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Changing RAFT_EXPLICT_* to CUVS_EXPLITI_* (rapidsai#141)
Changing this for now. We'll work on removing this macro in a future release. Authors: - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Ben Frederickson (https://github.com/benfred) - Divye Gala (https://github.com/divyegala) URL: rapidsai/cuvs#141
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| Original file line number | Diff line number | Diff line change |
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| /* | ||
| * Copyright (c) 2023-2024, 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. | ||
| */ | ||
|
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| #ifndef __MASKED_L2_NN_H | ||
| #define __MASKED_L2_NN_H | ||
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| #pragma once | ||
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| #include "detail/masked_nn.cuh" | ||
| #include "fused_distance_nn_helpers.cuh" | ||
| #include <raft/core/handle.hpp> | ||
| #include <raft/util/cuda_utils.cuh> | ||
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| #include <stdint.h> | ||
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| #include <limits> | ||
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| namespace cuvs { | ||
| namespace distance { | ||
| /** | ||
| * \defgroup masked_nn Masked 1-nearest neighbors | ||
| * @{ | ||
| */ | ||
|
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| /** | ||
| * @brief Parameter struct for masked_l2_nn function | ||
| * | ||
| * @tparam ReduceOpT Type of reduction operator in the epilogue. | ||
| * @tparam KVPReduceOpT Type of Reduction operation on key value pairs. | ||
| * | ||
| * Usage example: | ||
| * @code{.cpp} | ||
| * #include <cuvs/distance/masked_nn.cuh> | ||
| * | ||
| * using IdxT = int; | ||
| * using DataT = float; | ||
| * using RedOpT = cuvs::distance::MinAndDistanceReduceOp<IdxT, DataT>; | ||
| * using PairRedOpT = cuvs::distance::KVPMinReduce<IdxT, DataT>; | ||
| * using ParamT = cuvs::distance::masked_l2_nn_params<RedOpT, PairRedOpT>; | ||
| * | ||
| * bool init_out = true; | ||
| * bool sqrt = false; | ||
| * | ||
| * ParamT masked_l2_params{RedOpT{}, PairRedOpT{}, sqrt, init_out}; | ||
| * @endcode | ||
| * | ||
| * Prescribes how to reduce a distance to an intermediate type (`redOp`), and | ||
| * how to reduce two intermediate types (`pairRedOp`). Typically, a distance is | ||
| * mapped to an (index, value) pair and (index, value) pair with the lowest | ||
| * value (distance) is selected. | ||
| * | ||
| * In addition, prescribes whether to compute the square root of the distance | ||
| * (`sqrt`) and whether to initialize the output buffer (`initOutBuffer`). | ||
| */ | ||
| template <typename ReduceOpT, typename KVPReduceOpT> | ||
| struct masked_l2_nn_params { | ||
| /** Reduction operator in the epilogue */ | ||
| ReduceOpT redOp; | ||
| /** Reduction operation on key value pairs */ | ||
| KVPReduceOpT pairRedOp; | ||
| /** Whether the output `minDist` should contain L2-sqrt */ | ||
| bool sqrt; | ||
| /** Whether to initialize the output buffer before the main kernel launch */ | ||
| bool initOutBuffer; | ||
| }; | ||
|
|
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| /** | ||
| * @brief Masked L2 distance and 1-nearest-neighbor computation in a single call. | ||
| * | ||
| * This function enables faster computation of nearest neighbors if the | ||
| * computation of distances between certain point pairs can be skipped. | ||
| * | ||
| * We use an adjacency matrix that describes which distances to calculate. The | ||
| * points in `y` are divided into groups, and the adjacency matrix indicates | ||
| * whether to compute distances between points in `x` and groups in `y`. In other | ||
| * words, if `adj[i,k]` is true then distance between point `x_i`, and points in | ||
| * `group_k` will be calculated. | ||
| * | ||
| * **Performance considerations** | ||
| * | ||
| * The points in `x` are processed in tiles of `M` points (`M` is currently 64, | ||
| * but may change in the future). As a result, the largest compute time | ||
| * reduction occurs if all `M` points can skip a group. If only part of the `M` | ||
| * points can skip a group, then at most a minor compute time reduction and a | ||
| * modest energy use reduction can be expected. | ||
| * | ||
| * The points in `y` are also grouped into tiles of `N` points (`N` is currently | ||
| * 64, but may change in the future). As a result, group sizes should be larger | ||
| * than `N` to avoid wasting computational resources. If the group sizes are | ||
| * evenly divisible by `N`, then the computation is most efficient, although for | ||
| * larger group sizes this effect is minor. | ||
| * | ||
| * | ||
| * **Comparison to SDDM** | ||
| * | ||
| * [SDDMM](https://ieeexplore.ieee.org/document/8638042) (sampled dense-dense | ||
| * matrix multiplication) is a matrix-matrix multiplication where only part of | ||
| * the output is computed. Compared to masked_l2_nn, there are a few differences: | ||
| * | ||
| * - The output of masked_l2_nn is a single vector (of nearest neighbors) and not | ||
| * a sparse matrix. | ||
| * | ||
| * - The sampling in masked_l2_nn is expressed through intermediate "groups" | ||
| rather than a CSR format. | ||
| * | ||
| * @tparam DataT data type | ||
| * @tparam OutT output type to either store 1-NN indices and their minimum | ||
| * distances or store only the min distances. Accordingly, one | ||
| * has to pass an appropriate `ReduceOpT` | ||
| * @tparam IdxT indexing arithmetic type | ||
| * @tparam ReduceOpT A struct to perform the final needed reduction operation | ||
| * and also to initialize the output array elements with the | ||
| * appropriate initial value needed for reduction. | ||
| * | ||
| * @param handle RAFT handle for managing expensive resources | ||
| * @param params Parameter struct specifying the reduction operations. | ||
| * @param[in] x First matrix. Row major. Dim = `m x k`. | ||
| * (on device). | ||
| * @param[in] y Second matrix. Row major. Dim = `n x k`. | ||
| * (on device). | ||
| * @param[in] x_norm L2 squared norm of `x`. Length = `m`. (on device). | ||
| * @param[in] y_norm L2 squared norm of `y`. Length = `n`. (on device) | ||
| * @param[in] adj A boolean adjacency matrix indicating for each | ||
| * row of `x` and each group in `y` whether to compute the | ||
| * distance. Dim = `m x num_groups`. | ||
| * @param[in] group_idxs An array containing the *end* indices of each group | ||
| * in `y`. The value of group_idxs[j] indicates the | ||
| * start of group j + 1, i.e., it is the inclusive | ||
| * scan of the group lengths. The first group is | ||
| * always assumed to start at index 0 and the last | ||
| * group typically ends at index `n`. Length = | ||
| * `num_groups`. | ||
| * @param[out] out will contain the reduced output (Length = `m`) | ||
| * (on device) | ||
| */ | ||
| template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT, typename KVPReduceOpT> | ||
| void masked_l2_nn(raft::resources const& handle, | ||
| cuvs::distance::masked_l2_nn_params<ReduceOpT, KVPReduceOpT> params, | ||
| raft::device_matrix_view<const DataT, IdxT, raft::layout_c_contiguous> x, | ||
| raft::device_matrix_view<const DataT, IdxT, raft::layout_c_contiguous> y, | ||
| raft::device_vector_view<const DataT, IdxT, raft::layout_c_contiguous> x_norm, | ||
| raft::device_vector_view<const DataT, IdxT, raft::layout_c_contiguous> y_norm, | ||
| raft::device_matrix_view<const bool, IdxT, raft::layout_c_contiguous> adj, | ||
| raft::device_vector_view<const IdxT, IdxT, raft::layout_c_contiguous> group_idxs, | ||
| raft::device_vector_view<OutT, IdxT, raft::layout_c_contiguous> out) | ||
| { | ||
| IdxT m = x.extent(0); | ||
| IdxT n = y.extent(0); | ||
| IdxT k = x.extent(1); | ||
| IdxT num_groups = group_idxs.extent(0); | ||
|
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| // Match k dimension of x, y | ||
| RAFT_EXPECTS(x.extent(1) == y.extent(1), "Dimension of vectors in x and y must be equal."); | ||
| // Match x, x_norm and y, y_norm | ||
| RAFT_EXPECTS(m == x_norm.extent(0), "Length of `x_norm` must match input `x`."); | ||
| RAFT_EXPECTS(n == y_norm.extent(0), "Length of `y_norm` must match input `y` "); | ||
| // Match adj to x and group_idxs | ||
| RAFT_EXPECTS(m == adj.extent(0), "#rows in `adj` must match input `x`."); | ||
| RAFT_EXPECTS(num_groups == adj.extent(1), "#cols in `adj` must match length of `group_idxs`."); | ||
| // NOTE: We do not check if all indices in group_idxs actually points *inside* y. | ||
|
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| // If there is no work to be done, return immediately. | ||
| if (m == 0 || n == 0 || k == 0 || num_groups == 0) { return; } | ||
|
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| detail::masked_l2_nn_impl<DataT, OutT, IdxT, ReduceOpT>(handle, | ||
| out.data_handle(), | ||
| x.data_handle(), | ||
| y.data_handle(), | ||
| x_norm.data_handle(), | ||
| y_norm.data_handle(), | ||
| adj.data_handle(), | ||
| group_idxs.data_handle(), | ||
| num_groups, | ||
| m, | ||
| n, | ||
| k, | ||
| params.redOp, | ||
| params.pairRedOp, | ||
| params.sqrt, | ||
| params.initOutBuffer); | ||
| } | ||
|
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| /** @} */ | ||
|
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| } // namespace distance | ||
| } // namespace cuvs | ||
|
|
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| #endif |
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