cuvs 24.6.0

RAPIDS vector search library
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/*
 * Copyright (c) 2024, NVIDIA CORPORATION.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */


use std::io::{stderr, Write};

use crate::distance_type::DistanceType;
use crate::dlpack::ManagedTensor;
use crate::error::{check_cuvs, Result};
use crate::resources::Resources;

/// Compute pairwise distances between X and Y
///
/// # Arguments
///
/// * `res` - Resources to use
/// * `x` - A matrix in device memory - shape (m, k)
/// * `y` - A matrix in device memory - shape (n, k)
/// * `distances` - A matrix in device memory that receives the output distances - shape (m, n)
/// * `metric` - DistanceType to use for building the index
/// * `metric_arg` - Optional value of `p` for Minkowski distances
pub fn pairwise_distance(
    res: &Resources,
    x: &ManagedTensor,
    y: &ManagedTensor,
    distances: &ManagedTensor,
    metric: DistanceType,
    metric_arg: Option<f32>,
) -> Result<()> {
    unsafe {
        check_cuvs(ffi::cuvsPairwiseDistance(
            res.0,
            x.as_ptr(),
            y.as_ptr(),
            distances.as_ptr(),
            metric,
            metric_arg.unwrap_or(2.0),
        ))
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use ndarray::s;
    use ndarray_rand::rand_distr::Uniform;
    use ndarray_rand::RandomExt;

    #[test]
    fn test_pairwise_distance() {
        let res = Resources::new().unwrap();

        // Create a new random dataset to index
        let n_datapoints = 256;
        let n_features = 16;
        let dataset =
            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
        let dataset_device = ManagedTensor::from(&dataset).to_device(&res).unwrap();

        let mut distances_host = ndarray::Array::<f32, _>::zeros((n_datapoints, n_datapoints));
        let distances = ManagedTensor::from(&distances_host)
            .to_device(&res)
            .unwrap();
    
        pairwise_distance(&res, &dataset_device, &dataset_device, &distances, DistanceType::L2Expanded,
        None).unwrap();

        // Copy back to host memory
        distances.to_host(&res, &mut distances_host).unwrap();

        // Self distance should be 0
        assert_eq!(distances_host[[0, 0]], 0.0);
    }
}