going to have an even more efficient method, the dot product example from itertools should be replaced with something else.

GH-100485: Add extended accuracy test. Switch to faster fma() based v…

pythonGH-100485: Add extended accuracy test. Switch to faster fma() b…

GH-100485: Create an alternative code path when an accurate fma() imp…

@rhettinger

@rhettinger
I'd indeed like below:

The MS Excel implementation generalizes sumprod to handle an arbitrary number of input vectors. If there were valid use cases, we could do the same:
def sumprod(*vecs):
    return sum(map(math.prod, zip(*vecs, strict=True)))

>>> sumprod([10, 20, 30, 40], [2, 4, 6, 8], [1, 2, 3, 4])
2000
>>> 10*2*1 + 20*4*2 + 30*6*3 + 40*8*4
2000

Example use case (as variant to the motivating example at the very top message of this thread):
sum([price * quantity * (1 - discount) * (1 + tax_rate) for price, quantity, discount, tax_rate in zip(prices, quantities, discounts, tax_rates)])
which would then become:
sumprod(prices, quantities, (1 - discount for discount in discounts), (1 + tax_rate for tax_rate in tax_rates))

I'd welcome this generalisation, but perhaps my comment is too late because sumproduct was added to 3.12a6 and this issue was closed.

@Dutcho

It should not be too late for such an improvement yet. Beta 1 is the point when no new features are supposed to be added.
@Dutcho, maybe you should open a new issue, though.

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Consider adding sumproduct() or dotproduct() to the math module #100485

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PurityLake commented on Dec 24, 2022

tim-one commented on Dec 24, 2022

rhettinger commented on Dec 24, 2022

tim-one commented on Dec 24, 2022

rhettinger commented on Jan 3, 2023

rhettinger commented on Jan 7, 2023

rhettinger commented on Jan 8, 2023

gpshead commented on Jan 8, 2023

hugovk commented on Jan 8, 2023

rhettinger commented on Jan 8, 2023

rhettinger commented on Jan 9, 2023

TrangOul commented on Jan 12, 2023

Dutcho commented on Mar 12, 2023

TrangOul commented on Mar 20, 2023

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