Source code for ot.gpu.utils

# -*- coding: utf-8 -*-
Utility functions for GPU

# Author: Remi Flamary <>
#         Nicolas Courty <>
#         Leo Gautheron <>
# License: MIT License

import cupy as np  # np used for matrix computation
import cupy as cp  # cp used for cupy specific operations

def euclidean_distances(a, b, squared=False, to_numpy=True):
    Compute the pairwise euclidean distance between matrices a and b.

    If the input matrix are in numpy format, they will be uploaded to the
    GPU first which can incur significant time overhead.

    a : np.ndarray (n, f)
        first matrix
    b : np.ndarray (m, f)
        second matrix
    to_numpy : boolean, optional (default True)
        If true convert back the GPU array result to numpy format.
    squared : boolean, optional (default False)
        if True, return squared euclidean distance matrix

    c : (n x m) np.ndarray or cupy.ndarray
        pairwise euclidean distance distance matrix

    a, b = to_gpu(a, b)

    a2 = np.sum(np.square(a), 1)
    b2 = np.sum(np.square(b), 1)

    c = -2 *, b.T)
    c += a2[:, None]
    c += b2[None, :]

    if not squared:
        np.sqrt(c, out=c)
    if to_numpy:
        return to_np(c)
        return c

[docs]def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True): """Compute distance between samples in x1 and x2 on gpu Parameters ---------- x1 : np.array (n1,d) matrix with n1 samples of size d x2 : np.array (n2,d), optional matrix with n2 samples of size d (if None then x2=x1) metric : str Metric from 'sqeuclidean', 'euclidean', Returns ------- M : np.array (n1,n2) distance matrix computed with given metric """ if x2 is None: x2 = x1 if metric == "sqeuclidean": return euclidean_distances(x1, x2, squared=True, to_numpy=to_numpy) elif metric == "euclidean": return euclidean_distances(x1, x2, squared=False, to_numpy=to_numpy) else: raise NotImplementedError
[docs]def to_gpu(*args): """ Upload numpy arrays to GPU and return them""" if len(args) > 1: return (cp.asarray(x) for x in args) else: return cp.asarray(args[0])
[docs]def to_np(*args): """ convert GPU arras to numpy and return them""" if len(args) > 1: return (cp.asnumpy(x) for x in args) else: return cp.asnumpy(args[0])