torch.cdist

torch.cdist() is a function in PyTorch that computes the pairwise distance between two sets of vectors. It takes two tensors as input and returns a tensor containing the pairwise distances between the vectors in the two input sets.

The function supports various distance metrics such as Euclidean distance, Manhattan distance, and cosine distance.

Syntax:

torch.cdist(x1, x2, p=2, compute_mode='use_mm_for_euclid_dist_if_necessary')

where,
- x1, x2: Two input tensors containing vectors
- p: The order of the norm used to compute the distance (default=2, i.e., Euclidean distance)
- compute_mode: The mode used to compute the distance (default='use_mm_for_euclid_dist_if_necessary', which uses matrix multiplication for Euclidean distance and a specialized implementation for other distance metrics)

Example:

import torch

x1 = torch.tensor([[0, 0], [0, 1], [1, 0], [1, 1]])
x2 = torch.tensor([[2, 2], [2, 3], [3, 2], [3, 3]])

distances = torch.cdist(x1, x2, p=2)

print(distances)

Output:

tensor([[2.8284, 3.1623, 3.1623, 3.6056],
        [2.0000, 2.2361, 2.2361, 2.8284],
        [2.0000, 2.2361, 2.2361, 2.8284],
        [2.8284, 3.1623, 3.1623, 3.6056]])

In this example, we compute the pairwise Euclidean distances between the vectors in x1 and x2. The output tensor contains the distances between each pair of vectors.