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introduce a versatile sparse tensor type to MatX (experimental) #821
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| //////////////////////////////////////////////////////////////////////////////// | ||
| // BSD 3-Clause License | ||
| // | ||
| // Copyright (c) 2025, NVIDIA Corporation | ||
| // All rights reserved. | ||
| // | ||
| // Redistribution and use in source and binary forms, with or without | ||
| // modification, are permitted provided that the following conditions are met: | ||
| // | ||
| // 1. Redistributions of source code must retain the above copyright notice, this | ||
| // list of conditions and the following disclaimer. | ||
| // | ||
| // 2. Redistributions in binary form must reproduce the above copyright notice, | ||
| // this list of conditions and the following disclaimer in the documentation | ||
| // and/or other materials provided with the distribution. | ||
| // | ||
| // 3. Neither the name of the copyright holder nor the names of its | ||
| // contributors may be used to endorse or promote products derived from | ||
| // this software without specific prior written permission. | ||
| // | ||
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | ||
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | ||
| // DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE | ||
| // FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | ||
| // DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | ||
| // SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | ||
| // CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | ||
| // OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
| ///////////////////////////////////////////////////////////////////////////////// | ||
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| #include "matx.h" | ||
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| using namespace matx; | ||
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| int main([[maybe_unused]] int argc, [[maybe_unused]] char **argv) | ||
| { | ||
| MATX_ENTER_HANDLER(); | ||
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| cudaStream_t stream = 0; | ||
| cudaExecutor exec{stream}; | ||
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| // | ||
| // Creates a COO matrix for the following 4x8 dense matrix with 5 nonzero | ||
| // elements, using the factory method that uses MatX tensors for the 1-dim | ||
| // buffers. The sparse matrix resides in the same memory space as its buffer | ||
| // constituents. | ||
| // | ||
| // | 1, 2, 0, 0, 0, 0, 0, 0 | | ||
| // | 0, 0, 0, 0, 0, 0, 0, 0 | | ||
| // | 0, 0, 0, 0, 0, 0, 0, 0 | | ||
| // | 0, 0, 3, 4, 0, 5, 0, 0 | | ||
| // | ||
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| constexpr index_t m = 4; | ||
| constexpr index_t n = 8; | ||
| constexpr index_t nse = 5; | ||
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| tensor_t<float, 1> values{{nse}}; | ||
| tensor_t<int, 1> row_idx{{nse}}; | ||
| tensor_t<int, 1> col_idx{{nse}}; | ||
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| values.SetVals({ 1, 2, 3, 4, 5 }); | ||
| row_idx.SetVals({ 0, 0, 3, 3, 3 }); | ||
| col_idx.SetVals({ 0, 1, 2, 3, 5 }); | ||
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| // Note that sparse tensor support in MatX is still experimental. | ||
| auto Acoo = experimental::make_tensor_coo(values, row_idx, col_idx, {m, n}); | ||
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| // | ||
| // This shows: | ||
| // | ||
| // tensor_impl_2_f32: Tensor{float} Rank: 2, Sizes:[4, 8], Levels:[4, 8] | ||
| // nse = 5 | ||
| // format = ( d0, d1 ) -> ( d0 : compressed(non-unique), d1 : singleton ) | ||
| // crd[0] = ( 0 0 3 3 3 ) | ||
| // crd[1] = ( 0 1 2 3 5 ) | ||
| // values = ( 1.0000e+00 2.0000e+00 3.0000e+00 4.0000e+00 5.0000e+00 ) | ||
| // space = CUDA managed memory | ||
| // | ||
| print(Acoo); | ||
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| // TODO: operations on Acoo | ||
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| MATX_EXIT_HANDLER(); | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,104 @@ | ||
| //////////////////////////////////////////////////////////////////////////////// | ||
| // BSD 3-Clause License | ||
| // | ||
| // Copyright (c) 2025, NVIDIA Corporation | ||
| // All rights reserved. | ||
| // | ||
| // Redistribution and use in source and binary forms, with or without | ||
| // modification, are permitted provided that the following conditions are met: | ||
| // | ||
| // 1. Redistributions of source code must retain the above copyright notice, this | ||
| // list of conditions and the following disclaimer. | ||
| // | ||
| // 2. Redistributions in binary form must reproduce the above copyright notice, | ||
| // this list of conditions and the following disclaimer in the documentation | ||
| // and/or other materials provided with the distribution. | ||
| // | ||
| // 3. Neither the name of the copyright holder nor the names of its | ||
| // contributors may be used to endorse or promote products derived from | ||
| // this software without specific prior written permission. | ||
| // | ||
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | ||
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | ||
| // DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE | ||
| // FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | ||
| // DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | ||
| // SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | ||
| // CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | ||
| // OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
| ///////////////////////////////////////////////////////////////////////////////// | ||
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| #pragma once | ||
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| #include "matx/core/sparse_tensor.h" | ||
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| namespace matx { | ||
| namespace experimental { | ||
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| // | ||
| // MatX uses a single versatile sparse tensor type that uses a tensor format | ||
| // DSL (Domain Specific Language) to describe a vast space of storage formats. | ||
| // This file provides a number of convenience factory methods that construct | ||
| // sparse tensors in well-known storage formats, like COO, CSR, and CSC, | ||
| // directly from the constituent buffers. More factory methods can easily be | ||
| // added as the need arises. | ||
| // | ||
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| // Constructs a sparse matrix in COO format directly from the values and | ||
| // the two coordinates vectors. The entries should be sorted by row, then | ||
| // column. Duplicate entries should not occur. Explicit zeros may be stored. | ||
| template <typename ValTensor, typename CrdTensor> | ||
| auto make_tensor_coo(ValTensor &val, CrdTensor &row, CrdTensor &col, | ||
| const index_t (&shape)[2], bool owning = false) { | ||
| static_assert(ValTensor::Rank() == 1 && CrdTensor::Rank() == 1); | ||
| using VAL = typename ValTensor::value_type; | ||
| using CRD = typename CrdTensor::value_type; | ||
| using POS = int; // no positions, although some forms use [0,nse] | ||
| raw_pointer_buffer<POS, matx_allocator<POS>> emptyp{nullptr, 0, owning}; | ||
| basic_storage<decltype(emptyp)> ep{std::move(emptyp)}; | ||
| return sparse_tensor_t<VAL, CRD, POS, COO>( | ||
| shape, val.GetStorage(), {row.GetStorage(), col.GetStorage()}, {ep, ep}); | ||
| } | ||
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| // Constructs a sparse matrix in CSR format directly from the values, the row | ||
| // positions, and column coordinates vectors. The entries should be sorted by | ||
| // row, then column. Explicit zeros may be stored. | ||
| template <typename ValTensor, typename PosTensor, typename CrdTensor> | ||
| auto make_tensor_csr(ValTensor &val, PosTensor &rowp, CrdTensor &col, | ||
| const index_t (&shape)[2], bool owning = false) { | ||
| static_assert(ValTensor::Rank() == 1 && CrdTensor::Rank() == 1 && | ||
| PosTensor::Rank() == 1); | ||
| using VAL = typename ValTensor::value_type; | ||
| using CRD = typename CrdTensor::value_type; | ||
| using POS = typename PosTensor::value_type; | ||
| raw_pointer_buffer<CRD, matx_allocator<CRD>> emptyc{nullptr, 0, owning}; | ||
| basic_storage<decltype(emptyc)> ec{std::move(emptyc)}; | ||
| raw_pointer_buffer<POS, matx_allocator<POS>> emptyp{nullptr, 0, owning}; | ||
| basic_storage<decltype(emptyp)> ep{std::move(emptyp)}; | ||
| return sparse_tensor_t<VAL, CRD, POS, CSR>( | ||
| shape, val.GetStorage(), {ec, col.GetStorage()}, {ep, rowp.GetStorage()}); | ||
| } | ||
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| // Constructs a sparse matrix in CSC format directly from the values, | ||
| // the row coordinates, and column position vectors. The entries should | ||
| // be sorted by column, then row. Explicit zeros may be stored. | ||
| template <typename ValTensor, typename PosTensor, typename CrdTensor> | ||
| auto make_tensor_csc(ValTensor &val, CrdTensor &row, PosTensor &colp, | ||
| const index_t (&shape)[2], bool owning = false) { | ||
| static_assert(ValTensor::Rank() == 1 && CrdTensor::Rank() == 1 && | ||
| PosTensor::Rank() == 1); | ||
| using VAL = typename ValTensor::value_type; | ||
| using CRD = typename CrdTensor::value_type; | ||
| using POS = typename PosTensor::value_type; | ||
| raw_pointer_buffer<CRD, matx_allocator<CRD>> emptyc{nullptr, 0, owning}; | ||
| basic_storage<decltype(emptyc)> ec{std::move(emptyc)}; | ||
| raw_pointer_buffer<POS, matx_allocator<POS>> emptyp{nullptr, 0, owning}; | ||
| basic_storage<decltype(emptyp)> ep{std::move(emptyp)}; | ||
| return sparse_tensor_t<VAL, CRD, POS, CSC>( | ||
| shape, val.GetStorage(), {ec, row.GetStorage()}, {ep, colp.GetStorage()}); | ||
| } | ||
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| } // namespace experimental | ||
| } // namespace matx | ||
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