This lesson is being piloted (Beta version)

Generalised ufuncs

Overview

Teaching: 30 min
Exercises: 25 min
Questions
• What if I want performant functions operating on Numpy arrays that aren’t element-wise?

Objectives
• Be able to use Numba to write custom generalised universal functions

(Adapted from the Scipy 2017 Numba tutorial.)

In the previous episode we looked at writing our own ufuncs using Numba. These act on all elements of an array in the same way, broadcasting like a scalar. Sometimes, however, we would like a function that operates on more than one array element at once. Is there a way we can do this, but still keep the ability to broadcast to larger array shapes?

There is; such a function is called a generalised ufunc, and Numba lets us define these using the guvectorize decorator.

With great power, however, comes great responsibility. The increased flexibility of generalised ufuncs means that we need to give Numba more information to allow it to work. For example,

import numpy as np
from numba import guvectorize

@guvectorize('int64[:], int64, int64[:]', '(n),()->(n)')
def g(x, y, result):
for i in range(x.shape[0]):
result[i] = x[i] + y


Now, this could be done with a simple broadcast, but it demonstrates a point. We have had to make two declarations within the call to guvectorize: the first resembles what we saw for parallel ufuncs; we need to tell Numba what data types (and dimensionalities) we expect to receive and output. The second declaration tells Numba what dimensions are shared between inputs and outputs. In this case, we expect the array (or array slice) x to be the same size as the output array result, while y is a scalar number. Similarly to einsum, more letters indicate more array dimensions.

The function body has another change from the scalar ufunc case, too. We no longer have a return statement at the end; instead, we have an explicitly-passed result variable that we edit. This is necessary, as otherwise we would have to construct a data structure to hold the output, since the whole point of generalised ufuncs was to avoid being restricted to scalar outputs.

We can test this:

x = np.arange(10)
result = np.zeros_like(x)
result = g(x, 5, result)
print(result)

[ 5  6  7  8  9 10 11 12 13 14]


What happens if we try and use this like a more typical Numpy function, which uses a return value rather than taking the output as a parameter?

res = g(x, 5)
print(res)

[ 5  6  7  8  9 10 11 12 13 14]


This works, and is useful in cases where you are replacing an existing function and want to maintain the existing interface (rather than having to modify every calling point). However, it can be dangerous: this effectively uses np.empty to declare the output array, so unless you define all elements of the output array within the function, then the behaviour is unpredictable (the same as using an uninitialised variable in C-like languages).

Let’s look at another example, a generalised ufunc for matrix multiplication.

@guvectorize('float64[:,:], float64[:,:], float64[:,:]',
'(m,n),(n,p)->(m,p)')
def matmul(A, B, C):
m, n = A.shape
n, p = B.shape
for i in range(m):
for j in range(p):
C[i, j] = 0
for k in range(n):
C[i, j] += A[i, k] * B[k, j]


This now takes two rectangular arrays A and B, and returns a rectangular array C, all of type float64. As you might expect, the shapes are such that the first dimension of A and second dimension of B form the dimensions of C, while the second dimension of A must equal the first dimension of B.

As a sanity check, let’s confirm that the identity matrix works as we expect

matmul(np.identity(5), np.arange(25).reshape((5, 5)), np.zeros((5, 5)))

array([[ 0.,  1.,  2.,  3.,  4.],
[ 5.,  6.,  7.,  8.,  9.],
[10., 11., 12., 13., 14.],
[15., 16., 17., 18., 19.],
[20., 21., 22., 23., 24.]])


How does this perform on a slightly more substantial problem, and how does it compare to Numpy’s built-in matrix multiplication?

a = np.random.random((500, 500))
%timeit matmul(a, a, np.zeros_like(a))
%timeit a @ a

259 ms ± 4.04 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
24.7 ms ± 683 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)


The performance stil leaves a little to be desired—ten times slower than Numpy’s built-in matrix multiplier.

Writing signatures

The function ftcs below uses second-order finite differences to solve a heat transfer problem. Use guvectorize to turn ftcs into a generalised ufunc. ftcs has already been written from the point of view of acting as a gufunc, so only the signature needs to be provided, and the now-superfluous return removed.

import numpy
from numba import guvectorize

def ftcs(T, alpha, dt, dx, Tn):
I, J = T.shape
for i in range(1, I - 1):
for j in range(1, J - 1):
Tn[i, j] = (T[i, j] +
alpha *
(dt / dx**2 * (T[i + 1, j] - 2 * T[i, j] + T[i - 1, j]) +
dt / dx**2 * (T[i, j + 1] - 2 * T[i, j] + T[i, j - 1])))

for i in range(I):
Tn[i, 0] = T[i, 0]
Tn[i, J - 1] = Tn[i, J - 2]

for j in range(J):
Tn[0, j] = T[0, j]
Tn[I - 1, j] = Tn[I - 2, j]

#remove when vectorising
return Tn

def run_ftcs():
L = 1.0e-2
nx = 101
nt = 1000
dx = L / (nx - 1)
x = numpy.linspace(0, L, nx)
alpha = .0001
sigma = 0.25
dt = sigma * dx**2 / alpha

Ti = numpy.ones((nx, nx), dtype=numpy.float64)
Ti[0,:]= 100
Ti[:,0] = 100

for t in range(nt):
# creates an empty array with the same dimensions as Ti
Tn = numpy.empty_like(Ti)
Tn = ftcs(Ti, alpha, dt, dx, Tn)
Ti = Tn.copy()

return Tn, x

Tn, x = run_ftcs()


The following will plot the solution as a check that the function is working correctly:

from matplotlib import pyplot, cm
%matplotlib inline

pyplot.figure(figsize=(8, 8))
mx, my = numpy.meshgrid(x, x, indexing='ij')
pyplot.contourf(mx, my, Tn, 20, cmap=cm.viridis)
pyplot.axis('equal');


Solution

@guvectorize(['float64[:,:], float64, float64, float64, float64[:,:]'],
'(m,m),(),(),()->(m,m)', nopython=True)


Relocating loops

The above ftcs example performs an inner loop, but the outer loop in time t is still done outside of our generalised ufunc, using a plain Python loop. Plain Python loops are frequently the enemy of performance, so try removing this loop and instead implementing it within ftcs.

You will need to:

• Modify the parameter list to accept a number of timesteps nt
• Adjust the decorator’s signature to match the new parameter
• Add the extra loop over time
• Incorporate the Tn.copy() operation from the old outer loop
• Adjust run_ftcs to use a single call to the new function rather than a looped call as at present

How does this implementation compare performance-wise to the previous version using a pure Python outer loop?

Solution

@guvectorize(['float64[:,:], float64, float64, float64, int64, float64[:,:]'],
'(m,m),(),(),(),()->(m,m)', nopython=True)
def ftcs_loop(T, alpha, dt, dx, nt, Tn):
I, J = T.shape
for n in range(nt):
for i in range(1, I - 1):
for j in range(1, J - 1):
Tn[i,j] = (T[i, j] +
alpha *
(dt/dx**2 * (T[i + 1, j] - 2*T[i, j] + T[i - 1, j]) +
dt/dx**2 * (T[i, j + 1] - 2*T[i, j] + T[i, j - 1])))

for i in range(I):
Tn[i, 0] = T[i, 0]
Tn[i, J - 1] = Tn[i, J - 2]

for j in range(J):
Tn[0, j] = T[0, j]
Tn[I - 1, j] = Tn[I - 2, j]

T = Tn.copy()


Key Points

• Use the @guvectorize decorator to turn elemental functions into generalised ufuncs

• Both a signature (showing datatypes and dimensionalities) and a layout (showing relationships between indices) are required

• Explicitly initialise the output array within your generalised ufunc where possible