# Generic reductions¶

## Overview¶

The low-level interface of KeOps is the Genred module, which allows users to define and reduce generic operations. Depending on your framework, you may import Genred using either:

from pykeops.numpy import Genred  # for NumPy users, or...
from pykeops.torch import Genred  # for PyTorch users.


In both cases, Genred is a class with no methods: its instantiation simply returns a numerical function that can be called at will.

1. Instantiation: Genred(...) takes as input a bunch of strings that specify the desired computation. It returns a python function or PyTorch layer, callable on numpy arrays or torch tensors. The syntax is:

my_red = Genred(formula, aliases, reduction_op='Sum', axis=0, dtype='float32')

1. Call: The variable my_red now refers to a callable object wrapped around a set of custom Cuda routines. It may be used on any set of arrays (either NumPy arrays or Torch tensors) with the correct shapes, as described in the aliases argument:

result = my_red(arg_1, arg_2, ..., arg_p, backend='auto', device_id=-1, ranges=None)


## Documentation¶

See the numpy.Genred or torch.Genred API documentations for a complete description of the syntax at instantiation and call times.

## An example¶

Using the generic syntax, computing a Gaussian-RBF kernel product

$\text{for } i = 1, \cdots, 1000, \quad\quad a_i = \sum_{j=1}^{2000} \exp(-\gamma\|x_i-y_j\|^2) \,\cdot\, b_j.$

can be done with:

import torch
from pykeops.torch import Genred

# Notice that the parameter gamma is a dim-1 vector, *not* a scalar:
gamma  = torch.tensor([.5])
# Generate the data as pytorch tensors. If you intend to compute gradients, don't forget the requires_grad flag!
x = torch.randn(1000,3)
y = torch.randn(2000,3)
b = torch.randn(2000,2)

gaussian_conv = Genred('Exp(-G * SqDist(X,Y)) * B', # F(g,x,y,b) = exp( -g*|x-y|^2 ) * b
['G = Pm(1)',          # First arg  is a parameter,    of dim 1
'X = Vi(3)',          # Second arg is indexed by "i", of dim 3
'Y = Vj(3)',          # Third arg  is indexed by "j", of dim 3
'B = Vj(2)'],         # Fourth arg is indexed by "j", of dim 2
reduction_op='Sum',
axis=1)                # Summation over "j"

# N.B.: a.shape == [1000, 2]
a = gaussian_conv(gamma, x, y, b)

# By explicitly specifying the backend, you can try to optimize your pipeline:
a = gaussian_conv(gamma, x, y, b, backend='GPU')
a = gaussian_conv(gamma, x, y, b, backend='CPU')


More examples can be found in the gallery.