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Also refines some of the checks to make sure that the underlying libraries are never called for unsupported cases
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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 "assert.h" | ||
| #include "matx.h" | ||
| #include "test_types.h" | ||
| #include "utilities.h" | ||
| #include "gtest/gtest.h" | ||
|
|
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| using namespace matx; | ||
|
|
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| // | ||
| // Helper method to construct: | ||
| // | ||
| // | 1, 2, 0, 0, 0, 0, 0, 0 | | ||
| // | 0, 0, 0, 0, 0, 0, 0, 3 | | ||
| // | 0, 0, 0, 0, 0, 0, 4, 0 | | ||
| // | 0, 0, 5, 6, 0, 7, 0, 0 | | ||
| // | ||
| template <typename T> static auto makeA() { | ||
| const index_t m = 4; | ||
| const index_t n = 8; | ||
| tensor_t<T, 2> A = make_tensor<T>({m, n}); | ||
| for (index_t i = 0; i < m; i++) { | ||
| for (index_t j = 0; j < n; j++) { | ||
| A(i, j) = static_cast<T>(0); | ||
| } | ||
| } | ||
| A(0, 0) = static_cast<T>(1); | ||
| A(0, 1) = static_cast<T>(2); | ||
| A(1, 7) = static_cast<T>(3); | ||
| A(2, 6) = static_cast<T>(4); | ||
| A(3, 2) = static_cast<T>(5); | ||
| A(3, 3) = static_cast<T>(6); | ||
| A(3, 5) = static_cast<T>(7); | ||
| return A; | ||
| } | ||
|
|
||
| template <typename T> static auto makeB() { | ||
| const index_t m = 8; | ||
| const index_t n = 2; | ||
| tensor_t<T, 2> B = make_tensor<T>({m, n}); | ||
| B(0, 0) = static_cast<T>(1); B(0, 1) = static_cast<T>(2); | ||
| B(1, 0) = static_cast<T>(3); B(1, 1) = static_cast<T>(4); | ||
| B(2, 0) = static_cast<T>(5); B(2, 1) = static_cast<T>(6); | ||
| B(3, 0) = static_cast<T>(7); B(3, 1) = static_cast<T>(8); | ||
| B(4, 0) = static_cast<T>(9); B(4, 1) = static_cast<T>(10); | ||
| B(5, 0) = static_cast<T>(11); B(5, 1) = static_cast<T>(12); | ||
| B(6, 0) = static_cast<T>(13); B(6, 1) = static_cast<T>(14); | ||
| B(7, 0) = static_cast<T>(15); B(7, 1) = static_cast<T>(16); | ||
| return B; | ||
| } | ||
|
|
||
| template <typename T> static auto makeE() { | ||
| const index_t m = 4; | ||
| const index_t n = 2; | ||
| tensor_t<T, 2> E = make_tensor<T>({m, n}); | ||
| E(0, 0) = static_cast<T>(7); E(0, 1) = static_cast<T>(10); | ||
| E(1, 0) = static_cast<T>(45); E(1, 1) = static_cast<T>(48); | ||
| E(2, 0) = static_cast<T>(52); E(2, 1) = static_cast<T>(56); | ||
| E(3, 0) = static_cast<T>(144); E(3, 1) = static_cast<T>(162); | ||
| return E; | ||
| } | ||
|
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| template <typename T> class MatmulSparseTest : public ::testing::Test { | ||
| protected: | ||
| float thresh = 0.001f; | ||
| }; | ||
|
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| template <typename T> class MatmulSparseTestsAll : public MatmulSparseTest<T> { }; | ||
|
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| TYPED_TEST_SUITE(MatmulSparseTestsAll, MatXFloatNonHalfTypesCUDAExec); | ||
|
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| TYPED_TEST(MatmulSparseTestsAll, MatmulCOO) { | ||
| MATX_ENTER_HANDLER(); | ||
| using TestType = cuda::std::tuple_element_t<0, TypeParam>; | ||
| using ExecType = cuda::std::tuple_element_t<1, TypeParam>; | ||
|
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| ExecType exec{}; | ||
|
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| auto A = makeA<TestType>(); | ||
| auto B = makeB<TestType>(); | ||
| auto E = makeE<TestType>(); | ||
| const auto m = A.Size(0); | ||
| const auto k = A.Size(1); | ||
| const auto n = B.Size(1); | ||
|
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| // Convert dense A to sparse S. | ||
| auto S = experimental::make_zero_tensor_coo<TestType, index_t>({m, k}); | ||
| (S = dense2sparse(A)).run(exec); | ||
| ASSERT_EQ(S.Nse(), 7); | ||
| ASSERT_EQ(S.posSize(1), 0); | ||
|
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| // Matmul. | ||
| auto O = make_tensor<TestType>({m, n}); | ||
| (O = matmul(S, B)).run(exec); | ||
|
|
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| // Verify result. | ||
| exec.sync(); | ||
| for (index_t i = 0; i < m; i++) { | ||
| for (index_t j = 0; j < n; j++) { | ||
| if constexpr (is_complex_v<TestType>) { | ||
| ASSERT_NEAR(O(i, j).real(), E(i, j).real(), this->thresh); | ||
| ASSERT_NEAR(O(i, j).imag(), E(i,j ).imag(), this->thresh); | ||
| } | ||
| else { | ||
| ASSERT_NEAR(O(i, j), E(i, j), this->thresh); | ||
| } | ||
|
|
||
| } | ||
| } | ||
|
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| MATX_EXIT_HANDLER(); | ||
| } | ||
|
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| TYPED_TEST(MatmulSparseTestsAll, MatmulCSR) { | ||
| MATX_ENTER_HANDLER(); | ||
| using TestType = cuda::std::tuple_element_t<0, TypeParam>; | ||
| using ExecType = cuda::std::tuple_element_t<1, TypeParam>; | ||
|
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| ExecType exec{}; | ||
|
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| auto A = makeA<TestType>(); | ||
| auto B = makeB<TestType>(); | ||
| auto E = makeE<TestType>(); | ||
| const auto m = A.Size(0); | ||
| const auto k = A.Size(1); | ||
| const auto n = B.Size(1); | ||
|
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| // Convert dense A to sparse S. | ||
| auto S = experimental::make_zero_tensor_csr<TestType, index_t, index_t>({m, k}); | ||
| (S = dense2sparse(A)).run(exec); | ||
| ASSERT_EQ(S.Nse(), 7); | ||
| ASSERT_EQ(S.posSize(1), m + 1); | ||
|
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| // Matmul. | ||
| auto O = make_tensor<TestType>({m, n}); | ||
| (O = matmul(S, B)).run(exec); | ||
|
|
||
| // Verify result. | ||
| exec.sync(); | ||
| for (index_t i = 0; i < m; i++) { | ||
| for (index_t j = 0; j < n; j++) { | ||
| if constexpr (is_complex_v<TestType>) { | ||
| ASSERT_NEAR(O(i, j).real(), E(i, j).real(), this->thresh); | ||
| ASSERT_NEAR(O(i, j).imag(), E(i,j ).imag(), this->thresh); | ||
| } | ||
| else { | ||
| ASSERT_NEAR(O(i, j), E(i, j), this->thresh); | ||
| } | ||
|
|
||
| } | ||
| } | ||
|
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| MATX_EXIT_HANDLER(); | ||
| } | ||
|
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| TYPED_TEST(MatmulSparseTestsAll, MatmulCSC) { | ||
| MATX_ENTER_HANDLER(); | ||
| using TestType = cuda::std::tuple_element_t<0, TypeParam>; | ||
| using ExecType = cuda::std::tuple_element_t<1, TypeParam>; | ||
|
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| ExecType exec{}; | ||
|
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| auto A = makeA<TestType>(); | ||
| auto B = makeB<TestType>(); | ||
| auto E = makeE<TestType>(); | ||
| const auto m = A.Size(0); | ||
| const auto k = A.Size(1); | ||
| const auto n = B.Size(1); | ||
|
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| // Convert dense A to sparse S. | ||
| auto S = experimental::make_zero_tensor_csc<TestType, index_t, index_t>({m, k}); | ||
| (S = dense2sparse(A)).run(exec); | ||
| ASSERT_EQ(S.Nse(), 7); | ||
| ASSERT_EQ(S.posSize(1), k + 1); | ||
|
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| // Matmul. | ||
| auto O = make_tensor<TestType>({m, n}); | ||
| (O = matmul(S, B)).run(exec); | ||
|
|
||
| // Verify result. | ||
| exec.sync(); | ||
| for (index_t i = 0; i < m; i++) { | ||
| for (index_t j = 0; j < n; j++) { | ||
| if constexpr (is_complex_v<TestType>) { | ||
| ASSERT_NEAR(O(i, j).real(), E(i, j).real(), this->thresh); | ||
| ASSERT_NEAR(O(i, j).imag(), E(i,j ).imag(), this->thresh); | ||
| } | ||
| else { | ||
| ASSERT_NEAR(O(i, j), E(i, j), this->thresh); | ||
| } | ||
|
|
||
| } | ||
| } | ||
|
|
||
| MATX_EXIT_HANDLER(); | ||
| } |
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